WorldWideScience

Sample records for interaction network implications

  1. The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine.

    Directory of Open Access Journals (Sweden)

    Brett A McKinney

    Full Text Available Although many diseases and traits show large heritability, few genetic variants have been found to strongly separate phenotype groups by genotype. Complex regulatory networks of variants and expression of multiple genes lead to small individual-variant effects and difficulty replicating the effect of any single variant in an affected pathway. Interaction network modeling of GWAS identifies effects ignored by univariate models, but population differences may still cause specific genes to not replicate. Integrative network models may help detect indirect effects of variants in the underlying biological pathway. In this study, we used gene-level functional interaction information from the Integrative Multi-species Prediction (IMP tool to reveal important genes associated with a complex phenotype through evidence from epistasis networks and pathway enrichment. We test this method for augmenting variant-based network analyses with functional interactions by applying it to a smallpox vaccine immune response GWAS. The integrative analysis spotlights the role of genes related to retinoid X receptor alpha (RXRA, which has been implicated in a previous epistasis network analysis of smallpox vaccine.

  2. Dynamic and interacting complex networks

    Science.gov (United States)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  3. Network interactions within the canine intrinsic cardiac nervous system: implications for reflex control of regional cardiac function

    Science.gov (United States)

    Beaumont, Eric; Salavatian, Siamak; Southerland, E Marie; Vinet, Alain; Jacquemet, Vincent; Armour, J Andrew; Ardell, Jeffrey L

    2013-01-01

    The aims of the study were to determine how aggregates of intrinsic cardiac (IC) neurons transduce the cardiovascular milieu versus responding to changes in central neuronal drive and to determine IC network interactions subsequent to induced neural imbalances in the genesis of atrial fibrillation (AF). Activity from multiple IC neurons in the right atrial ganglionated plexus was recorded in eight anaesthetized canines using a 16-channel linear microelectrode array. Induced changes in IC neuronal activity were evaluated in response to: (1) focal cardiac mechanical distortion; (2) electrical activation of cervical vagi or stellate ganglia; (3) occlusion of the inferior vena cava or thoracic aorta; (4) transient ventricular ischaemia, and (5) neurally induced AF. Low level activity (ranging from 0 to 2.7 Hz) generated by 92 neurons was identified in basal states, activities that displayed functional interconnectivity. The majority (56%) of IC neurons so identified received indirect central inputs (vagus alone: 25%; stellate ganglion alone: 27%; both: 48%). Fifty per cent transduced the cardiac milieu responding to multimodal stressors applied to the great vessels or heart. Fifty per cent of IC neurons exhibited cardiac cycle periodicity, with activity occurring primarily in late diastole into isovolumetric contraction. Cardiac-related activity in IC neurons was primarily related to direct cardiac mechano-sensory inputs and indirect autonomic efferent inputs. In response to mediastinal nerve stimulation, most IC neurons became excessively activated; such network behaviour preceded and persisted throughout AF. It was concluded that stochastic interactions occur among IC local circuit neuronal populations in the control of regional cardiac function. Modulation of IC local circuit neuronal recruitment may represent a novel approach for the treatment of cardiac disease, including atrial arrhythmias. PMID:23818689

  4. Interacting neural networks

    Science.gov (United States)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  5. Networking activism: implications for Greece

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    Pantelis Vatikiotis

    2011-12-01

    Full Text Available The outbreak of December 2008 against police brutality through a wave of demonstrations and street protests in Athens, which was strongly advocated by protest activities and practices across the world, addresses several issues in relation to the transformative potentials of mediated collective action. The paper critically evaluates different accounts of December events, probing then into thevery networking of that movement. From this perspective, it points out another aspect of the local-global interplay in protest culture along new mediating practices (beyond the creation of transnational publics, that of the implications of transnational networking for local social activism and identification, addressing relevant questions in the Greek context.

  6. Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

  7. Networks and Interactivity

    DEFF Research Database (Denmark)

    Considine, Mark; Lewis, Jenny

    2012-01-01

    of `street-level' employment services staff for the impacts of this. Contrary to expectations, networking has generally declined over the last decade. There are signs of path dependence in networking patterns within each country, but also a convergence of patterns for the UK and Australia......The systemic reform of employment services in OECD countries was driven by New Public Management (NPM) and then post-NPM reforms, when first-phase changes such as privatization were amended with `joined up' processes to help manage fragmentation. This article examines the networking strategies......, but not The Netherlands. Networking appears to be mediated by policy and regulatory imperatives....

  8. Online Social Network Interactions:

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    Hui-Jung Chang

    2018-01-01

    Full Text Available A cross-cultural comparison of social networking structure on McDonald’s Facebook fan sites between Taiwan and the USA was conducted utilizing the individualism/collectivism dimension proposed by Hofstede. Four network indicators are used to describe the network structure of McDonald’s Facebook fan sites: size, density, clique and centralization. Individuals who post on both Facebook sites for the year of 2012 were considered as network participants for the purpose of the study. Due to the huge amount of data, only one thread of postings was sampled from each month of the year of 2012. The final data consists of 1002 postings written by 896 individuals and 5962 postings written by 5532 individuals from Taiwan and the USA respectively. The results indicated that the USA McDonald’s Facebook fan network has more fans, while Taiwan’s McDonald’s Facebook fan network is more densely connected. Cliques did form among the overall multiplex and within the individual uniplex networks in two countries, yet no significant differences were found between them. All the fan networks in both countries are relatively centralized, mostly on the site operators.

  9. Topology of molecular interaction networks

    NARCIS (Netherlands)

    Winterbach, W.; Van Mieghem, P.; Reinders, M.; Wang, H.; De Ridder, D.

    2013-01-01

    Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over

  10. Interactive Network Exploration with Orange

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    Miha Štajdohar

    2013-04-01

    Full Text Available Network analysis is one of the most widely used techniques in many areas of modern science. Most existing tools for that purpose are limited to drawing networks and computing their basic general characteristics. The user is not able to interactively and graphically manipulate the networks, select and explore subgraphs using other statistical and data mining techniques, add and plot various other data within the graph, and so on. In this paper we present a tool that addresses these challenges, an add-on for exploration of networks within the general component-based environment Orange.

  11. Implications of Network Centric Warfare

    National Research Council Canada - National Science Library

    Bailey, Alvin

    2004-01-01

    .... These areas of dependence also provide numerous vulnerabilities. This paper will focus specifically on Network Centric Warfare's vulnerabilities in terms of sensors cyberterrorism/ Electro-Magnetic Pulse (EMP...

  12. Temporal stability in human interaction networks

    Science.gov (United States)

    Fabbri, Renato; Fabbri, Ricardo; Antunes, Deborah Christina; Pisani, Marilia Mello; de Oliveira, Osvaldo Novais

    2017-11-01

    This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.

  13. Statistical Mechanics of Temporal and Interacting Networks

    Science.gov (United States)

    Zhao, Kun

    In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide

  14. Network Physiology: How Organ Systems Dynamically Interact.

    Science.gov (United States)

    Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.

  15. Network Physiology: How Organ Systems Dynamically Interact

    Science.gov (United States)

    Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  16. Vulnerability of networks of interacting Markov chains.

    Science.gov (United States)

    Kocarev, L; Zlatanov, N; Trajanov, D

    2010-05-13

    The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks--the network of infrastructures and the EU power grid--identifying the most vulnerable nodes of these networks.

  17. Climate Change and Interacting Stressors: Implications for ...

    Science.gov (United States)

    EPA announced the release of the final document, Climate Change and Interacting Stressors: Implications for Coral Reef Management in American Samoa. This report provides a synthesis of information on the interactive effects of climate change and other stressors on the reefs of American Samoa as well as an assessment of potential management responses. This report provides the coral reef managers of American Samoa, as well as other coral reef managers in the Pacific region, with some management options to help enhance the capacity of local coral reefs to resist the negative effects of climate change. This report was designed to take advantage of diverse research and monitoring efforts that are ongoing in American Samoa to: analyze and compile the results of multiple research projects that focus on understanding climate-related stressors and their effects on coral reef ecosystem degradation and recovery; and assess implications for coral reef managment of the combined information, including possible response options.

  18. Statistical physics of interacting neural networks

    Science.gov (United States)

    Kinzel, Wolfgang; Metzler, Richard; Kanter, Ido

    2001-12-01

    Recent results on the statistical physics of time series generation and prediction are presented. A neural network is trained on quasi-periodic and chaotic sequences and overlaps to the sequence generator as well as the prediction errors are calculated numerically. For each network there exists a sequence for which it completely fails to make predictions. Two interacting networks show a transition to perfect synchronization. A pool of interacting networks shows good coordination in the minority game-a model of competition in a closed market. Finally, as a demonstration, a perceptron predicts bit sequences produced by human beings.

  19. Implications of network structure on public health collaboratives.

    Science.gov (United States)

    Retrum, Jessica H; Chapman, Carrie L; Varda, Danielle M

    2013-10-01

    Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of

  20. Evolution of a protein domain interaction network

    International Nuclear Information System (INIS)

    Li-Feng, Gao; Jian-Jun, Shi; Shan, Guan

    2010-01-01

    In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases. (general)

  1. STATE NETWORK INTERNATIONAL POLITICAL INTERACTION

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    D. M. Feldman

    2011-01-01

    Full Text Available Abstract: The processes of fragmentation (regionalization and localization and globalization turn the state as the basic system forming element of the state-centric world political system into the component of the world political network. The political relations between actors of the world political network are ruled by the effectiveness and not by legitimacy (“victory rules”, what is different from the participatory principles of interstate relations (“participation rules” accepted by the Westphalian state system. The article argues that the post-Westphalian world political system will witness the clashes between victory rules and participation rules and their eventual coexistence since the very nature of the victory rules hinders its institutionalization, consolidation and legitimation. The article suggests that the new system of state relations regardless of the name will be not less Westphalian than the preceding one thus new participation rules will have to be formulated and codified.

  2. Unraveling spurious properties of interaction networks with tailored random networks.

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    Stephan Bialonski

    Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

  3. A conserved mammalian protein interaction network.

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    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  4. Synchronization in networks with multiple interaction layers

    Science.gov (United States)

    del Genio, Charo I.; Gómez-Gardeñes, Jesús; Bonamassa, Ivan; Boccaletti, Stefano

    2016-01-01

    The structure of many real-world systems is best captured by networks consisting of several interaction layers. Understanding how a multilayered structure of connections affects the synchronization properties of dynamical systems evolving on top of it is a highly relevant endeavor in mathematics and physics and has potential applications in several socially relevant topics, such as power grid engineering and neural dynamics. We propose a general framework to assess the stability of the synchronized state in networks with multiple interaction layers, deriving a necessary condition that generalizes the master stability function approach. We validate our method by applying it to a network of Rössler oscillators with a double layer of interactions and show that highly rich phenomenology emerges from this. This includes cases where the stability of synchronization can be induced even if both layers would have individually induced unstable synchrony, an effect genuinely arising from the true multilayer structure of the interactions among the units in the network. PMID:28138540

  5. Structure of the human chromosome interaction network.

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    Sergio Sarnataro

    Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

  6. Designing Networked Adaptive Interactive Hybrid Systems

    NARCIS (Netherlands)

    Kester, L.J.H.M.

    2008-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. In these systems humans and intelligent machines will, in close interaction, be able to reach their goals under

  7. Network Compression as a Quality Measure for Protein Interaction Networks

    Science.gov (United States)

    Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

    2012-01-01

    With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

  8. Network Interactions in the Great Altai Region

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    Lev Aleksandrovich Korshunov

    2017-12-01

    Full Text Available To improve the efficiency and competitiveness of the regional economy, an effective interaction between educational institutions in the Great Altai region is needed. The innovation growth can enhancing this interaction. The article explores the state of network structures in the economy and higher education in the border territories of the countries of Great Altai. The authors propose an updated approach to the three-level classification of network interaction. We analyze growing influence of the countries with emerging economies. We define the factors that impede the more stable and multifaceted regional development of these countries. Further, the authors determine indicators of the higher education systems and cooperation systems at the university level between the Shanghai Cooperation Organization countries (SCO and BRICS countries, showing the international rankings of the universities in these countries. The teaching language is important to overcome the obstacles in the interregional cooperation. The authors specify the problems of the development of the universities of the SCO and BRICS countries as global educational networks. The research applies basic scientific logical methods of analysis and synthesis, induction and deduction, as well as the SWOT analysis method. We have indentified and analyzed the existing economic and educational relations. To promote the economic innovation development of the border territories of the Great Altai, we propose a model of regional network university. Modern universities function in a new economic environment. Thus, in a great extent, they form the technological and social aspects of this environment. Innovative network structures contribute to the formation of a new network institutional environment of the regional economy, which impacts the macro- and microeconomic performance of the region as a whole. The results of the research can help to optimize the regional economies of the border

  9. Global Electricity Trade Network: Structures and Implications

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

    2016-01-01

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

  10. From network structure to network reorganization: implications for adult neurogenesis

    International Nuclear Information System (INIS)

    Schneider-Mizell, Casey M; Zochowski, Michal R; Sander, Leonard M; Parent, Jack M; Ben-Jacob, Eshel

    2010-01-01

    Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells

  11. The Global Alzheimer's Association Interactive Network.

    Science.gov (United States)

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

    2016-01-01

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

  12. Data management of protein interaction networks

    CERN Document Server

    Cannataro, Mario

    2012-01-01

    Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

  13. KNOWNET: Exploring Interactive Knowledge Networking across Insurance Supply Chains

    Directory of Open Access Journals (Sweden)

    Susan Grant

    2014-01-01

    Full Text Available Social media has become an extremely powerful phenomenon with millions of users who post status updates, blog, links and pictures on social networking sites such as Facebook, LinkedIn, and Twitter. However, social networking has so far spread mainly among consumers. Businesses are only now beginning to acknowledge the benefits of using social media to enhance employee and supplier collaboration to support new ideas and innovation through knowledge sharing across functions and organizational boundaries. Many businesses are still trying to understand the various implications of integrating internal communication systems with social media tools and private collaboration and networking platforms. Indeed, a current issue in organizations today is to explore the value of social media mechanisms across a range of functions within their organizations and across their supply chains.The KNOWNET project (an EC funded Marie Curie IAPP seeks to assess the value of social networking for knowledge exchange across Insurance supply chains. A key objective of the project being to develop and build a web based interactive environment - a Supplier Social Network or SSN, to support and facilitate exchange of good ideas, insights, knowledge, innovations etc across a diverse group of suppliers within a multi level supply chain within the Insurance sector.

  14. Tourist activated networks: Implications for dynamic packaging systems in tourism

    DEFF Research Database (Denmark)

    Zach, Florian; Gretzel, Ulrike; Fesenmaier, Daniel R.

    2008-01-01

    This paper discusses tourist activated networks as a concept to inform technological applications supporting dynamic bundling and en-route recommendations. Empirical data was collected from travellers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist activated network and provide implications for technology design and tourism...

  15. Repulsive interactions between two polyelectrolyte networks

    Science.gov (United States)

    Erbas, Aykut; Olvera de La Cruz, Monica; Olvera Group Collaboration

    Surfaces formed by charged polymeric species are highly_abundant in both synthetic and biological systems, for which maintaining_an optimum contact distance and a pressure balance is paramount. We investigate interactions between surfaces of two same-charged and_highly swollen polyelectrolyte gels, using extensive molecular dynamic_simulations and minimal analytical methods. The external-pressure_responses of the gels and the polymer-free ionic solvent layer separating_two surfaces are considered. Simulations confirmed that the surfaces are_held apart by osmotic pressure resulting from excess charges diffusing out_of the network. Both the solvent layer and pressure dependence are well_described by an analytical model based on the Poisson -Boltzmann solution for low and moderate electrostatic strengths. Our results can be of great importance for systems where charged gels or gel-like structures interact in various solvents, including systems encapsulated by gels and microgels in confinement.

  16. Some Implications of Human-Structure Interaction

    DEFF Research Database (Denmark)

    Pedersen, Lars

    2013-01-01

    On structures, humans may be active which may cause structural vibrations as human activity can excite structural vibration modes. However, humans may also be passive (sitting or standing on the structure). The paper addresses this subject and explores the implications of having passive humans...

  17. A Physical Interaction Network of Dengue Virus and Human Proteins*

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

    2011-01-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

  18. A physical interaction network of dengue virus and human proteins.

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  19. Co-creating value through agents interaction within service network

    Energy Technology Data Exchange (ETDEWEB)

    Okdinawati, L.; Simatupang, T.M.; Sunitiyoso, Y.

    2017-07-01

    The purpose of this paper is to gives further understanding on value co-creation mechanisms in B-to-B service network by reinforcing the processes, the relationships, and influences of other agents where Collaborative Transportation Management (CTM) forms might be best employed. Design/methodology/approach: In order to model the interactions among agents in the collaboration processes and the value co-creation processes, this research used three collaboration cases in Indonesia. Then, the agent-based simulation was used to capture both the collaboration process and the value co-creation process of the three collaboration cases. Findings: The interactions among the agents both inside and outside their collaboration environment determined agent’s role as a value co-creator. The willingness of an agent to accept the opinion of another agent determined the degree of their willingness to co-operate and to change their strategies, and perceptions. Therefore, influenced the size of the value obtained by them in each collaboration process. Research limitations/implications: The findings of the simulations subject to assumptions based on the collaboration cases. Further research is related to how to encourage agents to co-operate and adjust their perceptions. Practical implications: It is crucial for the practitioners to interact with another agent both inside and outside their collaboration environment. The opinions of another agent inside the collaboration environment also need to be considered. Originality/value: This research is derived from its emphasis on how a value is co-created by reinforcing both the collaborative processes and the interactions among agents as well as on how CTM might be best employed.

  20. Co-creating value through agents interaction within service network

    International Nuclear Information System (INIS)

    Okdinawati, L.; Simatupang, T.M.; Sunitiyoso, Y.

    2017-01-01

    The purpose of this paper is to gives further understanding on value co-creation mechanisms in B-to-B service network by reinforcing the processes, the relationships, and influences of other agents where Collaborative Transportation Management (CTM) forms might be best employed. Design/methodology/approach: In order to model the interactions among agents in the collaboration processes and the value co-creation processes, this research used three collaboration cases in Indonesia. Then, the agent-based simulation was used to capture both the collaboration process and the value co-creation process of the three collaboration cases. Findings: The interactions among the agents both inside and outside their collaboration environment determined agent’s role as a value co-creator. The willingness of an agent to accept the opinion of another agent determined the degree of their willingness to co-operate and to change their strategies, and perceptions. Therefore, influenced the size of the value obtained by them in each collaboration process. Research limitations/implications: The findings of the simulations subject to assumptions based on the collaboration cases. Further research is related to how to encourage agents to co-operate and adjust their perceptions. Practical implications: It is crucial for the practitioners to interact with another agent both inside and outside their collaboration environment. The opinions of another agent inside the collaboration environment also need to be considered. Originality/value: This research is derived from its emphasis on how a value is co-created by reinforcing both the collaborative processes and the interactions among agents as well as on how CTM might be best employed.

  1. Game theory in communication networks cooperative resolution of interactive networking scenarios

    CERN Document Server

    Antoniou, Josephina

    2012-01-01

    A mathematical tool for scientists and researchers who work with computer and communication networks, Game Theory in Communication Networks: Cooperative Resolution of Interactive Networking Scenarios addresses the question of how to promote cooperative behavior in interactive situations between heterogeneous entities in communication networking scenarios. It explores network design and management from a theoretical perspective, using game theory and graph theory to analyze strategic situations and demonstrate profitable behaviors of the cooperative entities. The book promotes the use of Game T

  2. Brain Networks Implicated in Seasonal Affective Disorder

    DEFF Research Database (Denmark)

    Nørgaard, Martin; Ganz, Melanie; Svarer, Claus

    2017-01-01

    , patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption...... without SAD; it included the right superior frontal gyrus, brainstem, globus pallidus (bilaterally) and the left hippocampus. Across seasons, female S' carriers without SAD showed nominally higher 5-HTT levels in these regions compared to female S' carriers with SAD, but the group difference was only...... winter compared to female S' carriers without SAD. Limitations: The study is preliminary and limited by small sample size in the SAD group (N = 6). Conclusions: These findings provide novel exploratory evidence for a wintertime state-dependent difference in 5-HTT levels that may leave SAD females...

  3. Annexin-Phospholipid Interactions. Functional Implications

    Directory of Open Access Journals (Sweden)

    Javier Turnay

    2013-01-01

    Full Text Available Annexins constitute an evolutionary conserved multigene protein superfamily characterized by their ability to interact with biological membranes in a calcium dependent manner. They are expressed by all living organisms with the exception of certain unicellular organisms. The vertebrate annexin core is composed of four (eight in annexin A6 homologous domains of around 70 amino acids, with the overall shape of a slightly bent ring surrounding a central hydrophilic pore. Calcium- and phospholipid-binding sites are located on the convex side while the N-terminus links domains I and IV on the concave side. The N-terminus region shows great variability in length and amino acid sequence and it greatly influences protein stability and specific functions of annexins. These proteins interact mainly with acidic phospholipids, such as phosphatidylserine, but differences are found regarding their affinity for lipids and calcium requirements for the interaction. Annexins are involved in a wide range of intra- and extracellular biological processes in vitro, most of them directly related with the conserved ability to bind to phospholipid bilayers: membrane trafficking, membrane-cytoskeleton anchorage, ion channel activity and regulation, as well as antiinflammatory and anticoagulant activities. However, the in vivo physiological functions of annexins are just beginning to be established.

  4. Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

    Science.gov (United States)

    Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

    2014-01-01

    Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

  5. Online networks, social interaction and segregation: An evolutionary approach

    OpenAIRE

    Antoci, Angelo; Sabatini, Fabio

    2018-01-01

    There is growing evidence that face-to-face interaction is declining in many countries, exacerbating the phenomenon of social isolation. On the other hand, social interaction through online networking sites is steeply rising. To analyze these societal dynamics, we have built an evolutionary game model in which agents can choose between three strategies of social participation: 1) interaction via both online social networks and face-to-face encounters; 2) interaction by exclusive means of face...

  6. Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

    Science.gov (United States)

    Miryala, Sravan Kumar; Anbarasu, Anand; Ramaiah, Sudha

    2018-02-05

    Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Cluster Approach to Network Interaction in Pedagogical University

    Science.gov (United States)

    Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.

    2016-01-01

    The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…

  8. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  9. Influence of degree correlations on network structure and stability in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

  10. Investigating physics learning with layered student interaction networks

    DEFF Research Database (Denmark)

    Bruun, Jesper; Traxler, Adrienne

    Centrality in student interaction networks (SINs) can be linked to variables like grades [1], persistence [2], and participation [3]. Recent efforts in the field of network science have been done to investigate layered - or multiplex - networks as mathematical objects [4]. These networks can be e......, this study investigates how target entropy [5,1] and pagerank [6,7] are affected when we take time and modes of interaction into account. We present our preliminary models and results and outline our future work in this area....

  11. Global patterns of interaction specialization in bird-flower networks

    DEFF Research Database (Denmark)

    Zanata, Thais B.; Dalsgaard, Bo; Passos, Fernando C.

    2017-01-01

    , such as plant species richness, asymmetry, latitude, insularity, topography, sampling methods and intensity. Results: Hummingbird–flower networks were more specialized than honeyeater–flower networks. Specifically, hummingbird–flower networks had a lower proportion of realized interactions (lower C), decreased...... in the interaction patterns with their floral resources. Location: Americas, Africa, Asia and Oceania/Australia. Methods: We compiled interaction networks between birds and floral resources for 79 hummingbird, nine sunbird and 33 honeyeater communities. Interaction specialization was quantified through connectance...... (C), complementary specialization (H2′), binary (QB) and weighted modularity (Q), with both observed and null-model corrected values. We compared interaction specialization among the three types of bird–flower communities, both independently and while controlling for potential confounding variables...

  12. Different cell fates from cell-cell interactions: core architectures of two-cell bistable networks.

    Science.gov (United States)

    Rouault, Hervé; Hakim, Vincent

    2012-02-08

    The acquisition of different fates by cells that are initially in the same state is central to development. Here, we investigate the possible structures of bistable genetic networks that can allow two identical cells to acquire different fates through cell-cell interactions. Cell-autonomous bistable networks have been previously sampled using an evolutionary algorithm. We extend this evolutionary procedure to take into account interactions between cells. We obtain a variety of simple bistable networks that we classify into major subtypes. Some have long been proposed in the context of lateral inhibition through the Notch-Delta pathway, some have been more recently considered and others appear to be new and based on mechanisms not previously considered. The results highlight the role of posttranscriptional interactions and particularly of protein complexation and sequestration, which can replace cooperativity in transcriptional interactions. Some bistable networks are entirely based on posttranscriptional interactions and the simplest of these is found to lead, upon a single parameter change, to oscillations in the two cells with opposite phases. We provide qualitative explanations as well as mathematical analyses of the dynamical behaviors of various created networks. The results should help to identify and understand genetic structures implicated in cell-cell interactions and differentiation. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  13. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  14. Specific non-monotonous interactions increase persistence of ecological networks.

    Science.gov (United States)

    Yan, Chuan; Zhang, Zhibin

    2014-03-22

    The relationship between stability and biodiversity has long been debated in ecology due to opposing empirical observations and theoretical predictions. Species interaction strength is often assumed to be monotonically related to population density, but the effects on stability of ecological networks of non-monotonous interactions that change signs have not been investigated previously. We demonstrate that for four kinds of non-monotonous interactions, shifting signs to negative or neutral interactions at high population density increases persistence (a measure of stability) of ecological networks, while for the other two kinds of non-monotonous interactions shifting signs to positive interactions at high population density decreases persistence of networks. Our results reveal a novel mechanism of network stabilization caused by specific non-monotonous interaction types through either increasing stable equilibrium points or reducing unstable equilibrium points (or both). These specific non-monotonous interactions may be important in maintaining stable and complex ecological networks, as well as other networks such as genes, neurons, the internet and human societies.

  15. Wireless network interface energy consumption implications of popular streaming formats

    Science.gov (United States)

    Chandra, Surendar

    2001-12-01

    With the proliferation of mobile streaming multimedia, available battery capacity constrains the end-user experience. Since streaming applications tend to be long running, wireless network interface card's (WNIC) energy consumption is particularly an acute problem. In this work, we explore the WNIC energy consumption implications of popular multimedia streaming formats from Microsoft (Windows media), Real (Real media) and Apple (Quick Time). We investigate the energy consumption under varying stream bandwidth and network loss rates. We also explore history-based client-side strategies to reduce the energy consumed by transitioning the WNICs to a lower power consuming sleep state. We show that Microsoft media tends to transmit packets at regular intervals; streams optimized for 28.8 Kbps can save over 80% in energy consumption with 2% data loss. A high bandwidth stream (768 Kbps) can still save 57% in energy consumption with less than 0.3% data loss. For high bandwidth streams, Microsoft media exploits network-level packet fragmentation, which can lead to excessive packet loss (and wasted energy) in a lossy network. Real stream packets tend to be sent closer to each other, especially at higher bandwidths. Quicktime packets sometimes arrive in quick succession; most likely an application level fragmentation mechanism. Such packets are harder to predict at the network level without understanding the packet semantics.

  16. Do networks of social interactions reflect patterns of kinship?

    Directory of Open Access Journals (Sweden)

    Joah R. MADDEN, Johanna F. NIELSEN, Tim H. CLUTTON-BROCK

    2012-04-01

    Full Text Available The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals, and is presumed to facilitate inclusive fitness benefits. Such structure may be evident at a finer, behavioural, scale with individuals preferentially interacting with kin. We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks: grooming, dominance or foraging competitions. Networks of dominance interactions were positively related to networks of kinship, with close relatives engaging in dominance interactions with each other. This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin, which are most likely to be able to discern kin through simple rules of thumb. Conversely, we found no relationship between kinship networks and either grooming networks or networks of foraging competitions. This is surprising because a positive association between kin in a grooming network, or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits. Indeed, the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members. We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits, and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2: 319-328, 2012].

  17. Do networks of social interactions reflect patterns of kinship?

    Institute of Scientific and Technical Information of China (English)

    Joah R. MADDEN; Johanna F. NIEL SEN; Tim H. CLUTTON-BROCK

    2012-01-01

    The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals,and is presumed to facilitate inclusive fitness benefits.Such structure may be evident at a finer,behavioural,scale with individuals preferentially interacting with kin.We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks:grooming,dominance or foraging competitions.Networks of dominance interactions were positively related to networks of kinship,with close relatives engaging in dominance interactions with each other.This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin,which are most likely to be able to discern kin through simple rules of thumb.Conversely,we found no relationship between kinship networks and either grooming networks or networks of foraging competitions.This is surprising because a positive association between kin in a grooming network,or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits.Indeed,the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members.We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits,and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2):319-328,2012].

  18. Influences of brain development and ageing on cortical interactive networks.

    Science.gov (United States)

    Zhu, Chengyu; Guo, Xiaoli; Jin, Zheng; Sun, Junfeng; Qiu, Yihong; Zhu, Yisheng; Tong, Shanbao

    2011-02-01

    To study the effect of brain development and ageing on the pattern of cortical interactive networks. By causality analysis of multichannel electroencephalograph (EEG) with partial directed coherence (PDC), we investigated the different neural networks involved in the whole cortex as well as the anterior and posterior areas in three age groups, i.e., children (0-10 years), mid-aged adults (26-38 years) and the elderly (56-80 years). By comparing the cortical interactive networks in different age groups, the following findings were concluded: (1) the cortical interactive network in the right hemisphere develops earlier than its left counterpart in the development stage; (2) the cortical interactive network of anterior cortex, especially at C3 and F3, is demonstrated to undergo far more extensive changes, compared with the posterior area during brain development and ageing; (3) the asymmetry of the cortical interactive networks declines during ageing with more loss of connectivity in the left frontal and central areas. The age-related variation of cortical interactive networks from resting EEG provides new insights into brain development and ageing. Our findings demonstrated that the PDC analysis of EEG is a powerful approach for characterizing the cortical functional connectivity during brain development and ageing. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  19. Mean field interaction in biochemical reaction networks

    KAUST Repository

    Tembine, Hamidou; Tempone, Raul; Vilanova, Pedro

    2011-01-01

    In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits

  20. Empirical evaluation of neutral interactions in host-parasite networks.

    Science.gov (United States)

    Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

    2014-04-01

    While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization.

  1. Interaction Networks: Generating High Level Hints Based on Network Community Clustering

    Science.gov (United States)

    Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

    2012-01-01

    We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

  2. Default network modulation and large-scale network interactivity in healthy young and old adults.

    Science.gov (United States)

    Spreng, R Nathan; Schacter, Daniel L

    2012-11-01

    We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands.

  3. Interacting Social Processes on Interconnected Networks.

    Directory of Open Access Journals (Sweden)

    Lucila G Alvarez-Zuzek

    Full Text Available We propose and study a model for the interplay between two different dynamical processes -one for opinion formation and the other for decision making- on two interconnected networks A and B. The opinion dynamics on network A corresponds to that of the M-model, where the state of each agent can take one of four possible values (S = -2,-1, 1, 2, describing its level of agreement on a given issue. The likelihood to become an extremist (S = ±2 or a moderate (S = ±1 is controlled by a reinforcement parameter r ≥ 0. The decision making dynamics on network B is akin to that of the Abrams-Strogatz model, where agents can be either in favor (S = +1 or against (S = -1 the issue. The probability that an agent changes its state is proportional to the fraction of neighbors that hold the opposite state raised to a power β. Starting from a polarized case scenario in which all agents of network A hold positive orientations while all agents of network B have a negative orientation, we explore the conditions under which one of the dynamics prevails over the other, imposing its initial orientation. We find that, for a given value of β, the two-network system reaches a consensus in the positive state (initial state of network A when the reinforcement overcomes a crossover value r*(β, while a negative consensus happens for r βc. We develop an analytical mean-field approach that gives an insight into these regimes and shows that both dynamics are equivalent along the crossover line (r*, β*.

  4. Personal Profiles: Enhancing Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

    2009-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

  5. Defaunation leads to interaction deficits, not interaction compensation, in an island seed dispersal network.

    Science.gov (United States)

    Fricke, Evan C; Tewksbury, Joshua J; Rogers, Haldre S

    2018-01-01

    Following defaunation, the loss of interactions with mutualists such as pollinators or seed dispersers may be compensated through increased interactions with remaining mutualists, ameliorating the negative cascading impacts on biodiversity. Alternatively, remaining mutualists may respond to altered competition by reducing the breadth or intensity of their interactions, exacerbating negative impacts on biodiversity. Despite the importance of these responses for our understanding of the dynamics of mutualistic networks and their response to global change, the mechanism and magnitude of interaction compensation within real mutualistic networks remains largely unknown. We examined differences in mutualistic interactions between frugivores and fruiting plants in two island ecosystems possessing an intact or disrupted seed dispersal network. We determined how changes in the abundance and behavior of remaining seed dispersers either increased mutualistic interactions (contributing to "interaction compensation") or decreased interactions (causing an "interaction deficit") in the disrupted network. We found a "rich-get-richer" response in the disrupted network, where remaining frugivores favored the plant species with highest interaction frequency, a dynamic that worsened the interaction deficit among plant species with low interaction frequency. Only one of five plant species experienced compensation and the other four had significant interaction deficits, with interaction frequencies 56-95% lower in the disrupted network. These results do not provide support for the strong compensating mechanisms assumed in theoretical network models, suggesting that existing network models underestimate the prevalence of cascading mutualism disruption after defaunation. This work supports a mutualist biodiversity-ecosystem functioning relationship, highlighting the importance of mutualist diversity for sustaining diverse and resilient ecosystems. © 2017 John Wiley & Sons Ltd.

