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Sample records for networks mixing temporal

  1. Mixed Neural Network Approach for Temporal Sleep Stage Classification.

    PubMed

    Dong, Hao; Supratak, Akara; Pan, Wei; Wu, Chao; Matthews, Paul M; Guo, Yike

    2017-07-28

    This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of sleep stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory (LSTM) network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electrooculogram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

  2. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

    SciTech Connect

    Rossi, R; Gallagher, B; Neville, J; Henderson, K

    2011-11-11

    Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

  3. Temporal networks

    NASA Astrophysics Data System (ADS)

    Holme, Petter; Saramäki, Jari

    2012-10-01

    A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered

  4. Mixing navigation on networks

    NASA Astrophysics Data System (ADS)

    Zhou, Tao

    2008-05-01

    In this article, we propose a mixing navigation mechanism, which interpolates between random-walk and shortest-path protocol. The navigation efficiency can be remarkably enhanced via a few routers. Some advanced strategies are also designed: For non-geographical scale-free networks, the targeted strategy with a tiny fraction of routers can guarantee an efficient navigation with low and stable delivery time almost independent of network size. For geographical localized networks, the clustering strategy can simultaneously increase efficiency and reduce the communication cost. The present mixing navigation mechanism is of significance especially for information organization of wireless sensor networks and distributed autonomous robotic systems.

  5. Coverage centralities for temporal networks*

    NASA Astrophysics Data System (ADS)

    Takaguchi, Taro; Yano, Yosuke; Yoshida, Yuichi

    2016-02-01

    Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of real-world temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2016-60498-7

  6. Helicity in supercritical temporal mixing layers

    NASA Technical Reports Server (NTRS)

    Bellan, J.; Okong'o, N.

    2003-01-01

    Databases of transitional states obtained from Direct Numerical Simulations (DNS) of temporal, supercritical mixing layers for two species systems, 02/H2 and C7Hle/N2, are analyzed to elucidate species-specific turbulence aspects.

  7. Navigability of multiplex temporal network

    NASA Astrophysics Data System (ADS)

    Wang, Yan; Song, Qiao-Zhen

    2017-01-01

    Real world complex systems have multiple levels of relationships and in many cases, they need to be modeled as multiplex networks where the same nodes can interact with each other in different layers, such as social networks. However, social relationships only appear at prescribed times so the temporal structures of edge activations can also affect the dynamical processes located above them. To consider both factors are simultaneously, we introduce multiplex temporal networks and propose three different walk strategies to investigate the concurrent dynamics of random walks and the temporal structure of multiplex networks. Thus, we derive analytical results for the multiplex centrality and coverage function in multiplex temporal networks. By comparing them with the numerical results, we show how the underlying topology of the layers and the walk strategy affect the efficiency when exploring the networks. In particular, the most interesting result is the emergence of a super-diffusion process, where the time scale of the multiplex is faster than that of both layers acting separately.

  8. Mixed deterministic and probabilistic networks

    PubMed Central

    Dechter, Rina

    2010-01-01

    The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user-interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model. PMID:20981243

  9. Executing Temporal Networks With Uncertainty

    NASA Technical Reports Server (NTRS)

    Morris, Paul; Muscettola, Nicola

    2000-01-01

    Simple Temporal Networks (STNs) have proved useful in applications that involve metric time. However, many applications involve events whose timing is uncertain in the sense that it is not controlled by the execution agent. In this paper we consider execution algorithms for temporal networks with events of uncertain duration. We present two such algorithms. The first retains maximum flexibility, but requires potentially costly updates during execution. The second surrenders some flexibility in order to obtain a fast execution comparable to that available for ordinary STNs.

  10. Effects of temporal fluctuations on mixing

    NASA Astrophysics Data System (ADS)

    Pool, Maria; Dentz, Marco; Post, Vincent E. A.; Simmons, Craig T.

    2016-04-01

    Mixing and dispersion in coastal aquifers are strongly influenced by periodic temporal flow fluctuations on multiple time-scales ranging from days (tides), seasons (pumping and recharge) to glacial cycles (regression and transgressions). Transient forcing effects lead to a complex space- ant time-dependent flow response which induces enhanced spreading and mixing of a dissolved substance. We study effective mixing and solute transport in temporally fluctuating one-dimensional flow for a stable stratification of two fluids of different density. We derive explicit expressions for the concentration distribution and variance to identify the controls and obtain realistic predictions of the coupling between mixing and oscillatory transient flow. We find that the magnitude of transient-driven mixing is mainly controlled by the hydraulic diffusivity, the period and the initial interface location. We also find a spatial dependence of the effective dispersion coefficient which at long times causes the concentration profile to become asymmetric. Sand column experiments under well-controlled laboratory conditions are presented to validate the theoretical effective model defined. The proposed formulation is found to provide very good predictions and correctly reproduces the experimental mixing dynamics.

  11. Temporal motifs in time-dependent networks

    NASA Astrophysics Data System (ADS)

    Kovanen, Lauri; Karsai, Márton; Kaski, Kimmo; Kertész, János; Saramäki, Jari

    2011-11-01

    Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as telecommunication, neural signal processing, biochemical reaction and human social interaction networks. We introduce the framework of temporal motifs to study the mesoscale topological-temporal structure of temporal networks in which the events of nodes do not overlap in time. Temporal motifs are classes of similar event sequences, where the similarity refers not only to topology but also to the temporal order of the events. We provide a mapping from event sequences to coloured directed graphs that enables an efficient algorithm for identifying temporal motifs. We discuss some aspects of temporal motifs, including causality and null models, and present basic statistics of temporal motifs in a large mobile call network.

  12. Statistical Mechanics of Temporal and Interacting Networks

    NASA Astrophysics Data System (ADS)

    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

  13. Delay synchronization of temporal Boolean networks

    NASA Astrophysics Data System (ADS)

    Wei, Qiang; Xie, Cheng-jun; Liang, Yi; Niu, Yu-jun; Lin, Da

    2016-01-01

    This paper investigates the delay synchronization between two temporal Boolean networks base on semi-tensor product method, which improve complete synchronization. Necessary and sufficient conditions for delay synchronization are drawn base on algebraic expression of temporal Boolean networks. A example is presented to show the effectiveness of theoretical analysis.

  14. Temporal correlation coefficient for directed networks.

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-01-01

    Previous studies dealing with network theory focused mainly on the static aggregation of edges over specific time window lengths. Thus, most of the dynamic information gets lost. To assess the quality of such a static aggregation the temporal correlation coefficient can be calculated. It measures the overall possibility for an edge to persist between two consecutive snapshots. Up to now, this measure is only defined for undirected networks. Therefore, we introduce the adaption of the temporal correlation coefficient to directed networks. This new methodology enables the distinction between ingoing and outgoing edges. Besides a small example network presenting the single calculation steps, we also calculated the proposed measurements for a real pig trade network to emphasize the importance of considering the edge direction. The farm types at the beginning of the pork supply chain showed clearly higher values for the outgoing temporal correlation coefficient compared to the farm types at the end of the pork supply chain. These farm types showed higher values for the ingoing temporal correlation coefficient. The temporal correlation coefficient is a valuable tool to understand the structural dynamics of these systems, as it assesses the consistency of the edge configuration. The adaption of this measure for directed networks may help to preserve meaningful additional information about the investigated network that might get lost if the edge directions are ignored.