  6. Development of Attention Networks and Their Interactions in Childhood

    Science.gov (United States)

    Pozuelos, Joan P.; Paz-Alonso, Pedro M.; Castillo, Alejandro; Fuentes, Luis J.; Rueda, M. Rosario

    2014-01-01

    In the present study, we investigated developmental trajectories of alerting, orienting, and executive attention networks and their interactions over childhood. Two cross-sectional experiments were conducted with different samples of 6-to 12-year-old children using modified versions of the attention network task (ANT). In Experiment 1 (N = 106),…

  7. Global Diffusion of Interactive Networks. The Impact of Culture

    OpenAIRE

    Maitland, Carleen

    1998-01-01

    The Internet and other interactive networks are diffusing across the globe at rates that vary from country to country. Typically, economic and market structure variables are used to explain these differences. The addition of culture to these variables will provide a more robust understanding of the differences in Internet and interactive network diffusion. Existing analyses that identify culture as a predictor of diffusion do not adequately specificy the dimensions of culture and their imp...

  8. Functional Interaction Network Construction and Analysis for Disease Discovery.

    Science.gov (United States)

    Wu, Guanming; Haw, Robin

    2017-01-01

    Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

  9. Unveiling protein functions through the dynamics of the interaction network.

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

    Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

  10. Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

    Directory of Open Access Journals (Sweden)

    Guo Hao

    2011-05-01

    Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

  11. Functional modules by relating protein interaction networks and gene expression.

    Science.gov (United States)

    Tornow, Sabine; Mewes, H W

    2003-11-01

    Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

  12. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  13. Building a glaucoma interaction network using a text mining approach.

    Science.gov (United States)

    Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

    2016-01-01

    The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

  14. Posting Behaviour Patterns in an Online Smoking Cessation Social Network: Implications for Intervention Design and Development

    Science.gov (United States)

    Healey, Benjamin; Hoek, Janet; Edwards, Richard

    2014-01-01

    Objectives Online Cessation Support Networks (OCSNs) are associated with increased quit success rates, but few studies have examined their use over time. We identified usage patterns in New Zealand's largest OCSN over two years and explored implications for OCSN intervention design and evaluation. Methods We analysed metadata relating to 133,096 OCSN interactions during 2011 and 2012. Metrics covered aggregate network activity, user posting activity and longevity, and between-user commenting. Binary logistic regression models were estimated to investigate the feasibility of predicting low user engagement using early interaction data. Results Repeating periodic peaks and troughs in aggregate activity related not only to seasonality (e.g., New Year), but also to day of the week. Out of 2,062 unique users, 69 Highly Engaged Users (180+ interactions each) contributed 69% of all OCSN interactions in 2012 compared to 1.3% contributed by 864 Minimally Engaged Users (metrics including posts and comments, this change did not coincide with large gains in first-time user persistence. Researchers assessing intervention effects should therefore examine multiple measures when evaluating changes in network dynamics over time. PMID:25192174

  15. Unified Alignment of Protein-Protein Interaction Networks.

    Science.gov (United States)

    Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

    2017-04-19

    Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

  16. Mean field interaction in biochemical reaction networks

    KAUST Repository

    Tembine, Hamidou

    2011-09-01

    In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits and illustrate them with numerical examples. © 2011 IEEE.

  17. EVALUATING AUSTRALIAN FOOTBALL LEAGUE PLAYER CONTRIBUTIONS USING INTERACTIVE NETWORK SIMULATION

    Directory of Open Access Journals (Sweden)

    Jonathan Sargent

    2013-03-01

    Full Text Available This paper focuses on the contribution of Australian Football League (AFL players to their team's on-field network by simulating player interactions within a chosen team list and estimating the net effect on final score margin. A Visual Basic computer program was written, firstly, to isolate the effective interactions between players from a particular team in all 2011 season matches and, secondly, to generate a symmetric interaction matrix for each match. Negative binomial distributions were fitted to each player pairing in the Geelong Football Club for the 2011 season, enabling an interactive match simulation model given the 22 chosen players. Dynamic player ratings were calculated from the simulated network using eigenvector centrality, a method that recognises and rewards interactions with more prominent players in the team network. The centrality ratings were recorded after every network simulation and then applied in final score margin predictions so that each player's match contribution-and, hence, an optimal team-could be estimated. The paper ultimately demonstrates that the presence of highly rated players, such as Geelong's Jimmy Bartel, provides the most utility within a simulated team network. It is anticipated that these findings will facilitate optimal AFL team selection and player substitutions, which are key areas of interest to coaches. Network simulations are also attractive for use within betting markets, specifically to provide information on the likelihood of a chosen AFL team list "covering the line".

  18. Structural stability of interaction networks against negative external fields

    Science.gov (United States)

    Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

    2018-04-01

    We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

  19. End of Interactive Emailing from the Technical Network

    CERN Multimedia

    2006-01-01

    According to the CNIC Security Policy for Control Systems (EDMS #584092), interactive emailing on PCs (and other devices) connected to the Technical Network is prohibited. Please note that from November 6th, neither reading emails nor sending emails interactively using e.g. Outlook or Pine mail clients on PCs connected to the Technical Network will be possible anymore. However, automatically generated emails will not be blocked and can still be sent off using CERNMX.CERN.CH as mail server. These restrictions DO NOT apply to PCs connected to any other network, like the General Purpose (or office) network. If you have questions, please do not hesitate to contact Uwe Epting, Pierre Charrue or Stefan Lueders (Technical-Network.Administrator@cern.ch). Your CNIC Working Group

  20. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  1. Evidence of probabilistic behaviour in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2008-01-01

    Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

  2. Transformational leadership and group interaction as climate antecedents: a social network analysis.

    Science.gov (United States)

    Zohar, Dov; Tenne-Gazit, Orly

    2008-07-01

    In order to test the social mechanisms through which organizational climate emerges, this article introduces a model that combines transformational leadership and social interaction as antecedents of climate strength (i.e., the degree of within-unit agreement about climate perceptions). Despite their longstanding status as primary variables, both antecedents have received limited empirical research. The sample consisted of 45 platoons of infantry soldiers from 5 different brigades, using safety climate as the exemplar. Results indicate a partially mediated model between transformational leadership and climate strength, with density of group communication network as the mediating variable. In addition, the results showed independent effects for group centralization of the communication and friendship networks, which exerted incremental effects on climate strength over transformational leadership. Whereas centralization of the communication network was found to be negatively related to climate strength, centralization of the friendship network was positively related to it. Theoretical and practical implications are discussed.

  3. Major component analysis of dynamic networks of physiologic organ interactions

    International Nuclear Information System (INIS)

    Liu, Kang K L; Ma, Qianli D Y; Ivanov, Plamen Ch; Bartsch, Ronny P

    2015-01-01

    The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function. (paper)

  4. Network traffic intelligence using a low interaction honeypot

    Science.gov (United States)

    Nyamugudza, Tendai; Rajasekar, Venkatesh; Sen, Prasad; Nirmala, M.; Madhu Viswanatham, V.

    2017-11-01

    Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

  5. A Global Protein Kinase and Phosphatase Interaction Network in Yeast

    Science.gov (United States)

    Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

    2011-01-01

    The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

  6. Specificity and evolvability in eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2007-02-01

    Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

  7. Interactively Evolving Compositional Sound Synthesis Networks

    DEFF Research Database (Denmark)

    Jónsson, Björn Þór; Hoover, Amy K.; Risi, Sebastian

    2015-01-01

    the space of potential sounds that can be generated through such compositional sound synthesis networks (CSSNs). To study the effect of evolution on subjective appreciation, participants in a listener study ranked evolved timbres by personal preference, resulting in preferences skewed toward the first......While the success of electronic music often relies on the uniqueness and quality of selected timbres, many musicians struggle with complicated and expensive equipment and techniques to create their desired sounds. Instead, this paper presents a technique for producing novel timbres that are evolved...

  8. Dynamic Interactions for Network Visualization and Simulation

    Science.gov (United States)

    2009-03-01

    projects.htm, Site accessed January 5, 2009. 12. John S. Weir, Major, USAF, Mediated User-Simulator Interactive Command with Visualization ( MUSIC -V). Master’s...Computing Sciences in Colleges, December 2005). 14. Enrique Campos -Nanez, “nscript user manual,” Department of System Engineer- ing University of

  9. Interactions In Online Education Implications For Theory & Practice

    Directory of Open Access Journals (Sweden)

    Askim KURT

    2007-04-01

    Full Text Available This book was edited by, Charles Juwah, Senior EducationDevelopment Officer at Robert Gordon University, where heruns the postgraduate learning and teaching qualificationcourse. It was published by Routledge in 2006.Interaction is very important in open and flexible learning,and apparent at all levels of engagement, whether betweenstudents, students and tutors, online learning materials orinterfacing with the learning environment. A student whoactively engages with learning materials, interactions helpto improve learning by fortifying knowledge and providingcontext, encouraging reflection, questioning and deeplyunderstanding of a subject.This book provides international perspectives on key topics including analyzing and designing e-learning interactions, social and conceptual dimensions of learning, interactions in online discussions, interactions in pair learning, and professional development of online facilitators. In this book a collection of research and innovative case material drawn from practitioners and academicians and it covers the theory and the practical implications of related issues. It is essential reading for all those involved in the design,implementation, management and use of open and flexible learning.

  10. Protein interaction networks by proteome peptide scanning.

    Directory of Open Access Journals (Sweden)

    Christiane Landgraf

    2004-01-01

    Full Text Available A substantial proportion of protein interactions relies on small domains binding to short peptides in the partner proteins. Many of these interactions are relatively low affinity and transient, and they impact on signal transduction. However, neither the number of potential interactions mediated by each domain nor the degree of promiscuity at a whole proteome level has been investigated. We have used a combination of phage display and SPOT synthesis to discover all the peptides in the yeast proteome that have the potential to bind to eight SH3 domains. We first identified the peptides that match a relaxed consensus, as deduced from peptides selected by phage display experiments. Next, we synthesized all the matching peptides at high density on a cellulose membrane, and we probed them directly with the SH3 domains. The domains that we have studied were grouped by this approach into five classes with partially overlapping specificity. Within the classes, however, the domains display a high promiscuity and bind to a large number of common targets with comparable affinity. We estimate that the yeast proteome contains as few as six peptides that bind to the Abp1 SH3 domain with a dissociation constant lower than 100 microM, while it contains as many as 50-80 peptides with corresponding affinity for the SH3 domain of Yfr024c. All the targets of the Abp1 SH3 domain, identified by this approach, bind to the native protein in vivo, as shown by coimmunoprecipitation experiments. Finally, we demonstrate that this strategy can be extended to the analysis of the entire human proteome. We have developed an approach, named WISE (whole interactome scanning experiment, that permits rapid and reliable identification of the partners of any peptide recognition module by peptide scanning of a proteome. Since the SPOT synthesis approach is semiquantitative and provides an approximation of the dissociation constants of the several thousands of interactions that are

  11. Depressive Symptoms and Their Interactions With Emotions and Personality Traits Over Time: Interaction Networks in a Psychiatric Clinic.

    Science.gov (United States)

    Semino, Laura N; Marksteiner, Josef; Brauchle, Gernot; Danay, Erik

    2017-04-13

    Associations between depression, personality traits, and emotions are complex and reciprocal. The aim of this study is to explore these interactions in dynamical networks and in a linear way over time depending on the severity of depression. Participants included 110 patients with depressive symptoms (DSM-5 criteria) who were recruited between October 2015 and February 2016 during their inpatient stay in a general psychiatric hospital in Hall in Tyrol, Austria. The patients filled out the Beck Depression Inventory-II, a German emotional competence questionnaire (Emotionale Kompetenz Fragebogen), Positive and Negative Affect Schedule, and the German versions of the Big Five Inventory-short form and State-Trait-Anxiety-Depression Inventory regarding symptoms, emotions, and personality during their inpatient stay and at a 3-month follow-up by mail. Network and regression analyses were performed to explore interactions both in a linear and a dynamical way at baseline and 3 months later. Regression analyses showed that emotions and personality traits gain importance for the prediction of depressive symptoms with decreasing symptomatology at follow-up (personality: baseline, adjusted R2 = 0.24, P personality traits is significantly denser and more interconnected (network comparison test: P = .03) at follow-up than at baseline, meaning that with decreased symptoms interconnections get stronger. During depression, personality traits and emotions are walled off and not strongly interconnected with depressive symptoms in networks. With decreasing depressive symptomatology, interfusing of these areas begins and interconnections become stronger. This finding has practical implications for interventions in an acute depressive state and with decreased symptoms. The network approach offers a new perspective on interactions and is a way to make the complexity of these interactions more tangible. © Copyright 2017 Physicians Postgraduate Press, Inc.

  12. Detecting Friendship Within Dynamic Online Interaction Networks

    OpenAIRE

    Merritt, Sears; Jacobs, Abigail Z.; Mason, Winter; Clauset, Aaron

    2013-01-01

    In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Recovering these hidden ties, particularly for casual users who are relatively less active, would enable a wide variety of friendship-aware applications in domains where labeled data are often unavailable, including online advertising and national security. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal...

  13. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  14. Interacting epidemics and coinfection on contact networks.

    Science.gov (United States)

    Newman, M E J; Ferrario, Carrie R

    2013-01-01

    The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other pathogens. Such coinfection processes, when combined with nontrivial structure in the contact networks over which diseases spread, can lead to complex patterns of epidemiological behavior. Here we consider a mathematical model of two diseases spreading through a single population, where infection with one disease is dependent on prior infection with the other. We solve exactly for the sizes of the outbreaks of both diseases in the limit of large population size, along with the complete phase diagram of the system. Among other things, we use our model to demonstrate how diseases can be controlled not only by reducing the rate of their spread, but also by reducing the spread of other infections upon which they depend.

  15. Interacting epidemics and coinfection on contact networks.

    Directory of Open Access Journals (Sweden)

    M E J Newman

    Full Text Available The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other pathogens. Such coinfection processes, when combined with nontrivial structure in the contact networks over which diseases spread, can lead to complex patterns of epidemiological behavior. Here we consider a mathematical model of two diseases spreading through a single population, where infection with one disease is dependent on prior infection with the other. We solve exactly for the sizes of the outbreaks of both diseases in the limit of large population size, along with the complete phase diagram of the system. Among other things, we use our model to demonstrate how diseases can be controlled not only by reducing the rate of their spread, but also by reducing the spread of other infections upon which they depend.

  16. Estrogen-cholinergic interactions: Implications for cognitive aging.

    Science.gov (United States)

    Newhouse, Paul; Dumas, Julie

    2015-08-01

    This article is part of a Special Issue "Estradiol and Cognition". While many studies in humans have investigated the effects of estrogen and hormone therapy on cognition, potential neurobiological correlates of these effects have been less well studied. An important site of action for estrogen in the brain is the cholinergic system. Several decades of research support the critical role of CNS cholinergic systems in cognition in humans, particularly in learning and memory formation and attention. In humans, the cholinergic system has been implicated in many aspects of cognition including the partitioning of attentional resources, working memory, inhibition of irrelevant information, and improved performance on effort-demanding tasks. Studies support the hypothesis that estradiol helps to maintain aspects of attention and verbal and visual memory. Such cognitive domains are exactly those modulated by cholinergic systems and extensive basic and preclinical work over the past several decades has clearly shown that basal forebrain cholinergic systems are dependent on estradiol support for adequate functioning. This paper will review recent human studies from our laboratories and others that have extended preclinical research examining estrogen-cholinergic interactions to humans. Studies examined include estradiol and cholinergic antagonist reversal studies in normal older women, examinations of the neural representations of estrogen-cholinergic interactions using functional brain imaging, and studies of the ability of selective estrogen receptor modulators such as tamoxifen to interact with cholinergic-mediated cognitive performance. We also discuss the implications of these studies for the underlying hypotheses of cholinergic-estrogen interactions and cognitive aging, and indications for prophylactic and therapeutic potential that may exploit these effects. Published by Elsevier Inc.

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

    Directory of Open Access Journals (Sweden)

    Menizibeya O. Welcome

    2015-01-01

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

  18. Speech networks at rest and in action: interactions between functional brain networks controlling speech production

    Science.gov (United States)

    Fuertinger, Stefan

    2015-01-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

  19. Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

    Science.gov (United States)

    Simonyan, Kristina; Fuertinger, Stefan

    2015-04-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.

  20. Greed and Fear in Network Reciprocity: Implications for Cooperation among Organizations

    Science.gov (United States)

    Kitts, James A.; Leal, Diego F.; Felps, Will; Jones, Thomas M.; Berman, Shawn L.

    2016-01-01

    Extensive interdisciplinary literatures have built on the seminal spatial dilemmas model, which depicts the evolution of cooperation on regular lattices, with strategies propagating locally by relative fitness. In this model agents may cooperate with neighbors, paying an individual cost to enhance their collective welfare, or they may exploit cooperative neighbors and diminish collective welfare. Recent research has extended the model in numerous ways, incorporating behavioral noise, implementing other network topologies or adaptive networks, and employing alternative dynamics of replication. Although the underlying dilemma arises from two distinct dimensions—the gains for exploiting cooperative partners (Greed) and the cost of cooperating with exploitative partners (Fear)–most work following from the spatial dilemmas model has argued or assumed that the dilemma can be represented with a single parameter: This research has typically examined Greed or Fear in isolation, or a composite such as the K-index of Cooperation or the ratio of the benefit to cost of cooperation. We challenge this claim on theoretical grounds—showing that embedding interaction in networks generally leads Greed and Fear to have divergent, interactive, and highly nonlinear effects on cooperation at the macro level, even when individuals respond identically to Greed and Fear. Using computational experiments, we characterize both dynamic local behavior and long run outcomes across regions of this space. We also simulate interventions to investigate changes of Greed and Fear over time, showing how model behavior changes asymmetrically as boundaries in payoff space are crossed, leading some interventions to have irreversible effects on cooperation. We then replicate our experiments on inter-organizational network data derived from links through shared directors among 2,400 large US corporations, thus demonstrating our findings for Greed and Fear on a naturally-occurring network. In closing

  1. Integrated multimedia information system on interactive CATV network

    Science.gov (United States)

    Lee, Meng-Huang; Chang, Shin-Hung

    1998-10-01

    In the current CATV system architectures, they provide one- way delivery of a common menu of entertainment to all the homes through the cable network. Through the technologies evolution, the interactive services (or two-way services) can be provided in the cable TV systems. They can supply customers with individualized programming and support real- time two-way communications. With a view to the service type changed from the one-way delivery systems to the two-way interactive systems, `on demand services' is a distinct feature of multimedia systems. In this paper, we present our work of building up an integrated multimedia system on interactive CATV network in Shih Chien University. Besides providing the traditional analog TV programming from the cable operator, we filter some channels to reserve them as our campus information channels. In addition to the analog broadcasting channel, the system also provides the interactive digital multimedia services, e.g. Video-On- Demand (VOD), Virtual Reality, BBS, World-Wide-Web, and Internet Radio Station. These two kinds of services are integrated in a CATV network by the separation of frequency allocation for the analog broadcasting service and the digital interactive services. Our ongoing work is to port our previous work of building up a VOD system conformed to DAVIC standard (for inter-operability concern) on Ethernet network into the current system.

  2. Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

    Science.gov (United States)

    Gill, Joel C.; Malamud, Bruce D.

    2016-08-01

    This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability

  3. Identification of the BRD1 interaction network and its impact on mental disorder risk

    DEFF Research Database (Denmark)

    Fryland, Tue; Christensen, Jane H; Pallesen, Jonatan

    2016-01-01

    and regulates expression of numerous genes, many of which are involved with brain development and susceptibility to mental disorders. Our findings indicate that BRD1 acts as a regulatory hub in a comprehensive schizophrenia risk network which plays a role in many brain regions throughout life, implicating e......Background: The bromodomain containing 1 (BRD1) gene has been implicated with transcriptional regulation, brain development, and susceptibility to schizophrenia and bipolar disorder. To advance the understanding of BRD1 and its role in mental disorders, we characterized the protein and chromatin...... functional molecular data were integrated with human genomic and transcriptomic data using available GWAS, exome-sequencing datasets as well as spatiotemporal transcriptomic datasets from the human brain. Results: We present several novel protein interactions of BRD1, including isoform-specific interactions...

  4. Modeling human dynamics of face-to-face interaction networks

    OpenAIRE

    Starnini, Michele; Baronchelli, Andrea; Pastor-Satorras, Romualdo

    2013-01-01

    Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of inter-conversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here ...

  5. Epidemic spreading in networks with nonrandom long-range interactions

    Science.gov (United States)

    Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

    2011-09-01

    An “infection,” understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both “close” contacts and “casual” encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called “conductance” controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

  6. Epidemic spreading in networks with nonrandom long-range interactions.

    Science.gov (United States)

    Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

    2011-09-01

    An "infection," understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both "close" contacts and "casual" encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called "conductance" controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

  7. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  8. Simulating market dynamics: interactions between consumer psychology and social networks.

    Science.gov (United States)

    Janssen, Marco A; Jager, Wander

    2003-01-01

    Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

  9. Drug-domain interaction networks in myocardial infarction.

    Science.gov (United States)

    Wang, Haiying; Zheng, Huiru; Azuaje, Francisco; Zhao, Xing-Ming

    2013-09-01

    It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p drugs, their known targets, and seemingly unrelated proteins can be revealed.

  10. Robust collaborative process interactions under system crash and network failures

    NARCIS (Netherlands)

    Wang, Lei; Wombacher, Andreas; Ferreira Pires, Luis; van Sinderen, Marten J.; Chi, Chihung

    2013-01-01

    With the possibility of system crashes and network failures, the design of robust client/server interactions for collaborative process execution is a challenge. If a business process changes its state, it sends messages to the relevant processes to inform about this change. However, server crashes

  11. Characterizing interactions in online social networks during exceptional events

    Science.gov (United States)

    Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

    2015-08-01

    Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

  12. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  13. The ecological and evolutionary implications of merging different types of networks

    NARCIS (Netherlands)

    Fontaine, C.; Guimaraes, P.R.; Kéfi, S.; Loeuille, N.; Memmott, J.; Putten, van der W.H.; Veen, F.J.; Thébault, E.

    2011-01-01

    Interactions among species drive the ecological and evolutionary processes in ecological communities. These interactions are effectively key components of biodiversity. Studies that use a network approach to study the structure and dynamics of communities of interacting species have revealed many

  14. Crucial role of strategy updating for coexistence of strategies in interaction networks

    Science.gov (United States)

    Zhang, Jianlei; Zhang, Chunyan; Cao, Ming; Weissing, Franz J.

    2015-04-01

    Network models are useful tools for studying the dynamics of social interactions in a structured population. After a round of interactions with the players in their local neighborhood, players update their strategy based on the comparison of their own payoff with the payoff of one of their neighbors. Here we show that the assumptions made on strategy updating are of crucial importance for the strategy dynamics. In the first step, we demonstrate that seemingly small deviations from the standard assumptions on updating have major implications for the evolutionary outcome of two cooperation games: cooperation can more easily persist in a Prisoner's Dilemma game, while it can go more easily extinct in a Snowdrift game. To explain these outcomes, we develop a general model for the updating of states in a network that allows us to derive conditions for the steady-state coexistence of states (or strategies). The analysis reveals that coexistence crucially depends on the number of agents consulted for updating. We conclude that updating rules are as important for evolution on a network as network structure and the nature of the interaction.

  15. Patterns of interactions of a large fish-parasite network in a tropical floodplain.

    Science.gov (United States)

    Lima, Dilermando P; Giacomini, Henrique C; Takemoto, Ricardo M; Agostinho, Angelo A; Bini, Luis M

    2012-07-01

    1. Describing and explaining the structure of species interaction networks is of paramount importance for community ecology. Yet much has to be learned about the mechanisms responsible for major patterns, such as nestedness and modularity in different kinds of systems, of which large and diverse networks are a still underrepresented and scarcely studied fraction. 2. We assembled information on fishes and their parasites living in a large floodplain of key ecological importance for freshwater ecosystems in the Paraná River basin in South America. The resulting fish-parasite network containing 72 and 324 species of fishes and parasites, respectively, was analysed to investigate the patterns of nestedness and modularity as related to fish and parasite features. 3. Nestedness was found in the entire network and among endoparasites, multiple-host life cycle parasites and native hosts, but not in networks of ectoparasites, single-host life cycle parasites and non-native fishes. All networks were significantly modular. Taxonomy was the major host's attribute influencing both nestedness and modularity: more closely related host species tended to be associated with more nested parasite compositions and had greater chance of belonging to the same network module. Nevertheless, host abundance had a positive relationship with nestedness when only native host species pairs of the same network module were considered for analysis. 4. These results highlight the importance of evolutionary history of hosts in linking patterns of nestedness and formation of modules in the network. They also show that functional attributes of parasites (i.e. parasitism mode and life cycle) and origin of host populations (i.e. natives versus non-natives) are crucial to define the relative contribution of these two network properties and their dependence on other ecological factors (e.g. host abundance), with potential implications for community dynamics and stability. © 2012 The Authors

  16. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  17. How People Interact in Evolving Online Affiliation Networks

    Science.gov (United States)

    Gallos, Lazaros K.; Rybski, Diego; Liljeros, Fredrik; Havlin, Shlomo; Makse, Hernán A.

    2012-07-01

    The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks.

  18. Exact tensor network ansatz for strongly interacting systems

    Science.gov (United States)

    Zaletel, Michael P.

    It appears that the tensor network ansatz, while not quite complete, is an efficient coordinate system for the tiny subset of a many-body Hilbert space which can be realized as a low energy state of a local Hamiltonian. However, we don't fully understand precisely which phases are captured by the tensor network ansatz, how to compute their physical observables (even numerically), or how to compute a tensor network representation for a ground state given a microscopic Hamiltonian. These questions are algorithmic in nature, but their resolution is intimately related to understanding the nature of quantum entanglement in many-body systems. For this reason it is useful to compute the tensor network representation of various `model' wavefunctions representative of different phases of matter; this allows us to understand how the entanglement properties of each phase are expressed in the tensor network ansatz, and can serve as test cases for algorithm development. Condensed matter physics has many illuminating model wavefunctions, such as Laughlin's celebrated wave function for the fractional quantum Hall effect, the Bardeen-Cooper-Schrieffer wave function for superconductivity, and Anderson's resonating valence bond ansatz for spin liquids. This thesis presents some results on exact tensor network representations of these model wavefunctions. In addition, a tensor network representation is given for the time evolution operator of a long-range one-dimensional Hamiltonian, which allows one to numerically simulate the time evolution of power-law interacting spin chains as well as two-dimensional strips and cylinders.

  19. Cannabinoid and opioid interactions: implications for opiate dependence and withdrawal.

    Science.gov (United States)

    Scavone, J L; Sterling, R C; Van Bockstaele, E J

    2013-09-17

    Withdrawal from opiates, such as heroin or oral narcotics, is characterized by a host of aversive physical and emotional symptoms. High rates of relapse and limited treatment success rates for opiate addiction have prompted a search for new approaches. For many opiate addicts, achieving abstinence may be further complicated by poly-drug use and co-morbid mental disorders. Research over the past decade has shed light on the influence of endocannabinoids (ECs) on the opioid system. Evidence from both animal and clinical studies point toward an interaction between these two systems, and suggest that targeting the EC system may provide novel interventions for managing opiate dependence and withdrawal. This review will summarize the literature surrounding the molecular effects of cannabinoids and opioids on the locus coeruleus-norepinephrine system, a key circuit implicated in the negative sequelae of opiate addiction. A consideration of the trends and effects of marijuana use in those seeking treatment to abstain from opiates in the clinical setting will also be presented. In summary, the present review details how cannabinoid-opioid interactions may inform novel interventions in the management of opiate dependence and withdrawal. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  20. Digital Ecology: Coexistence and Domination among Interacting Networks

    Science.gov (United States)

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2015-05-01

    The overwhelming success of Web 2.0, within which online social networks are key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of Web 2.0 services has allowed researchers to quantify large-scale social patterns for the first time. However, the mechanisms that determine the fate of networks at the system level are still poorly understood. For instance, the simultaneous existence of multiple digital services naturally raises questions concerning which conditions these services can coexist under. Analogously to the case of population dynamics, the digital world forms a complex ecosystem of interacting networks. The fitness of each network depends on its capacity to attract and maintain users’ attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits stable coexistence of several networks as well as the dominance of an individual one, in contrast to the competitive exclusion principle. Interestingly, our theory also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.

  1. Myths on Bi-direction Communication of Web 2.0 Based Social Networks: Is Social Network Truly Interactive?

    Science.gov (United States)

    2011-03-10

    more and more social interactions are happening on the on-line. Especially recent uptake of the social network sites (SNSs), such as Facebook (http...Smart phones • Live updates within social networks • Facebook & Twitters Solution: WebMon for Risk Management Need for New WebMon for Social Networks ...Title: Myths on bi-direction communication of Web 2.0 based social networks : Is social network truly interactive

  2. Self-organization of social hierarchy on interaction networks

    International Nuclear Information System (INIS)

    Fujie, Ryo; Odagaki, Takashi

    2011-01-01

    In order to examine the effects of interaction network structures on the self-organization of social hierarchy, we introduce the agent-based model: each individual as on a node of a network has its own power and its internal state changes by fighting with its neighbors and relaxation. We adopt three different networks: regular lattice, small-world network and scale-free network. For the regular lattice, we find the emergence of classes distinguished by the internal state. The transition points where each class emerges are determined analytically, and we show that each class is characterized by the local ranking relative to their neighbors. We also find that the antiferromagnetic-like configuration emerges just above the critical point. For the heterogeneous networks, individuals become winners (or losers) in descending order of the number of their links. By using mean-field analysis, we reveal that the transition point is determined by the maximum degree and the degree distribution in its neighbors

  3. Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

    Science.gov (United States)

    Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

    2016-11-01

    Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

  4. Feed forward neural networks modeling for K-P interactions

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2003-01-01

    Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

  5. Auditing Medical Records Accesses via Healthcare Interaction Networks

    Science.gov (United States)

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

    Healthcare organizations are deploying increasingly complex clinical information systems to support patient care. Traditional information security practices (e.g., role-based access control) are embedded in enterprise-level systems, but are insufficient to ensure patient privacy. This is due, in part, to the dynamic nature of healthcare, which makes it difficult to predict which care providers need access to what and when. In this paper, we show that modeling operations at a higher level of granularity (e.g., the departmental level) are stable in the context of a relational network, which may enable more effective auditing strategies. We study three months of access logs from a large academic medical center to illustrate that departmental interaction networks exhibit certain invariants, such as the number, strength, and reciprocity of relationships. We further show that the relations extracted from the network can be leveraged to assess the extent to which a patient’s care satisfies expected organizational behavior. PMID:23304277

  6. Games as Actors - Interaction, Play, Design, and Actor Network Theory

    DEFF Research Database (Denmark)

    Jessen, Jari Due; Jessen, Carsten

    2014-01-01

    When interacting with computer games, users are forced to follow the rules of the game in return for the excitement, joy, fun, or other pursued experiences. In this paper, we investigate how games a chieve these experiences in the perspective of Actor Network Theory (ANT). Based on a qualitative......, and by doing so they create in humans what in modern play theory is known as a “state of play”...

  7. Protein Annotation from Protein Interaction Networks and Gene Ontology

    OpenAIRE

    Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2011-01-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

  8. Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions.

    Science.gov (United States)

    Meesters, Stephan; Ossenblok, Pauly; Colon, Albert; Wagner, Louis; Schijns, Olaf; Boon, Paul; Florack, Luc; Fuster, Andrea

    2018-06-01

    The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions. To identify the synchronized neuronal activity underlying the IEDs, the variation in correlation over time of the SEEG signals is related to the occurrence of IEDs using the general linear model. The interdependency is assessed of the brain areas that reflect highly synchronized neural activity by applying independent component analysis, followed by cluster analysis of the spatial distributions of the independent components. The spatiotemporal interactions of the spike clusters reveal the leading or lagging of brain areas. The analysis framework was evaluated for five successfully operated patients, showing that the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone. The additional value of the framework was demonstrated for two more patients, who were MRI-negative and for whom surgery was not successful. A network approach is promising in case of complex epilepsies. Analysis of IEDs is considered a valuable addition to routine review of SEEG recordings, with the potential to increase the success rate of epilepsy surgery. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  9. Network characteristics emerging from agent interactions in balanced distributed system.

    Science.gov (United States)

    Salman, Mahdi Abed; Bertelle, Cyrille; Sanlaville, Eric

    2015-01-01

    A distributed computing system behaves like a complex network, the interactions between nodes being essential information exchanges and migrations of jobs or services to execute. These actions are performed by software agents, which behave like the members of social networks, cooperating and competing to obtain knowledge and services. The load balancing consists in distributing the load evenly between system nodes. It aims at enhancing the resource usage. A load balancing strategy specifies scenarios for the cooperation. Its efficiency depends on quantity, accuracy, and distribution of available information. Nevertheless, the distribution of information on the nodes, together with the initial network structure, may create different logical network structures. In this paper, different load balancing strategies are tested on different network structures using a simulation. The four tested strategies are able to distribute evenly the load so that the system reaches a steady state (the mean response time of the jobs is constant), but it is shown that a given strategy indeed behaves differently according to structural parameters and information spreading. Such a study, devoted to distributed computing systems (DCSs), can be useful to understand and drive the behavior of other complex systems.

  10. Oligomeric protein structure networks: insights into protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

  11. Peptide microarrays to probe for competition for binding sites in a protein interaction network

    NARCIS (Netherlands)

    Sinzinger, M.D.S.; Ruttekolk, I.R.R.; Gloerich, J.; Wessels, H.; Chung, Y.D.; Adjobo-Hermans, M.J.W.; Brock, R.E.

    2013-01-01

    Cellular protein interaction networks are a result of the binding preferences of a particular protein and the entirety of interactors that mutually compete for binding sites. Therefore, the reconstruction of interaction networks by the accumulation of interaction networks for individual proteins

  12. Exploring drug-target interaction networks of illicit drugs.

    Science.gov (United States)

    Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming

    2013-01-01

    Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. This study presents the first systematic review of the network

  13. Network of interactions between ciliates and phytoplankton during spring

    Directory of Open Access Journals (Sweden)

    Thomas ePosch

    2015-11-01

    Full Text Available The annually recurrent spring phytoplankton blooms in freshwater lakes initiate pronounced successions of planktonic ciliate species. Although there is considerable knowledge on the taxonomic diversity of these ciliates, their species-specific interactions with other microorganisms are still not well understood. Here we present the succession patterns of 20 morphotypes of ciliates during spring in Lake Zurich, Switzerland, and we relate their abundances to phytoplankton genera, flagellates, heterotrophic bacteria, and abiotic parameters. Interspecific relationships were analyzed by contemporaneous correlations and time-lagged co-occurrence and visualized as association networks. The contemporaneous network pointed to the pivotal role of distinct ciliate species (e.g., Balanion planctonicum, Rimostrombidium humile as primary consumers of cryptomonads, revealed a clear overclustering of mixotrophic / omnivorous species, and highlighted the role of Halteria / Pelagohalteria as important bacterivores. By contrast, time-lagged statistical approaches (like local similarity analyses, LSA proved to be inadequate for the evaluation of high-frequency sampling data. LSA led to a conspicuous inflation of significant associations, making it difficult to establish ecologically plausible interactions between ciliates and other microorganisms. Nevertheless, if adequate statistical procedures are selected, association networks can be powerful tools to formulate testable hypotheses about the autecology of only recently described ciliate species.

  14. Systems pharmacology - Towards the modeling of network interactions.

    Science.gov (United States)

    Danhof, Meindert

    2016-10-30

    Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and

  15. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

    Directory of Open Access Journals (Sweden)

    Brendan Chambers

    2016-08-01

    Full Text Available Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.

  16. Interactive social contagions and co-infections on complex networks

    Science.gov (United States)

    Liu, Quan-Hui; Zhong, Lin-Feng; Wang, Wei; Zhou, Tao; Eugene Stanley, H.

    2018-01-01

    What we are learning about the ubiquitous interactions among multiple social contagion processes on complex networks challenges existing theoretical methods. We propose an interactive social behavior spreading model, in which two behaviors sequentially spread on a complex network, one following the other. Adopting the first behavior has either a synergistic or an inhibiting effect on the spread of the second behavior. We find that the inhibiting effect of the first behavior can cause the continuous phase transition of the second behavior spreading to become discontinuous. This discontinuous phase transition of the second behavior can also become a continuous one when the effect of adopting the first behavior becomes synergistic. This synergy allows the second behavior to be more easily adopted and enlarges the co-existence region of both behaviors. We establish an edge-based compartmental method, and our theoretical predictions match well with the simulation results. Our findings provide helpful insights into better understanding the spread of interactive social behavior in human society.

  17. Supply Chain Management: from Linear Interactions to Networked Processes

    Directory of Open Access Journals (Sweden)

    Doina FOTACHE

    2006-01-01

    Full Text Available Supply Chain Management is a distinctive product, with a tremendous impact on the software applications market. SCM applications are back-end solutions intended to link suppliers, manufacturers, distributors and resellers in a production and distribution network, which allows the enterprise to track and consolidate the flows of materials and data trough the process of manufacturing and distribution of goods/services. The advent of the Web as a major means of conducting business transactions and business-tobusiness communications, coupled with evolving web-based supply chain management (SCM technology, has resulted in a transition period from “linear” supply chain models to "networked" supply chain models. The technologies to enable dynamic process changes and real time interactions between extended supply chain partners are emerging and being deployed at an accelerated pace.

  18. Modern Social Support Structures: Online Social Networks and their Implications for Social Workers

    Directory of Open Access Journals (Sweden)

    Kala Chakradhar

    2009-03-01

    Full Text Available Mapping and assessing social networks and the quality of their social support is a valuable intervention strategy for social workers. These networks have now spread onto the digital realm in the form of Online Social Networks (OSNs. This study investigated the nature of social support provided by such networks to their users in a rural mid-South University (USA and explored parallels with the current understanding of social support in conventional social networks. A web-based survey administered to college students revealed that users of these online networks were predominantly undergraduate first year students, female, single, unemployed and from a variety of academic disciplines. The examination of the components of OSNs appears to mirror those of offline networks. They also seem to complement the effects of each other while contributing to an individual's support system. The paper concludes with critical implications of such online social networking for University students and social workers in practice and education.

  19. Highly dynamic animal contact network and implications on disease transmission

    OpenAIRE

    Shi Chen; Brad J. White; Michael W. Sanderson; David E. Amrine; Amiyaal Ilany; Cristina Lanzas

    2014-01-01

    Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmissio...

  20. Visualization of protein interaction networks: problems and solutions

    Directory of Open Access Journals (Sweden)

    Agapito Giuseppe

    2013-01-01

    Full Text Available Abstract Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins and edges (interactions, the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i technology, i.e. availability/license of the software and supported OS (Operating System platforms; (ii interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the

  1. Interacting loop-current model of superconducting networks

    International Nuclear Information System (INIS)

    Chi, C.C.; Santhanam, P.; Bloechl, P.E.

    1992-01-01

    The authors review their recent approximation scheme to calculate the normal-superconducting phase boundary, T c (H), of a superconducting wire network in a magnetic field in terms of interacting loop currents. The theory is based on the London approximation of the linearized Ginzburg-Landau equation. An approximate general formula is derived for any two-dimensional space-filling lattice comprising tiles of two shapes. Many examples are provided illustrating the use of this method, with a particular emphasis on the fluxoid distribution. In addition to periodic lattices, quasiperiodic lattices and fractal Sierpinski gaskets are also discussed

  2. Protein annotation from protein interaction networks and Gene Ontology.

    Science.gov (United States)

    Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

    2011-10-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Coevolution of Synchronization and Cooperation in Costly Networked Interactions

    Science.gov (United States)

    Antonioni, Alberto; Cardillo, Alessio

    2017-06-01

    Despite the large number of studies on synchronization, the hypothesis that interactions bear a cost for involved individuals has seldom been considered. The introduction of costly interactions leads, instead, to the formulation of a dichotomous scenario in which an individual may decide to cooperate and pay the cost in order to get synchronized with the rest of the population. Alternatively, the same individual can decide to free ride, without incurring any cost, waiting for others to get synchronized to his or her state. Thus, the emergence of synchronization may be seen as the byproduct of an evolutionary game in which individuals decide their behavior according to the benefit-to-cost ratio they accrued in the past. We study the onset of cooperation and synchronization in networked populations of Kuramoto oscillators and report how topology is essential in order for cooperation to thrive. We also display how different classes of topology foster synchronization differently both at microscopic and macroscopic levels.

  4. Messaging Performance of FIPA Interaction Protocols in Networked Embedded Controllers

    Directory of Open Access Journals (Sweden)

    García JoséAPérez

    2008-01-01

    Full Text Available Abstract Agent-based technologies in production control systems could facilitate seamless reconfiguration and integration of mechatronic devices/modules into systems. Advances in embedded controllers which are continuously improving computational capabilities allow for software modularization and distribution of decisions. Agent platforms running on embedded controllers could hide the complexity of bootstrap and communication. Therefore, it is important to investigate the messaging performance of the agents whose main motivation is the resource allocation in manufacturing systems (i.e., conveyor system. The tests were implemented using the FIPA-compliant JADE-LEAP agent platform. Agent containers were distributed through networked embedded controllers, and agents were communicating using request and contract-net FIPA interaction protocols. The test scenarios are organized in intercontainer and intracontainer communications. The work shows the messaging performance for the different test scenarios using both interaction protocols.

  5. Messaging Performance of FIPA Interaction Protocols in Networked Embedded Controllers

    Directory of Open Access Journals (Sweden)

    Omar Jehovani López Orozco

    2007-12-01

    Full Text Available Agent-based technologies in production control systems could facilitate seamless reconfiguration and integration of mechatronic devices/modules into systems. Advances in embedded controllers which are continuously improving computational capabilities allow for software modularization and distribution of decisions. Agent platforms running on embedded controllers could hide the complexity of bootstrap and communication. Therefore, it is important to investigate the messaging performance of the agents whose main motivation is the resource allocation in manufacturing systems (i.e., conveyor system. The tests were implemented using the FIPA-compliant JADE-LEAP agent platform. Agent containers were distributed through networked embedded controllers, and agents were communicating using request and contract-net FIPA interaction protocols. The test scenarios are organized in intercontainer and intracontainer communications. The work shows the messaging performance for the different test scenarios using both interaction protocols.

  6. Chaos in generically coupled phase oscillator networks with nonpairwise interactions

    Energy Technology Data Exchange (ETDEWEB)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana [Centre for Systems, Dynamics and Control and Department of Mathematics, University of Exeter, Exeter EX4 4QF (United Kingdom)

    2016-09-15

    The Kuramoto–Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling—including three and four-way interactions of the oscillator phases—that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  7. Chaos in generically coupled phase oscillator networks with nonpairwise interactions.

    Science.gov (United States)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-01

    The Kuramoto-Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling-including three and four-way interactions of the oscillator phases-that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  8. Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

    International Nuclear Information System (INIS)

    Blenk, Steffen; Engelmann, Julia C; Pinkert, Stefan; Weniger, Markus; Schultz, Jörg; Rosenwald, Andreas; Müller-Hermelink, Hans K; Müller, Tobias; Dandekar, Thomas

    2008-01-01

    Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data

  9. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  10. Sequence memory based on coherent spin-interaction neural networks.

    Science.gov (United States)

    Xia, Min; Wong, W K; Wang, Zhijie

    2014-12-01

    Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

  11. Graph theoretic analysis of protein interaction networks of eukaryotes

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2005-11-01

    Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

  12. The paradox of caffeine-zolpidem interaction: a network analysis.

    Science.gov (United States)

    Myslobodsky, Michael

    2009-10-01

    A widely prescribed and potent short-acting hypnotic, zolpidem has become the mainstay for the treatment of middle-of-the-night sleeplessness. It is expected to be antagonized by caffeine. Paradoxically, in some cases caffeine appears to slightly enhance zolpidem sedation. The pharmacokinetic and pharmacodynamic nature of this odd effect remains unexplored. The purpose of this study is to reproduce a hypothetical molecular network recruited by caffeine when co-administered with zolpidem using Ingenuity Pathway Analysis. Thus generated, network drew attention to several possible contributors to caffeine sedation, such as tachykinin precursor 1, cannabinoid, and GABA receptors. The present overview is centered on the possibility that caffeine potentiation of zolpidem sedation does not involve a centralized interaction of specific neurotransmitters, but rather is contributed by its antioxidant capacity. It is proposed that by modifying the cellular redox state, caffeine ultimately reduces the pool of reactive oxygen species, thereby increasing the bioavailability of endogenous melatonin for interaction with zolpidem. This side effect of caffeine encourages further studies of multiple antioxidants as an attractive way to potentially increasing somnolence.

  13. Accessing Wireless Sensor Networks Via Dynamically Reconfigurable Interaction Models

    Directory of Open Access Journals (Sweden)

    Maria Cecília Gomes

    2012-12-01

    Full Text Available The Wireless Sensor Networks (WSNs technology is already perceived as fundamental for science across many domains, since it provides a low cost solution for environment monitoring. WSNs representation via the service concept and its inclusion in Web environments, e.g. through Web services, supports particularly their open/standard access and integration. Although such Web enabled WSNs simplify data access, network parameterization and aggregation, the existing interaction models and run-time adaptation mechanisms available to clients are still scarce. Nevertheless, applications increasingly demand richer and more flexible accesses besides the traditional client/server. For instance, applications may require a streaming model in order to avoid sequential data requests, or the asynchronous notification of subscribed data through the publish/subscriber. Moreover, the possibility to automatically switch between such models at runtime allows applications to define flexible context-based data acquisition. To this extent, this paper discusses the relevance of the session and pattern abstractions on the design of a middleware prototype providing richer and dynamically reconfigurable interaction models to Web enabled WSNs.

  14. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  15. Evaluation of clustering algorithms for protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    van Helden Jacques

    2006-11-01

    Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

  16. Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartite networks.

    Science.gov (United States)

    Astegiano, Julia; Altermatt, Florian; Massol, François

    2017-11-13

    Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.

  17. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    Science.gov (United States)

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  18. Tourist activated networks: Implications for dynamic bundling and en-route recommendations

    DEFF Research Database (Denmark)

    Zach, Florian; Gretzel, Ulrike

    2011-01-01

    This article discusses tourist-activated networks as a concept to inform technological applications supporting dynamic bundling and en route recommendations. Empirical data were collected from travelers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist-activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist-activated network and provide implications for technology design and tourism...

  19. The A. Q. Khan Network: Causes and Implications

    National Research Council Canada - National Science Library

    Clary, Christopher O

    2005-01-01

    .... For the first time in history, all of the keys to a nuclear weapon -- the supplier networks, the material, the enrichment technology, and the warhead designs -- were outside of state oversight and control...

  20. Computer networks and their implications for nuclear data

    International Nuclear Information System (INIS)

    Carlson, J.

    1992-01-01

    Computer networks represent a valuable resource for accessing information. Just as the computer has revolutionized the ability to process and analyze information, networks have and will continue to revolutionize data collection and access. A number of services are in routine use that would not be possible without the presence of an (inter)national computer network (which will be referred to as the internet). Services such as electronic mail, remote terminal access, and network file transfers are almost a required part of any large scientific/research organization. These services only represent a small fraction of the potential uses of the internet; however, the remainder of this paper discusses some of these uses and some technological developments that may influence these uses

  1. IMPLICATIONS OF SOCIAL RESPONSIBILITY DISCLOSURE ON GLOBAL PRODUCTION NETWORK

    OpenAIRE

    Le Bo; Dan Shen; Jin Jun Bo

    2014-01-01

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

  2. The advertising creations network : implications to society and organizations

    OpenAIRE

    Παζαρζή, Νίνα Ε.

    2007-01-01

    This article investigates television-advertising production in the context of the systemic thinking. Advertising rhetoric is based on shared codes that are used by particular professional networks of the «urban elites». New technologies, new cinematographic techniques, latest fads, reformed postmodern arguments, etc are developed by «lead users» and subsequently are passed along those networks. In this respect, particular innovative practices can gradually become a part of the ...

  3. Social Networking Sites as Communication, Interaction, and Learning Environments: Perceptions and Preferences of Distance Education Students

    Science.gov (United States)

    Bozkurt, Aras; Karadeniz, Abdulkadir; Kocdar, Serpil

    2017-01-01

    The advent of Web 2.0 technologies transformed online networks into interactive spaces in which user-generated content has become the core material. With the possibilities that emerged from Web 2.0, social networking sites became very popular. The capability of social networking sites promises opportunities for communication and interaction,…

  4. User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

    Science.gov (United States)

    Ko, Moo Nam

    2011-01-01

    Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

  5. Modeling attacker-defender interactions in information networks.

    Energy Technology Data Exchange (ETDEWEB)

    Collins, Michael Joseph

    2010-09-01

    The simplest conceptual model of cybersecurity implicitly views attackers and defenders as acting in isolation from one another: an attacker seeks to penetrate or disrupt a system that has been protected to a given level, while a defender attempts to thwart particular attacks. Such a model also views all non-malicious parties as having the same goal of preventing all attacks. But in fact, attackers and defenders are interacting parts of the same system, and different defenders have their own individual interests: defenders may be willing to accept some risk of successful attack if the cost of defense is too high. We have used game theory to develop models of how non-cooperative but non-malicious players in a network interact when there is a substantial cost associated with effective defensive measures. Although game theory has been applied in this area before, we have introduced some novel aspects of player behavior in our work, including: (1) A model of how players attempt to avoid the costs of defense and force others to assume these costs; (2) A model of how players interact when the cost of defending one node can be shared by other nodes; and (3) A model of the incentives for a defender to choose less expensive, but less effective, defensive actions.

  6. Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-01-01

    Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with

  7. A networks analysis of terrorism in Africa: implications for Kenya

    Directory of Open Access Journals (Sweden)

    Steven Kigen Morumbasi

    2016-12-01

    Full Text Available This paper highlights the challenges that the international community faces in responding to the terrorists and the need to change tactics to respond more effectively to an increasingly nebulous enemy. Terrorism can take different forms and is perpetrated by both state and non-state actors. This research looks into the network structure of terrorism and terrorist groups. In the contemporary setting, terrorist organizations operate transnationally hence the use of the term ‘terrorism without borders’. An enabling factor of terrorism today is the network structure that it has adopted which gives it the ability to both project its reach and prevent easy infiltration. The network structure has also brought about renewed interests in Africa, where global terror networks such as al-Qaeda and the Islamic State compete for influence. Boko Haram in West Africa is an affiliate of the Islamic State and this provides possible linkages with the Islamic State in Libya. Boko Haram refers to itself as the Islamic State’s Western Province. Al-Shabaab has dominated headlines by carrying out deadly attacks in East Africa. The al-Qaeda affiliate has however faced resistance from a section of its members who seek ties with the Islamic State. This resulted in the formation of Jabha East Africa, a group that aligns itself to the Islamic State. The Sinai Peninsula has also witnessed an upsurge of terror attacks perpetrated by the Sinai Province, which views itself as a province of the Islamic State. This surmounts to a complex network structure of terrorist networks in Africa and the growing threat to militant Islam. The special attention is paid to analysis of terrorist challenges in Kenia.

  8. NatalieQ: A web server for protein-protein interaction network querying

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  9. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2016-01-01

    Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  10. Determine point-to-point networking interactions using regular expressions

    Directory of Open Access Journals (Sweden)

    Konstantin S. Deev

    2015-06-01

    Full Text Available As Internet growth and becoming more popular, the number of concurrent data flows start to increasing, which makes sense in bandwidth requested. Providers and corporate customers need ability to identify point-to-point interactions. The best is to use special software and hardware implementations that distribute the load in the internals of the complex, using the principles and approaches, in particular, described in this paper. This paper represent the principles of building system, which searches for a regular expression match using computing on graphics adapter in server station. A significant computing power and capability to parallel execution on modern graphic processor allows inspection of large amounts of data through sets of rules. Using the specified characteristics can lead to increased computing power in 30…40 times compared to the same setups on the central processing unit. The potential increase in bandwidth capacity could be used in systems that provide packet analysis, firewalls and network anomaly detectors.

  11. Specialization of mutualistic interaction networks decreases toward tropical latitudes

    DEFF Research Database (Denmark)

    Schleuning, M.; Fründ, J.; Klein, A.-M.

    2012-01-01

    that current conditions have a stronger effect on biotic specialization than historical community stability. Biotic specialization decreased with increasing local and regional plant diversity. This suggests that high specialization of mutualistic interactions is a response of pollinators and seed dispersers......] or differences in plant diversity [10, 11]. Thus, the direction of the latitudinal specialization gradient remains contentious. With an unprecedented global data set, we investigated how biotic specialization between plants and animal pollinators or seed dispersers is associated with latitude, past...... and contemporary climate, and plant diversity. We show that in contrast to expectation, biotic specialization of mutualistic networks is significantly lower at tropical than at temperate latitudes. Specialization was more closely related to contemporary climate than to past climate stability, suggesting...

  12. The brain's default network: origins and implications for the study of psychosis.

    Science.gov (United States)

    Buckner, Randy L

    2013-09-01

    The brain's default network is a set of regions that is spontaneously active during passive moments. The network is also active during directed tasks that require participants to remember past events or imagine upcoming events. One hypothesis is that the network facilitates construction of mental models (simulations) that can be used adaptively in many contexts. Extensive research has considered whether disruption of the default network may contribute to disease. While an intriguing possibility, a specific challenge to this notion is the fact that it is difficult to accurately measure the default network in patients where confounds of head motion and compliance are prominent. Nonetheless, some intriguing recent findings suggest that dysfunctional interactions between front-oparietal control systems and the default network contribute to psychosis. Psychosis may be a network disturbance that manifests as disordered thought partly because it disrupts the fragile balance between the default network and competing brain systems.

  13. China's evolving role in global production networks: Implications for Trump's trade war

    OpenAIRE

    Athukorala, Prema-chandra

    2017-01-01

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

  14. Pleistocene megafaunal interaction networks became more vulnerable after human arrival.

    Science.gov (United States)

    Pires, Mathias M; Koch, Paul L; Fariña, Richard A; de Aguiar, Marcus A M; dos Reis, Sérgio F; Guimarães, Paulo R

    2015-09-07

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions. © 2015 The Author(s).

  15. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    Science.gov (United States)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  16. Social Capital Theory: Implications for Women's Networking and Learning

    Science.gov (United States)

    Alfred, Mary V.

    2009-01-01

    This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

  17. Networking in the Workplace: Implications for Women's Career Development

    Science.gov (United States)

    Wang, Jia

    2009-01-01

    Although the value of social capital for organization and individual career success is widely recognized, gender as a moderator in the building of social capital in organizational settings has not received adequate research attention. This chapter looks at how professional women use one aspect of social capital--networks--to assist with their…

  18. A value network development model and implications for innovation and production network management

    NARCIS (Netherlands)

    Vermeulen, B.; Kok, de A.G.

    2013-01-01

    In managing their value network, firms have to balance current and future value concerns and own and network partners’ concerns. Firms generate immediate value through manufacturing and selling the current generation of products together with other firms in its production network and generate future

  19. Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy.

    Directory of Open Access Journals (Sweden)

    Tejaswini Narayanan

    Full Text Available Duchenne Muscular Dystrophy (DMD is an important pathology associated with the human skeletal muscle and has been studied extensively. Gene expression measurements on skeletal muscle of patients afflicted with DMD provides the opportunity to understand the underlying mechanisms that lead to the pathology. Community structure analysis is a useful computational technique for understanding and modeling genetic interaction networks. In this paper, we leverage this technique in combination with gene expression measurements from normal and DMD patient skeletal muscle tissue to study the structure of genetic interactions in the context of DMD. We define a novel framework for transforming a raw dataset of gene expression measurements into an interaction network, and subsequently apply algorithms for community structure analysis for the extraction of topological communities. The emergent communities are analyzed from a biological standpoint in terms of their constituent biological pathways, and an interpretation that draws correlations between functional and structural organization of the genetic interactions is presented. We also compare these communities and associated functions in pathology against those in normal human skeletal muscle. In particular, differential enhancements are observed in the following pathways between pathological and normal cases: Metabolic, Focal adhesion, Regulation of actin cytoskeleton and Cell adhesion, and implication of these mechanisms are supported by prior work. Furthermore, our study also includes a gene-level analysis to identify genes that are involved in the coupling between the pathways of interest. We believe that our results serve to highlight important distinguishing features in the structural/functional organization of constituent biological pathways, as it relates to normal and DMD cases, and provide the mechanistic basis for further biological investigations into specific pathways differently regulated

  20. The effect of volume exclusion on the formation of DNA minicircle networks: implications to kinetoplast DNA

    International Nuclear Information System (INIS)

    Diao, Y; Hinson, K; Sun, Y; Arsuaga, J

    2015-01-01

    Kinetoplast DNA (kDNA) is the mitochondrial of DNA of disease causing organisms such as Trypanosoma Brucei (T. Brucei) and Trypanosoma Cruzi (T. Cruzi). In most organisms, KDNA is made of thousands of small circular DNA molecules that are highly condensed and topologically linked forming a gigantic planar network. In our previous work we have developed mathematical and computational models to test the confinement hypothesis, that is that the formation of kDNA minicircle networks is a product of the high DNA condensation achieved in the mitochondrion of these organisms. In these studies we studied three parameters that characterize the growth of the network topology upon confinement: the critical percolation density, the mean saturation density and the mean valence (i.e. the number of mini circles topologically linked to any chosen minicircle). Experimental results on insect-infecting organisms showed that the mean valence is equal to three, forming a structure similar to those found in medieval chain-mails. These same studies hypothesized that this value of the mean valence was driven by the DNA excluded volume. Here we extend our previous work on kDNA by characterizing the effects of DNA excluded volume on the three descriptive parameters. Using computer simulations of polymer swelling we found that (1) in agreement with previous studies the linking probability of two minicircles does not decrease linearly with the distance between the two minicircles, (2) the mean valence grows linearly with the density of minicircles and decreases with the thickness of the excluded volume, (3) the critical percolation and mean saturation densities grow linearly with the thickness of the excluded volume. Our results therefore suggest that the swelling of the DNA molecule, due to electrostatic interactions, has relatively mild implications on the overall topology of the network. Our results also validate our topological descriptors since they appear to reflect the changes in the

  1. The effect of volume exclusion on the formation of DNA minicircle networks: implications to kinetoplast DNA

    Science.gov (United States)

    Diao, Y.; Hinson, K.; Sun, Y.; Arsuaga, J.

    2015-10-01

    Kinetoplast DNA (kDNA) is the mitochondrial of DNA of disease causing organisms such as Trypanosoma Brucei (T. Brucei) and Trypanosoma Cruzi (T. Cruzi). In most organisms, KDNA is made of thousands of small circular DNA molecules that are highly condensed and topologically linked forming a gigantic planar network. In our previous work we have developed mathematical and computational models to test the confinement hypothesis, that is that the formation of kDNA minicircle networks is a product of the high DNA condensation achieved in the mitochondrion of these organisms. In these studies we studied three parameters that characterize the growth of the network topology upon confinement: the critical percolation density, the mean saturation density and the mean valence (i.e. the number of mini circles topologically linked to any chosen minicircle). Experimental results on insect-infecting organisms showed that the mean valence is equal to three, forming a structure similar to those found in medieval chain-mails. These same studies hypothesized that this value of the mean valence was driven by the DNA excluded volume. Here we extend our previous work on kDNA by characterizing the effects of DNA excluded volume on the three descriptive parameters. Using computer simulations of polymer swelling we found that (1) in agreement with previous studies the linking probability of two minicircles does not decrease linearly with the distance between the two minicircles, (2) the mean valence grows linearly with the density of minicircles and decreases with the thickness of the excluded volume, (3) the critical percolation and mean saturation densities grow linearly with the thickness of the excluded volume. Our results therefore suggest that the swelling of the DNA molecule, due to electrostatic interactions, has relatively mild implications on the overall topology of the network. Our results also validate our topological descriptors since they appear to reflect the changes in the

  2. Opinion dynamics on interacting networks: media competition and social influence.

    Science.gov (United States)

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-05-27

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

  3. Opinion dynamics on interacting networks: media competition and social influence

    Science.gov (United States)

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-05-01

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

  4. The design implications of social interaction in a workplace setting

    OpenAIRE

    A Backhouse; P Drew

    1992-01-01

    Space has been traditionally conceptualised as a passive host to its user activities. Increasingly, however, it is recognised that patterns of human interaction are derivative of the spatial configuration of which they are an integral part. This work is almost wholly confined to computational statistics of undifferentiated interactional encounters. In this paper specifically qualitative techniques will be used to extend and refine this model in order to demonstrate empirically that user behav...

  5. A scored human protein-protein interaction network to catalyze genomic interpretation

    DEFF Research Database (Denmark)

    Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

  6. A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

    Directory of Open Access Journals (Sweden)

    Mengqu Ge

    2016-02-01

    Full Text Available As one large class of non-coding RNAs (ncRNAs, long ncRNAs (lncRNAs have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI. LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR and protein-based collaborative filtering (ProCF. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

  7. Students' Participation in Social Networking Sites: Implications for Social Work Education

    Science.gov (United States)

    Mukherjee, Dhrubodhi; Clark, Janet

    2012-01-01

    Social work students have few guidelines to help them evaluate the implication of their posted information on Internet-based social networking sites (SNSs). There is a national trend among employers of human services to cross-check publicly available online information on applicants. Based on data from a survey of 105 baccalaureate and master's…

  8. Airline network development in Europe and its implications for airport planning

    NARCIS (Netherlands)

    Burghouwt, G.

    2007-01-01

    Order by Fax Printer Friendly PDF Brochure Send to Friend Enquire before Buying Hard Copy Airline Network Development in Europe and its Implications for Airport Planning Ashgate Publishing, March 2007, Pages: 300 Description Table of Contents Enquire before Buying Send to a Friend The ongoing

  9. Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry

    NARCIS (Netherlands)

    Liu, Fan; Heck, Albert J R

    2015-01-01

    Proteins are involved in almost all processes of the living cell. They are organized through extensive networks of interaction, by tightly bound macromolecular assemblies or more transiently via signaling nodes. Therefore, revealing the architecture of protein complexes and protein interaction

  10. Weighted Protein Interaction Network Analysis of Frontotemporal Dementia.

    Science.gov (United States)

    Ferrari, Raffaele; Lovering, Ruth C; Hardy, John; Lewis, Patrick A; Manzoni, Claudia

    2017-02-03

    The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein-protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.

  11. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  12. Cancer signaling networks and their implications for personalized medicine

    DEFF Research Database (Denmark)

    Creixell, Pau

    Amongst the unique features of cancer cells perhaps the most crucial one is the change in the cellular decision-making process. While both non-cancer and cancer cells are constantly integrating different external cues that reach them and computing cellular decisions (e.g. proliferation or apoptosis......) based on the integration of these cues; this integration and consequently the cellular decisions taken by cancer cells are arguably very distinct from the decisions that would be expected from non-cancer cells. Since cellular signaling networks and its different states are the computational circuits...

  13. Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

    Science.gov (United States)

    Wuchty, Stefan

    2006-05-23

    While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

  14. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  15. The brain-gut interaction: the conversation and the implications ...

    African Journals Online (AJOL)

    Bi-directional interactions between the gut and the brain play a role in health and disease. It is involved in glucose homeostasis, satiety and obesity, functional gastrointestinal disorders and possibly in inflammatory disorders such as inflammatory bowel disease. Data is starting to elucidate the conversation between the mini ...

  16. Interactive Whiteboards and Implications for Use in Education

    Science.gov (United States)

    Gibson, Danita C.

    2013-01-01

    Interactive whiteboards (IWBs) have increasingly become a technology tool used in the educational field. IWBs are touch-sensitive screens that work in conjunction with a computer and a projector, and which are used to display information from a computer. As a qualitative case study, this study investigated the SMART Board-infused instructional…

  17. Pedagogical implications on interactive techniques of teaching non ...

    African Journals Online (AJOL)

    owner

    the ESP course, create proper learning environment and evoke students' interest ... (ESL) in a broad context and teaching students professional terminology in .... practice of foreign language education, but applying interactive methods of ... students acquire the experience in cross-cultural competence, which is essential in.

  18. The Implications of Interactive eBooks on Comprehension

    Science.gov (United States)

    Frye, Sheila K.

    2014-01-01

    This study examined how the interactive features of eBooks affect comprehension, the behaviors that participants engage in during the Read-to-Me and Read-and-Play reading modes, and the affordances and constraints of these reading modes. A repeated measures design was used to analyze the reading behaviors of 30 second grade, lower-level readers…

  19. Interactions of nanomaterials and biological systems: implications to personalized nanomedicine☆

    Science.gov (United States)

    Zhang, Xue-Qing; Xu, Xiaoyang; Bertrand, Nicolas; Pridgen, Eric; Swami, Archana; Farokhzad, Omid C.