  15. Error and attack vulnerability of temporal networks

    NASA Astrophysics Data System (ADS)

    Trajanovski, S.; Scellato, S.; Leontiadis, I.

    2012-06-01

    The study of real-world communication systems via complex network models has greatly expanded our understanding on how information flows, even in completely decentralized architectures such as mobile wireless networks. Nonetheless, static network models cannot capture the time-varying aspects and, therefore, various temporal metrics have been introduced. In this paper, we investigate the robustness of time-varying networks under various failures and intelligent attacks. We adopt a methodology to evaluate the impact of such events on the network connectivity by employing temporal metrics in order to select and remove nodes based on how critical they are considered for the network. We also define the temporal robustness range, a new metric that quantifies the disruption caused by an attack strategy to a given temporal network. Our results show that in real-world networks, where some nodes are more dominant than others, temporal connectivity is significantly more affected by intelligent attacks than by random failures. Moreover, different intelligent attack strategies have a similar effect on the robustness: even small subsets of highly connected nodes act as a bottleneck in the temporal information flow, becoming critical weak points of the entire system. Additionally, the same nodes are the most important across a range of different importance metrics, expressing the correlation between highly connected nodes and those that trigger most of the changes in the optimal information spreading. Contrarily, we show that in randomly generated networks, where all the nodes have similar properties, random errors and intelligent attacks exhibit similar behavior. These conclusions may help us in design of more robust systems and fault-tolerant network architectures.

  16. Temporal Coding in Realistic Neural Networks

    NASA Astrophysics Data System (ADS)

    Gerasyuta, S. M.; Ivanov, D. V.

    1995-10-01

    The modification of realistic neural network model have been proposed. The model differs from the Hopfield model because of the two characteristic contributions to synaptic efficacious: the short-time contribution which is determined by the chemical reactions in the synapses and the long-time contribution corresponding to the structural changes of synaptic contacts. The approximation solution of the realistic neural network model equations is obtained. This solution allows us to calculate the postsynaptic potential as function of input. Using the approximate solution of realistic neural network model equations the behaviour of postsynaptic potential of realistic neural network as function of time for the different temporal sequences of stimuli is described. The various outputs are obtained for the different temporal sequences of the given stimuli. These properties of the temporal coding can be exploited as a recognition element capable of being selectively tuned to different inputs.

  17. Random walk centrality for temporal networks

    NASA Astrophysics Data System (ADS)

    Rocha, Luis E. C.; Masuda, Naoki

    2014-06-01

    Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

  18. Temporal efficiency evaluation and small-worldness characterization in temporal networks

    NASA Astrophysics Data System (ADS)

    Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

    2016-09-01

    Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

  19. Temporal efficiency evaluation and small-worldness characterization in temporal networks

    PubMed Central

    Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

    2016-01-01

    Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

  20. Time series analysis of temporal networks

    NASA Astrophysics Data System (ADS)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

  1. Importance of individual events in temporal networks

    NASA Astrophysics Data System (ADS)

    Takaguchi, Taro; Sato, Nobuo; Yano, Kazuo; Masuda, Naoki

    2012-09-01

    Records of time-stamped social interactions between pairs of individuals (e.g. face-to-face conversations, e-mail exchanges and phone calls) constitute a so-called temporal network. A remarkable difference between temporal networks and conventional static networks is that time-stamped events rather than links are the unit elements generating the collective behavior of nodes. We propose an importance measure for single interaction events. By generalizing the concept of the advance of events proposed by Kossinets et al (2008 Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining p 435), we propose that an event is central when it carries new information about others to the two nodes involved in the event. We find that the proposed measure properly quantifies the importance of events in connecting nodes along time-ordered paths. Because of strong heterogeneity in the importance of events present in real data, a small fraction of highly important events is necessary and sufficient to sustain the connectivity of temporal networks. Nevertheless, in contrast to the behavior of scale-free networks against link removal, this property mainly results from bursty activity patterns and not heterogeneous degree distributions.

  2. Accessibility and delay in random temporal networks

    NASA Astrophysics Data System (ADS)

    Tajbakhsh, Shahriar Etemadi; Coon, Justin P.; Simmons, David E.

    2017-09-01

    In a wide range of complex networks, the links between the nodes are temporal and may sporadically appear and disappear. This temporality is fundamental to analyzing the formation of paths within such networks. Moreover, the presence of the links between the nodes is a random process induced by nature in many real-world networks. In this paper, we study random temporal networks at a microscopic level and formulate the probability of accessibility from a node i to a node j after a certain number of discrete time units T . While solving the original problem is computationally intractable, we provide an upper and two lower bounds on this probability for a very general case with arbitrary time-varying probabilities of the links' existence. Moreover, for a special case where the links have identical probabilities across the network at each time slot, we obtain the exact probability of accessibility between any two nodes. Finally, we discuss scenarios where the information regarding the presence and absence of links is initially available in the form of time duration (of presence or absence intervals) continuous probability distributions rather than discrete probabilities over time slots. We provide a method for transforming such distributions to discrete probabilities, which enables us to apply the given bounds in this paper to a broader range of problem settings.

  3. Temporal node centrality in complex networks

    NASA Astrophysics Data System (ADS)

    Kim, Hyoungshick; Anderson, Ross

    2012-02-01

    Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

  4. Temporal fidelity in dynamic social networks

    NASA Astrophysics Data System (ADS)

    Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex `Sandy'; Lehmann, Sune

    2015-10-01

    It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.

  5. Network Analysis Using Spatio-Temporal Patterns

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele H. B.; Machicao, Jeaneth; Bruno, Odemir M.

    2016-08-01

    Different network models have been proposed along the last years inspired by real-world topologies. The characterization of these models implies the understanding of the underlying network phenomena, which accounts structural and dynamic properties. Several mathematical tools can be employed to characterize such properties as Cellular Automata (CA), which can be defined as dynamical systems of discrete nature composed by spatially distributed units governed by deterministic rules. In this paper, we proposed a method based on the modeling of one specific CA over distinct network topologies in order to perform the classification of the network model. The proposed methodology consists in the modeling of a binary totalistic CA over a network. The transition function that governs each CA cell is based on the density of living neighbors. Secondly, the distribution of the Shannon entropy is obtained from the evolved spatio-temporal pattern of the referred CA and used as a network descriptor. The experiments were performed using a dataset composed of four different types of networks: random, small-world, scale-free and geographical. We also used cross-validation for training purposes. We evaluated the accuracy of classification as a function of the initial number of living neighbors, and, also, as a function of a threshold parameter related to the density of living neighbors. The results show high accuracy values in distinguishing among the network models which demonstrates the feasibility of the proposed method.