    2012-01-01

    The application of nanotechnology to personalized medicine provides an unprecedented opportunity to improve the treatment of many diseases. Nanomaterials offer several advantages as therapeutic and diagnostic tools due to design flexibility, small sizes, large surface-to-volume ratio, and ease of surface modification with multivalent ligands to increase avidity for target molecules. Nanomaterials can be engineered to interact with specific biological components, allowing them to benefit from the insights provided by personalized medicine techniques. To tailor these interactions, a comprehensive knowledge of how nanomaterials interact with biological systems is critical. Herein, we discuss how the interactions of nanomaterials with biological systems can guide their design for diagnostic, imaging and drug delivery purposes. A general overview of nanomaterials under investigation is provided with an emphasis on systems that have reached clinical trials. Finally, considerations for the development of personalized nanomedicines are summarized such as the potential toxicity, scientific and technical challenges in fabricating them, and regulatory and ethical issues raised by the utilization of nanomaterials. PMID:22917779

  20. Tumor - host immune interactions in Ewing sarcoma : implications for therapy

    NARCIS (Netherlands)

    Berghuis, Dagmar

    2012-01-01

    In this thesis, we report on various aspects of tumor - host (immune) interactions in Ewing sarcoma patients with the aim to obtain leads for immunotherapeutic or targeted treatment strategies. We demonstrate a key role for interferon gamma (IFNg) in enhancing both Ewing sarcoma immunogenicity and

  1. The Implications of Pervasive Computing on Network Design

    Science.gov (United States)

    Briscoe, R.

    Mark Weiser's late-1980s vision of an age of calm technology with pervasive computing disappearing into the fabric of the world [1] has been tempered by an industry-driven vision with more of a feel of conspicuous consumption. In the modified version, everyone carries around consumer electronics to provide natural, seamless interactions both with other people and with the information world, particularly for eCommerce, but still through a pervasive computing fabric.

  2. Effect of interaction strength on robustness of controlling edge dynamics in complex networks

    Science.gov (United States)

    Pang, Shao-Peng; Hao, Fei

    2018-05-01

    Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

  3. Albumin-drug interaction and its clinical implication.

    Science.gov (United States)

    Yamasaki, Keishi; Chuang, Victor Tuan Giam; Maruyama, Toru; Otagiri, Masaki

    2013-12-01

    Human serum albumin acts as a reservoir and transport protein for endogenous (e.g. fatty acids or bilirubin) and exogenous compounds (e.g. drugs or nutrients) in the blood. The binding of a drug to albumin is a major determinant of its pharmacokinetic and pharmacodynamic profile. The present review discusses recent findings regarding the nature of drug binding sites, drug-albumin binding in certain diseased states or in the presence of coadministered drugs, and the potential of utilizing albumin-drug interactions in clinical applications. Drug-albumin interactions appear to predominantly occur at one or two specific binding sites. The nature of these drug binding sites has been fundamentally investigated as to location, size, charge, hydrophobicity or changes that can occur under conditions such as the content of the endogenous substances in question. Such findings can be useful tools for the analysis of drug-drug interactions or protein binding in diseased states. A change in protein binding is not always a problem in terms of drug therapy, but it can be used to enhance the efficacy of therapeutic agents or to enhance the accumulation of radiopharmaceuticals to targets for diagnostic purposes. Furthermore, several extracorporeal dialysis procedures using albumin-containing dialysates have proven to be an effective tool for removing endogenous toxins or overdosed drugs from patients. Recent findings related to albumin-drug interactions as described in this review are useful for providing safer and efficient therapies and diagnoses in clinical settings. This article is part of a Special Issue entitled Serum Albumin. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Interactions In Online Education Implications For Theory & Practice

    OpenAIRE

    Askim KURT

    2007-01-01

    This book was edited by, Charles Juwah, Senior EducationDevelopment Officer at Robert Gordon University, where heruns the postgraduate learning and teaching qualificationcourse. It was published by Routledge in 2006.Interaction is very important in open and flexible learning,and apparent at all levels of engagement, whether betweenstudents, students and tutors, online learning materials orinterfacing with the learning environment. A student whoactively engages with learning materials, interac...

  5. Triactome: neuro-immune-adipose interactions. Implication in vascular biology

    Directory of Open Access Journals (Sweden)

    George Nikov Chaldakov

    2014-04-01

    Full Text Available Understanding how the precise interactions of nerves, immune cells and adipose tissue account for cardiovascular and metabolic biology is a central aim of biomedical research at present. A long standing paradigm holds that the vascular wall is composed of three concentric tissue coats (tunicae: intima, media, and adventitia. However, large- and medium-sized arteries, where usually atherosclerotic lesions develop, are consistently surrounded by periadventitial adipose tissue, we recently designated tunica adiposa (in brief, adiposa like intima, media, adventitia. According to present paradigm, atherosclerosis is an immune-mediated inflammatory disease featured by endothelial dysfunction/intimal thickening, medial atrophy and adventitial lesions associated with adipose dysfunction, whereas hypertension is characterized by hyperinnervation-associated medial thickening due to smooth muscle cell hypertrophy/hyperplasia. Periadventitial adipose tissue expansion is associated with increased infiltration of immune cells, both adipocytes and immunocytes secreting pro-inflammatory and anti-inflammatory (metabotrophic signaling proteins collectively dubbed adipokines. However, the role of perivascular nerves and their interactions with immune cells and paracrine adipose tissue is not yet evaluated in such an integrated way. The present review attempts to briefly highlight the findings in basic and translational sciences in this area focusing on neuro-immune-adipose interactions, herein referred to as triactome. Triactome-targeted pharmacology may provide a novel therapeutic approach in cardiovascular disease.

  6. Host-Microbe Interactions in Microgravity: Assessment and Implications

    Directory of Open Access Journals (Sweden)

    Jamie S. Foster

    2014-05-01

    Full Text Available Spaceflight imposes several unique stresses on biological life that together can have a profound impact on the homeostasis between eukaryotes and their associated microbes. One such stressor, microgravity, has been shown to alter host-microbe interactions at the genetic and physiological levels. Recent sequencing of the microbiomes associated with plants and animals have shown that these interactions are essential for maintaining host health through the regulation of several metabolic and immune responses. Disruptions to various environmental parameters or community characteristics may impact the resiliency of the microbiome, thus potentially driving host-microbe associations towards disease. In this review, we discuss our current understanding of host-microbe interactions in microgravity and assess the impact of this unique environmental stress on the normal physiological and genetic responses of both pathogenic and mutualistic associations. As humans move beyond our biosphere and undergo longer duration space flights, it will be essential to more fully understand microbial fitness in microgravity conditions in order to maintain a healthy homeostasis between humans, plants and their respective microbiomes.

  7. Host-microbe interactions in microgravity: assessment and implications.

    Science.gov (United States)

    Foster, Jamie S; Wheeler, Raymond M; Pamphile, Regine

    2014-05-26

    Spaceflight imposes several unique stresses on biological life that together can have a profound impact on the homeostasis between eukaryotes and their associated microbes. One such stressor, microgravity, has been shown to alter host-microbe interactions at the genetic and physiological levels. Recent sequencing of the microbiomes associated with plants and animals have shown that these interactions are essential for maintaining host health through the regulation of several metabolic and immune responses. Disruptions to various environmental parameters or community characteristics may impact the resiliency of the microbiome, thus potentially driving host-microbe associations towards disease. In this review, we discuss our current understanding of host-microbe interactions in microgravity and assess the impact of this unique environmental stress on the normal physiological and genetic responses of both pathogenic and mutualistic associations. As humans move beyond our biosphere and undergo longer duration space flights, it will be essential to more fully understand microbial fitness in microgravity conditions in order to maintain a healthy homeostasis between humans, plants and their respective microbiomes.

  8. The interaction between network ties and business modeling : Case studies of sustainability-oriented innovations

    NARCIS (Netherlands)

    Oskam, Inge; Bossink, Bart; de Man, Ard Pieter

    2018-01-01

    A stream of literature is emerging where network development and business modeling intersect. Various authors emphasize that networks influence business models. This paper extends this stream of literature by studying two cases in which we analyze how business modeling and networking interact over

  9. The Interaction between network ties and business modeling : case studies of sustainability-oriented innovations

    NARCIS (Netherlands)

    Oskam, Inge; Bossink, Bart; de Man, Ard-Pieter

    2018-01-01

    A stream of literature is emerging where network development and business modeling intersect. Various authors emphasize that networks influence business models. This paper extends this stream of literature by studying two cases in which we analyze how business modeling and networking interact over

  10. Strategic interactions in DRAM and RISC technology: A network approach

    NARCIS (Netherlands)

    Duysters, G.M.; Vanhaverbeke, W.P.M.

    1996-01-01

    Interorganizational cooperation in some high-tech industries is no longer confined to two-company alliances, but entails industry-wide alliance networks. This article examines how industry analysis and network analysis can be combined to provide a thorough understanding of how network positions, and

  11. Interaction in agent-based economics: A survey on the network approach

    Science.gov (United States)

    Bargigli, Leonardo; Tedeschi, Gabriele

    2014-04-01

    In this paper we aim to introduce the reader to some basic concepts and instruments used in a wide range of economic networks models. In particular, we adopt the theory of random networks as the main tool to describe the relationship between the organization of interaction among individuals within different components of the economy and overall aggregate behavior. The focus is on the ways in which economic agents interact and the possible consequences of their interaction on the system. We show that network models are able to introduce complex phenomena in economic systems by allowing for the endogenous evolution of networks.

  12. Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

    This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

  13. Impedance-Based Harmonic Instability Assessment in Multiple Electric Trains and Traction Network Interaction System

    DEFF Research Database (Denmark)

    Tao, Haidong; Hu, Haitao; Wang, Xiongfei

    2018-01-01

    This paper presents an impedance-based method to systematically investigate the interaction between multi-train and traction networks, focusing on evaluating the harmonic instability problems. Firstly, the interaction mechanism of multi-train and the traction network is represented as a feedback ...

  14. Non-criticality of interaction network over system's crises: A percolation analysis.

    Science.gov (United States)

    Shirazi, Amir Hossein; Saberi, Abbas Ali; Hosseiny, Ali; Amirzadeh, Ehsan; Toranj Simin, Pourya

    2017-11-20

    Extraction of interaction networks from multi-variate time-series is one of the topics of broad interest in complex systems. Although this method has a wide range of applications, most of the previous analyses have focused on the pairwise relations. Here we establish the potential of such a method to elicit aggregated behavior of the system by making a connection with the concepts from percolation theory. We study the dynamical interaction networks of a financial market extracted from the correlation network of indices, and build a weighted network. In correspondence with the percolation model, we find that away from financial crises the interaction network behaves like a critical random network of Erdős-Rényi, while close to a financial crisis, our model deviates from the critical random network and behaves differently at different size scales. We perform further analysis to clarify that our observation is not a simple consequence of the growth in correlations over the crises.

  15. The Medial Ventrothalamic Circuitry: Cells Implicated in a Bimodal Network

    Directory of Open Access Journals (Sweden)

    Tomas Vega-Zuniga

    2018-02-01

    Full Text Available Previous avian thalamic studies have shown that the medial ventral thalamus is composed of several nuclei located close to the lateral wall of the third ventricle. Although the general connectivity is known, detailed morphology and connectivity pattern in some regions are still elusive. Here, using the intracellular filling technique in the chicken, we focused on two neural structures, namely, the retinorecipient neuropil of the n. geniculatus lateralis pars ventralis (GLv, and the adjacent n. intercalatus thalami (ICT. We found that the GLv-ne cells showed two different neuronal types: projection cells and horizontal interneurons. The projection cells showed variable morphologies and dendritic arborizations with axons that targeted the n. lentiformis mesencephali (LM, griseum tectale (GT, ICT, n. principalis precommissuralis (PPC, and optic tectum (TeO. The horizontal cells showed a widespread mediolateral neural process throughout the retinorecipient GLv-ne. The ICT cells, on the other hand, had multipolar somata with wide dendritic fields that extended toward the lamina interna of the GLv, and a projection pattern that targeted the n. laminaris precommissuralis (LPC. Together, these results elucidate the rich complexity of the connectivity pattern so far described between the GLv, ICT, pretectum, and tectum. Interestingly, the implication of some of these neural structures in visuomotor and somatosensory roles strongly suggests that the GLv and ICT are part of a bimodal circuit that may be involved in the generation/modulation of saccades, gaze control, and space perception.

  16. Diet-Microbiota Interactions and Their Implications for Healthy Living

    Directory of Open Access Journals (Sweden)

    Ian B. Jeffery

    2013-01-01

    Full Text Available It is well established that diet influences the health of an individual and that a diet rich in plant-based foods has many advantages in relation to the health and well-being of an individual. What has been unclear until recently is the large contribution of the gut microbiota to this effect. As well as providing basic nutritional requirements, the long-term diet of an animal modifies its gut microbiota. In adults, diets that have a high proportion of fruit and vegetables and a low consumption of meat are associated with a highly diverse microbiota and are defined by a greater abundance of Prevotella compared to Bacteroides, while the reverse is associated with a diet that contains a low proportion of plant-based foods. Furthermore, it is becoming increasingly clear that the effect of the microbial ecology of the gut goes beyond the local gut immune system and is implicated in immune-related disorders, such as IBS, diabetes and inflamm-ageing. In this review, we investigate the evidence that a balanced diet leads to a balanced, diverse microbiota with significant consequences for healthy ageing by focusing on conditions of interest.

  17. Interactions between diabetes and anxiety and depression: implications for treatment.

    Science.gov (United States)

    Bystritsky, Alexander; Danial, Jessica; Kronemyer, David

    2014-03-01

    Anxiety or depression may be a risk factor for the development of diabetes. This relationship may occur through a combination of genetic predispositions; epigenetic contingencies; exacerbating conditions such as metabolic syndrome (a precursor to diabetes); and other serious medical conditions. Medications used to treat anxiety and depression have significant side effects, such as weight gain, further increasing the possibility of developing diabetes. These components combine, interact, and reassemble to create a precarious system for persons with, or predisposed to, diabetes. Clinicians must be aware of these interrelationships to adequately treat the disease. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Interactions between neural networks: a mechanism for tuning chaos and oscillations.

    Science.gov (United States)

    Wang, Lipo

    2007-06-01

    We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability.

  19. Astroglial networks and implications for therapeutic neuromodulation of epilepsy

    Directory of Open Access Journals (Sweden)

    Mark R Witcher

    2012-08-01

    Full Text Available Epilepsy is a common chronic neurologic disorder affecting approximately 1 percent of the world population. More than one-third of all epilepsy patients have incompletely controlled seizures or debilitating medication side effects in spite of optimal medical management. Medically refractory epilepsy is associated with excess injury and mortality, psychosocial dysfunction, and significant cognitive impairment. Effective treatment options for these patients can be limited. The cellular mechanisms underlying seizure activity are incompletely understood, though we here describe multiple lines of evidence supporting the likely contribution of astroglia to epilepsy, with focus on individual astrocytes and their network functions. Of the emerging therapeutic modalities for epilepsy, one of the most intriguing is the field of neuromodulation. Neuromodulatory treatment, which consists of administering electrical pulses to neural tissue to modulate its activity leading to a beneficial effect, may be an option for these patients. Current modalities consist of vagal nerve stimulation, open and closed loop stimulation, and transcranial magnetic stimulation. Due to their unique properties, we here present astrocytes as likely important targets for the developing field of neuromodulation in the treatment of epilepsy.

  20. Astroglial networks and implications for therapeutic neuromodulation of epilepsy.

    Science.gov (United States)

    Witcher, Mark R; Ellis, Thomas L

    2012-01-01

    Epilepsy is a common chronic neurologic disorder affecting approximately 1% of the world population. More than one-third of all epilepsy patients have incompletely controlled seizures or debilitating medication side effects in spite of optimal medical management. Medically refractory epilepsy is associated with excess injury and mortality, psychosocial dysfunction, and significant cognitive impairment. Effective treatment options for these patients can be limited. The cellular mechanisms underlying seizure activity are incompletely understood, though we here describe multiple lines of evidence supporting the likely contribution of astroglia to epilepsy, with focus on individual astrocytes and their network functions. Of the emerging therapeutic modalities for epilepsy, one of the most intriguing is the field of neuromodulation. Neuromodulatory treatment, which consists of administering electrical pulses to neural tissue to modulate its activity leading to a beneficial effect, may be an option for these patients. Current modalities consist of vagal nerve stimulation, open and closed-loop stimulation, and transcranial magnetic stimulation. Due to their unique properties, we here present astrocytes as likely important targets for the developing field of neuromodulation in the treatment of epilepsy.

  1. Interacting with Networks : How Does Structure Relate to Controllability in Single-Leader, Consensus Networks?

    NARCIS (Netherlands)

    Egerstedt, Magnus; Martini, Simone; Cao, Ming; Camlibel, Kanat; Bicchi, Antonio

    As networked dynamical systems appear around us at an increasing rate, questions concerning how to manage and control such systems are becoming more important. Examples include multiagent robotics, distributed sensor networks, interconnected manufacturing chains, and data networks. In response to

  2. JV Task 77 - Health Implications of Mercury - Selenium Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Nicholas Ralstion; Laura Raymond

    2007-12-15

    Exposure to mercury (Hg) commonly results from eating fish containing bioaccumulated methylmercury (MeHg). However, conflicting observations and conclusions have arisen from the ongoing human studies of MeHg exposure from fish consumption. Resolving these uncertainties has important implications for human health since significant nutritional benefits will be lost if fish consumption is needlessly avoided. Selenium (Se), an important nutrient that is abundant in ocean fish, has a potent protective effect against Hg toxicity. This protective effect was thought to be due to the high binding affinities between Hg and Se resulting in Se sequestration of Hg to prevent its harmful effects. However, it is imperative to consider the opposing effect of Hg on Se physiology. Crucial proteins that require Se normally protect the brain and hormone-producing glands from oxidative damage. MeHg is able to cross all biological barriers and enter cells in these tissues, where its high Se affinity results in Se sequestration. Sequestration in association with Hg prevents Se from participating in proteins that perform essential antioxidant activities. Supplemental dietary Se is able to replace Se sequestered by Hg and maintain normal antioxidant protection of brain and glands. The goal of this research project was to assess the potency of normal dietary levels of Se in protection against MeHg toxicity. Results from this project indicate that MeHg toxicity is only evident in situations resulting in Hg occurring in high molar excess of Se. Additionally, the common method of MeHg risk assessments using measurements of toenail and blood levels of Hg was shown to provide an accurate reflection of Hg exposure but did not accurately indicate risk of toxicity resulting from that exposure. Instead, Hg:Se molar ratios are proposed as a superior means of assessing risks associated with MeHg exposure.

  3. Semantic integration to identify overlapping functional modules in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2007-07-01

    Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

  4. Bowling alone but tweeting together: the evolution of human interaction in the social networking era

    OpenAIRE

    Antoci, Angelo; Sabatini, Fabio; Sodini, Mauro

    2011-01-01

    The objective of this paper is to theoretically analyze how human interaction may evolve in a world characterized by the explosion of online networking and other Web-mediated ways of building and nurturing relationships. The analysis shows that online networking yields a storage mechanism through which any individual contribution - e.g. a blog post, a comment, or a photo - is stored within a particular network and ready for virtual access by each member who connects to the network. When someo...

  5. The management of interaction networks. The ???in-between??? concept within social work and counseling

    OpenAIRE

    Hern??ndez-Aristu, Jes??s

    2015-01-01

    We are familiar with the field of group interaction through the traditional work of Kurt Lewin and also systemic thinking talks about network interaction that builds up the system. Martin Buber also discusses the ???in-between??? concept as the third element.The therapist or counselor, social worker and clients are part of an interaction network, representing therapeutic and social working situations. Success in treatment and reflective processes, depends on the perception and managemen...

  6. Soy and Gut Microbiota: Interaction and Implication for Human Health.

    Science.gov (United States)

    Huang, Haiqiu; Krishnan, Hari B; Pham, Quynhchi; Yu, Liangli Lucy; Wang, Thomas T Y

    2016-11-23

    Soy (Glycine max) is a major commodity in the United States, and soy foods are gaining popularity due to their reported health-promoting effects. In the past two decades, soy and soy bioactive components have been studied for their health-promoting/disease-preventing activities and potential mechanisms of action. Recent studies have identified gut microbiota as an important component in the human body ecosystem and possibly a critical modulator of human health. Soy foods' interaction with the gut microbiota may critically influence many aspects of human development, physiology, immunity, and nutrition at different stages of life. This review summarizes current knowledge on the effects of soy foods and soy components on gut microbiota population and composition. It was found, although results vary in different studies, in general, both animal and human studies have shown that consumption of soy foods can increase the levels of bifidobacteria and lactobacilli and alter the ratio between Firmicutes and Bacteroidetes. These changes in microbiota are consistent with reported reductions in pathogenic bacteria populations in the gut, thereby lowering the risk of diseases and leading to beneficial effects on human health.

  7. Dynamics of Moment Neuronal Networks with Intra- and Inter-Interactions

    Directory of Open Access Journals (Sweden)

    Xuyan Xiang

    2015-01-01

    Full Text Available A framework of moment neuronal networks with intra- and inter-interactions is presented. It is to show how the spontaneous activity is propagated across the homogeneous and heterogeneous network. The input-output firing relationship and the stability are first explored for a homogeneous network. For heterogeneous network without the constraint of the correlation coefficients between neurons, a more sophisticated dynamics is then explored. With random interactions, the network gets easily synchronized. However, desynchronization is produced by a lateral interaction such as Mexico hat function. It is the external intralayer input unit that offers a more sophisticated and unexpected dynamics over the predecessors. Hence, the work further opens up the possibility of carrying out a stochastic computation in neuronal networks.

  8. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

    Directory of Open Access Journals (Sweden)

    Aaron R Wolen

    response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders.

  9. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

    Directory of Open Access Journals (Sweden)

    Oyang Yen-Jen

    2010-10-01

    Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

  10. Deciphering microbial interactions and detecting keystone species with co-occurrence networks

    Directory of Open Access Journals (Sweden)

    David eBerry

    2014-05-01

    Full Text Available Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics, construct co-occurrence networks, and evaluate how well networks reveal the underlying interactions, and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

  11. Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

    Science.gov (United States)

    Berry, David; Widder, Stefanie

    2014-01-01

    Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

  12. DyNet: visualization and analysis of dynamic molecular interaction networks.

    Science.gov (United States)

    Goenawan, Ivan H; Bryan, Kenneth; Lynn, David J

    2016-09-01

    : The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most 'rewired' nodes across many network states. DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). david.lynn@sahmri.com Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  13. Vitamin D deficiency and childhood obesity: interactions, implications, and recommendations

    Directory of Open Access Journals (Sweden)

    Peterson CA

    2015-02-01

    Full Text Available Catherine A Peterson Department of Nutrition and Exercise Physiology, University of Missouri-Columbia, Columbia, MO, USA Abstract: Vitamin D deficiency and childhood obesity have been classified as epidemics throughout the world, and both share some common risk factors including poor diet and inactivity. Observational and clinical studies show that vitamin D status and fat mass are inversely correlated. It is not clear whether vitamin D deficiency contributes to, or is a consequence of obesity, or whether there are regulatory interactions between excess adiposity and vitamin D activity. The effects of this deficiency in childhood obesity appear to have negative influences on overall health, including insulin resistance, inflammation, and impeded bone mineralization, as well as increased future risk of type 2 diabetes, cardiovascular disease, and osteoporosis. The rather ubiquitous distribution of the vitamin D receptor and the 25-hydroxyvitamin D 1a-hydroxylase throughout the body, including evidence for a role of vitamin D in adipogenesis and adipocyte metabolism, may in part explain these widespread effects. Most of the findings to date suggest that the vitamin D needs of obese children are greater than the nonobese. Although ultraviolet B-induced skin synthesis is a main source of vitamin D, its use is neither feasible nor prudent due to limited sun availability for many and concerns for skin cancer. Likewise, obtaining adequate vitamin D from natural food sources alone is generally not achievable, and even in countries that allow fortification, vitamin D intakes are low. Therefore, in obese children, vitamin D supplementation is warranted. Weight loss interventions using energy restriction and physical activity may also improve the poor vitamin D status associated with obesity. More research is needed to define optimal vitamin D status in this vulnerable population, including investigations to determine the efficacy of vitamin D

  14. Interaction between Ailanthus altissima and Native Robinia pseudoacacia in Early Succession: Implications for Forest Management

    Science.gov (United States)

    Erik Nilsen; Cynthia Huebner; David Carr; Zhe Bao

    2018-01-01

    The goal of this study was to discover the nature and intensity of the interaction between an exotic invader Ailanthus altissima (Mill.) Swingle and its coexisting native Robinia pseudoacacia L. and consider management implications. The study occurred in the Mid-Appalachian region of the eastern United States. ...

  15. A human protein interaction network shows conservation of aging processes between human and invertebrate species.

    Directory of Open Access Journals (Sweden)

    Russell Bell

    2009-03-01

    Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

  16. A global genetic interaction network maps a wiring diagram of cellular function.

    Science.gov (United States)

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

    2016-09-23

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

  17. A global interaction network maps a wiring diagram of cellular function

    Science.gov (United States)

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

    2017-01-01

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

  18. Self-organized criticality in a network of interacting neurons

    NARCIS (Netherlands)

    Cowan, J.D.; Neuman, J.; Kiewiet, B.; van Drongelen, W.

    2013-01-01

    This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway.

  19. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

  20. Limitation of degree information for analyzing the interaction evolution in online social networks

    Science.gov (United States)

    Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

    2014-04-01

    Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

  1. Drug-drug interactions of antifungal agents and implications for patient care.

    Science.gov (United States)

    Gubbins, Paul O; Amsden, Jarrett R

    2005-10-01

    Drug interactions in the gastrointestinal tract, liver and kidneys result from alterations in pH, ionic complexation, and interference with membrane transport proteins and enzymatic processes involved in intestinal absorption, enteric and hepatic metabolism, renal filtration and excretion. Azole antifungals can be involved in drug interactions at all the sites, by one or more of the above mechanisms. Consequently, azoles interact with a vast array of compounds. Drug-drug interactions associated with amphotericin B formulations are predictable and result from the renal toxicity and electrolyte disturbances associated with these compounds. The echinocandins are unknown cytochrome P450 substrates and to date are relatively devoid of significant drug-drug interactions. This article reviews drug interactions involving antifungal agents that affect other agents and implications for patient care are highlighted.

  2. Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

    Science.gov (United States)

    Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

    2006-01-01

    Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

  3. Assessing Group Interaction with Social Language Network Analysis

    Science.gov (United States)

    Scholand, Andrew J.; Tausczik, Yla R.; Pennebaker, James W.

    In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

  4. Unravelling Darwin's entangled bank: architecture and robustness of mutualistic networks with multiple interaction types.

    Science.gov (United States)

    Dáttilo, Wesley; Lara-Rodríguez, Nubia; Jordano, Pedro; Guimarães, Paulo R; Thompson, John N; Marquis, Robert J; Medeiros, Lucas P; Ortiz-Pulido, Raul; Marcos-García, Maria A; Rico-Gray, Victor

    2016-11-30

    Trying to unravel Darwin's entangled bank further, we describe the architecture of a network involving multiple forms of mutualism (pollination by animals, seed dispersal by birds and plant protection by ants) and evaluate whether this multi-network shows evidence of a structure that promotes robustness. We found that species differed strongly in their contributions to the organization of the multi-interaction network, and that only a few species contributed to the structuring of these patterns. Moreover, we observed that the multi-interaction networks did not enhance community robustness compared with each of the three independent mutualistic networks when analysed across a range of simulated scenarios of species extinction. By simulating the removal of highly interacting species, we observed that, overall, these species enhance network nestedness and robustness, but decrease modularity. We discuss how the organization of interlinked mutualistic networks may be essential for the maintenance of ecological communities, and therefore the long-term ecological and evolutionary dynamics of interactive, species-rich communities. We suggest that conserving these keystone mutualists and their interactions is crucial to the persistence of species-rich mutualistic assemblages, mainly because they support other species and shape the network organization. © 2016 The Author(s).

  5. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  6. 77 FR 9946 - Draft Guidance for Industry on Drug Interaction Studies-Study Design, Data Analysis, Implications...

    Science.gov (United States)

    2012-02-21

    ... industry entitled ``Drug Interaction Studies--Study Design, Data Analysis, Implications for Dosing, and... data analysis in the context of identifying potential drug interactions. The guidance also addresses... Studies--Study Design, Data Analysis, and Implications for Dosing and Labeling.'' Comments were received...

  7. An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

    Science.gov (United States)

    Mallik, Mrinmay Kumar

    2018-02-07

    Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that

  8. Method for designing networking adaptive interactive hybrid systems

    NARCIS (Netherlands)

    Kester, L. J.H.M.

    2010-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public

  9. Creating networking adaptive interactive hybrid systems : A methodic approach

    NARCIS (Netherlands)

    Kester, L.J.

    2011-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defense, crisis management, traffic management, public

  10. KNOWNET: Exploring Interactive Knowledge Networking across Insurance Supply Chains

    OpenAIRE

    Grant, Susan

    2014-01-01

    [EN] Social media has become an extremely powerful phenomenon with millions of users who post status updates, blog, links and pictures on social networking sites such as Facebook, LinkedIn, and Twitter. However, social networking has so far spread mainly among consumers. Businesses are only now beginning to acknowledge the benefits of using social media to enhance employee and supplier collaboration to support new ideas and innovation through knowledge sharing across functions and organizatio...

  11. Organization of Multi-controller Interaction in Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Sergey V. Morzhov

    2018-01-01

    Full Text Available Software Defined Networking (SDN is a promising paradigm for network management. It is a centralized network intelligence on a dedicated server, which runs network operating system, and is called SDN controller. It was assumed that such an architecture should have an improved network performance and monitoring. However, the centralized control architecture of the SDNs brings novel challenges to reliability, scalability, fault tolerance and interoperability. These problems are especially acute for large data center networks and can be solved by combining SDN controllers into clusters, called multi-controllers. Multi-controller architecture became very important for SDN-enabled networks nowadays. This paper gives a comprehensive overview of SDN multi-controller architectures. The authors review several most popular distributed controllers in order to indicate their strengths and weaknesses. They also investigate and classify approaches used. This paper explains in details the difference among various types of multi-controller architectures, the distribution method and the communication system. Furthermore, it provides already implemented architectures and some examples of architectures under consideration by describing their design, communication process, and performance results. In this paper, the authors show their own classification of multi-controllers and claim that, despite the existence of undeniable advantages, all reviewed controllers have serious drawbacks, which must be eliminated. These drawbacks hamper the development of multi-controllers and their widespread adoption in corporate networks. In the end, the authors conclude that now it is impossible to find a solution capable to solve all the tasks assigned to it adequately and fully. The article is published in the authors’ wording.

  12. Exploring drug-target interaction networks of illicit drugs

    OpenAIRE

    Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming

    2013-01-01

    Background Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit dru...

  13. Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.

    Directory of Open Access Journals (Sweden)

    Tommi Aho

    2010-05-01

    Full Text Available Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our

  14. Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

    Directory of Open Access Journals (Sweden)

    Boucher Charles AB

    2010-07-01

    Full Text Available Abstract Background The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. Results Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. Conclusions HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

  15. Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; He, Yongqun

    2017-03-14

    Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

  16. Structural Modeling and Characteristics Analysis of Flow Interaction Networks in the Internet

    International Nuclear Information System (INIS)

    Wu Xiao-Yu; Gu Ren-Tao; Pan Zhuo-Ya; Jin Wei-Qi; Ji Yue-Feng

    2015-01-01

    Applying network duality and elastic mechanics, we investigate the interactions among Internet flows by constructing a weighted undirected network, where the vertices and the edges represent the flows and the mutual dependence between flows, respectively. Based on the obtained flow interaction network, we find the existence of ‘super flow’ in the Internet, indicating that some flows have a great impact on a huge number of other flows; moreover, one flow can spread its influence to another through a limited quantity of flows (less than 5 in the experimental simulations), which shows strong small-world characteristics like the social network. To reflect the flow interactions in the physical network congestion evaluation, the ‘congestion coefficient’ is proposed as a new metric which shows a finer observation on congestion than the conventional one. (paper)

  17. Reconstructing consensus Bayesian network structures with application to learning molecular interaction networks

    NARCIS (Netherlands)

    Fröhlich, H.; Klau, G.W.

    2013-01-01

    Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies

  18. Ensemble Sensitivity Analysis of a Severe Downslope Windstorm in Complex Terrain: Implications for Forecast Predictability Scales and Targeted Observing Networks

    Science.gov (United States)

    2013-09-01

    observations, linear regression finds the straight line that explains the linear relationship of the sample. This line is given by the equation y = mx + b...SENSITIVITY ANALYSIS OF A SEVERE DOWNSLOPE WINDSTORM IN COMPLEX TERRAIN: IMPLICATIONS FOR FORECAST PREDICTABILITY SCALES AND TARGETED OBSERVING...SENSITIVITY ANALYSIS OF A SEVERE DOWNSLOPE WINDSTORM IN COMPLEX TERRAIN: IMPLICATIONS FOR FORECAST PREDICTABILITY SCALES AND TARGETED OBSERVING NETWORKS

  19. Changes in the interaction of resting-state neural networks from adolescence to adulthood.

    Science.gov (United States)

    Stevens, Michael C; Pearlson, Godfrey D; Calhoun, Vince D

    2009-08-01

    This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. (c) 2009 Wiley-Liss, Inc.

  20. NASCENT: an automatic protein interaction network generation tool for non-model organisms.

    Science.gov (United States)

    Banky, Daniel; Ordog, Rafael; Grolmusz, Vince

    2009-04-24

    Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software. http://nascent.pitgroup.org.

  1. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Aljarah, Ibrahim; Brewer, James

    2013-01-01

    networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional

  2. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

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

    2013-01-01

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness

  3. ANSIBLE: A Network of Social Interactions for Bilateral Life Enhancement, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — ANSIBLE (A Network of Social Interactions for Bilateral Life Enhancement) can be used pre, during, and post flight to connect the flight crew with their family,...

  4. On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

    Science.gov (United States)

    Schmitt, Michael

    2004-09-01

    We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

  5. Network graph analysis of gene-gene interactions in genome-wide association study data.

    Science.gov (United States)

    Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

    2012-12-01

    Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  6. The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

    Science.gov (United States)

    Özgür, Arzucan; Hur, Junguk; He, Yongqun

    2016-01-01

    The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: 'has literature mining keywords' and 'has keyword dependency pattern'. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A

  7. Interaction of chimera states in a multilayered network of nonlocally coupled oscillators

    Science.gov (United States)

    Goremyko, M. V.; Maksimenko, V. A.; Makarov, V. V.; Ghosh, D.; Bera, B.; Dana, S. K.; Hramov, A. E.

    2017-08-01

    The processes of formation and evolution of chimera states in the model of a multilayered network of nonlinear elements with complex coupling topology are studied. A two-layered network of nonlocally intralayer-coupled Kuramoto-Sakaguchi phase oscillators is taken as the object of investigation. Different modes implemented in this system upon variation of the degree of interlayer interaction are demonstrated.

  8. Study of Personalized Network Tutoring System Based on Emotional-cognitive Interaction

    Science.gov (United States)

    Qi, Manfei; Ma, Ding; Wang, Wansen

    Aiming at emotion deficiency in present Network tutoring system, a lot of negative effects is analyzed and corresponding countermeasures are proposed. The model of Personalized Network tutoring system based on Emotional-cognitive interaction is constructed in the paper. The key techniques of realizing the system such as constructing emotional model and adjusting teaching strategies are also introduced.

  9. The Social Fabric of Elementary Schools: A Network Typology of Social Interaction among Teachers

    Science.gov (United States)

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

    2012-01-01

    While researchers are currently studying various forms of social network interaction among teachers for their impact on educational policy implementation and practice, knowledge on how various types of networks are interrelated is limited. The goal of this study is to understand the dimensionality that may underlie various types of social networks…

  10. The social fabric of elementary schools: a network typology of social interaction among teachers

    NARCIS (Netherlands)

    Moolenaar, N.M.; Sleegers, P.J.C.; Karsten, S.; Daly, A.J.

    2012-01-01

    While researchers are currently studying various forms of social network interaction among teachers for their impact on educational policy implementation and practice, knowledge on how various types of networks are interrelated is limited. The goal of this study is to understand the dimensionality

  11. The Effect of Social Interaction on Learning Engagement in a Social Networking Environment

    Science.gov (United States)

    Lu, Jie; Churchill, Daniel

    2014-01-01

    This study investigated the impact of social interactions among a class of undergraduate students on their learning engagement in a social networking environment. Thirteen undergraduate students enrolled in a course in a university in Hong Kong used an Elgg-based social networking platform throughout a semester to develop their digital portfolios…

  12. The social fabric of elementary schools: A network typology of social interaction among teachers

    NARCIS (Netherlands)

    Moolenaar, Nienke; Sleegers, P.J.C.; Karsten, Sjoerd; Daly, Alan J.; Daly, A.J.

    2012-01-01

    While researchers are currently studying various forms of social network interaction among teachers for their impact on educational policy implementation and practice, knowledge on how various types of networks are interrelated is limited. The goal of this study is to understand the dimensionality

  13. Flower-Visiting Social Wasps and Plants Interaction: Network Pattern and Environmental Complexity

    Directory of Open Access Journals (Sweden)

    Mateus Aparecido Clemente

    2012-01-01

    Full Text Available Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland. Results showed that the landscapes differed in species richness and composition, and also the interaction networks between wasps and plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants.

  14. Financial Stability and Interacting Networks of Financial Institutions and Market Infrastructures

    NARCIS (Netherlands)

    Léon, C.; Berndsen, R.J.; Renneboog, L.D.R.

    2014-01-01

    An interacting network coupling financial institutions’ multiplex (i.e. multi-layer) and financial market infrastructures’ single-layer networks gives an accurate picture of a financial system’s true connective architecture. We examine and compare the main properties of Colombian multiplex and

  15. Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2015-01-01

    Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

  16. Problematic use of social network sites: the interactive relationship between gratifications sought and privacy concerns.

    Science.gov (United States)

    Chen, Hsuan-Ting; Kim, Yonghwan

    2013-11-01

    Problematic Internet use has long been a matter of concern; however, few studies extend this line of research from general Internet use to the use of social network sites (SNSs), or explicate the problematic use of SNSs by understanding what factors may enhance or reduce users' compulsive behaviors and excessive form of use on SNSs. Building on literature that found a positive relationship between gratifications sought from the Internet and problematic Internet use, this study first explores the types of gratifications sought from SNSs and examines their relationship with problematic SNS use. It found that three types of gratifications-diversion, self-presentation, and relationship building-were positively related to problematic SNS use. In addition, with a growing body of research on SNS privacy, a moderating role of privacy concerns on SNSs has been proposed to understand how it can influence the relationship between gratifications sought from SNSs and problematic SNS use. The findings suggest that different subdimensions of privacy concerns interact with gratifications sought in different manners. In other words, privacy concerns, including unauthorized secondary use and improper access, play a more influential role in constraining the positive relationship between gratifications sought and problematic SNS use when individuals seek to build relationships on SNSs. However, if individuals seek to have diversion on SNSs, their privacy concerns will be overridden by their gratifications sought, which in turn leads to problematic SNS use. Implications of these findings for future research are discussed.

  17. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  18. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Science.gov (United States)

    Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

    2013-01-01

    Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  19. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Directory of Open Access Journals (Sweden)

    Cristina Tur

    Full Text Available Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them. Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i linkage level (number of interactions, (ii diversity of interactions, and (iii closeness centrality (a measure of how much a species is connected to other plants via shared pollinators. Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  20. Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

    Directory of Open Access Journals (Sweden)

    Hong Li

    2017-05-01

    Full Text Available Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein–protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs. We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant–pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

  1. A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

    Science.gov (United States)

    Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D; Hao, Ke; Summa, Keith C; Yang, He S; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

    2015-05-05

    Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  2. The role of interactions along the flood process chain and implications for risk assessment

    Science.gov (United States)

    Vorogushyn, Sergiy; Apel, Heiko; Viet Nguyen, Dung; Guse, Björn; Kreibich, Heidi; Lüdtke, Stefan; Schröter, Kai; Merz, Bruno

    2017-04-01

    Floods with their manifold characteristics are shaped by various processes along the flood process chain - from triggering meteorological extremes through catchment and river network process down to impacts on societies. In flood risk systems numerous interactions and feedbacks along the process chain may occur which finally shape spatio-temporal flood patterns and determine the ultimate risk. In this talk, we review some important interactions in the atmosphere-catchment, river-dike-floodplain and vulnerability compartments of the flood risk system. We highlight the importance of spatial interactions for flood hazard and risk assessment. For instance, the role of spatial rainfall structure or wave superposition in river networks is elucidated with selected case studies. In conclusion, we show the limits of current methods in assessment of large-scale flooding and outline the approach to more comprehensive risk assessment based on our regional flood risk model (RFM) for Germany.

  3. Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

    Science.gov (United States)

    Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

    2017-01-01

    The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

  4. The IRIS network of excellence : Integrating research in interactive storytelling

    NARCIS (Netherlands)

    Cavazza, Marc; Donikian, Stéphane; Christie, Marc; Spierling, Ulrike; Szilas, Nicolas; Vorderer, Peter; Hartmann, Tilo; Klimmt, Christoph; André, Elisabeth; Champagnat, Ronan; Petta, Paolo; Olivier, Patrick

    2008-01-01

    Interactive Storytelling is a major endeavour to develop new media which could offer a radically new user experience, with a potential to revolutionise digital entertainment. European research in Interactive Storytelling has played a leading role in the development of the field, and this creates a

  5. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    2017-01-01

    is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...

  6. Estimating the Local Size and Coverage of Interaction Network Regions

    Science.gov (United States)

    Eagle, Michael; Barnes, Tiffany

    2015-01-01

    Interactive problem solving environments, such as intelligent tutoring systems and educational video games, produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled the student-tutor interactions using complex network…

  7. Leadership networks in Catholic parishes: implications for implementation research in health.

    Science.gov (United States)

    Negrón, Rosalyn; Leyva, Bryan; Allen, Jennifer; Ospino, Hosffman; Tom, Laura; Rustan, Sarah

    2014-12-01

    Through two case studies of Catholic parishes in Massachusetts, this study explores the implications of leader-centered versus distributed leadership in Catholic parishes for the implementation of evidence-based health interventions. The two parishes involved in the study differ from each other in several ways. In the first, parishioners are less engaged in leadership activities at the decision-making level in the parish. A small group of lay volunteers work with the parish priest and other ordained leaders on parish activities. In the second parish, a large and active lay volunteer leadership have forged an organizational structure that allows more independence from the pastor's direct oversight. In this parish, lay volunteer leaders are the prime drivers of organizational programs and events. In 2012-2013, three types of networks were assessed at each parish: discussion, collaboration, and outside-of-parish ties. The contrasts between each parish include differences in density of collaboration, in frequency of discussion, and network centrality of the respective parish priests. We further identified key actors in the network structures at each parish. We discuss the implications of these findings for understanding organizational capacity in the context of health program implementation. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh

    2012-04-06

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  9. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh; Tan, Sin Lam; Zhang, Jinfeng; Karnik, Shreyas; Bajic, Vladimir B.; Liu, Jun S.

    2012-01-01

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  10. Dynamic interactions of the cortical networks during thought suppression.

    Science.gov (United States)

    Aso, Toshihiko; Nishimura, Kazuo; Kiyonaka, Takashi; Aoki, Takaaki; Inagawa, Michiyo; Matsuhashi, Masao; Tobinaga, Yoshikazu; Fukuyama, Hidenao

    2016-08-01

    Thought suppression has spurred extensive research in clinical and preclinical fields, particularly with regard to the paradoxical aspects of this behavior. However, the involvement of the brain's inhibitory system in the dynamics underlying the continuous effort to suppress thoughts has yet to be clarified. This study aims to provide a unified perspective for the volitional suppression of internal events incorporating the current understanding of the brain's inhibitory system. Twenty healthy volunteers underwent functional magnetic resonance imaging while they performed thought suppression blocks alternating with visual imagery blocks. The whole dataset was decomposed by group-independent component analysis into 30 components. After discarding noise components, the 20 valid components were subjected to further analysis of their temporal properties including task-relatedness and between-component residual correlation. Combining a long task period and a data-driven approach, we observed a right-side-dominant, lateral frontoparietal network to be strongly suppression related. This network exhibited increased fluctuation during suppression, which is compatible with the well-known difficulty of suppression maintenance. Between-network correlation provided further insight into the coordinated engagement of the executive control and dorsal attention networks, as well as the reciprocal activation of imagery-related components, thus revealing neural substrates associated with the rivalry between intrusive thoughts and the suppression process.

  11. Effects of residential move on interaction frequency with social network

    NARCIS (Netherlands)

    Sharmeen, F.; Timmermans, H.J.P.; Sze, N.N.; Szeto, W.Y.; Wong, S.C.

    2011-01-01

    Size and composition of social networks have recently been identified in the transportation literature as important triggers of travel demand in general and travel generation in particular. With the field of activity-based modeling of travel demand moving from static to dynamic models, this implies

  12. Frameworks for Understanding the Nature of Interactions, Networking, and Community in a Social Networking Site for Academic Practice

    Directory of Open Access Journals (Sweden)

    Grainne Conole

    2011-03-01

    Full Text Available This paper describes a new social networking site, Cloudworks, which has been developed to enable discussion and sharing of learning and teaching ideas/designs and to promote reflective academic practice. The site aims to foster new forms of social and participatory practices (peer critiquing, sharing, user-generated content, aggregation, and personalisation within an educational context. One of the key challenges in the development of the site has been to understand the user interactions and the changing patterns of user behaviour as it evolves. The paper explores the extent to which four frameworks that have been used in researching networked learning contexts can provide insights into the patterns of user behaviour that we see in Cloudworks. The paper considers this within the current debate about the new types of interactions, networking, and community being observed as users adapt to and appropriate new technologies.

  13. Large Scale Molecular Simulation of Nanoparticle-Biomolecule Interactions and their Implications in Nanomedicine

    Science.gov (United States)

    Zhou, Ruhong

    Nanoscale particles have become promising materials in various biomedical applications, however, in order to stimulate and facilitate these applications, there is an urgent need for a better understanding of their biological effects and related molecular mechanism/physics as well. In this talk, I will discuss some of our recent works, mostly molecular modelling, on nanotoxicity and their implications in de novo design of nanomedicine. We show that carbon-based nanoparticles (carbon nanotubes, graphene nanosheets, and fullerenes) can interact and disrupt the structures and functions of many important proteins. The hydrophobic interactions between the carbon nanotubes and hydrophobic residues, particularly aromatic residues through the so-called π- π stacking interactions, are found to play key roles. Meanwhile, metallofullerenol Gd@C82(OH)22 is found to inhibit tumour growth and metastases with both experimental and theoretical approaches. Graphene and graphene oxide (GO) nanosheets show strong destructive interactions to E. coli cell membranes (antibacterial activity) and A β amyloid fibrils (anti-AD, Alzheimer's disease, capability) with unique molecular mechanisms, while on the other hand, they also show a strong supportive role in enzyme immobilisation such as lipases through lid opening. In particular, the lid opening is assisted by lipase's sophisticated interaction with GO, which allows the adsorbed lipase to enhance its enzyme activity. The lipase enzymatic activity can be further optimized through fine tuning of the GO surface hydrophobicity. These findings might provide a better understanding of ``nanotoxicity'' at the molecular level with implications in de novo nanomedicine design.

  14. Implications of the Dynamics of the New Networked Economy for E-Business Start-Ups: The Case of Philips' Access Point.

    Science.gov (United States)

    Tovstiga, George; Fantner, Ernest J.

    2000-01-01

    Examines implications of the networked economy for e-commerce business start-ups. Revisits the notion of "value" and "value creation" in a network context. Examines "value" relative to technological innovation. Looks at implications of the network environment for the organization and transformation of the enterprise's…

  15. LETTER TO THE EDITOR: Dynamics of interacting neural networks

    Science.gov (United States)

    Kinzel, W.; Metzler, R.; Kanter, I.

    2000-04-01

    The dynamics of interacting perceptrons is solved analytically. For a directed flow of information the system runs into a state which has a higher symmetry than the topology of the model. A symmetry-breaking phase transition is found with increasing learning rate. In addition, it is shown that a system of interacting perceptrons which is trained on the history of its minority decisions develops a good strategy for the problem of adaptive competition known as the bar problem or minority game.

  16. An analysis pipeline for the inference of protein-protein interaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

    2009-12-01

    An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

  17. Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes.

    Science.gov (United States)

    Timóteo, Sérgio; Correia, Marta; Rodríguez-Echeverría, Susana; Freitas, Helena; Heleno, Ruben

    2018-01-10

    Species interaction networks are traditionally explored as discrete entities with well-defined spatial borders, an oversimplification likely impairing their applicability. Using a multilayer network approach, explicitly accounting for inter-habitat connectivity, we investigate the spatial structure of seed-dispersal networks across the Gorongosa National Park, Mozambique. We show that the overall seed-dispersal network is composed by spatially explicit communities of dispersers spanning across habitats, functionally linking the landscape mosaic. Inter-habitat connectivity determines spatial structure, which cannot be accurately described with standard monolayer approaches either splitting or merging habitats. Multilayer modularity cannot be predicted by null models randomizing either interactions within each habitat or those linking habitats; however, as habitat connectivity increases, random processes become more important for overall structure. The importance of dispersers for the overall network structure is captured by multilayer versatility but not by standard metrics. Highly versatile species disperse many plant species across multiple habitats, being critical to landscape functional cohesion.

  18. In silico modeling of the yeast protein and protein family interaction network

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2004-03-01

    Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

  19. Defining the protein interaction network of human malaria parasite Plasmodium falciparum

    KAUST Repository

    Ramaprasad, Abhinay

    2012-02-01

    Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.

  20. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

    Science.gov (United States)

    de Vos, Marjon G. J.; Bollenbach, Tobias

    2017-01-01

    Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. PMID:28923953

  1. Crucial role of strategy updating for coexistence of strategies in interaction networks

    NARCIS (Netherlands)

    Zhang, Jianlei; Zhang, Chunyan; Cao, Ming; Weissing, Franz J.

    2015-01-01

    Network models are useful tools for studying the dynamics of social interactions in a structured population. After a round of interactions with the players in their local neighborhood, players update their strategy based on the comparison of their own payoff with the payoff of one of their

  2. Social networks, social interactions, and activity-travel behavior: a framework for microsimulation

    NARCIS (Netherlands)

    Arentze, T.A.; Timmermans, H.J.P.

    2007-01-01

    We argue that the social networks and activity-travel patterns of people interact and coevolve over time. Through social interaction, people exchange information about activity-travel choice alternatives and adapt their latent and overt preferences for alternatives to each other. At the same time,

  3. QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Asa Thibodeau

    2016-06-01

    Full Text Available Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1 building and visualizing chromatin interaction networks, 2 annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3 querying network components based on gene name or chromosome location, and 4 utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

  4. QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

    Science.gov (United States)

    Thibodeau, Asa; Márquez, Eladio J; Luo, Oscar; Ruan, Yijun; Menghi, Francesca; Shin, Dong-Guk; Stitzel, Michael L; Vera-Licona, Paola; Ucar, Duygu

    2016-06-01

    Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

  5. Future Mission Trends and their Implications for the Deep Space Network

    Science.gov (United States)

    Abraham, Douglas S.

    2006-01-01

    Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.

  6. Asymmetrically interacting spreading dynamics on complex layered networks.

    Science.gov (United States)

    Wang, Wei; Tang, Ming; Yang, Hui; Younghae Do; Lai, Ying-Cheng; Lee, GyuWon

    2014-05-29

    The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.

  7. Characterization of Schizophrenia Adverse Drug Interactions through a Network Approach and Drug Classification

    Directory of Open Access Journals (Sweden)

    Jingchun Sun

    2013-01-01

    Full Text Available Antipsychotic drugs are medications commonly for schizophrenia (SCZ treatment, which include two groups: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the “alimentary tract and metabolism” category while SCZ atypical drugs had significant interactions with drugs of the categories “nervous system” and “antiinfectives for systemic uses.” This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.

  8. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

    International Nuclear Information System (INIS)

    Ba, Qian; Li, Junyang; Huang, Chao; Li, Jingquan; Chu, Ruiai; Wu, Yongning; Wang, Hui

    2015-01-01

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified

  9. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

    Energy Technology Data Exchange (ETDEWEB)

    Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

    2015-03-01

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.

  10. Evaluating the Network: A Workflow for Tracking Twitter Interactions Using Social Networking Analysis

    Science.gov (United States)

    Goodier, Sarah

    2018-01-01

    Networking plays an important role in research projects to build a community and audience around a research area. Using social media is popular in project communication as it provides the ability to engage with a group of followers daily. Such online networking tools provide the advantage of providing nearrealtime data, which can be used to…

  11. NEURAL NETWORK INTERACTIONS AND INGESTIVE BEHAVIOR CONTROL DURING ANOREXIA

    Science.gov (United States)

    Watts, Alan G.; Salter, Dawna S.; Neuner, Christina M.

    2007-01-01

    Many models have been proposed over the years to explain how motivated feeding behavior is controlled. One of the most compelling is based on the original concepts of Eliot Stellar whereby sets of interosensory and exterosensory inputs converge on a hypothalamic control network that can either stimulate or inhibit feeding. These inputs arise from information originating in the blood, the viscera, and the telencephalon. In this manner the relative strengths of the hypothalamic stimulatory and inhibitory networks at a particular time dictates how an animal feeds. Anorexia occurs when the balance within the networks consistently favors the restraint of feeding. This article discusses experimental evidence supporting a model whereby the increases in plasma osmolality that result from drinking hypertonic saline activate pathways projecting to neurons in the paraventricular nucleus of the hypothalamus (PVH) and lateral hypothalamic area (LHA). These neurons constitute the hypothalamic controller for ingestive behavior, and receive a set of afferent inputs from regions of the brain that process sensory information that is critical for different aspects of feeding. Important sets of inputs arise in the arcuate nucleus, the hindbrain, and in the telencephalon. Anorexia is generated in dehydrated animals by way of osmosensitive projections to the behavior control neurons in the PVH and LHA, rather than by actions on their afferent inputs. PMID:17531275

  12. Integration and visualization of non-coding RNA and protein interaction networks

    OpenAIRE

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian; Pan, Xiaoyong; Santos Delgado, Alberto; Anthon, Christian; Alkan, Ferhat; von Mering, Christian; Workman, Christopher; Jensen, Lars Juhl; Gorodkin, Jan

    2015-01-01

    Non-coding RNAs (ncRNAs) fulfill a diverse set of biological functions relying on interactions with other molecular entities. The advent of new experimental and computational approaches makes it possible to study ncRNAs and their associations on an unprecedented scale. We present RAIN (RNA Association and Interaction Networks) - a database that combines ncRNA-ncRNA, ncRNA-mRNA and ncRNA-protein interactions with large-scale protein association networks available in the STRING database. By int...

  13. Modeling and Detecting Feature Interactions among Integrated Services of Home Network Systems

    Science.gov (United States)

    Igaki, Hiroshi; Nakamura, Masahide

    This paper presents a framework for formalizing and detecting feature interactions (FIs) in the emerging smart home domain. We first establish a model of home network system (HNS), where every networked appliance (or the HNS environment) is characterized as an object consisting of properties and methods. Then, every HNS service is defined as a sequence of method invocations of the appliances. Within the model, we next formalize two kinds of FIs: (a) appliance interactions and (b) environment interactions. An appliance interaction occurs when two method invocations conflict on the same appliance, whereas an environment interaction arises when two method invocations conflict indirectly via the environment. Finally, we propose offline and online methods that detect FIs before service deployment and during execution, respectively. Through a case study with seven practical services, it is shown that the proposed framework is generic enough to capture feature interactions in HNS integrated services. We also discuss several FI resolution schemes within the proposed framework.

  14. [Network clusters of symptoms as elementary syndromes of psychopathology: implications for clinical practice].

    Science.gov (United States)

    Goekoop, R; Goekoop, J G

    2016-01-01

    In a recent publication we reported the existence of around 11 (to 15) 'elementary syndromes' that may combine in various ways, rather like 'building blocks', to explain the wide range of psychiatric symptoms. 'Bridge symptoms' seem to be responsible both for combining large sets of symptoms into elementary syndromes and for combining the various elementary syndromes to form one globally connected network structure. To discuss the implication of these findings for clinical practice. We performed a network analysis of symptom scores. Elementary syndromes provide a massive simplification of the description of psychiatric disease. Instead of the more than 300 categories in DSM-5, we now need to consider only a handful of elementary syndromes and personality domains. This modular representation of psychiatric illnesses allows us to make a complete, systematic and efficient assessment of patients and a systematic review of treatment options. Clinicians, patients, managerial staff and insurance companies can verify whether symptom reduction is taking place in the most important domains of psychopathology. Unlike classic multidimensional methods of disease description, network models of psychopathology can be used to explain comorbidity patterns, predict the clinical course of psychopathology and to designate primary targets for therapeutic interventions. A network view on psychopathology could significantly improve everyday clinical practice.

  15. Dendritic Connectivity, Heterogeneity, and Scaling in Urban Stormwater Networks: Implications for Socio-Hydrology

    Science.gov (United States)

    Mejia, A.; Jovanovic, T.; Hale, R. L.; Gironas, J. A.

    2017-12-01

    Urban stormwater networks (USNs) are unique dendritic (tree-like) structures that combine both artificial (e.g., swales and pipes) and natural (e.g., streams and wetlands) components. They are central to stream ecosystem structure and function in urban watersheds. The emphasis of conventional stormwater management, however, has been on localized, temporal impacts (e.g., changes to hydrographs at discrete locations), and the performance of individual stormwater control measures. This is the case even though control measures are implemented to prevent impacts on the USN. We develop a modeling approach to retrospectively study hydrological fluxes and states in USNs and apply the model to an urban watershed in Scottsdale, Arizona, USA. Using outputs from the model, we analyze over space and time the network properties of dendritic connectivity, heterogeneity, and scaling. Results show that as the network growth over time, due to increasing urbanization, it tends to become more homogenous in terms of topological features but increasingly heterogeneous in terms of dynamic features. We further use the modeling results to address socio-hydrological implications for USNs. We find that the adoption over time of evolving management strategies (e.g., widespread implementation of vegetated swales and retention ponds versus pipes) may be locally beneficial to the USN but benefits may not propagate systematically through the network. The latter can be reinforced by sudden, perhaps unintended, changes to the overall dendritic connectivity.

  16. Ecological network analysis for economic systems: growth and development and implications for sustainable development.

    Science.gov (United States)

    Huang, Jiali; Ulanowicz, Robert E

    2014-01-01

    The quantification of growth and development is an important issue in economics, because these phenomena are closely related to sustainability. We address growth and development from a network perspective in which economic systems are represented as flow networks and analyzed using ecological network analysis (ENA). The Beijing economic system is used as a case study and 11 input-output (I-O) tables for 1985-2010 are converted into currency networks. ENA is used to calculate system-level indices to quantify the growth and development of Beijing. The contributions of each direct flow toward growth and development in 2010 are calculated and their implications for sustainable development are discussed. The results show that during 1985-2010, growth was the main attribute of the Beijing economic system. Although the system grew exponentially, its development fluctuated within only a small range. The results suggest that system ascendency should be increased in order to favor more sustainable development. Ascendency can be augmented in two ways: (1) strengthen those pathways with positive contributions to increasing ascendency and (2) weaken those with negative effects.

  17. Autoshaped choice in artificial neural networks: implications for behavioral economics and neuroeconomics.

    Science.gov (United States)

    Burgos, José E; García-Leal, Óscar

    2015-05-01

    An existing neural network model of conditioning was used to simulate autoshaped choice. In this phenomenon, pigeons first receive an autoshaping procedure with two keylight stimuli X and Y separately paired with food in a forward-delay manner, intermittently for X and continuously for Y. Then pigeons receive unreinforced choice test trials of X and Y concurrently present. Most pigeons choose Y. This preference for a more valuable response alternative is a form of economic behavior that makes the phenomenon relevant to behavioral economics. The phenomenon also suggests a role for Pavlovian contingencies in economic behavior. The model used, in contrast to others, predicts autoshaping and automaintenance, so it is uniquely positioned to predict autoshaped choice. The model also contemplates neural substrates of economic behavior in neuroeconomics, such as dopaminergic and hippocampal systems. A feedforward neural network architecture was designed to simulate a neuroanatomical differentiation between two environment-behavior relations X-R1 and Y-R2, [corrected] where R1 and R2 denote two different emitted responses (not unconditionally elicited by the reward). Networks with this architecture received a training protocol that simulated an autoshaped-choice procedure. Most networks simulated the phenomenon. Implications for behavioral economics and neuroeconomics, limitations, and the issue of model appraisal are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Identification of human disease genes from interactome network using graphlet interaction.

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Wang

    Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

  19. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  20. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed

    2013-05-01

    Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

  1. Fluctuating interaction network and time-varying stability of a natural fish community

    Science.gov (United States)

    Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

    2018-02-01

    Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

  2. Social networks, web-based tools and diseases: implications for biomedical research.

    Science.gov (United States)

    Costa, Fabricio F

    2013-03-01

    Advances in information technology have improved our ability to gather, collect and analyze information from individuals online. Social networks can be seen as a nonlinear superposition of a multitude of complex connections between people where the nodes represent individuals and the links between them capture a variety of different social interactions. The emergence of different types of social networks has fostered connections between individuals, thus facilitating data exchange in a variety of fields. Therefore, the question posed now is "can these same tools be applied to life sciences in order to improve scientific and medical research?" In this article, I will review how social networks and other web-based tools are changing the way we approach and track diseases in biomedical research. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  4. Systematic discovery of new recognition peptides mediating protein interaction networks

    DEFF Research Database (Denmark)

    Neduva, Victor; Linding, Rune; Su-Angrand, Isabelle

    2005-01-01

    Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP). Many domains...... by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length...

  5. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  6. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  7. Establishing Interaction between Machine and Medaka using Deep Q-Network

    Directory of Open Access Journals (Sweden)

    Ryo Nishimura

    2016-05-01

    Full Text Available Social interaction is the basic ability for animals to survive. It is difficult for a machine to interact with human or other animals because it is not clear how the machine should interact. This paper examines whether an artificial dot controlled by a machine can interact with a medaka and induce a desired behavior. The dot is displayed on a monitor. We use deep Q network (DQN to learn how to move the dot. As a result, the DQN could learn some basic elements to interact with the medaka and the desired behavior could be induced.

  8. Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks

    Science.gov (United States)

    Liu, Quan-Hui; Wang, Wei; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

    2018-02-01

    Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

  9. Interaction between Ailanthus altissima and Native Robinia pseudoacacia in Early Succession: Implications for Forest Management

    Directory of Open Access Journals (Sweden)

    Erik T. Nilsen

    2018-04-01

    Full Text Available The goal of this study was to discover the nature and intensity of the interaction between an exotic invader Ailanthus altissima (Mill. Swingle and its coexisting native Robinia pseudoacacia L. and consider management implications. The study occurred in the Mid-Appalachian region of the eastern United States. Ailanthus altissima can have a strong negative influence on community diversity and succession due to its allelopathic nature while R. pseudoacacia can have a positive effect on community diversity and succession because of its ability to fix nitrogen. How these trees interact and the influence of the interaction on succession will have important implications for forests in many regions of the world. An additive-replacement series common garden experiment was established to identify the type and extent of interactions between these trees over a three-year period. Both A. altissima and R. pseudoacacia grown in monoculture were inhibited by intraspecific competition. In the first year, A. altissima grown with R. pseudoacacia tended to be larger than A. altissima in monoculture, suggesting that R. pseudoacacia may facilitate the growth of A. altissima at the seedling stage. After the second year, R. pseudoacacia growth decreased as the proportion of coexisting A. altissima increased, indicating inhibition of R. pseudoacacia by A. altissima even though the R. pseudoacacia plants were much larger aboveground than the A. altissima plants. In early successional sites A. altissima should be removed, particularly in the presence of R. pseudoacacia in order to promote long-term community succession.

  10. Macroeconomic policy interaction: State dependency and implications for financial stability in UK: A systemic review

    Directory of Open Access Journals (Sweden)

    Muhammad Ali Nasir

    2016-12-01

    Full Text Available The association between economic and financial stabilities and influence of macroeconomic policies on the financial sector creates scope of active policy role in financial stability. As a contribution to the existing body of knowledge, this study has analysed the implications of macroeconomic policy interaction/coordination for financial stability, proxied by financial assets, i.e. equity and bonds price oscillation. The critical review and analysis of the existing literature on the subject suggests that there is also ample evidence of interdependence between monetary and fiscal policies and this interrelation necessitates coordination between them for the sake of financial stability. There is also a case for analysing the symmetry of financial markets responses to macroeconomic policy interaction. On methodological and empirical grounds, it is vital to test the robustness of policy recommendations to overcome the limitation of a single empirical approach (Jeffrey–Lindley’s paradox. Hence, the Frequentist and Bayesian approaches should be used in commentary manner. The policy interaction and optimal policy combination should also be analysed in the context of institutional design and major financial events to gain insight into the implications of policy interaction in the periods of stable economic and financial environments as well as period of financial and economic distress.

  11. The MicroRNA Interaction Network of Lipid Diseases

    Science.gov (United States)

    Kandhro, Abdul H.; Shoombuatong, Watshara; Nantasenamat, Chanin; Prachayasittikul, Virapong; Nuchnoi, Pornlada

    2017-01-01

    Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets. PMID:29018475

  12. Networked Mobilities and new sites of mediated interaction

    DEFF Research Database (Denmark)

    Jensen, Ole B.

    2008-01-01

    everyday life experiences the movement is much more than a travel from point A to point B. The mobile experiences of the contemporary society are practices that are meaningful and normatively embedded. That is to say, mobility is seen as a cultural phenomenon shaping notions of self and other as well......This paper takes point of departure in an understanding of mobility as an important cultural dimension to contemporary life. The movement of objects, signs, and people constitutes material sites of networked relationships. However, as an increasing number of mobility practices are making up our...