  6. The Temporal Configuration of Airline Networks

    NASA Technical Reports Server (NTRS)

    Burghouwt, Guillaume; deWit, Jaap

    2003-01-01

    The deregulation of US aviation in 1978 resulted in the reconfiguration of airline networks into hub-and-spoke systems, spatially concentrated around a small number of central airports or 'hubs' through which an airline operates a number of daily waves of flights. A hub-and-spoke network requires a concentration of traffic in both space and time. In contrast to the U.S. airlines, European airlines had entered the phase of spatial network concentration long before deregulation. Bilateral negotiation of traffic fights between governments forced European airlines to focus their networks spatially on small number of 'national' airports. In general, these star-shaped networks were not coordinated in time. Transfer opportunities at central airports were mostly created 'by accident'. With the deregulation of the EU air transport market from 1988 on, a second phase of airline network concentration started. European airlines concentrated their networks in time by adopting or intensifying wave-system structures in their flight schedules. Temporal concentration may increase the competitive position of the network in a deregulated market because of certain cost and demand advantages.

  7. Structural Controllability of Temporal Networks with a Single Switching Controller.

    PubMed

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance.

  8. Structural Controllability of Temporal Networks with a Single Switching Controller

    PubMed Central

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance. PMID:28107538

  9. Cyber War Game in Temporal Networks

    PubMed Central

    Cho, Jin-Hee; Gao, Jianxi

    2016-01-01

    In a cyber war game where a network is fully distributed and characterized by resource constraints and high dynamics, attackers or defenders often face a situation that may require optimal strategies to win the game with minimum effort. Given the system goal states of attackers and defenders, we study what strategies attackers or defenders can take to reach their respective system goal state (i.e., winning system state) with minimum resource consumption. However, due to the dynamics of a network caused by a node’s mobility, failure or its resource depletion over time or action(s), this optimization problem becomes NP-complete. We propose two heuristic strategies in a greedy manner based on a node’s two characteristics: resource level and influence based on k-hop reachability. We analyze complexity and optimality of each algorithm compared to optimal solutions for a small-scale static network. Further, we conduct a comprehensive experimental study for a large-scale temporal network to investigate best strategies, given a different environmental setting of network temporality and density. We demonstrate the performance of each strategy under various scenarios of attacker/defender strategies in terms of win probability, resource consumption, and system vulnerability. PMID:26859840

  10. Cyber War Game in Temporal Networks.

    PubMed

    Cho, Jin-Hee; Gao, Jianxi

    2016-01-01

    In a cyber war game where a network is fully distributed and characterized by resource constraints and high dynamics, attackers or defenders often face a situation that may require optimal strategies to win the game with minimum effort. Given the system goal states of attackers and defenders, we study what strategies attackers or defenders can take to reach their respective system goal state (i.e., winning system state) with minimum resource consumption. However, due to the dynamics of a network caused by a node's mobility, failure or its resource depletion over time or action(s), this optimization problem becomes NP-complete. We propose two heuristic strategies in a greedy manner based on a node's two characteristics: resource level and influence based on k-hop reachability. We analyze complexity and optimality of each algorithm compared to optimal solutions for a small-scale static network. Further, we conduct a comprehensive experimental study for a large-scale temporal network to investigate best strategies, given a different environmental setting of network temporality and density. We demonstrate the performance of each strategy under various scenarios of attacker/defender strategies in terms of win probability, resource consumption, and system vulnerability.

  11. Reconstructing propagation networks with temporal similarity

    PubMed Central

    Liao, Hao; Zeng, An

    2015-01-01

    Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method. PMID:26086198

  12. Reconstructing propagation networks with temporal similarity.

    PubMed

    Liao, Hao; Zeng, An

    2015-06-18

    Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.

  13. Learning oncogenetic networks by reducing to mixed integer linear programming.

    PubMed

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

    Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

  14. Higher order temporal finite element methods through mixed formalisms.

    PubMed

    Kim, Jinkyu

    2014-01-01

    The extended framework of Hamilton's principle and the mixed convolved action principle provide new rigorous weak variational formalism for a broad range of initial boundary value problems in mathematical physics and mechanics. In this paper, their potential when adopting temporally higher order approximations is investigated. The classical single-degree-of-freedom dynamical systems are primarily considered to validate and to investigate the performance of the numerical algorithms developed from both formulations. For the undamped system, all the algorithms are symplectic and unconditionally stable with respect to the time step. For the damped system, they are shown to be accurate with good convergence characteristics.

  15. Representing operations procedures using temporal dependency networks

    NASA Technical Reports Server (NTRS)

    Fayyad, Kristina E.; Cooper, Lynne P.

    1993-01-01

    DSN Link Monitor & Control (LMC) operations consist primarily of executing procedures to configure, calibrate, test, and operate a communications link between an interplanetary spacecraft and its mission control center. Currently the LMC operators are responsible for integrating procedures into an end-to-end series of steps. The research presented in this paper is investigating new ways of specifying operations procedures that incorporate the insight of operations, engineering, and science personnel to improve mission operations. The paper describes the rationale for using Temporal Dependency Networks (TDN's) to represent the procedures, a description of how the data is acquired, and the knowledge engineering effort required to represent operations procedures. Results of operational tests of this concept, as implemented in the LMC Operator Assistant Prototype (LMCOA), are also presented.

  16. Mixed reality temporal bone surgical dissector: mechanical design.

    PubMed

    Hochman, Jordan Brent; Sepehri, Nariman; Rampersad, Vivek; Kraut, Jay; Khazraee, Milad; Pisa, Justyn; Unger, Bertram

    2014-08-08

    The Development of a Novel Mixed Reality (MR) Simulation. An evolving training environment emphasizes the importance of simulation. Current haptic temporal bone simulators have difficulty representing realistic contact forces and while 3D printed models convincingly represent vibrational properties of bone, they cannot reproduce soft tissue. This paper introduces a mixed reality model, where the effective elements of both simulations are combined; haptic rendering of soft tissue directly interacts with a printed bone model. This paper addresses one aspect in a series of challenges, specifically the mechanical merger of a haptic device with an otic drill. This further necessitates gravity cancelation of the work assembly gripper mechanism. In this system, the haptic end-effector is replaced by a high-speed drill and the virtual contact forces need to be repositioned to the drill tip from the mid wand. Previous publications detail generation of both the requisite printed and haptic simulations. Custom software was developed to reposition the haptic interaction point to the drill tip. A custom fitting, to hold the otic drill, was developed and its weight was offset using the haptic device. The robustness of the system to disturbances and its stable performance during drilling were tested. The experiments were performed on a mixed reality model consisting of two drillable rapid-prototyped layers separated by a free-space. Within the free-space, a linear virtual force model is applied to simulate drill contact with soft tissue. Testing illustrated the effectiveness of gravity cancellation. Additionally, the system exhibited excellent performance given random inputs and during the drill's passage between real and virtual components of the model. No issues with registration at model boundaries were encountered. These tests provide a proof of concept for the initial stages in the development of a novel mixed-reality temporal bone simulator.