  13. Experimental FSO network availability estimation using interactive fog condition monitoring

    Science.gov (United States)

    Turán, Ján.; Ovseník, Łuboš

    2016-12-01

    Free Space Optics (FSO) is a license free Line of Sight (LOS) telecommunication technology which offers full duplex connectivity. FSO uses infrared beams of light to provide optical broadband connection and it can be installed literally in a few hours. Data rates go through from several hundreds of Mb/s to several Gb/s and range is from several 100 m up to several km. FSO link advantages: Easy connection establishment, License free communication, No excavation are needed, Highly secure and safe, Allows through window connectivity and single customer service and Compliments fiber by accelerating the first and last mile. FSO link disadvantages: Transmission media is air, Weather and climate dependence, Attenuation due to rain, snow and fog, Scattering of laser beam, Absorption of laser beam, Building motion and Air pollution. In this paper FSO availability evaluation is based on long term measured data from Fog sensor developed and installed at TUKE experimental FSO network in TUKE campus, Košice, Slovakia. Our FSO experimental network has three links with different physical distances between each FSO heads. Weather conditions have a tremendous impact on FSO operation in terms of FSO availability. FSO link availability is the percentage of time over a year that the FSO link will be operational. It is necessary to evaluate the climate and weather at the actual geographical location where FSO link is going to be mounted. It is important to determine the impact of a light scattering, absorption, turbulence and receiving optical power at the particular FSO link. Visibility has one of the most critical influences on the quality of an FSO optical transmission channel. FSO link availability is usually estimated using visibility information collected from nearby airport weather stations. Raw data from fog sensor (Fog Density, Relative Humidity, Temperature measured at each ms) are collected and processed by FSO Simulator software package developed at our Department. Based

  14. Diversity in a complex ecological network with two interaction types

    Czech Academy of Sciences Publication Activity Database

    Melián, C. J.; Bascompte, J.; Jordano, P.; Křivan, Vlastimil

    2009-01-01

    Roč. 118, č. 1 (2009), s. 122-130 ISSN 0030-1299 R&D Projects: GA AV ČR IAA100070601 Grant - others:University of California(US) DEB-0553768; The Spanish Ministry of Science and Technology (ES) REN2003-04774; The Spanish Ministry of Science and Technology (ES) REN2003-00273 Institutional research plan: CEZ:AV0Z50070508 Keywords : complex ecological network Subject RIV: EH - Ecology, Behaviour Impact factor: 3.147, year: 2009

  15. Interaction between fatty acid and the elastin network

    NARCIS (Netherlands)

    Vreeswijk, van J.

    1995-01-01

    The aim of the present study was to investigate the interaction between salts of fatty acids (FAS) and elastin. Absorption of fatty acids in elastin may affect the elasticity of elastin-containing tissue. Such phenomena could, for instance, be of relevance for the understanding of the

  16. Protein-lipid interactions: from membrane domains to cellular networks

    National Research Council Canada - National Science Library

    Tamm, Lukas K

    2005-01-01

    ... membranes is the lipid bilayer. Embedded in the fluid lipid bilayer are proteins of various shapes and traits. This volume illuminates from physical, chemical and biological angles the numerous - mostly quite weak - interactions between lipids, proteins, and proteins and lipids that define the delicate, highly dynamic and yet so stable fabri...

  17. Ariadne's Thread - Interactive Navigation in a World of Networked Information

    NARCIS (Netherlands)

    Koopman, Rob; Wang, Shenghui; Scharnhorst, Andrea; Englebienne, Gwenn

    2015-01-01

    This work-in-progress paper introduces an interface for the interactive visual exploration of the context of queries using the ArticleFirst database, a product of OCLC. We describe a workflow which allows the user to browse live entities associated with 65 million articles. In the on-line interface,

  18. Effect of dataset selection on the topological interpretation of protein interaction networks

    Directory of Open Access Journals (Sweden)

    Robertson David L

    2005-09-01

    Full Text Available Abstract Background Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. Results We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. Conclusion When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected

  19. Bird-plant interaction networks: a study on frugivory in Brazilian urban areas

    Directory of Open Access Journals (Sweden)

    Diego Silva Freitas Oliveira

    2015-12-01

    Full Text Available In Brazil, few studies compare the consumption of native and exotic fruits, especially in an urban environment. The Network Theory may be useful in such studies, because it allows evaluating many bird and plant species involved in interactions. The goals of this study were: evaluate a bird frugivory interaction network in an urban environment; checking the role played by native and exotic plants in the network and comparing the consumer assemblies of these two plant groups. A literature review on bird frugivory in Brazilian urban areas was conducted, as well as an analysis to create an interaction network on a regional scale. The analysis included 15 papers with 70 bird species eating fruits from 15 plant species (6 exotic and 9 native. The exotic and native fruit consumers did not form different groups and the interaction network was significantly nested (NODF = 0.30; p < 0.01 and not modular (M = 0.36; p = 0.16. Two exotic plant species are in the generalist core of the frugivory network (Ficus microcarpa and Michelia champaca. The results point out that a relatively diversified bird group eats fruits in Brazilian urban areas in an opportunistic way, with no preference for native or exotic plants.

  20. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  1. Interaction between hopping and static spins in a discrete network

    Energy Technology Data Exchange (ETDEWEB)

    Ciccarello, Francesco, E-mail: francesco.ciccarello@sns.it [CNISM and Dipartimento di Fisica, Universita' degli Studi di Palermo, Viale delle Scienze, Edificio 18, I-90128 Palermo (Italy); NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126 Pisa (Italy)

    2011-06-27

    We consider a process where a spin hops across a discrete network and at certain sites couples to static spins. While this setting is implementable in various scenarios (e.g. quantum dots or coupled cavities) the physics of such processes is still basically unknown. Here, we take a first step along this line by scrutinizing a two-site and a three-site lattices, each with two static spins. Despite a generally complex dynamics occurs, we show a regime such that the spin dynamics is described by an effective three-spin chain. Tasks such as entanglement generation and quantum state transfer can be achieved accordingly. -- Highlights: → We study mobile spins hopping in a discrete network and coupled to static spins. → This setting can be implemented in various scenarios. → We address a two-site and a three-site lattice, each with two static spins. → We show a regime where the setup can be described by an effective three-spin chain. → Accordingly, it is prone to be exploited for some QIP applications.

  2. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2007-10-01

    Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

  3. Cytoscape: a software environment for integrated models of biomolecular interaction networks.

    Science.gov (United States)

    Shannon, Paul; Markiel, Andrew; Ozier, Owen; Baliga, Nitin S; Wang, Jonathan T; Ramage, Daniel; Amin, Nada; Schwikowski, Benno; Ideker, Trey

    2003-11-01

    Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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

    Science.gov (United States)

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

    2010-07-01

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

  5. Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

    Science.gov (United States)

    Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

    2008-10-01

    Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

  6. Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment.

    Science.gov (United States)

    Chand, Ganesh B; Wu, Junjie; Hajjar, Ihab; Qiu, Deqiang

    2017-09-01

    Previous functional magnetic resonance imaging (fMRI) investigations suggest that the intrinsically organized large-scale networks and the interaction between them might be crucial for cognitive activities. A triple network model, which consists of the default-mode network, salience network, and central-executive network, has been recently used to understand the connectivity patterns of the cognitively normal brains versus the brains with disorders. This model suggests that the salience network dynamically controls the default-mode and central-executive networks in healthy young individuals. However, the patterns of interactions have remained largely unknown in healthy aging or those with cognitive decline. In this study, we assess the patterns of interactions between the three networks using dynamical causal modeling in resting state fMRI data and compare them between subjects with normal cognition and mild cognitive impairment (MCI). In healthy elderly subjects, our analysis showed that the salience network, especially its dorsal subnetwork, modulates the interaction between the default-mode network and the central-executive network (Mann-Whitney U test; p control correlated significantly with lower overall cognitive performance measured by Montreal Cognitive Assessment (r = 0.295; p control, especially the dorsal salience network, over other networks provides a neuronal basis for cognitive decline and may be a candidate neuroimaging biomarker of cognitive impairment.

  7. Temporal variation in bat-fruit interactions: Foraging strategies influence network structure over time

    Science.gov (United States)

    Zapata-Mesa, Natalya; Montoya-Bustamante, Sebastián; Murillo-García, Oscar E.

    2017-11-01

    Mutualistic interactions, such as seed dispersal, are important for the maintenance of structure and stability of tropical communities. However, there is a lack of information about spatial and temporal variation in plant-animal interaction networks. Thus, our goal was to assess the effect of bat's foraging strategies on temporal variation in the structure and robustness of bat-fruit networks in both a dry and a rain tropical forest. We evaluated monthly variation in bat-fruit networks by using seven structure metrics: network size, average path length, nestedness, modularity, complementary specialization, normalized degree and betweenness centrality. Seed dispersal networks showed variations in size, species composition and modularity; did not present nested structures and their complementary specialization was high compared to other studies. Both networks presented short path lengths, and a constantly high robustness, despite their monthly variations. Sedentary bat species were recorded during all the study periods and occupied more central positions than nomadic species. We conclude that foraging strategies are important structuring factors that affect the dynamic of networks by determining the functional roles of frugivorous bats over time; thus sedentary bats are more important than nomadic species for the maintenance of the network structure, and their conservation is a must.

  8. Social network analysis of character interaction in the Stargate and Star Trek television series

    Science.gov (United States)

    Tan, Melody Shi Ai; Ujum, Ephrance Abu; Ratnavelu, Kuru

    This paper undertakes a social network analysis of two science fiction television series, Stargate and Star Trek. Television series convey stories in the form of character interaction, which can be represented as “character networks”. We connect each pair of characters that exchanged spoken dialogue in any given scene demarcated in the television series transcripts. These networks are then used to characterize the overall structure and topology of each series. We find that the character networks of both series have similar structure and topology to that found in previous work on mythological and fictional networks. The character networks exhibit the small-world effects but found no significant support for power-law. Since the progression of an episode depends to a large extent on the interaction between each of its characters, the underlying network structure tells us something about the complexity of that episode’s storyline. We assessed the complexity using techniques from spectral graph theory. We found that the episode networks are structured either as (1) closed networks, (2) those containing bottlenecks that connect otherwise disconnected clusters or (3) a mixture of both.

  9. Reconstructing past ecological networks: the reconfiguration of seed-dispersal interactions after megafaunal extinction.

    Science.gov (United States)

    Pires, Mathias M; Galetti, Mauro; Donatti, Camila I; Pizo, Marco A; Dirzo, Rodolfo; Guimarães, Paulo R

    2014-08-01

    The late Quaternary megafaunal extinction impacted ecological communities worldwide, and affected key ecological processes such as seed dispersal. The traits of several species of large-seeded plants are thought to have evolved in response to interactions with extinct megafauna, but how these extinctions affected the organization of interactions in seed-dispersal systems is poorly understood. Here, we combined ecological and paleontological data and network analyses to investigate how the structure of a species-rich seed-dispersal network could have changed from the Pleistocene to the present and examine the possible consequences of such changes. Our results indicate that the seed-dispersal network was organized into modules across the different time periods but has been reconfigured in different ways over time. The episode of megafaunal extinction and the arrival of humans changed how seed dispersers were distributed among network modules. However, the recent introduction of livestock into the seed-dispersal system partially restored the original network organization by strengthening the modular configuration. Moreover, after megafaunal extinctions, introduced species and some smaller native mammals became key components for the structure of the seed-dispersal network. We hypothesize that such changes in network structure affected both animal and plant assemblages, potentially contributing to the shaping of modern ecological communities. The ongoing extinction of key large vertebrates will lead to a variety of context-dependent rearranged ecological networks, most certainly affecting ecological and evolutionary processes.

  10. Spatial interactions reveal inhibitory cortical networks in human amblyopia.

    Science.gov (United States)

    Wong, Erwin H; Levi, Dennis M; McGraw, Paul V

    2005-10-01

    Humans with amblyopia have a well-documented loss of sensitivity for first-order, or luminance defined, visual information. Recent studies show that they also display a specific loss of sensitivity for second-order, or contrast defined, visual information; a type of image structure encoded by neurons found predominantly in visual area A18/V2. In the present study, we investigate whether amblyopia disrupts the normal architecture of spatial interactions in V2 by determining the contrast detection threshold of a second-order target in the presence of second-order flanking stimuli. Adjacent flanks facilitated second-order detectability in normal observers. However, in marked contrast, they suppressed detection in each eye of the majority of amblyopic observers. Furthermore, strabismic observers with no loss of visual acuity show a similar pattern of detection suppression. We speculate that amblyopia results in predominantly inhibitory cortical interactions between second-order neurons.

  11. INCAS—Interactive Teleconsultation Network for Worldwide Healthcare Services

    Science.gov (United States)

    Castelli, A.; Colombo, C.; Garlaschelli, A.; Pepe, G.

    2001-01-01

    The INCAS Project arises from the needs of an Italian oil company in order to support the doctors responsible for the healthcare in remote drilling sites. The INCAS telemedicine1 system implements a prototype of teleconsultation medical service allowing for the interactive on-line connection with Italian healthcare reference centres in order to: • provide support to the expatriate doctor with the diagnoses and treatment of routine complaints; • contribute to the general improvement of healthcare in remote areas.

  12. Generalist Bee Species on Brazilian Bee-Plant Interaction Networks

    Directory of Open Access Journals (Sweden)

    Astrid de Matos Peixoto Kleinert

    2012-01-01

    Full Text Available Determining bee and plant interactions has an important role on understanding general biology of bee species as well as the potential pollinating relationship between them. Bee surveys have been conducted in Brazil since the end of the 1960s. Most of them applied standardized methods and had identified the plant species where the bees were collected. To analyze the most generalist bees on Brazilian surveys, we built a matrix of bee-plant interactions. We estimated the most generalist bees determining the three bee species of each surveyed locality that presented the highest number of interactions. We found 47 localities and 39 species of bees. Most of them belong to Apidae (31 species and Halictidae (6 families and to Meliponini (14 and Xylocopini (6 tribes. However, most of the surveys presented Apis mellifera and/or Trigona spinipes as the most generalist species. Apis mellifera is an exotic bee species and Trigona spinipes, a native species, is also widespread and presents broad diet breath and high number of individuals per colony.

  13. Model-free inference of direct network interactions from nonlinear collective dynamics.

    Science.gov (United States)

    Casadiego, Jose; Nitzan, Mor; Hallerberg, Sarah; Timme, Marc

    2017-12-19

    The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

  14. Emergence of structural patterns out of synchronization in networks with competitive interactions

    Science.gov (United States)

    Assenza, Salvatore; Gutiérrez, Ricardo; Gómez-Gardeñes, Jesús; Latora, Vito; Boccaletti, Stefano

    2011-09-01

    Synchronization is a collective phenomenon occurring in systems of interacting units, and is ubiquitous in nature, society and technology. Recent studies have enlightened the important role played by the interaction topology on the emergence of synchronized states. However, most of these studies neglect that real world systems change their interaction patterns in time. Here, we analyze synchronization features in networks in which structural and dynamical features co-evolve. The feedback of the node dynamics on the interaction pattern is ruled by the competition of two mechanisms: homophily (reinforcing those interactions with other correlated units in the graph) and homeostasis (preserving the value of the input strength received by each unit). The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale-free structures, together with a striking enhancement of local synchronization in systems with no global order.

  15. The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis.

    Directory of Open Access Journals (Sweden)

    Matthew D Dyer

    2010-08-01

    Full Text Available Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.

  16. Network interactions: non-geniculate input to V1.

    Science.gov (United States)

    Muckli, Lars; Petro, Lucy S

    2013-04-01

    The strongest connections to V1 are fed back from neighbouring area V2 and from a network of higher cortical areas (e.g. V3, V5, LOC, IPS and A1), transmitting the results of cognitive operations such as prediction, attention and imagination. V1 is therefore at the receiving end of a complex cortical processing cascade and not only at the entrance stage of cortical processing of retinal input. One elegant strategy to investigate this information-rich feedback to V1 is to eliminate feedforward input, that is, exploit V1's retinotopic organisation to isolate subregions receiving no direct bottom-up stimulation. We highlight the diverse mechanisms of cortical feedback, ranging from gain control to predictive coding, and conclude that V1 is involved in rich internal communication processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. The missing part of seed dispersal networks: structure and robustness of bat-fruit interactions.

    Directory of Open Access Journals (Sweden)

    Marco Aurelio Ribeiro Mello

    2011-02-01

    Full Text Available Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i some bat species depend more on fruits than others, and (ii that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H(2' = 0.37±0.10, mean ± SD and similar nestedness (NODF = 0.56±0.12 than pollination networks. All networks were modular (M = 0.32±0.07, and had on average four cohesive subgroups (modules of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum, although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55±0.10 and plants (R = 0.68±0.09. Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks.

  18. A Preliminary Examination of the Relationship Between Social Networking Interactions, Internet Use, and Thwarted Belongingness.

    Science.gov (United States)

    Moberg, Fallon B; Anestis, Michael D

    2015-01-01

    Joiner's (2005) interpersonal-psychological theory of suicide hypothesizes that suicidal desire develops in response to the joint presence of thwarted belongingness and perceived burdensomeness. To consider the potential influence of online interactions and behaviors on these outcomes. To address this, we administered an online protocol assessing suicidal desire and online interactions in a sample of 305 undergraduates (83.6% female). We hypothesized negative interactions on social networking sites and a preference for online social interactions would be associated with thwarted belongingness. We also conducted an exploratory analysis examining the associations between Internet usage and perceived burdensomeness. Higher levels of negative interactions on social networking sites, but no other variables, significantly predicted thwarted belongingness. Our exploratory analysis showed that none of our predictors were associated with perceived burdensomeness after accounting for demographics, depression, and thwarted belongingness. Our findings indicate that a general tendency to have negative interactions on social networking sites could possibly impact suicidal desire and that these effects are significant above and beyond depression symptoms. Furthermore, no other aspect of problematic Internet use significantly predicted our outcomes in multivariate analyses, indicating that social networking in particular may have a robust effect on thwarted belongingness.

  19. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

    Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

  20. HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

    Science.gov (United States)

    Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

    2015-04-01

    The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. When the Web meets the cell: using personalized PageRank for analyzing protein interaction networks.

    Science.gov (United States)

    Iván, Gábor; Grolmusz, Vince

    2011-02-01

    Enormous and constantly increasing quantity of biological information is represented in metabolic and in protein interaction network databases. Most of these data are freely accessible through large public depositories. The robust analysis of these resources needs novel technologies, being developed today. Here we demonstrate a technique, originating from the PageRank computation for the World Wide Web, for analyzing large interaction networks. The method is fast, scalable and robust, and its capabilities are demonstrated on metabolic network data of the tuberculosis bacterium and the proteomics analysis of the blood of melanoma patients. The Perl script for computing the personalized PageRank in protein networks is available for non-profit research applications (together with sample input files) at the address: http://uratim.com/pp.zip.

  2. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  3. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

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

    2013-01-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  4. Evolution of quantum and classical strategies on networks by group interactions

    International Nuclear Information System (INIS)

    Li Qiang; Chen Minyou; Iqbal, Azhar; Abbott, Derek

    2012-01-01

    In this paper, quantum strategies are introduced within evolutionary games in order to investigate the evolution of quantum and classical strategies on networks in the public goods game. Comparing the results of evolution on a scale-free network and a square lattice, we find that a quantum strategy outperforms the classical strategies, regardless of the network. Moreover, a quantum strategy dominates the population earlier in group interactions than it does in pairwise interactions. In particular, if the hub node in a scale-free network is occupied by a cooperator initially, the strategy of cooperation will prevail in the population. However, in other situations, a quantum strategy can defeat the classical ones and finally becomes the dominant strategy in the population. (paper)

  5. Integrating Micro-level Interactions with Social Network Analysis in Tie Strength Research

    DEFF Research Database (Denmark)

    Torre, Osku; Gupta, Jayesh Prakash; Kärkkäinen, Hannu

    2017-01-01

    of tie strength based on reciprocal interaction from publicly available Facebook data, and suggest that this approach could work as a basis for further tie strength studies. Our approach makes use of weak tie theory, and enables researchers to study micro-level interactions (i.e. discussions, messages......A social tie is a target for ongoing, high-level scientific debate. Measuring the tie strength in social networks has been an important topic for academic studies since Mark Granovetter's seminal papers in 1970's. However, it is still a problematic issue mainly for two reasons: 1) existing tie...... strengthening process in online social networks. Therefore, we suggest a new approach to tie strength research, which focuses on studying communication patterns (edges) more rather than actors (nodes) in a social network. In this paper we build a social network analysis-based approach to enable the evaluation...

  6. Social network analysis as a method for analyzing interaction in collaborative online learning environments

    Directory of Open Access Journals (Sweden)

    Patricia Rice Doran

    2011-12-01

    Full Text Available Social network analysis software such as NodeXL has been used to describe participation and interaction in numerous social networks, but it has not yet been widely used to examine dynamics in online classes, where participation is frequently required rather than optional and participation patterns may be impacted by the requirements of the class, the instructor’s activities, or participants’ intrinsic engagement with the subject matter. Such social network analysis, which examines the dynamics and interactions among groups of participants in a social network or learning group, can be valuable in programs focused on teaching collaborative and communicative skills, including teacher preparation programs. Applied to these programs, social network analysis can provide information about instructional practices likely to facilitate student interaction and collaboration across diverse student populations. This exploratory study used NodeXL to visualize students’ participation in an online course, with the goal of identifying (1 ways in which NodeXL could be used to describe patterns in participant interaction within an instructional setting and (2 identifying specific patterns in participant interaction among students in this particular course. In this sample, general education teachers demonstrated higher measures of connection and interaction with other participants than did those from specialist (ESOL or special education backgrounds, and tended to interact more frequently with all participants than the majority of participants from specialist backgrounds. We recommend further research to delineate specific applications of NodeXL within an instructional context, particularly to identify potential patterns in student participation based on variables such as gender, background, cultural and linguistic heritage, prior training and education, and prior experience so that instructors can ensure their practice helps to facilitate student interaction

  7. Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

    Directory of Open Access Journals (Sweden)

    Sungyoung Lee

    2012-12-01

    Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  8. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo

    2013-06-21

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The

  9. Prediction of interface residue based on the features of residue interaction network.

    Science.gov (United States)

    Jiao, Xiong; Ranganathan, Shoba

    2017-11-07

    Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  11. Establishing Network Interaction between Resource Training Centers for People with Disabilities and Partner Universities

    Directory of Open Access Journals (Sweden)

    Panyukova S.V.,

    2018-05-01

    Full Text Available The paper focuses on the problem of accessibility and quality of higher education for students with disabilities. We describe our experience in organising network interaction between the MSUPE Resource and Training Center for Disabled People established in 2016-2017 and partner universities in ‘fixed territories’. The need for cooperation and network interaction arises from the high demand for the cooperation of efforts of leading experts, researchers, methodologists and instructors necessary for improving the quality and accessibility of higher education for persons with disabilities. The Resource and Training Center offers counseling for the partner universities, arranges advanced training for those responsible for teaching of the disabled, and offers specialized equipment for temporary use. In this article, we emphasize the importance of organizing network interactions with universities and social partners in order to ensure accessibility of higher education for students with disabilities.

  12. Emergence of modularity and disassortativity in protein-protein interaction networks.

    Science.gov (United States)

    Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

    2010-12-01

    In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

  13. Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity.

    Science.gov (United States)

    Pardikes, Nicholas A; Lumpkin, Will; Hurtado, Paul J; Dyer, Lee A

    2018-01-01

    Most of earth's biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships

  14. Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

    Science.gov (United States)

    Tsai, Kuo-Ting; Hu, Chin-Kun; Li, Kuan-Wei; Hwang, Wen-Liang; Chou, Ya-Hui

    2018-05-23

    Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.

  15. Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

    Directory of Open Access Journals (Sweden)

    Christian Nowke

    2018-06-01

    Full Text Available Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

  16. Topological and functional properties of the small GTPases protein interaction network.

    Directory of Open Access Journals (Sweden)

    Anna Delprato

    Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

  17. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    Science.gov (United States)

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

    Science.gov (United States)

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

    2015-01-01

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

  19. Interactions between Financial and Environmental Networks in OECD Countries.

    Directory of Open Access Journals (Sweden)

    Franco Ruzzenenti

    Full Text Available We analysed a multiplex of financial and environmental networks between OECD countries from 2002 to 2010. Foreign direct investments and portfolio investment showing the flows in equity securities, short-term, long-term and total debt, these securities represent the financial layers; emissions of NOx, PM10, SO2, CO2 equivalent and the water footprint associated with international trade represent the environmental layers. We present a new measure of cross-layer correlations between flows in different layers based on reciprocity. For the assessment of results, we implement a null model for this measure based on the exponential random graph theory. We find that short-term financial flows are more correlated with environmental flows than long-term investments. Moreover, the correlations between reverse financial and environmental flows (i.e. the flows of different layers going in opposite directions are generally stronger than correlations between synergic flows (flows going in the same direction. This suggests a trade-off between financial and environmental layers, where, more financialised countries display higher correlations between outgoing financial flows and incoming environmental flows than from lower financialised countries. Five countries are identified as hubs in this finance-environment multiplex: The United States, France, Germany, Belgium-Luxembourg and United Kingdom.

  20. Insights into the fold organization of TIM barrel from interaction energy based structure networks.

    Science.gov (United States)

    Vijayabaskar, M S; Vishveshwara, Saraswathi

    2012-01-01

    There are many well-known examples of proteins with low sequence similarity, adopting the same structural fold. This aspect of sequence-structure relationship has been extensively studied both experimentally and theoretically, however with limited success. Most of the studies consider remote homology or "sequence conservation" as the basis for their understanding. Recently "interaction energy" based network formalism (Protein Energy Networks (PENs)) was developed to understand the determinants of protein structures. In this paper we have used these PENs to investigate the common non-covalent interactions and their collective features which stabilize the TIM barrel fold. We have also developed a method of aligning PENs in order to understand the spatial conservation of interactions in the fold. We have identified key common interactions responsible for the conservation of the TIM fold, despite high sequence dissimilarity. For instance, the central beta barrel of the TIM fold is stabilized by long-range high energy electrostatic interactions and low-energy contiguous vdW interactions in certain families. The other interfaces like the helix-sheet or the helix-helix seem to be devoid of any high energy conserved interactions. Conserved interactions in the loop regions around the catalytic site of the TIM fold have also been identified, pointing out their significance in both structural and functional evolution. Based on these investigations, we have developed a novel network based phylogenetic analysis for remote homologues, which can perform better than sequence based phylogeny. Such an analysis is more meaningful from both structural and functional evolutionary perspective. We believe that the information obtained through the "interaction conservation" viewpoint and the subsequently developed method of structure network alignment, can shed new light in the fields of fold organization and de novo computational protein design.

  1. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Robust Weak Chimeras in Oscillator Networks with Delayed Linear and Quadratic Interactions

    Science.gov (United States)

    Bick, Christian; Sebek, Michael; Kiss, István Z.

    2017-10-01

    We present an approach to generate chimera dynamics (localized frequency synchrony) in oscillator networks with two populations of (at least) two elements using a general method based on a delayed interaction with linear and quadratic terms. The coupling design yields robust chimeras through a phase-model-based design of the delay and the ratio of linear and quadratic components of the interactions. We demonstrate the method in the Brusselator model and experiments with electrochemical oscillators. The technique opens the way to directly bridge chimera dynamics in phase models and real-world oscillator networks.

  3. Network structure beyond food webs: mapping non-trophic and trophic interactions on Chilean rocky shores.

    Science.gov (United States)

    Sonia Kéfi; Berlow, Eric L; Wieters, Evie A; Joppa, Lucas N; Wood, Spencer A; Brose, Ulrich; Navarrete, Sergio A

    2015-01-01

    How multiple types of non-trophic interactions map onto trophic networks in real communities remains largely unknown. We present the first effort, to our knowledge, describing a comprehensive ecological network that includes all known trophic and diverse non-trophic links among >100 coexisting species for the marine rocky intertidal community of the central Chilean coast. Our results suggest that non-trophic interactions exhibit highly nonrandom structures both alone and with respect to food web structure. The occurrence of different types of interactions, relative to all possible links, was well predicted by trophic structure and simple traits of the source and target species. In this community, competition for space and positive interactions related to habitat/refuge provisioning by sessile and/or basal species were by far the most abundant non-trophic interactions. If these patterns are orroborated in other ecosystems, they may suggest potentially important dynamic constraints on the combined architecture of trophic and non-trophic interactions. The nonrandom patterning of non-trophic interactions suggests a path forward for developing a more comprehensive ecological network theory to predict the functioning and resilience of ecological communities.

  4. Dynamical analysis of yeast protein interaction network during the sake brewing process.

    Science.gov (United States)

    Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

    2011-12-01

    Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.

  5. Musical Imagery Involves Wernicke's Area in Bilateral and Anti-Correlated Network Interactions in Musicians.

    Science.gov (United States)

    Zhang, Yizhen; Chen, Gang; Wen, Haiguang; Lu, Kun-Han; Liu, Zhongming

    2017-12-06

    Musical imagery is the human experience of imagining music without actually hearing it. The neural basis of this mental ability is unclear, especially for musicians capable of engaging in accurate and vivid musical imagery. Here, we created a visualization of an 8-minute symphony as a silent movie and used it as real-time cue for musicians to continuously imagine the music for repeated and synchronized sessions during functional magnetic resonance imaging (fMRI). The activations and networks evoked by musical imagery were compared with those elicited by the subjects directly listening to the same music. Musical imagery and musical perception resulted in overlapping activations at the anterolateral belt and Wernicke's area, where the responses were correlated with the auditory features of the music. Whereas Wernicke's area interacted within the intrinsic auditory network during musical perception, it was involved in much more complex networks during musical imagery, showing positive correlations with the dorsal attention network and the motor-control network and negative correlations with the default-mode network. Our results highlight the important role of Wernicke's area in forming vivid musical imagery through bilateral and anti-correlated network interactions, challenging the conventional view of segregated and lateralized processing of music versus language.

  6. Synchronization unveils the organization of ecological networks with positive and negative interactions

    Science.gov (United States)

    Girón, Andrea; Saiz, Hugo; Bacelar, Flora S.; Andrade, Roberto F. S.; Gómez-Gardeñes, Jesús

    2016-06-01

    Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

  7. Synchronization unveils the organization of ecological networks with positive and negative interactions.

    Science.gov (United States)

    Girón, Andrea; Saiz, Hugo; Bacelar, Flora S; Andrade, Roberto F S; Gómez-Gardeñes, Jesús

    2016-06-01

    Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

  8. Rainwater Harvesting and Social Networks: Visualising Interactions for Niche Governance, Resilience and Sustainability

    Directory of Open Access Journals (Sweden)

    Sarah Ward

    2016-11-01

    Full Text Available Visualising interactions across urban water systems to explore transition and change processes requires the development of methods and models at different scales. This paper contributes a model representing the network interactions of rainwater harvesting (RWH infrastructure innovators and other organisations in the UK RWH niche to identify how resilience and sustainability feature within niche governance in practice. The RWH network interaction model was constructed using a modified participatory social network analysis (SNA. The SNA was further analysed through the application of a two-part analytical framework based on niche management and the safe, resilient and sustainable (‘Safe and SuRe’ framework. Weak interactions between some RWH infrastructure innovators and other organisations highlighted reliance on a limited number of persuaders to influence the regime and landscape, which were underrepresented. Features from niche creation and management were exhibited by the RWH network interaction model, though some observed characteristics were not represented. Additional Safe and SuRe features were identified covering diverse innovation, responsivity, no protection, unconverged expectations, primary influencers, polycentric or adaptive governance and multiple learning-types. These features enable RWH infrastructure innovators and other organisations to reflect on improving resilience and sustainability, though further research in other sectors would be useful to verify and validate observation of the seven features.

  9. Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

    Science.gov (United States)

    Naderi, Elnaz; Mostafaei, Mehdi; Pourshams, Akram

    2014-01-01

    Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches. PMID:24895587

  10. Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

    Directory of Open Access Journals (Sweden)

    Elnaz Naderi

    2014-01-01

    Full Text Available Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches.

  11. Reliability of transmission networks : Impact of EHV underground cables & interaction of offshore-onshore networks

    NARCIS (Netherlands)

    Tuinema, B.W.

    2017-01-01

    For the future, several developments of the power system are expected. The transition towards a more sustainable energy supply puts new requirements on the design and operation of power systems, and the transmission network in particular. Offshore, a transmission grid will be implemented to collect

  12. Social implications of children's smartphone addiction: The role of support networks and social engagement.

    Science.gov (United States)

    Ihm, Jennifer

    2018-06-05

    Background and aims Most studies have regarded smartphone addiction as a condition stemming from individuals' psychological issues, so research has rarely examined it in relation to a lack of social resources and its social impacts. However, this study reinterprets smartphone addiction as a social problem stemming from a lack of offline social networks and resulting in a decline of social engagement. Methods This study drew on a survey of 2,000 children in Korea consisting of 991 males and 1,009 females with an average age of 12 years old. Using the STATA 14 structural equation modeling program, this study examined the relationships between children's lack of social networks, smartphone addiction, and social engagement. Results Social network variables, such as formal organizational membership, quality of relationship with parents, size of the peer group, and peer support, decrease smartphone addiction. Simply having good relationships and reciprocal feelings with peers do not have any influence on the smartphone addiction. The more the children become addicted to smartphones, the less they participate in social engagement. Discussion and conclusions This study provides a new understanding of smartphone addiction by focusing on its social aspects, augmenting prior studies that have addressed psychological factors. Findings suggest that children's lack of social networks may inhibit comfortable social interactions and feelings of support in the offline environment, which can heighten their desire to escape to smartphones. These children, unlike non-addicts, may not take advantage of the media to enrich their social lives and increase their level of social engagement.

  13. Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

    Science.gov (United States)

    Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

    2009-03-11

    Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

  14. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

    Science.gov (United States)

    Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

    2015-01-01

    The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

  15. STITCH 2: an interaction network database for small molecules and proteins

    DEFF Research Database (Denmark)

    Kuhn, Michael; Szklarczyk, Damian; Franceschini, Andrea

    2010-01-01

    Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug......-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other...... chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/....

  16. Global product development interaction between local networks: A study of the Danish food industry

    DEFF Research Database (Denmark)

    Kristensen, Preben Sander

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

  17. Mechanisms of radiation interaction with DNA: Potential implications for radiation protection

    International Nuclear Information System (INIS)

    Sinclair, W.K.; Fry, R.J.M.

    1987-01-01

    An overview of presentations and discussions which took place at the US Department of Energy/Commission of European Communities (DOE/CEC) workshop on ''Mechanisms of Radiation Interaction with DNA: Potential Implications for Radiation Protection,'' held at San Diego, California, January 21-22, 1987, is provided. The Department has traditionally supported fundamental research on interactions of ionizing radiation with different biological systems and at all levels of biological organization. The aim of this workshop was to review the base of knowledge in the area of mechanisms of radiation action at the DNA level, and to explore ways in which this information can be applied to the development of scientifically sound concepts and procedures for use in the field of radiation protection

  18. Teacher interaction in psychosocial learning environments: cultural differences and their implications in science instruction

    Science.gov (United States)

    Khine, Myint Swe; Fisher, Darrell L.

    2004-01-01

    The purpose of this study was to examine interpersonal behaviour in psychosocial learning environments and to determine the associations between science students' perceptions of their interactions with their teachers, the cultural background of teachers and their attitudinal outcomes. A sample of 1188 students completed the Questionnaire on Teacher Interaction instrument. The responses to two subscales of Test of Science-related Attitudes were used as attitudinal measures. Significant associations between students' perceptions of teacher interpersonal behaviour and the cultural background of teachers were detected. The results showed that students perceived a more favourable interpersonal relationship with Western teachers in the secondary science classrooms. The students in the classes of Western teachers indicated that they enjoyed science lessons more than those in the classes of Asian teachers. Some implications for science instruction in this context are discussed.

  19. Network-based identification of biomarkers coexpressed with multiple pathways.

    Science.gov (United States)

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

  20. Chemical-gene interaction networks and causal reasoning for ...

    Science.gov (United States)

    Evaluating the potential human health and ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrate chemical monitoring and biological effects data to evaluate risks associated with chemicals present in the environment. Here, we used prior knowledge about chemical-gene interactions to develop a knowledge assembly model for detected chemicals at five locations near the North Branch and Chisago wastewater treatment plants (WWTP) in the St. Croix River Basin, MN and WI. The assembly model was used to generate hypotheses about the biological impacts of the chemicals at each location. The hypotheses were tested using empirical hepatic gene expression data from fathead minnows exposed for 12 d at each location. Empirical gene expression data were also mapped to the assembly models to evaluate the likelihood of a chemical contributing to the observed biological responses using richness and concordance statistics. The prior knowledge approach was able predict the observed biological pathways impacted at one site but not the other. Atrazine was identified as a potential contributor to the observed gene expression responses at a location upstream of the North Branch WTTP. Four chemicals were identified as contributors to the observed biological responses at the effluent and downstream o

  1. Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

    Energy Technology Data Exchange (ETDEWEB)

    Gonzalez-Barrios, P.; Castro, M.; Pérez, O.; Vilaró, D.; Gutiérrez, L.

    2017-07-01

    Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.

  2. Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma.

    Directory of Open Access Journals (Sweden)

    Yuewen Han

    Full Text Available Epidemiological studies have validated the association between hepatitis C virus (HCV infection and hepatocellular carcinoma (HCC. An increasing number of studies show that protein-protein interactions (PPIs between HCV proteins and host proteins play a vital role in infection and mediate HCC progression. In this work, we collected all published interaction between HCV and human proteins, which include 455 unique human proteins participating in 524 HCV-human interactions. Then, we construct the HCV-human and HCV-HCC protein interaction networks, which display the biological knowledge regarding the mechanism of HCV pathogenesis, particularly with respect to pathogenesis of HCC. Through in-depth analysis of the HCV-HCC interaction network, we found that interactors are enriched in the JAK/STAT, p53, MAPK, TNF, Wnt, and cell cycle pathways. Using a random walk with restart algorithm, we predicted the importance of each protein in the HCV-HCC network and found that AKT1 may play a key role in the HCC progression. Moreover, we found that NS5A promotes HCC cells proliferation and metastasis by activating AKT/GSK3β/β-catenin pathway. This work provides a basis for a detailed map tracking new cellular interactions of HCV and identifying potential targets for HCV-related hepatocellular carcinoma treatment.

  3. Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

    Science.gov (United States)

    DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

    Directory of Open Access Journals (Sweden)

    Neszt Michael

    2008-07-01

    Full Text Available Abstract Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q. Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes.

  5. Modeling interacting dynamic networks: I. Preferred degree networks and their characteristics

    International Nuclear Information System (INIS)

    Liu, Wenjia; Schmittmann, Beate; Zia, R K P; Jolad, Shivakumar

    2013-01-01

    We study a simple model of dynamic networks, characterized by a set preferred degree, κ. Each node with degree k attempts to maintain its κ and will add (cut) a link with probability w(k;κ) (1 − w(k;κ)). As a starting point, we consider a homogeneous population, where each node has the same κ, and examine several forms of w(k;κ), inspired by Fermi–Dirac functions. Using Monte Carlo simulations, we find the degree distribution in the steady state. In contrast to the well known Erdős–Rényi network, our degree distribution is not a Poisson distribution; yet its behavior can be understood by an approximate theory. Next, we introduce a second preferred degree network and couple it to the first by establishing a controllable fraction of inter-group links. For this model, we find both understandable and puzzling features. Generalizing the prediction for the homogeneous population, we are able to explain the total degree distributions well, but not the intra- or inter-group degree distributions. When monitoring the total number of inter-group links, X, we find very surprising behavior. X explores almost the full range between its maximum and minimum allowed values, resulting in a flat steady-state distribution, reminiscent of a simple random walk confined between two walls. Both simulation results and analytic approaches will be discussed. (paper)

  6. The role of asymmetric interactions on the effect of habitat destruction in mutualistic networks.

    Directory of Open Access Journals (Sweden)

    Guillermo Abramson

    Full Text Available Plant-pollinator mutualistic networks are asymmetric in their interactions: specialist plants are pollinated by generalist animals, while generalist plants are pollinated by a broad range involving specialists and generalists. It has been suggested that this asymmetric--or disassortative--assemblage could play an important role in determining the observed equal susceptibility of specialist and generalist plants under habitat destruction. At the core of the analysis of the phenomenon lies the observation that specialist plants, otherwise candidates to extinction, could cope with the disruption thanks to their interaction with a few generalist pollinators. We present a theoretical framework that supports this thesis. We analyze a dynamical model of a system of mutualistic plants and pollinators, subject to the destruction of their habitat. We analyze and compare two families of interaction topologies, ranging from highly assortative to highly disassortative ones, as well as real pollination networks. We found that several features observed in natural systems are predicted by the mathematical model. First, there is a tendency to increase the asymmetry of the network as a result of the extinctions. Second, an entropy measure of the differential susceptibility to extinction of specialist and generalist species show that they tend to balance when the network is disassortative. Finally, the disappearance of links in the network, as a result of extinctions, shows that specialist plants preserve more connections than the corresponding plants in an assortative system, enabling them to resist the disruption.

  7. Data on overlapping brain disorders and emerging drug targets in human Dopamine Receptors Interaction Network

    Directory of Open Access Journals (Sweden)

    Avijit Podder

    2017-06-01

    Full Text Available Intercommunication of Dopamine Receptors (DRs with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled “Human Dopamine Receptors Interaction Network (DRIN: a systems biology perspective on topology, stability and functionality of the network” (Podder et al., 2014 [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

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

    Directory of Open Access Journals (Sweden)

    Kirouac Daniel C

    2012-05-01

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

  9. Mirid (Hemiptera: Heteroptera) specialists of sticky plants: adaptations, interactions, and ecological implications.

    Science.gov (United States)

    Wheeler, Alfred G; Krimmel, Billy A

    2015-01-07

    Sticky plants-those having glandular trichomes (hairs) that produce adhesive, viscous exudates-can impede the movement of, and entrap, generalist insects. Disparate arthropod groups have adapted to these widespread and taxonomically diverse plants, yet their interactions with glandular hosts rarely are incorporated into broad ecological theory. Ecologists and entomologists might be unaware of even well-documented examples of insects that are sticky-plant specialists. The hemipteran family Miridae (more specifically, the omnivorous Dicyphini: Dicyphina) is the best-known group of arthropods that specializes on sticky plants. In the first synthesis of relationships with glandular plants for any insect family, we review mirid interactions with sticky hosts, including their adaptations (behavioral, morphological, and physiological) and mutualisms with carnivorous plants, and the ecological and agricultural implications of mirid-sticky plant systems. We propose that mirid research applies generally to tritrophic interactions on trichome-defended plants, enhances an understanding of insect-plant interactions, and provides information useful in managing crop pests.

  10. Drug-drug interactions involving lysosomes: mechanisms and potential clinical implications.

    Science.gov (United States)

    Logan, Randall; Funk, Ryan S; Axcell, Erick; Krise, Jeffrey P

    2012-08-01

    Many commercially available, weakly basic drugs have been shown to be lysosomotropic, meaning they are subject to extensive sequestration in lysosomes through an ion trapping-type mechanism. The extent of lysosomal trapping of a drug is an important therapeutic consideration because it can influence both activity and pharmacokinetic disposition. The administration of certain drugs can alter lysosomes such that their accumulation capacity for co-administered and/or secondarily administered drugs is altered. In this review the authors explore what is known regarding the mechanistic basis for drug-drug interactions involving lysosomes. Specifically, the authors address the influence of drugs on lysosomal pH, volume and lipid processing. Many drugs are known to extensively accumulate in lysosomes and significantly alter their structure and function; however, the therapeutic and toxicological implications of this remain controversial. The authors propose that drug-drug interactions involving lysosomes represent an important potential source of variability in drug activity and pharmacokinetics. Most evaluations of drug-drug interactions involving lysosomes have been performed in cultured cells and isolated tissues. More comprehensive in vivo evaluations are needed to fully explore the impact of this drug-drug interaction pathway on therapeutic outcomes.

  11. Restoring fish ecological quality in estuaries: Implication of interactive and cumulative effects among anthropogenic stressors.

    Science.gov (United States)

    Teichert, Nils; Borja, Angel; Chust, Guillem; Uriarte, Ainhize; Lepage, Mario

    2016-01-15

    Estuaries are subjected to multiple anthropogenic stressors, which have additive, antagonistic or synergistic effects. Current challenges include the use of large databases of biological monitoring surveys (e.g. the European Water Framework Directive) to help environmental managers prioritizing restoration measures. This study investigated the impact of nine stressor categories on the fish ecological status derived from 90 estuaries of the North East Atlantic countries. We used a random forest model to: 1) detect the dominant stressors and their non-linear effects; 2) evaluate the ecological benefits expected from reducing pressure from stressors; and 3) investigate the interactions among stressors. Results showed that largest restoration benefits were expected when mitigating water pollution and oxygen depletion. Non-additive effects represented half of pairwise interactions among stressors, and antagonisms were the most common. Dredged sediments, flow changes and oxygen depletion were predominantly implicated in non-additive interactions, whereas the remainder stressors often showed additive impacts. The prevalence of interactive impacts reflects a complex scenario for estuaries management; hence, we proposed a step-by-step restoration scheme focusing on the mitigation of stressors providing the maximum of restoration benefits under a multi-stress context. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. LIVING IN NETWORK: AN ANALYSIS OF THE IMPLICATIONS OF THE USE OF SOCIAL MEDIA BY UNIVERSITY STUDENTS

    Directory of Open Access Journals (Sweden)

    Ana Cristina Alves Lima

    2017-10-01

    Full Text Available The work sought to analyze and discuss, from a predominantly quantitative approach, the repercussion caused by the use of social media in the life of students of a University located in the city of Araraquara (SP. After the bibliographic review on the subject, a field research was carried out with students who use social media such as Facebook, Twiter and Instagram, trying to identify the perceptions of users about benefits and damages due to the use. The study counted on the participation of 140 students of the Psychology course, 92 participants of the Biology course and 51 participants of the course of Computer Engineering. The students answered a questionnaire on the subject. The study found that most (95.76% of the participants use social networks often and say that this does not lead to significant changes or implications in their lives. They present as benefits the use in order to reduce the physical distance, improving their interaction and communication, ease in the exchange and obtaining of information, mainly in the acquisition of knowledge, education and entertainment. As for the damages caused, they indicate excessive exposure of personal issues in daily life, dependence on the use of the Internet in carrying out its activities and alienation from concrete situations.

  13. Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

    Science.gov (United States)

    LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

    2015-01-01

    High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

  14. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Jan eHahne

    2015-09-01

    Full Text Available Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy...

  15. Independent and Interactive Effects of Neighborhood Disadvantage and Social Network Characteristics on Problem Drinking after Treatment.

    Science.gov (United States)

    Mericle, Amy A; Kaskutas, Lee A; Polcin, Doug L; Karriker-Jaffe, Katherine J

    2018-01-01

    Socioecological approaches to public health problems like addiction emphasize the importance of person-environment interactions. Neighborhood and social network characteristics may influence the likelihood of relapse among individuals in recovery, but these factors have been understudied, particularly with respect to conceptualizing social network characteristics as moderators of neighborhood disadvantage. Drawing from a larger prospective study of individuals recruited from outpatient treatment (N=451) and interviewed 1, 3, 5, and 7 years later, the aim of this study was to examine the independent and interactive effects of neighborhood and social network characteristics on continued problem drinking after treatment. Models using generalized estimating equations controlling for demographic and other risk factors found the number of heavy drinkers in one's network increases risk of relapse, with the effects being significantly stronger among those living in disadvantaged neighborhoods than among those in non-disadvantaged neighborhoods. No independent effects were found for neighborhood disadvantage or for the number of network members supporting reduced drinking. Future research is needed to examine potential protective factors in neighborhoods which may offset socioeconomic disadvantage as well as to investigate the functions that network members serve in helping to improve long-term treatment outcomes.

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

    Science.gov (United States)

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

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

  17. Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

    Science.gov (United States)

    Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

    2017-01-01

    In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

  18. A 3D network of helicates fully assembled by pi-stacking interactions.

    Science.gov (United States)

    Vázquez, Miguel; Taglietti, Angelo; Gatteschi, Dante; Sorace, Lorenzo; Sangregorio, Claudio; González, Ana M; Maneiro, Marcelino; Pedrido, Rosa M; Bermejo, Manuel R

    2003-08-07

    The neutral dinuclear dihelicate [Cu2(L)2] x 2CH3CN (1) forms a unique 3D network in the solid state due to pi-stacking interactions, which are responsible for intermolecular antiferromagnetic coupling between Cu(II) ions.

  19. On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana

    2008-01-01

    Berlanga, A. J., Bitter-Rijpkema, M. E., Brouns F., & Sloep, P. B. (2008). On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks. Presented at the IADIS International Conference on Web Based Communities 2008. July, 24-26, 2008, Amsterdam, The Netherlands.

  20. On the importance of personal profiles to enhance social interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter

    2008-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns F., & Sloep, P.B. (2008). On the importance of personal profiles to enhance social interaction in Learning Networks. In P. Kommers (Ed.), Proceedings of Web Based Communities Conference (WEBC 2008) (pp. 55-62). July, 24-26, 2008, Amsterdam, The

  1. Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

    Directory of Open Access Journals (Sweden)

    Alexandru Topirceanu

    2016-01-01

    Full Text Available One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual’s inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

  2. Robust client/server shared state interactions of collaborative process with system crash and network failures

    NARCIS (Netherlands)

    Wang, Lei; Wombacher, Andreas; Ferreira Pires, Luis; van Sinderen, Marten J.; Chi, Chihung

    With the possibility of system crashes and network failures, the design of robust client/server interactions for collaborative process execution is a challenge. If a business process changes state, it sends messages to relevant processes to inform about this change. However, server crashes and

  3. Control of Grid Interactive PV Inverters for High Penetration in Low Voltage Distribution Networks

    DEFF Research Database (Denmark)

    Demirok, Erhan

    Regarding of high density deployment of PV installations in electricity grids, new technical challenges such as voltage rise, thermal loading of network components, voltage unbalance, harmonic interaction and fault current contributions are being added to tasks list of distribution system operators...... of these inverters may depend on grid connection rules which are forced by DSOs. Minimum requirement expected from PV inverters is to transfer maximum power by taking direct current (DC) form from PV modules and release it into AC grid and also continuously keep the inverters synchronized to the grid even under...... for this problem but PV inverters connected to highly capacitive networks are able to employ extra current and voltage harmonics compensation to avoid triggering network resonances at low order frequencies. The barriers such as harmonics interaction, flicker, fault current contribution and dc current injections...

  4. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

    DEFF Research Database (Denmark)

    Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens

    2008-01-01

    is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology......Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...

  5. Implications of a neural network model of early sensori-motor development for the field of developmental neurology

    NARCIS (Netherlands)

    van Heijst, JJ; Touwen, BCL; Vos, JE

    This paper reports on a neural network model for early sensori-motor development and on the possible implications of this research for our understanding and, eventually, treatment of motor disorders like cerebral palsy. We recapitulate the results we published in detail in a series of papers [1-4].

  6. Network theory-based analysis of risk interactions in large engineering projects

    International Nuclear Information System (INIS)

    Fang, Chao; Marle, Franck; Zio, Enrico; Bocquet, Jean-Claude

    2012-01-01

    This paper presents an approach based on network theory to deal with risk interactions in large engineering projects. Indeed, such projects are exposed to numerous and interdependent risks of various nature, which makes their management more difficult. In this paper, a topological analysis based on network theory is presented, which aims at identifying key elements in the structure of interrelated risks potentially affecting a large engineering project. This analysis serves as a powerful complement to classical project risk analysis. Its originality lies in the application of some network theory indicators to the project risk management field. The construction of the risk network requires the involvement of the project manager and other team members assigned to the risk management process. Its interpretation improves their understanding of risks and their potential interactions. The outcomes of the analysis provide a support for decision-making regarding project risk management. An example of application to a real large engineering project is presented. The conclusion is that some new insights can be found about risks, about their interactions and about the global potential behavior of the project. - Highlights: ► The method addresses the modeling of complexity in project risk analysis. ► Network theory indicators enable other risks than classical criticality analysis to be highlighted. ► This topological analysis improves project manager's understanding of risks and risk interactions. ► This helps project manager to make decisions considering the position in the risk network. ► An application to a real tramway implementation project in a city is provided.

  7. Structural breakdown of specialized plant-herbivore interaction networks in tropical forest edges

    Directory of Open Access Journals (Sweden)

    Bruno Ximenes Pinho

    2017-10-01

    Full Text Available Plant-herbivore relationships are essential for ecosystem functioning, typically forming an ecological network with a compartmentalized (i.e. modular structure characterized by highly specialized interactions. Human disturbances can favor habitat generalist species and thus cause the collapse of this modular structure, but its effects are rarely assessed using a network-based approach. We investigate how edge proximity alters plant-insect herbivore networks by comparing forest edge and interior in a large remnant (3.500 ha of the Brazilian Atlantic forest. Given the typical dominance of pioneer plants and generalist herbivores in edge-affected habitats, we test the hypothesis that the specialized structure of plant-herbivore networks collapse in forest edges, resulting in lower modularity and herbivore specialization. Despite no differences in the number of species and interactions, the network structure presented marked differences between forest edges and interior. Herbivore specialization, modularity and number of modules were significantly higher in forest interior than edge-affected habitats. When compared to a random null model, two (22.2% and eight (88.8% networks were significantly modular in forest edge and interior, respectively. The loss of specificity and modularity in plant-herbivore networks in forest edges may be related to the loss of important functions, such as density-dependent control of superior plant competitors, which is ultimately responsible for the maintenance of biodiversity and ecosystem functions. Our results support previous warnings that focusing on traditional community measures only (e.g. species diversity may overlook important modifications in species interactions and ecosystem functioning.

  8. On the Connectivity of Wireless Network Systems and an Application in Teacher-Student Interactive Platforms

    Directory of Open Access Journals (Sweden)

    Xun Ge

    2014-01-01

    Full Text Available A wireless network system is a pair (U;B, where B is a family of some base stations and U is a set of their users. To investigate the connectivity of wireless network systems, this paper takes covering approximation spaces as mathematical models of wireless network systems. With the help of covering approximation operators, this paper characterizes the connectivity of covering approximation spaces by their definable subsets. Furthermore, it is obtained that a wireless network system is connected if and only if the relevant covering approximation space has no nonempty definable proper subset. As an application of this result, the connectivity of a teacher-student interactive platform is discussed, which is established in the School of Mathematical Sciences of Soochow University. This application further demonstrates the usefulness of rough set theory in pedagogy and makes it possible to research education by logical methods and mathematical methods.

  9. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

    DEFF Research Database (Denmark)

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae......, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While...... demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease....

  10. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

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    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  11. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

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    Recep Colak

    2010-10-01

    Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely

  12. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Science.gov (United States)

    Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

    2010-10-25

    Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large

  13. Network Interaction of Universities in Higher Education System of Ural Macro-Region

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    Garold Efimovich Zborovsky

    2017-06-01

    Full Text Available The subject-matter of the analysis are the characteristics and forms of cooperation between universities of Ural Federal District on the basis of their typology. The purpose of the article is to substantiate the necessity and possibility of network interaction between universities of the macro-region. We prove the importance and potential effectiveness of universities network interaction in the terms of socio-economic uncertainty of the development of Ural Federal District and its higher education. Networking interaction and multilateral cooperation are considered as a new type of inter-universities relations, which can be activated and intensified by strengthening the relations of universities with stakeholders. The authors examine certain concrete forms and formats of network interaction and cooperation between universities and discuss selected cases of new type of relations. In it, they see the real and potential innovation of higher school nonlinear development processes. The statements of the article allow to confirm the hypothesis about the reality of strengthening the network interaction in macro-region. It can transform higher education in the driver of socio-economic development of Ural Federal District; ensure the competitiveness of higher education of the macro-region in the Russian and global educational space; enhance its role in the society; become one of the most significant elements of nonlinear models of higher education development in the country. The authors’ research is based on the interdisciplinary methodology including the potential of theoretical sociology, sociology of higher education, economic sociology, management theory, regional economics. The results of the study can form the basis for the improvement of the Ural Federal District’s educational policy.

  14. Analyzing complex wake-terrain interactions and its implications on wind-farm performance.

    Science.gov (United States)

    Tabib, Mandar; Rasheed, Adil; Fuchs, Franz

    2016-09-01

    Rotating wind turbine blades generate complex wakes involving vortices (helical tip-vortex, root-vortex etc.).These wakes are regions of high velocity deficits and high turbulence intensities and they tend to degrade the performance of down-stream turbines. Hence, a conservative inter-turbine distance of up-to 10 times turbine diameter (10D) is sometimes used in wind-farm layout (particularly in cases of flat terrain). This ensures that wake-effects will not reduce the overall wind-farm performance, but this leads to larger land footprint for establishing a wind-farm. In-case of complex-terrain, within a short distance (say 10D) itself, the nearby terrain can rise in altitude and be high enough to influence the wake dynamics. This wake-terrain interaction can happen either (a) indirectly, through an interaction of wake (both near tip vortex and far wake large-scale vortex) with terrain induced turbulence (especially, smaller eddies generated by small ridges within the terrain) or (b) directly, by obstructing the wake-region partially or fully in its flow-path. Hence, enhanced understanding of wake- development due to wake-terrain interaction will help in wind farm design. To this end the current study involves: (1) understanding the numerics for successful simulation of vortices, (2) understanding fundamental vortex-terrain interaction mechanism through studies devoted to interaction of a single vortex with different terrains, (3) relating influence of vortex-terrain interactions to performance of a wind-farm by studying a multi-turbine wind-farm layout under different terrains. The results on interaction of terrain and vortex has shown a much faster decay of vortex for complex terrain compared to a flatter-terrain. The potential reasons identified explaining the observation are (a) formation of secondary vortices in flow and its interaction with the primary vortex and (b) enhanced vorticity diffusion due to increased terrain-induced turbulence. The implications of

  15. Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks.

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    Sumeet Agarwal

    2010-06-01

    Full Text Available The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

  16. The RING 2.0 web server for high quality residue interaction networks.

    Science.gov (United States)

    Piovesan, Damiano; Minervini, Giovanni; Tosatto, Silvio C E

    2016-07-08

    Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico-chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain-domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π-π stacking and π-cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical re-parameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  17. Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs

    LENUS (Irish Health Repository)

    Casey, Fergal

    2011-08-22

    Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.

  18. Protein complex prediction based on k-connected subgraphs in protein interaction network

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    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  19. The specificity of host-bat fly interaction networks across vegetation and seasonal variation.

    Science.gov (United States)

    Zarazúa-Carbajal, Mariana; Saldaña-Vázquez, Romeo A; Sandoval-Ruiz, César A; Stoner, Kathryn E; Benitez-Malvido, Julieta

    2016-10-01

    Vegetation type and seasonality promote changes in the species composition and abundance of parasite hosts. However, it is poorly known how these variables affect host-parasite interaction networks. This information is important to understand the dynamics of parasite-host relationships according to biotic and abiotic changes. We compared the specialization of host-bat fly interaction networks, as well as bat fly and host species composition between upland dry forest and riparian forest and between dry and rainy seasons in a tropical dry forest in Jalisco, Mexico. Bat flies were surveyed by direct collection from bats. Our results showed that host-bat fly interaction networks were more specialized in upland dry forest compared to riparian forest. Bat fly species composition was different between the dry and rainy seasons, while host species composition was different between upland dry forest and riparian forest. The higher specialization in upland dry forest could be related to the differences in bat host species composition and their respective roosting habits. Variation in the composition of bat fly species between dry and rainy seasons coincides with the seasonal shifts in their species richness. Our study confirms the high specialization of host-bat fly interactions and shows the importance of biotic and abiotic factors to understand the dynamics of parasite-host interactions.

  20. MINER: exploratory analysis of gene interaction networks by machine learning from expression data

    Directory of Open Access Journals (Sweden)

    Sivieng Jane

    2009-12-01

    Full Text Available Abstract Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation, an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

  1. Personalized Social Network Activity Feeds for Increased Interaction and Content Contribution

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    Shlomo eBerkovsky

    2015-10-01

    Full Text Available Online social networks were originally conceived as means of sharing information and activities with friends, and their success has been one of the primary contributors of the tremendous growth of the Web. Social network activity feeds were devised as a means to aggregate recent actions of friends into a convenient list. But the volume of actions and content generated by social network users is overwhelming, such that keeping users up-to-date with friend activities is an ongoing challenge for social network providers. Personalization has been proposed as a solution to combat social network information overload and help users to identify the nuggets of relevant information in the incoming flood of network activities. In this paper, we propose and thoroughly evaluate a personalized model for predicting the relevance of the activity feed items, which informs the ranking of the feeds and facilitates personalization. Results of a live study show that the proposed feed personalization approach successfully identifies and promotes relevant feed items and boosts the uptake of the feeds. In addition, it increases the contribution of user-generated content to the social network and spurs interaction between users.

  2. The role of exon shuffling in shaping protein-protein interaction networks

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    França Gustavo S

    2010-12-01

    Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

  3. Global innovation networks and university-firm interactions: an exploratory survey analysis

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    Gustavo Britto

    2015-02-01

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

  4. Correlated miR-mRNA expression signatures of mouse hematopoietic stem and progenitor cell subsets predict "Stemness" and "Myeloid" interaction networks.

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    Diane Heiser

    Full Text Available Several individual miRNAs (miRs have been implicated as potent regulators of important processes during normal and malignant hematopoiesis. In addition, many miRs have been shown to fine-tune intricate molecular networks, in concert with other regulatory elements. In order to study hematopoietic networks as a whole, we first created a map of global miR expression during early murine hematopoiesis. Next, we determined the copy number per cell for each miR in each of the examined stem and progenitor cell types. As data is emerging indicating that miRs function robustly mainly when they are expressed above a certain threshold (∼100 copies per cell, our database provides a resource for determining which miRs are expressed at a potentially functional level in each cell type. Finally, we combine our miR expression map with matched mRNA expression data and external prediction algorithms, using a Bayesian modeling approach to create a global landscape of predicted miR-mRNA interactions within each of these hematopoietic stem and progenitor cell subsets. This approach implicates several interaction networks comprising a "stemness" signature in the most primitive hematopoietic stem cell (HSC populations, as well as "myeloid" patterns associated with two branches of myeloid development.

  5. Correlated miR-mRNA expression signatures of mouse hematopoietic stem and progenitor cell subsets predict "Stemness" and "Myeloid" interaction networks.

    Science.gov (United States)

    Heiser, Diane; Tan, Yee Sun; Kaplan, Ian; Godsey, Brian; Morisot, Sebastien; Cheng, Wen-Chih; Small, Donald; Civin, Curt I

    2014-01-01

    Several individual miRNAs (miRs) have been implicated as potent regulators of important processes during normal and malignant hematopoiesis. In addition, many miRs have been shown to fine-tune intricate molecular networks, in concert with other regulatory elements. In order to study hematopoietic networks as a whole, we first created a map of global miR expression during early murine hematopoiesis. Next, we determined the copy number per cell for each miR in each of the examined stem and progenitor cell types. As data is emerging indicating that miRs function robustly mainly when they are expressed above a certain threshold (∼100 copies per cell), our database provides a resource for determining which miRs are expressed at a potentially functional level in each cell type. Finally, we combine our miR expression map with matched mRNA expression data and external prediction algorithms, using a Bayesian modeling approach to create a global landscape of predicted miR-mRNA interactions within each of these hematopoietic stem and progenitor cell subsets. This approach implicates several interaction networks comprising a "stemness" signature in the most primitive hematopoietic stem cell (HSC) populations, as well as "myeloid" patterns associated with two branches of myeloid development.

  6. The influence of gender, age, matriline and hierarchical rank on individual social position, role and interactional patterns in Macaca sylvanus at ‘La Forêt des singes’: A multilevel social network approach

    Directory of Open Access Journals (Sweden)

    Sebastian OROZCO SOSA

    2016-04-01

    Full Text Available A society is a complex system composed of individuals that can be characterized by their own attributes that influence their behaviors. In this study, a specific analytical protocol based on social network analysis was adopted to investigate the influence of four attributes (gender, age, matriline, and hierarchical rank on affiliative (allogrooming and agonistic networks in a non-human primate species, Macaca sylvanus, at the park La Forêt des Singes in France. The results show significant differences with respect to the position (i.e. centric, peripheral and role (i.e. implication in the network cohesiveness of an individual within a social network and hence interactional patterns. Females are more central, more active, and have a denser ego network in the affiliative social network tan males; thus, they contribute in a greater way to the cohesive structure of the network. High-ranking individuals are likely to receive fewer agonistic behaviors than low-ranking individuals, and high-ranking females receive more allogrooming. I also observe homophily for affiliative interactions regarding all attributes and homophily for agonistic interactions regarding gender and age. Revealing the positions, the roles, and the interactional behavioral patterns of individuals can help understand the mechanisms that shape the overall structure of a social network.

  7. The Influence of Gender, Age, Matriline and Hierarchical Rank on Individual Social Position, Role and Interactional Patterns in Macaca sylvanus at 'La Forêt des Singes': A Multilevel Social Network Approach.