  17. Mixed reality temporal bone surgical dissector: mechanical design

    PubMed Central

    2014-01-01

    Objective The Development of a Novel Mixed Reality (MR) Simulation. An evolving training environment emphasizes the importance of simulation. Current haptic temporal bone simulators have difficulty representing realistic contact forces and while 3D printed models convincingly represent vibrational properties of bone, they cannot reproduce soft tissue. This paper introduces a mixed reality model, where the effective elements of both simulations are combined; haptic rendering of soft tissue directly interacts with a printed bone model. This paper addresses one aspect in a series of challenges, specifically the mechanical merger of a haptic device with an otic drill. This further necessitates gravity cancelation of the work assembly gripper mechanism. In this system, the haptic end-effector is replaced by a high-speed drill and the virtual contact forces need to be repositioned to the drill tip from the mid wand. Previous publications detail generation of both the requisite printed and haptic simulations. Method Custom software was developed to reposition the haptic interaction point to the drill tip. A custom fitting, to hold the otic drill, was developed and its weight was offset using the haptic device. The robustness of the system to disturbances and its stable performance during drilling were tested. The experiments were performed on a mixed reality model consisting of two drillable rapid-prototyped layers separated by a free-space. Within the free-space, a linear virtual force model is applied to simulate drill contact with soft tissue. Results Testing illustrated the effectiveness of gravity cancellation. Additionally, the system exhibited excellent performance given random inputs and during the drill’s passage between real and virtual components of the model. No issues with registration at model boundaries were encountered. Conclusion These tests provide a proof of concept for the initial stages in the development of a novel mixed-reality temporal bone

  18. Applying temporal network analysis to the venture capital market

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene

    2015-10-01

    Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.

  19. Reconstruction of stochastic temporal networks through diffusive arrival times

    NASA Astrophysics Data System (ADS)

    Li, Xun; Li, Xiang

    2017-06-01

    Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications.

  20. Applications of Temporal Graph Metrics to Real-World Networks

    NASA Astrophysics Data System (ADS)

    Tang, John; Leontiadis, Ilias; Scellato, Salvatore; Nicosia, Vincenzo; Mascolo, Cecilia; Musolesi, Mirco; Latora, Vito

    Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

  1. Spatio-temporal networks: reachability, centrality and robustness

    PubMed Central

    Musolesi, Mirco

    2016-01-01

    Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks. PMID:27429776

  2. Locating the source of spreading in temporal networks

    NASA Astrophysics Data System (ADS)

    Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Yi, Dongyun

    2017-02-01

    The topological structure of many real networks changes with time. Thus, locating the sources of a temporal network is a creative and challenging problem, as the enormous size of many real networks makes it unfeasible to observe the state of all nodes. In this paper, we propose an algorithm to solve this problem, named the backward temporal diffusion process. The proposed algorithm calculates the shortest temporal distance to locate the transmission source. We assume that the spreading process can be modeled as a simple diffusion process and by consensus dynamics. To improve the location accuracy, we also adopt four strategies to select which nodes should be observed by ranking their importance in the temporal network. Our paper proposes a highly accurate method for locating the source in temporal networks and is, to the best of our knowledge, a frontier work in this field. Moreover, our framework has important significance for controlling the transmission of diseases or rumors and formulating immediate immunization strategies.

  3. Centrality measures in temporal networks with time series analysis

    NASA Astrophysics Data System (ADS)

    Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun

    2017-05-01

    The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.

  4. Spatio-Temporal Clustering of Monitoring Network

    NASA Astrophysics Data System (ADS)

    Hussain, I.; Pilz, J.

    2009-04-01

    Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters

  5. On the viscosity stratification in temporal mixing layer

    NASA Astrophysics Data System (ADS)

    Danaila, Luminita; Taguelmimt, Noureddine; Hadjadj, Abdellah; Turbulence Team

    2015-11-01

    We assess the effects of viscosity variations in low-speed temporally-evolving turbulent mixing layer. The two streams are density-matched, but the slow fluid is Rv times more viscous than the rapid stream. Direct Numerical Simulations (DNS) are performed for several viscosity ratios, Rv varying between 1 and 9. The space-time evolution of Variable-Viscosity Flow (VVF) is compared with that of the Constant-Viscosity Flow (CVF). The velocity fluctuations occur earlier and are more enhanced for VVF. In particular, the kinetic energy peaks earlier and is up to three times larger for VVF than for CVF at the earliest stages of the flow. Over the first stages of the flow, the temporal growth rate of the fluctuations kinetic energy is exponential, in full agreement with linear stability theory. The transport equation for the fluctuations kinetic energy is favourably compared with simulations data. The enhanced kinetic energy for VVF is mainly due to an increased production at the interface between the two fluids, in tight correlation with enlarged values of mean velocity gradient at the inflection point of the mean velocity profile. The transport equations of the one-and two-point kinetic energy show that self-preservation cannot be complete in variable-viscosity flows. ANR is acknowledged for financial support.

  6. Dynamic-Sensitive centrality of nodes in temporal networks

    PubMed Central

    Huang, Da-Wen; Yu, Zu-Guo

    2017-01-01

    Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold bc, especially for the temporal scale-free networks. PMID:28150735

  7. Structural Controllability and Controlling Centrality of Temporal Networks

    PubMed Central

    Pan, Yujian; Li, Xiang

    2014-01-01

    Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks. PMID:24747676

  8. Structural controllability and controlling centrality of temporal networks.

    PubMed

    Pan, Yujian; Li, Xiang

    2014-01-01

    Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks.

  9. Dynamic-Sensitive centrality of nodes in temporal networks

    NASA Astrophysics Data System (ADS)

    Huang, Da-Wen; Yu, Zu-Guo

    2017-02-01

    Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold bc, especially for the temporal scale-free networks.

  10. Strong, Long-Term Temporal Dynamics of an Ecological Network

    PubMed Central

    Olesen, Jens M.; Stefanescu, Constantí; Traveset, Anna

    2011-01-01

    Nature is organized into complex, dynamical networks of species and their interactions, which may influence diversity and stability. However, network research is, generally, short-term and depict ecological networks as static structures only, devoid of any dynamics. This hampers our understanding of how nature responds to larger disturbances such as changes in climate. In order to remedy this we studied the long-term (12-yrs) dynamics of a flower-visitation network, consisting of flower-visiting butterflies and their nectar plants. Global network properties, i.e. numbers of species and links, as well as connectance, were temporally stable, whereas most species and links showed a strong temporal dynamics. However, species of butterflies and plants varied bimodally in their temporal persistance: Sporadic species, being present only 1–2(-5) years, and stable species, being present (9-)11–12 years, dominated the networks. Temporal persistence and linkage level of species, i.e. number of links to other species, made up two groups of species: Specialists with a highly variable temporal persistence, and temporally stable species with a highly variable linkage level. Turnover of links of specialists was driven by species turnover, whereas turnover of links among generalists took place through rewiring, i.e. by reshuffling existing interactions. However, in spite of this strong internal dynamics of species and links the network appeared overall stable. If this global stability-local instability phenomenon is general, it is a most astonishing feature of ecological networks. PMID:22125597

  11. Lightweight simulation of air traffic control using simple temporal networks

    NASA Technical Reports Server (NTRS)

    Knight, Russell

    2005-01-01

    We provide a formulation of the air traffic control problem and a solver for this problem that makes use of temporal constraint networks and simple geometric reasoning. We provide results showing that this approach is practical for realistic simulated problems.

  12. Lightweight simulation of air traffic control using simple temporal networks

    NASA Technical Reports Server (NTRS)

    Knight, Russell

    2005-01-01

    We provide a formulation of the air traffic control problem and a solver for this problem that makes use of temporal constraint networks and simple geometric reasoning. We provide results showing that this approach is practical for realistic simulated problems.