    Science.gov (United States)

    Sosa, Sebastian

    2016-01-01

    A society is a complex system composed of individuals that can be characterized by their own attributes that influence their behaviors. In this study, a specific analytical protocol based on social network analysis was adopted to investigate the influence of four attributes (gender, age, matriline, and hierarchical rank) on affiliative (allogrooming) and agonistic networks in a non-human primate species, Macaca sylvanus, at the park La Forêt des Singes in France. The results show significant differences with respect to the position (i.e., centric, peripheral) and role (i.e., implication in the network cohesiveness) of an individual within a social network and hence interactional patterns. Females are more central, more active, and have a denser ego network in the affiliative social network tan males; thus, they contribute in a greater way to the cohesive structure of the network. High-ranking individuals are likely to receive fewer agonistic behaviors than low-ranking individuals, and high-ranking females receive more allogrooming. I also observe homophily for affiliative interactions regarding all attributes and homophily for agonistic interactions regarding gender and age. Revealing the positions, the roles, and the interactional behavioral patterns of individuals can help understand the mechanisms that shape the overall structure of a social network.

  8. Building dynamic capabilities in large global advertising agency networks: managing the shift from mass communication to digital interactivity

    DEFF Research Database (Denmark)

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

    2018-01-01

    Interactive digital technologies result in significant managerial challenges for the largest global advertising agency networks. This paper, based on original data from in-depth case research in three of the largest global advertising networks, investigates how advertising agency networks manage...

  9. Top-Down Influences on Local Networks: Basic Theory with Experimental Implications

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    Ramesh eSrinivasan

    2013-04-01

    Full Text Available The response of a population of sensory neurons to an external stimulus depends not only on the receptive field properties of the neurons, but also the level of arousal and attention or goal-oriented cognitive biases that guide information processing. These top-down effects on the sensory neurons bias the output of the neurons and affect behavioral outcomes such as stimulus detection, discrimination, and response time. In any physiological study, neural dynamics are observed in a specific brain state; the background state partly determines neuronal excitability. Experimental studies in humans and animal models have also demonstrated that slow oscillations (typically in the alpha or theta bands modulate the fast oscillations (gamma band associated with local networks of neurons. Cross-frequency interaction is of interest as a mechanism for top-down or bottom-up interactions between systems at different spatial scales. We develop a generic model of top-down influences on local networks appropriate for comparison with EEG. EEG provides excellent temporal resolution to investigate neuronal oscillations but is space-averaged on the cm scale. Thus, appropriate EEG models are developed in terms of population synaptic activity. We used the Wilson-Cowan population model to investigate fast (gamma band oscillations generated by a local network of excitatory and inhibitory neurons. We modified the Wilson-Cowan equations to make them more physiologically realistic by explicitly incorporating background state variables into the model. We found that the population response is strongly influenced by the background state. We apply the model to reproduce the modulation of gamma rhythms by theta rhythms as has been observed in animal models and human ECoG and EEG studies. The concept of a dynamic background state presented here using the Wilson-Cowan model can be readily applied to incorporate top-down modulation in more detailed models of specific sensory

  10. Isolation and evolution of labile sulfur allotropes via kinetic encapsulation in interactive porous networks

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    Hakuba Kitagawa

    2016-07-01

    Full Text Available The isolation and characterization of small sulfur allotropes have long remained unachievable because of their extreme lability. This study reports the first direct observation of disulfur (S2 with X-ray crystallography. Sulfur gas was kinetically trapped and frozen into the pores of two Cu-based porous coordination networks containing interactive iodide sites. Stabilization of S2 was achieved either through physisorption or chemisorption on iodide anions. One of the networks displayed shape selectivity for linear molecules only, therefore S2 was trapped and remained stable within the material at room temperature and higher. In the second network, however, the S2 molecules reacted further to produce bent-S3 species as the temperature was increased. Following the thermal evolution of the S2 species in this network using X-ray diffraction and Raman spectroscopy unveiled the generation of a new reaction intermediate never observed before, the cyclo-trisulfur dication (cyclo-S32+. It is envisaged that kinetic guest trapping in interactive crystalline porous networks will be a promising method to investigate transient chemical species.

  11. Comprehension through explanation as the interaction of the brain’s coherence and cognitive control networks

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    Jarrod eMoss

    2015-10-01

    Full Text Available Discourse comprehension processes attempt to produce an elaborate and well-connected representation in the reader’s mind. A common network of regions including the angular gyrus, posterior cingulate, and dorsal frontal cortex appears to be involved in constructing coherent representations in a variety of tasks including social cognition tasks, narrative comprehension, and expository text comprehension. Reading strategies that require the construction of explicit inferences are used in the present research to examine how this coherence network interacts with other brain regions. A psychophysiological interaction analysis was used to examine regions showing changed functional connectivity with this coherence network when participants were engaged in either a non-inferencing reading strategy, paraphrasing, or a strategy requiring coherence-building inferences, self-explanation. Results of the analysis show that the coherence network increases in functional connectivity with a cognitive control network that may be specialized for the manipulation of semantic representations and the construction of new relations among these representations.

  12. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

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    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  13. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Directory of Open Access Journals (Sweden)

    Brian R Granger

    2016-04-01

    Full Text Available The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space, a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  14. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Science.gov (United States)

    Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

    2016-04-01

    The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  15. An Atomic Force Microscopy Study of the Interactions Involving Polymers and Silane Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo L. Oréfice

    1998-12-01

    Full Text Available ABSTRACT: Silane coupling agents have been frequently used as interfacial agents in polymer composites to improve interfacial strength and resistance to fluid migration. Although the capability of these agents in improving properties and performance of composites has been reported, there are still many uncertainties regarding the processing-structure-property relationships and the mechanisms of coupling developed by silane agents. In this work, an Atomic Force Microscope (AFM was used to measure interactions between polymers and silica substrates, where silane networks with a series of different structures were processed. The influence of the structure of silane networks on the interactions with polymers was studied and used to determine the mechanisms involved in the coupling phenomenon. The AFM results showed that phenomena such as chain penetration, entanglements, intersegment bonding, chain conformation in the vicinities of rigid surfaces were identified as being relevant for the overall processes of adhesion and adsorption of polymeric chains within a silane network. AFM adhesion curves showed that penetration of polymeric chains through a more open silane network can lead to higher levels of interactions between polymer and silane agents.

  16. Exploring overlapping functional units with various structure in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Xiao-Fei Zhang

    Full Text Available Revealing functional units in protein-protein interaction (PPI networks are important for understanding cellular functional organization. Current algorithms for identifying functional units mainly focus on cohesive protein complexes which have more internal interactions than external interactions. Most of these approaches do not handle overlaps among complexes since they usually allow a protein to belong to only one complex. Moreover, recent studies have shown that other non-cohesive structural functional units beyond complexes also exist in PPI networks. Thus previous algorithms that just focus on non-overlapping cohesive complexes are not able to present the biological reality fully. Here, we develop a new regularized sparse random graph model (RSRGM to explore overlapping and various structural functional units in PPI networks. RSRGM is principally dominated by two model parameters. One is used to define the functional units as groups of proteins that have similar patterns of connections to others, which allows RSRGM to detect non-cohesive structural functional units. The other one is used to represent the degree of proteins belonging to the units, which supports a protein belonging to more than one revealed unit. We also propose a regularizer to control the smoothness between the estimators of these two parameters. Experimental results on four S. cerevisiae PPI networks show that the performance of RSRGM on detecting cohesive complexes and overlapping complexes is superior to that of previous competing algorithms. Moreover, RSRGM has the ability to discover biological significant functional units besides complexes.

  17. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    Science.gov (United States)

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  18. CONCERNING THE NETWORKING INTERACTION EXPERIENCE OF TEACHERS AND STUDENTS OF PEDAGOGICAL UNIVERSITY

    Directory of Open Access Journals (Sweden)

    E. A. Dmitrieva

    2015-01-01

    Full Text Available The purpose of the research is to identify the possibilities for the formation knowledge and practical skills related to the use of the professional activity of software and network resource of teaching communities in the pedagogical sphere.Methods. The methods involve the analysis of the literary sources, regulatory documents, Internet resources within the researched problem; an analysis of the practical experience of teachers of secondary schools, work of high school teachers and establishment of training teachers on the research problem; the experimental work and monitoring the learning process.Results. The process of teachers’ training inYaroslavl, in particular preparation of students-biologists at theYaroslavlStatePedagogicalUniversityis reflected. Activity of network pedagogical community of Yaroslavl is considered as a platform for network interaction; the analysis of such platform, use of its resources, and also conversations with subject teachers and students have shown that the given electronic and communication resources cause a great interest for practicing teachers and future experts, however, they not always possess necessary knowledge and abilities concerning its operation.Scientific novelty. The author describes in detail the process of forming a competence of networking of professional interaction in terms of its methodological support that is relevant to the educational process, both in the high school, and post-graduate education.Practical significance. The research implementations can be useful while developing specific guidelines to explain the content and methodology of the training network of professional interaction with examples of practicing teachers and students ofPedagogicalUniversity– future teachers of biology.The article is addressed to researchers, dealing with networking, specialists of teaching service centers (institutions of educational development, the practicing subject teachers and teachers of high

  19. Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

    Science.gov (United States)

    McClelland, James L

    2013-01-01

    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

  20. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human--Robot Interaction

    Directory of Open Access Journals (Sweden)

    Tatsuro Yamada

    2016-07-01

    Full Text Available To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language--behavior relationships and the temporal patterns of interaction. Here, ``internal dynamics'' refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language--behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language--behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  1. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

    Science.gov (United States)

    Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

    2016-01-01

    To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  2. Metabolic network modeling of microbial interactions in natural and engineered environmental systems

    Directory of Open Access Journals (Sweden)

    Octavio ePerez-Garcia

    2016-05-01

    Full Text Available We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA, experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e. i lumped networks, ii compartment per guild networks, iii bi-level optimization simulations and iv dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial

  3. Social networks, personal values, and creativity: evidence for curvilinear and interaction effects.

    Science.gov (United States)

    Zhou, Jing; Shin, Shung Jae; Brass, Daniel J; Choi, Jaepil; Zhang, Zhi-Xue

    2009-11-01

    Taking an interactional perspective on creativity, the authors examined the influence of social networks and conformity value on employees' creativity. They theorized and found a curvilinear relationship between number of weak ties and creativity such that employees exhibited greater creativity when their number of weak ties was at intermediate levels rather than at lower or higher levels. In addition, employees' conformity value moderated the curvilinear relationship between number of weak ties and creativity such that employees exhibited greater creativity at intermediate levels of number of weak ties when conformity was low than when it was high. A proper match between personal values and network ties is critical for understanding creativity.

  4. Computer Networks E-learning Based on Interactive Simulations and SCORM

    Directory of Open Access Journals (Sweden)

    Francisco Andrés Candelas

    2011-05-01

    Full Text Available This paper introduces a new set of compact interactive simulations developed for the constructive learning of computer networks concepts. These simulations, which compose a virtual laboratory implemented as portable Java applets, have been created by combining EJS (Easy Java Simulations with the KivaNS API. Furthermore, in this work, the skills and motivation level acquired by the students are evaluated and measured when these simulations are combined with Moodle and SCORM (Sharable Content Object Reference Model documents. This study has been developed to improve and stimulate the autonomous constructive learning in addition to provide timetable flexibility for a Computer Networks subject.

  5. The cultural implications of growth: Modeling nonlinear interaction of trait selection and population dynamics

    Science.gov (United States)

    Antoci, Angelo; Galeotti, Marcello; Russu, Paolo; Luigi Sacco, Pier

    2018-05-01

    In this paper, we study a nonlinear model of the interaction between trait selection and population dynamics, building on previous work of Ghirlanda et al. [Theor. Popul. Biol. 77, 181-188 (2010)] and Antoci et al. [Commun. Nonlinear Sci. Numer. Simul. 58, 92-106 (2018)]. We establish some basic properties of the model dynamics and present some simulations of the fine-grained structure of alternative dynamic regimes for chosen combinations of parameters. The role of the parameters that govern the reinforcement/corruption of maladaptive vs. adaptive traits is of special importance in determining the model's dynamic evolution. The main implication of this result is the need to pay special attention to the structural forces that may favor the emergence and consolidation of maladaptive traits in contemporary socio-economies, as it is the case, for example, for the stimulation of dysfunctional consumption habits and lifestyles in the pursuit of short-term profits.

  6. The cultural implications of growth: Modeling nonlinear interaction of trait selection and population dynamics.

    Science.gov (United States)

    Antoci, Angelo; Galeotti, Marcello; Russu, Paolo; Luigi Sacco, Pier

    2018-05-01

    In this paper, we study a nonlinear model of the interaction between trait selection and population dynamics, building on previous work of Ghirlanda et al. [Theor. Popul. Biol. 77, 181-188 (2010)] and Antoci et al. [Commun. Nonlinear Sci. Numer. Simul. 58, 92-106 (2018)]. We establish some basic properties of the model dynamics and present some simulations of the fine-grained structure of alternative dynamic regimes for chosen combinations of parameters. The role of the parameters that govern the reinforcement/corruption of maladaptive vs. adaptive traits is of special importance in determining the model's dynamic evolution. The main implication of this result is the need to pay special attention to the structural forces that may favor the emergence and consolidation of maladaptive traits in contemporary socio-economies, as it is the case, for example, for the stimulation of dysfunctional consumption habits and lifestyles in the pursuit of short-term profits.

  7. Pharmacokinetic drug-drug interaction and their implication in clinical management.

    Science.gov (United States)

    Palleria, Caterina; Di Paolo, Antonello; Giofrè, Chiara; Caglioti, Chiara; Leuzzi, Giacomo; Siniscalchi, Antonio; De Sarro, Giovambattista; Gallelli, Luca

    2013-07-01

    Drug-drug interactions (DDIs) are one of the commonest causes of medication error in developed countries, particularly in the elderly due to poly-therapy, with a prevalence of 20-40%. In particular, poly-therapy increases the complexity of therapeutic management and thereby the risk of clinically important DDIs, which can both induce the development of adverse drug reactions or reduce the clinical efficacy. DDIs can be classify into two main groups: pharmacokinetic and pharmacodynamic. In this review, using Medline, PubMed, Embase, Cochrane library and Reference lists we searched articles published until June 30 2012, and we described the mechanism of pharmacokinetic DDIs focusing the interest on their clinical implications.

  8. Pharmacokinetic drug-drug interaction and their implication in clinical management

    Directory of Open Access Journals (Sweden)

    Palleria Caterina

    2013-01-01

    Full Text Available Drug-drug interactions (DDIs are one of the commonest causes of medication error in developed countries, particularly in the elderly due to poly-therapy, with a prevalence of 20-40%. In particular, poly-therapy increases the complexity of therapeutic management and thereby the risk of clinically important DDIs, which can both induce the development of adverse drug reactions or reduce the clinical efficacy. DDIs can be classify into two main groups: pharmacokinetic and pharmacodynamic. In this review, using Medline, PubMed, Embase, Cochrane library and Reference lists we searched articles published until June 30 2012, and we described the mechanism of pharmacokinetic DDIs focusing the interest on their clinical implications.

  9. Equilibrium switching and mathematical properties of nonlinear interaction networks with concurrent antagonism and self-stimulation

    International Nuclear Information System (INIS)

    Rabajante, Jomar Fajardo; Talaue, Cherryl Ortega

    2015-01-01

    Graphical abstract: Display Omitted -- Highlights: •Properties of n-dimensional decision model of competitive interaction networks. •Graphical technique for component-wise and steady state stability analysis. •Search for parameter conditions that control equilibrium switching. •Illustrations of multi-stable systems and repressilators. -- Abstract: Concurrent decision-making model (CDM) of interaction networks with more than two antagonistic components represents various biological systems, such as gene interaction, species competition and mental cognition. The CDM model assumes sigmoid kinetics where every component stimulates itself but concurrently represses the others. Here we prove generic mathematical properties (e.g., location and stability of steady states) of n-dimensional CDM with either symmetric or asymmetric reciprocal antagonism between components. Significant modifications in parameter values serve as biological regulators for inducing steady state switching by driving a temporal state to escape an undesirable equilibrium. Increasing the maximal growth rate and decreasing the decay rate can expand the basin of attraction of a steady state that contains the desired dominant component. Perpetually adding an external stimulus could shut down multi-stability of the system which increases the robustness of the system against stochastic noise. We further show that asymmetric interaction forming a repressilator-type network generates oscillatory behavior

  10. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Layered vanadyl (IV) nitroprusside: Magnetic interaction through a network of hydrogen bonds

    Energy Technology Data Exchange (ETDEWEB)

    Gil, D.M. [Instituto de Química Física, Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán, San Lorenzo 456, T4000CAN San Miguel de Tucumán (Argentina); Osiry, H. [Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Legaria, Instituto Politécnico Nacional, México (Mexico); Pomiro, F.; Varetti, E.L. [CEQUINOR (CONICET-UNLP), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, 47 and 115, 1900, La Plata (Argentina); Carbonio, R.E. [INFIQC – CONICET, Departamento de Físico Química, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Haya de la Torre esq, Medina Allende, Ciudad Universitaria, X5000HUA Córdoba (Argentina); Alejandro, R.R. [Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Legaria, Instituto Politécnico Nacional, México (Mexico); Ben Altabef, A. [INQUINOA-UNT-CONICET, Instituto de Química Física, Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán, San Lorenzo 456, T4000CAN San Miguel de Tucumán (Argentina); and others

    2016-07-15

    The hydrogen bond and π-π stacking are two non-covalent interactions able to support cooperative magnetic ordering between paramagnetic centers. This contribution reports the crystal structure and related magnetic properties for VO[Fe(CN){sub 5}NO]·2H{sub 2}O, which has a layered structure. This solid crystallizes with an orthorhombic unit cell, in the Pna2{sub 1} space group, with cell parameters a=14.1804(2), b=10.4935(1), c=7.1722(8) Å and four molecules per unit cell (Z=4). Its crystal structure was solved and refined from powder X-ray diffraction data. Neighboring layers remain linked through a network of hydrogen bonds involving a water molecule coordinated to the axial position for the V atom and the unbridged axial NO and CN ligands. An uncoordinated water molecule is found forming a triple bridge between these last two ligands and the coordinated water molecule. The magnetic measurements, recorded down to 2 K, shows a ferromagnetic interaction between V atoms located at neighboring layers, with a Curie-Weiss constant of 3.14 K. Such ferromagnetic behavior was interpreted as resulting from a superexchange interaction through the network of strong OH····O{sub H2O}, OH····N{sub CN}, and OH····O{sub NO} hydrogen bonds that connects neighboring layers. The interaction within the layer must be of antiferromagnetic nature and it was detected close to 2 K. - Graphical abstract: Coordination environment for the metals in vanadyl (II) nitroprusside dihydrate. Display Omitted - Highlights: • Crystal structure of vanadyl nitroprusside dehydrate. • Network of hydrogen bonds. • Magnetic interactions through a network of hydrogen bonds. • Layered transition metal nitroprussides.

  12. Prediction of Narcissism, Perception of Social Interactions and Marital Conflicts Based on the Use of Social Networks

    OpenAIRE

    رویا رضاپور; محمد مهدی ذاکری; لقمان ابراهیمی

    2017-01-01

    The prevalence of social networks and the excessive use of them by couples have had a significant impact on various aspects of their lives. The aim of this study was to investigate the role of social networks in the formation of narcissism, perception of social interaction and marital conflicts in couples who use these social networks. The study design was correlational and the statistical population included couples of Zanjan city who use social networks. 120 couples which widely used social...

  13. Quasispecies dynamics on a network of interacting genotypes and idiotypes: formulation of the model

    Science.gov (United States)

    Barbosa, Valmir C.; Donangelo, Raul; Souza, Sergio R.

    2015-01-01

    A quasispecies is the stationary state of a set of interrelated genotypes that evolve according to the usual principles of selection and mutation. Quasispecies studies have for the most part concentrated on the possibility of errors during genotype replication and their role in promoting either the survival or the demise of the quasispecies. In a previous work, we introduced a network model of quasispecies dynamics, based on a single probability parameter (p) and capable of addressing several plausibility issues of previous models. Here we extend that model by pairing its network with another one aimed at modeling the dynamics of the immune system when confronted with the quasispecies. The new network is based on the idiotypic-network model of immunity and, together with the previous one, constitutes a network model of interacting genotypes and idiotypes. The resulting model requires further parameters and as a consequence leads to a vast phase space. We have focused on a particular niche in which it is possible to observe the trade-offs involved in the quasispecies' survival or destruction. Within this niche, we give simulation results that highlight some key preconditions for quasispecies survival. These include a minimum initial abundance of genotypes relative to that of the idiotypes and a minimum value of p. The latter, in particular, is to be contrasted with the stand-alone quasispecies network of our previous work, in which arbitrarily low values of p constitute a guarantee of quasispecies survival.

  14. A caspase-2-RFXANK interaction and its implication for MHC class II expression.

    Science.gov (United States)

    Forsberg, Jeremy; Li, Xinge; Akpinar, Birce; Salvatori, Roger; Ott, Martin; Zhivotovsky, Boris; Olsson, Magnus

    2018-01-23

    Despite recent achievements implicating caspase-2 in tumor suppression, the enzyme stands out from the apoptotic caspase family as a factor whose function requires further clarification. To specify enzyme characteristics through the definition of interacting proteins in apoptotic or non-apoptotic settings, a yeast 2-hybrid (Y2H) screen was performed using the full-length protein as bait. The current report describes the analysis of a captured prey and putative novel caspase-2 interacting factor, the regulatory factor X-associated ankyrin-containing protein (RFXANK), previously associated with CIITA, the transactivator regulating cell-type specificity and inducibility of MHC class II gene expression. The interaction between caspase-2 and RFXANK was verified by co-immunoprecipitations using both exogenous and endogenous proteins, where the latter approach suggested that binding of the components occurs in the cytoplasm. Cellular co-localization was confirmed by transfection of fluorescently conjugated proteins. Enhanced caspase-2 processing in RFXANK-overexpressing HEK293T cells treated with chemotherapeutic agents further supported Y2H data. Yet, no distinct differences with respect to MHC class II expression were observed in plasma membranes of antigen-presenting cells derived from wild type and caspase-2 -/- mice. In contrast, increased levels of the total MHC class II protein was evident in protein lysates from caspase-2 RNAi-silenced leukemia cell lines and B-cells isolated from gene-targeted mice. Together, these data identify a novel caspase-2-interacting factor, RFXANK, and indicate a potential non-apoptotic role for the enzyme in the control of MHC class II gene regulation.

  15. Plant-insect interactions under bacterial influence: ecological implications and underlying mechanisms.

    Science.gov (United States)

    Sugio, Akiko; Dubreuil, Géraldine; Giron, David; Simon, Jean-Christophe

    2015-02-01

    Plants and insects have been co-existing for more than 400 million years, leading to intimate and complex relationships. Throughout their own evolutionary history, plants and insects have also established intricate and very diverse relationships with microbial associates. Studies in recent years have revealed plant- or insect-associated microbes to be instrumental in plant-insect interactions, with important implications for plant defences and plant utilization by insects. Microbial communities associated with plants are rich in diversity, and their structure greatly differs between below- and above-ground levels. Microbial communities associated with insect herbivores generally present a lower diversity and can reside in different body parts of their hosts including bacteriocytes, haemolymph, gut, and salivary glands. Acquisition of microbial communities by vertical or horizontal transmission and possible genetic exchanges through lateral transfer could strongly impact on the host insect or plant fitness by conferring adaptations to new habitats. Recent developments in sequencing technologies and molecular tools have dramatically enhanced opportunities to characterize the microbial diversity associated with plants and insects and have unveiled some of the mechanisms by which symbionts modulate plant-insect interactions. Here, we focus on the diversity and ecological consequences of bacterial communities associated with plants and herbivorous insects. We also highlight the known mechanisms by which these microbes interfere with plant-insect interactions. Revealing such mechanisms in model systems under controlled environments but also in more natural ecological settings will help us to understand the evolution of complex multitrophic interactions in which plants, herbivorous insects, and micro-organisms are inserted. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions

  16. Interaction between the Cockayne syndrome B and p53 proteins: implications for aging.

    Science.gov (United States)

    Frontini, Mattia; Proietti-De-Santis, Luca

    2012-02-01

    The CSB protein plays a role in the transcription coupled repair (TCR) branch of the nucleotide excision repair pathway. CSB is very often found mutated in Cockayne syndrome, a segmental progeroid genetic disease characterized by organ degeneration and growth failure. The tumor suppressor p53 plays a pivotal role in triggering senescence and apoptosis and suppressing tumorigenesis. Although p53 is very important to avoid cancer, its excessive activity can be detrimental for the lifespan of the organism. This is why a network of positive and negative feedback loops, which most likely evolved to fine-tune the activity of this tumor suppressor, modulate its induction and activation. Accordingly, an unbalanced p53 activity gives rise to premature aging or cancer. The physical interaction between CSB and p53 proteins has been known for more than a decade but, despite several hypotheses, nobody has been able to show the functional consequences of this interaction. In this review we resume recent advances towards a more comprehensive understanding of the critical role of this interaction in modulating p53’s levels and activity, therefore helping the system find a reasonable equilibrium between the beneficial and the detrimental effects of its activity. This crosstalk re-establishes the physiological balance towards cell proliferation and survival instead of towards cell death, after stressors of a broad nature. Accordingly, cells bearing mutations in the csb gene are unable to re-establish this physiological balance and to properly respond to some stress stimuli and undergo massive apoptosis.

  17. Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

    Science.gov (United States)

    Freilich, Mara A; Wieters, Evie; Broitman, Bernardo R; Marquet, Pablo A; Navarrete, Sergio A

    2018-03-01

    Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction

  18. The Influence of Ethnic Diversity on Social Network Structure in a Common-Pool Resource System: Implications for Collaborative Management

    Directory of Open Access Journals (Sweden)

    Michele Barnes-Mauthe

    2013-03-01

    Full Text Available Social networks have recently been identified as key features in facilitating or constraining collaborative arrangements that can enhance resource governance and adaptability in complex social-ecological systems. Nonetheless, the effect of ethnicity on social network structure in an ethnically diverse common-pool resource system is virtually unknown. We characterize the entire social network of Hawaii's longline fishery, an ethnically diverse competitive pelagic fishery, and investigate network homophily, network structure, and cross-scale linkages. Results show that ethnicity significantly influences social network structure and is responsible for a homophily effect, which can create challenges for stakeholder collaboration across groups. Our analysis also suggests that ethnicity influences the formation of diverse network structures, and can affect the level of linkages to outside industry leaders, government or management officials, and members of the scientific community. This study provides the first empirical examination of the impact of ethnic diversity on resource user's social networks in the common-pool resource literature, having important implications for collaborative resource management.

  19. Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

    Directory of Open Access Journals (Sweden)

    Liu Lin

    2009-12-01

    Full Text Available Abstract Background microRNAs (miRNAs regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. Results We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT. Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. Conclusions This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and

  20. Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

    Science.gov (United States)

    Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

    2014-12-01

    Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

  1. Building Social Interactions as a Creation of Networks in an RDF Repository

    Directory of Open Access Journals (Sweden)

    Eun G Park

    2018-02-01

    Full Text Available Humanities scholars are not likely to be thinking about their research findings as data, and the predominant models of organizing documents remain generally archival or bibliographic in nature for text-based documents. Although the linked data movement has greatly influenced information organization and search queries on the Web, in comparison to other fields, the adoption of the linked data approach to humanities collections is unequally paced.  This study intends to explain how people or actors make social interactions, and how social interactions are formed in a type of network through the example of the Making Publics (MaPs project. The objective of the MaPs project is to build collaborative common environments for tracing social interactions between people, things, places and times. To build social interactions, the Networked Event Model was designed in a collaborative environment. Events were defined as six types of nodes (e.g., people, organizations, places, things, events, and literals in the RDF (Resource Description Framework triple statements. The interaction vocabulary list is made of 173 verbs and predicates, offering 510 traceable events. The RDF repository runs on a Sesame server and MySQL architecture. Users can use digital tools to select and document events and visually present the selected events in interactive social web forms. The MaPs project sought to extract the network extant in the works of prose in large collaborative humanities documents. In this way, the dissemination of and access to humanities data can be made more connectable, available and accessible to both academic and non-academic communities.

  2. Forbidden versus permitted interactions: Disentangling processes from patterns in ecological network analysis.

    Science.gov (United States)

    Strona, Giovanni; Veech, Joseph A

    2017-07-01

    Several studies have identified the tendency for species to share interacting partners as a key property to the functioning and stability of ecological networks. However, assessing this pattern has proved challenging in several regards, such as finding proper metrics to assess node overlap (sharing), and using robust null modeling to disentangle significance from randomness. Here, we bring attention to an additional, largely neglected challenge in assessing species' tendency to share interacting partners. In particular, we discuss and illustrate with two different case studies how identifying the set of "permitted" interactions for a given species (i.e. interactions that are not impeded, e.g. by lack of functional trait compatibility) is paramount to understand the ecological and co-evolutionary processes at the basis of node overlap and segregation patterns.

  3. Carnegie Mellon's STUDIO for Creative Inquiry [and] The Interdisciplinary Teaching Network (ITeN) [and] Interactive Fiction [and] The Networked Virtual Art Museum.

    Science.gov (United States)

    Holden, Lynn; And Others

    1992-01-01

    Explains the STUDIO for Creative Inquiry, an interdisciplinary center at Carnegie Mellon University that supports experimental activities in the arts, and its Interdisciplinary Teaching Network. Three STUDIO projects are described: the Ancient Egypt Prototype application of the network; an interactive fiction system based on artificial…

  4. The Protein Interaction Network of Bacteriophage Lambda with Its Host, Escherichia coli

    Science.gov (United States)

    Blasche, Sonja; Wuchty, Stefan; Rajagopala, Seesandra V.

    2013-01-01

    Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens. PMID:24049175

  5. Redrawing the map of Great Britain from a network of human interactions.

    Science.gov (United States)

    Ratti, Carlo; Sobolevsky, Stanislav; Calabrese, Francesco; Andris, Clio; Reades, Jonathan; Martino, Mauro; Claxton, Rob; Strogatz, Steven H

    2010-12-08

    Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

  6. Redrawing the map of Great Britain from a network of human interactions.

    Directory of Open Access Journals (Sweden)

    Carlo Ratti

    2010-12-01

    Full Text Available Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

  7. A global network of RNA and protein interactions in Fronto Temporal Dementia

    Directory of Open Access Journals (Sweden)

    Francesca eFontana

    2015-03-01

    Full Text Available Fronto Temporal Dementia (FTD is a neurodegenerative disorder characterized by degeneration of the fronto temporal lobes and abnormal protein inclusions. It exhibits a broad clinicopathological spectrum and has been linked to mutations in seven different genes. We will provide a picture, which connects the products of these genes, albeit diverse in nature and function, in a network. Despite the paucity of information available for some of these genes, we believe that RNA processing and post-transcriptional regulation of gene expression might constitute a common theme in the network. Recent studies have unraveled the role of mutations affecting the functions of RNA binding proteins and regulation of microRNAs. This review will combine all the recent findings on genes involved in the pathogenesis of FTD, highlighting the importance of a common network of interactions in order to study and decipher the heterogeneous clinical manifestations associated with FTD. This approach could be helpful for the research of potential therapeutic strategies.

  8. Collective Phenomena Emerging from the Interactions between Dynamical Processes in Multiplex Networks.

    Science.gov (United States)

    Nicosia, Vincenzo; Skardal, Per Sebastian; Arenas, Alex; Latora, Vito

    2017-03-31

    We introduce a framework to intertwine dynamical processes of different nature, each with its own distinct network topology, using a multilayer network approach. As an example of collective phenomena emerging from the interactions of multiple dynamical processes, we study a model where neural dynamics and nutrient transport are bidirectionally coupled in such a way that the allocation of the transport process at one layer depends on the degree of synchronization at the other layer, and vice versa. We show numerically, and we prove analytically, that the multilayer coupling induces a spontaneous explosive synchronization and a heterogeneous distribution of allocations, otherwise not present in the two systems considered separately. Our framework can find application to other cases where two or more dynamical processes such as synchronization, opinion formation, information diffusion, or disease spreading, are interacting with each other.

  9. Depth of interaction detection with enhanced position-sensitive proportional resistor network

    International Nuclear Information System (INIS)

    Lerche, Ch.W.; Benlloch, J.M.; Sanchez, F.; Pavon, N.; Gimenez, N.; Fernandez, M.; Gimenez, M.; Sebastia, A.; Martinez, J.; Mora, F.J.

    2005-01-01

    A new method of determining the depth of interaction of γ-rays in thick inorganic scintillation crystals was tested experimentally. The method uses the strong correlation between the width of the scintillation light distribution within large continuous crystals and the γ-ray's interaction depth. This behavior was successfully reproduced by a theoretical model distribution based on the inverse square law. For the determination of the distribution's width, its standard deviation σ is computed using an enhanced position-sensitive proportional resistor network which is often used in γ-ray-imaging devices. Minor changes of this known resistor network allow the analog and real-time determination of the light distribution's 2nd moment without impairing the measurement of the energy and centroid. First experimental results are presented that confirm that the described method works correctly. Since only some cheap electronic components, but no additional detectors or crystals are required, the main advantage of this method is its low cost

  10. Collective Phenomena Emerging from the Interac