  13. Altered cortical anatomical networks in temporal lobe epilepsy

    NASA Astrophysics Data System (ADS)

    Lv, Bin; He, Huiguang; Lu, Jingjing; Li, Wenjing; Dai, Dai; Li, Meng; Jin, Zhengyu

    2011-03-01

    Temporal lobe epilepsy (TLE) is one of the most common epilepsy syndromes with focal seizures generated in the left or right temporal lobes. With the magnetic resonance imaging (MRI), many evidences have demonstrated that the abnormalities in hippocampal volume and the distributed atrophies in cortical cortex. However, few studies have investigated if TLE patients have the alternation in the structural networks. In the present study, we used the cortical thickness to establish the morphological connectivity networks, and investigated the network properties using the graph theoretical methods. We found that all the morphological networks exhibited the small-world efficiency in left TLE, right TLE and normal groups. And the betweenness centrality analysis revealed that there were statistical inter-group differences in the right uncus region. Since the right uncus located at the right temporal lobe, these preliminary evidences may suggest that there are topological alternations of the cortical anatomical networks in TLE, especially for the right TLE.

  14. Identify Dynamic Network Modules with Temporal and Spatial Constraints

    SciTech Connect

    Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J

    2007-09-24

    Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.

  15. Duplication: a Mechanism Producing Disassortative Mixing Networks in Biology

    NASA Astrophysics Data System (ADS)

    Zhao, Dan; Liu, Zeng-Rong; Wang, Jia-Zeng

    2007-10-01

    Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks (PPI), gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks. Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.

  16. Classification of behavior using unsupervised temporal neural networks

    SciTech Connect

    Adair, K.L.; Argo, P.

    1998-03-01

    Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem.

  17. Inferring Complex Network Topology from Spatio-Temporal Spike Patterns

    NASA Astrophysics Data System (ADS)

    van Bussel, Frank; Kriener, Birgit; Timme, Marc

    2011-03-01

    The problem of reconstructing or reverse-engineering the connectivity of networks consisting of dynamically interacting units has become an active area of study in fields such as genetics, ecology, and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence) of individual interactions, but there is commonly no direct way to probe for their existence. We present an explicit method for reconstructing neuronal networks from their spiking activity. The approach works well for networks in simple collective states, but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required.

  18. Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities

    NASA Astrophysics Data System (ADS)

    Scholtes, Ingo; Wider, Nicolas; Garas, Antonios

    2016-03-01

    Despite recent advances in the study of temporal networks, the analysis of time-stamped network data is still a fundamental challenge. In particular, recent studies have shown that correlations in the ordering of links crucially alter causal topologies of temporal networks, thus invalidating analyses based on static, time-aggregated representations of time-stamped data. These findings not only highlight an important dimension of complexity in temporal networks, but also call for new network-analytic methods suitable to analyze complex systems with time-varying topologies. Addressing this open challenge, here we introduce a novel framework for the study of path-based centralities in temporal networks. Studying betweenness, closeness and reach centrality, we first show than an application of these measures to time-aggregated, static representations of temporal networks yields misleading results about the actual importance of nodes. To overcome this problem, we define path-based centralities in higher-order aggregate networks, a recently proposed generalization of the commonly used static representation of time-stamped data. Using data on six empirical temporal networks, we show that the resulting higher-order measures better capture the true, temporal centralities of nodes. Our results demonstrate that higher-order aggregate networks constitute a powerful abstraction, with broad perspectives for the design of new, computationally efficient data mining techniques for time-stamped relational data.

  19. Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

    NASA Astrophysics Data System (ADS)

    Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

    2017-09-01

    Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

  20. Temporal and spatial stability in translation invariant linear resistive networks.

    PubMed

    Solak, M K

    1997-01-01

    Simple algebraic methods are proposed to evaluate the temporal and spatial stability of translation invariant linear resistive networks. Temporal stability is discussed for a finite number of nodes n. The proposed method evaluates stability of a Toeplitz pencil A(n)(a)+muB(n)(b) in terms of parameters a(i ) and b(i). In many cases a simple method allows one to verify positive definition of B(n)(b) in terms of b(i) only.

  1. Cyber War Game in Temporal Networks

    DTIC Science & Technology

    2016-02-09

    Boston, Massachusetts 02115, United States of America * jianxi.gao@gmail.com Abstract In a cyber war game where a network is fully distributed and... game with minimum effort. Given the system goal states of attackers and defenders, we study what strategies attackers or defenders can take to reach

  2. Roles of mixing patterns in the network reconstruction.

    PubMed

    Guo, Qiang; Liang, Guang; Fu, Jia-Qi; Han, Jing-Ti; Liu, Jian-Guo

    2016-11-01

    Compressive sensing is an effective way to reconstruct the network structure. In this paper, we investigate the effect of the mixing patterns, measured by the assortative coefficient, on the performance of network reconstruction. First, we present a model to generate networks with different assortativity coefficients, then we reconstruct the network structure by using the compressive sensing method. The experimental results show that when the assortativity coefficient r=0.2, the accuracy of the network reconstruction reaches the maximum value, which suggests that the compressive sensing is more effective for uncovering the links of social networks. Moreover, the accuracy of the network reconstruction will be higher as the network size increases.

  3. Roles of mixing patterns in the network reconstruction

    NASA Astrophysics Data System (ADS)

    Guo, Qiang; Liang, Guang; Fu, Jia-Qi; Han, Jing-Ti; Liu, Jian-Guo

    2016-11-01

    Compressive sensing is an effective way to reconstruct the network structure. In this paper, we investigate the effect of the mixing patterns, measured by the assortative coefficient, on the performance of network reconstruction. First, we present a model to generate networks with different assortativity coefficients, then we reconstruct the network structure by using the compressive sensing method. The experimental results show that when the assortativity coefficient r =0.2 , the accuracy of the network reconstruction reaches the maximum value, which suggests that the compressive sensing is more effective for uncovering the links of social networks. Moreover, the accuracy of the network reconstruction will be higher as the network size increases.

  4. Perfusion network shift during seizures in medial temporal lobe epilepsy.

    PubMed

    Sequeira, Karen M; Tabesh, Ali; Sainju, Rup K; DeSantis, Stacia M; Naselaris, Thomas; Joseph, Jane E; Ahlman, Mark A; Spicer, Kenneth M; Glazier, Steve S; Edwards, Jonathan C; Bonilha, Leonardo

    2013-01-01

    Medial temporal lobe epilepsy (MTLE) is associated with limbic atrophy involving the hippocampus, peri-hippocampal and extra-temporal structures. While MTLE is related to static structural limbic compromise, it is unknown whether the limbic system undergoes dynamic regional perfusion network alterations during seizures. In this study, we aimed to investigate state specific (i.e. ictal versus interictal) perfusional limbic networks in patients with MTLE. We studied clinical information and single photon emission computed tomography (SPECT) images obtained with intravenous infusion of the radioactive tracer Technetium- Tc 99 m Hexamethylpropyleneamine Oxime (Tc-99 m HMPAO) during ictal and interictal state confirmed by video-electroencephalography (VEEG) in 20 patients with unilateral MTLE (12 left and 8 right MTLE). Pair-wise voxel-based analyses were used to define global changes in tracer between states. Regional tracer uptake was calculated and state specific adjacency matrices were constructed based on regional correlation of uptake across subjects. Graph theoretical measures were applied to investigate global and regional state specific network reconfigurations. A significant increase in tracer uptake was observed during the ictal state in the medial temporal region, cerebellum, thalamus, insula and putamen. From network analyses, we observed a relative decreased correlation between the epileptogenic temporal region and remaining cortex during the interictal state, followed by a surge of cross-correlated perfusion in epileptogenic temporal-limbic structures during a seizure, corresponding to local network integration. These results suggest that MTLE is associated with a state specific perfusion and possibly functional organization consisting of a surge of limbic cross-correlated tracer uptake during a seizure, with a relative disconnection of the epileptogenic temporal lobe in the interictal period. This pattern of state specific shift in metabolic networks in

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

    PubMed Central

    Masuda, Naoki

    2016-01-01

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

  6. Imaging structural and functional brain networks in temporal lobe epilepsy

    PubMed Central

    Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

    2013-01-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

  7. Temporal representation for gene networks: towards a qualitative temporal data mining.

    PubMed

    Turenne, Nicolas; Schwer, Sylviane R

    2008-01-01

    Processing literature (i.e., text corpora) to capture gene regulation events is not easy and can be driven by the final data representation. We propose to build, manually, an example of temporal representation (whole gene networks for coat formation in Bacillus Subtilis). Our temporal representation is based on a generalised formal language theory (S-languages). We propose an algorithm to link bags of relations with representation, by ordering interactions. In this paper, starting from the network made manually from text data, we show that S-languages are quite relevant to encapsulate gene properties, and infer knowledge across timestamped gene relations found in texts.

  8. A distributed recurrent network contributes to temporally precise vocalizations

    PubMed Central

    Hamaguchi, Kosuke; Tanaka, Masashi; Mooney, Richard

    2016-01-01

    SUMMARY How do forebrain and brainstem circuits interact to produce temporally precise and reproducible behaviors? Birdsong is an elaborate, temporally precise and stereotyped vocal behavior controlled by a network of forebrain and brainstem nuclei. An influential idea is that song premotor neurons in a forebrain nucleus (HVC) form a synaptic chain that dictates song timing in a top-down manner. Here we combine physiological, dynamical and computational methods to show that song timing is not generated solely by a mechanism localized to HVC but instead is the product of a distributed and recurrent synaptic network spanning the forebrain and brainstem, of which HVC is a component. PMID:27397518

  9. Temporal dynamics of connectivity and epidemic properties of growing networks

    NASA Astrophysics Data System (ADS)

    Fotouhi, Babak; Shirkoohi, Mehrdad Khani

    2016-01-01

    Traditional mathematical models of epidemic disease had for decades conventionally considered static structure for contacts. Recently, an upsurge of theoretical inquiry has strived towards rendering the models more realistic by incorporating the temporal aspects of networks of contacts, societal and online, that are of interest in the study of epidemics (and other similar diffusion processes). However, temporal dynamics have predominantly focused on link fluctuations and nodal activities, and less attention has been paid to the growth of the underlying network. Many real networks grow: Online networks are evidently in constant growth, and societal networks can grow due to migration flux and reproduction. The effect of network growth on the epidemic properties of networks is hitherto unknown, mainly due to the predominant focus of the network growth literature on the so-called steady state. This paper takes a step towards alleviating this gap. We analytically study the degree dynamics of a given arbitrary network that is subject to growth. We use the theoretical findings to predict the epidemic properties of the network as a function of time. We observe that the introduction of new individuals into the network can enhance or diminish its resilience against endemic outbreaks and investigate how this regime shift depends upon the connectivity of newcomers and on how they establish connections to existing nodes. Throughout, theoretical findings are corroborated with Monte Carlo simulations over synthetic and real networks. The results shed light on the effects of network growth on the future epidemic properties of networks and offers insights for devising a priori immunization strategies.

  10. Spatial-Temporal Quantification of Interdependencies Across Infrastructure Networks

    NASA Astrophysics Data System (ADS)

    Chan, Christopher; Dueñas-Osorio, Leonardo

    As infrastructure networks become more complex and intertwined, the relevance of network interdependency research is increasingly evident. Interconnected networks bring about efficiencies during normal operations but also come with risks of cascading failures with disaster events. An adequate understanding of network interdependencies and realistic multi-system modeling capabilities enable the exploration of practical operation strategies and mitigation efforts applicable to existing or future coupled networked systems. This chapter examines recent efforts in quantifying infrastructure network interdependencies through spatial and time-series analyses to reveal the heterogeneity and complexity in their coupling. Furthermore, a combined spatial-temporal methodology is recommended for the future calibration and validation of theoretical and computational models of interdependent networks of networks. An example case study is demonstrated using data derived from the 2010 Chilean Earthquake in the Talcahuano-Concepción region, which highlights the richness in coupling strengths across infrastructure systems, both as a function of time and geographical extent. Insights for design and control of coupled networks are also derivable from joint spatial-temporal analyses of infrastructure interdependence and its evolution.

  11. Models, Entropy and Information of Temporal Social Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

    Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

  12. Dynamics on networks: competition of temporal and topological correlations

    NASA Astrophysics Data System (ADS)

    Artime, Oriol; Ramasco, José J.; San Miguel, Maxi

    2017-02-01

    Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way.

  13. Dynamics on networks: competition of temporal and topological correlations

    PubMed Central

    Artime, Oriol; Ramasco, José J.; San Miguel, Maxi

    2017-01-01

    Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way. PMID:28150708

  14. Anterior temporal lobe degeneration produces widespread network-driven dysfunction.

    PubMed

    Guo, Christine C; Gorno-Tempini, Maria Luisa; Gesierich, Benno; Henry, Maya; Trujillo, Andrew; Shany-Ur, Tal; Jovicich, Jorge; Robinson, Simon D; Kramer, Joel H; Rankin, Katherine P; Miller, Bruce L; Seeley, William W

    2013-10-01

    The neural organization of semantic memory remains much debated. A 'distributed-only' view contends that semantic knowledge is represented within spatially distant, modality-selective primary and association cortices. Observations in semantic variant primary progressive aphasia have inspired an alternative model featuring the anterior temporal lobe as an amodal hub that supports semantic knowledge by linking distributed modality-selective regions. Direct evidence has been lacking, however, to support intrinsic functional interactions between an anterior temporal lobe hub and upstream sensory regions in humans. Here, we examined the neural networks supporting semantic knowledge by performing a multimodal brain imaging study in healthy subjects and patients with semantic variant primary progressive aphasia. In healthy subjects, the anterior temporal lobe showed intrinsic connectivity to an array of modality-selective primary and association cortices. Patients showed focal anterior temporal lobe degeneration but also reduced physiological integrity throughout distributed modality-selective regions connected with the anterior temporal lobe in healthy controls. Physiological deficits outside the anterior temporal lobe correlated with scores on semantic tasks and with anterior temporal subregion atrophy, following domain-specific and connectivity-based predictions. The findings provide a neurophysiological basis for the theory that semantic processing is orchestrated through interactions between a critical anterior temporal lobe hub and modality-selective processing nodes.

  15. Spectral properties of the temporal evolution of brain network structure.

    PubMed

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  16. Spectral properties of the temporal evolution of brain network structure

    NASA Astrophysics Data System (ADS)

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  17. Default Network Connectivity in Medial Temporal Lobe Amnesia

    PubMed Central

    Hayes, Scott M.; Salat, David H.; Verfaellie, Mieke

    2012-01-01

    There is substantial overlap between the brain regions supporting episodic memory and the default network. However, in humans the impact of bilateral medial temporal lobe (MTL) damage on a large-scale neural network such as the default mode network is unknown. To examine this issue, resting functional magnetic resonance imaging (fMRI) was performed with amnesic patients and control participants. Seed-based functional connectivity analyses revealed robust default network connectivity in amnesia in cortical default network regions such as medial prefrontal cortex, posterior medial cortex, and lateral parietal cortex, as well as evidence of connectivity to residual MTL tissue. Relative to control participants, decreased posterior cingulate cortex connectivity to MTL and increased connectivity to cortical default network regions including lateral parietal and medial prefrontal cortex was observed in amnesia. In contrast, somatomotor network connectivity was intact in amnesia, indicating bilateral MTL lesions may selectively impact the default network. Changes in default network connectivity in amnesia were largely restricted to the MTL subsystem, providing preliminary support from MTL amnesic patients that the default network can be fractionated into functionally and structurally distinct components. To our knowledge, this is the first examination of the default network in amnesia. PMID:23077048

  18. Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

    DTIC Science & Technology

    2008-03-01

    IT Insider Threat . . . . . . . . . . . . . . . . . . . . . . . . 7 AFRL Air Force Reasearch Laboratory . . . . . . . . . . . . . . . 14 C3 Command...associated with various aspects of IW/IO. The OODA Loop concept suffers due to the unique environmental factors of cyberspace. Causes and problems...could be programmed to detect any environmental stimulus (i.e. TCP packets, network traffic volume, destinations, etc.). [9–11,14] • Temporal

  19. Temporal modulation of collective cell behavior controls vascular network topology

    PubMed Central

    Kur, Esther; Kim, Jiha; Tata, Aleksandra; Comin, Cesar H; Harrington, Kyle I; Costa, Luciano da F; Bentley, Katie; Gu, Chenghua

    2016-01-01

    Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology. DOI: http://dx.doi.org/10.7554/eLife.13212.001 PMID:26910011

  20. Temporal modulation of collective cell behavior controls vascular network topology.

    PubMed

    Kur, Esther; Kim, Jiha; Tata, Aleksandra; Comin, Cesar H; Harrington, Kyle I; Costa, Luciano da F; Bentley, Katie; Gu, Chenghua

    2016-02-24

    Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology.

  1. Synchronization in output-coupled temporal Boolean networks

    NASA Astrophysics Data System (ADS)

    Lu, Jianquan; Zhong, Jie; Tang, Yang; Huang, Tingwen; Cao, Jinde; Kurths, Jürgen

    2014-09-01

    This paper presents an analytical study of synchronization in an array of output-coupled temporal Boolean networks. A temporal Boolean network (TBN) is a logical dynamic system developed to model Boolean networks with regulatory delays. Both state delay and output delay are considered, and these two delays are assumed to be different. By referring to the algebraic representations of logical dynamics and using the semi-tensor product of matrices, the output-coupled TBNs are firstly converted into a discrete-time algebraic evolution system, and then the relationship between the states of coupled TBNs and the initial state sequence is obtained. Then, some necessary and sufficient conditions are derived for the synchronization of an array of TBNs with an arbitrary given initial state sequence. Two numerical examples including one epigenetic model are finally given to illustrate the obtained results.

  2. Synchronization in output-coupled temporal Boolean networks

    PubMed Central

    Lu, Jianquan; Zhong, Jie; Tang, Yang; Huang, Tingwen; Cao, Jinde; Kurths, Jürgen

    2014-01-01

    This paper presents an analytical study of synchronization in an array of output-coupled temporal Boolean networks. A temporal Boolean network (TBN) is a logical dynamic system developed to model Boolean networks with regulatory delays. Both state delay and output delay are considered, and these two delays are assumed to be different. By referring to the algebraic representations of logical dynamics and using the semi-tensor product of matrices, the output-coupled TBNs are firstly converted into a discrete-time algebraic evolution system, and then the relationship between the states of coupled TBNs and the initial state sequence is obtained. Then, some necessary and sufficient conditions are derived for the synchronization of an array of TBNs with an arbitrary given initial state sequence. Two numerical examples including one epigenetic model are finally given to illustrate the obtained results. PMID:25189531

  3. Random vs. nonrandom mixing in network epidemic models.

    PubMed

    Zaric, Gregory S

    2002-04-01

    In this paper we compare random and nonrandom mixing patterns for network epidemic models. Several of studies have examined the impact of different mixing patterns using compartmental epidemic models. We extend the work on compartmental models to the case of network epidemic models. We define two nonrandom mixing patterns for a network epidemic model and investigate the impact that these mixing patterns have on a number of epidemic outcomes when compared to random mixing. We find that different mixing assumptions lead to small but statistically significant differences in disease prevalence, cumulative number of new infections, final population size, and network structure. Significant differences in outcomes were more likely to be observed for larger populations and longer time horizons. Sensitivity analysis revealed that greater differences in outcomes between random and nonrandom mixing were associated with a larger incremental mortality rate among infected individuals, a larger average number of partners, and a greater probability of forming new partnerships. When adjusted for the initial population size, differences between random and nonrandom mixing models were approximately constant across all population sizes considered. We also considered the impact that differences between mixing models might have on the cost effectiveness ratio for epidemic control interventions.

  4. Detailed temporal structure of communication networks in groups of songbirds.

    PubMed

    Stowell, Dan; Gill, Lisa; Clayton, David

    2016-06-01

    Animals in groups often exchange calls, in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group. We introduce a model-based analysis for temporal patterns of animal call timing, originally developed for networks of firing neurons. This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals. It also has advantages over standard Markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls. Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taeniopygia guttata) individuals. We find that the communication network in these groups has stable structure that persists from one day to the next, and that 'kernels' reflecting the temporal range of influence have a characteristic structure for a calling individual's effect on itself, its partner and on others in the group. We further find characteristic patterns of influences by call type as well as by individual.

  5. Detailed temporal structure of communication networks in groups of songbirds

    PubMed Central

    Clayton, David

    2016-01-01

    Animals in groups often exchange calls, in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group. We introduce a model-based analysis for temporal patterns of animal call timing, originally developed for networks of firing neurons. This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals. It also has advantages over standard Markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls. Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taeniopygia guttata) individuals. We find that the communication network in these groups has stable structure that persists from one day to the next, and that ‘kernels’ reflecting the temporal range of influence have a characteristic structure for a calling individual's effect on itself, its partner and on others in the group. We further find characteristic patterns of influences by call type as well as by individual. PMID:27335223

  6. Dynamics of history-dependent epidemics in temporal networks

    NASA Astrophysics Data System (ADS)

    Sunny, Albert; Kotnis, Bhushan; Kuri, Joy

    2015-08-01

    The structural properties of temporal networks often influence the dynamical processes that occur on these networks, e.g., bursty interaction patterns have been shown to slow down epidemics. In this paper, we investigate the effect of link lifetimes on the spread of history-dependent epidemics. We formulate an analytically tractable activity-driven temporal network model that explicitly incorporates link lifetimes. For Markovian link lifetimes, we use mean-field analysis for computing the epidemic threshold, while the effect of non-Markovian link lifetimes is studied using simulations. Furthermore, we also study the effect of negative correlation between the number of links spawned by an individual and the lifetimes of those links. Such negative correlations may arise due to the finite cognitive capacity of the individuals. Our investigations reveal that heavy-tailed link lifetimes slow down the epidemic, while negative correlations can reduce epidemic prevalence. We believe that our results help shed light on the role of link lifetimes in modulating diffusion processes on temporal networks.

  7. Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks.

    PubMed

    Hernandez-Leal, Pablo; Rios-Flores, Alma; Avila-Rios, Santiago; Reyes-Terán, Gustavo; Gonzalez, Jesus A; Fiedler-Cameras, Lindsey; Orihuela-Espina, Felipe; Morales, Eduardo F; Sucar, L Enrique

    2013-03-01

    The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy. We apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance. In a first experiment, we compared the TNBN approach with other models such as static Bayesian networks, dynamic Bayesian networks and association rules. TNBN achieved a 64.2% sparser structure over the static network. In a second experiment, the TNBN model was applied to a dataset associating antiretroviral drugs with mutations developed under different antiretroviral regimes. The learned models captured previously described mutational pathways and associations between antiretroviral drugs and drug resistance mutations. Predictive accuracy reached 90.5%. Our results suggest possible applications of TNBN for studying drug-mutation and mutation-mutation networks in the context of antiretroviral therapy, with direct impact on the clinical management of patients under antiretroviral therapy. This opens new horizons for predicting HIV mutational pathways in immune selection with relevance for antiretroviral drug development and therapy plan. Copyright © 2013 Elsevier B.V. All rights

  8. The Implications of a Mixed Media Network for Information Interchange.

    ERIC Educational Resources Information Center

    Meaney, John W.

    A mixed media network for information interchange is what we are always likely to have. Amid the current permutations of the storage and distribution media we see the emergence of two trends -- toward the common denominators of electronic display on the TV system and of digital processing and control. The economic implications of a mixed network…

  9. Spatial-temporal modeling of malware propagation in networks.

    PubMed

    Chen, Zesheng; Ji, Chuanyi

    2005-09-01

    Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

  10. Associative Memory Neural Network with Low Temporal Spiking Rates

    NASA Astrophysics Data System (ADS)

    Amit, Daniel J.; Treves, A.

    1989-10-01

    We describe a modified attractor neural network in which neuronal dynamics takes place on a time scale of the absolute refractory period but the mean temporal firing rate of any neuron in the network is lower by an arbitrary factor that characterizes the strength of the effective inhibition. It operates by encoding information on the excitatory neurons only and assuming the inhibitory neurons to be faster and to inhibit the excitatory ones by an effective postsynaptic potential that is expressed in terms of the activity of the excitatory neurons themselves. Retrieval is identified as a nonergodic behavior of the network whose consecutive states have a significantly enhanced activity rate for the neurons that should be active in a stored pattern and a reduced activity rate for the neurons that are inactive in the memorized pattern. In contrast to the Hopfield model the network operates away from fixed points and under the strong influence of noise. As a consequence, of the neurons that should be active in a pattern, only a small fraction is active in any given time cycle and those are randomly distributed, leading to reduced temporal rates. We argue that this model brings neural network models much closer to biological reality. We present the results of detailed analysis of the model as well as simulations.

  11. Multiprocess network logic with temporal and spatial modalities. revised

    SciTech Connect

    Reif, J.H.; Sistla, A.P.

    1982-10-01

    We introduce a modal logic which can be used to formally reason about synchronous fixed connection multiprocess networks such as VLSI. Our logic has both temporal and spatial modal operators. The various temporal modal operators allow us to relate properties of the current state of a given process with properties of succeeding states of the given process. Also, the spatial modal operators allow us to relate properties of the current state of a given process with properties of the current state of neighboring processes. Many interesting properties for multiprocessor networks can be elegantly expressed in our logic. We give examples of the diverse applications of our logic to packet routing firing squad problems, and systolic algorithms.

  12. Mixing properties of growing networks and Simpson's paradox.

    PubMed

    Capocci, Andrea; Colaiori, Francesca

    2006-08-01

    The mixing properties of networks are usually inferred by comparing the degree of a node with the average degree of its neighbors. This kind of analysis often leads to incorrect conclusions: Assortative patterns may appear reversed by a mechanism known as Simpson's paradox. We prove this fact by analytical calculations and simulations on three classes of growing networks based on preferential attachment and fitness, where the disassortative behavior observed is a spurious effect. Our results give a crucial contribution to the debate about the origin of disassortative mixing, since networks previously classified as disassortative reveal instead assortative behavior to a careful analysis.

  13. Complex earthquake networks: Hierarchical organization and assortative mixing

    NASA Astrophysics Data System (ADS)

    Abe, Sumiyoshi; Suzuki, Norikazu

    2006-08-01

    To characterize the dynamical features of seismicity as a complex phenomenon, the seismic data are mapped to a growing random graph, which is a small-world scale-free network. Here, hierarchical and mixing properties of such a network are studied. The clustering coefficient is found to exhibit asymptotic power-law decay with respect to connectivity, showing hierarchical organization. This structure is supported by not only main shocks but also small shocks, and may have its origin in the combined effect of vertex fitness and deactivation by stress release at faults. The nearest-neighbor average connectivity and the Pearson correlation coefficient are also calculated. It is found that the earthquake network has assortative mixing. This is a main difference of the earthquake network from the Internet with disassortative mixing. Physical implications of these results are discussed.

  14. From blickets to synapses: inferring temporal causal networks by observation.

    PubMed

    Fernando, Chrisantha

    2013-01-01

    How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla-Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.

  15. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    PubMed

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

  16. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

    PubMed Central

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

  17. Sparse Temporally Dynamic Resting-State Functional Connectivity Networks for Early MCI Identification

    PubMed Central

    Wee, Chong-Yaw; Yang, Sen; Yap, Pew-Thian; Shen, Dinggang

    2015-01-01

    In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI subseries. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients. PMID:26123390

  18. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.

    PubMed

    Wee, Chong-Yaw; Yang, Sen; Yap, Pew-Thian; Shen, Dinggang

    2016-06-01

    In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI sub-series. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients.

  19. Spatio-temporal statistical models for river monitoring networks.

    PubMed

    Clement, L; Thas, O; Vanrolleghem, P A; Ottoy, J P

    2006-01-01

    When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

  20. Temporal-varying failures of nodes in networks

    NASA Astrophysics Data System (ADS)

    Knight, Georgie; Cristadoro, Giampaolo; Altmann, Eduardo G.

    2015-08-01

    We consider networks in which random walkers are removed because of the failure of specific nodes. We interpret the rate of loss as a measure of the importance of nodes, a notion we denote as failure centrality. We show that the degree of the node is not sufficient to determine this measure and that, in a first approximation, the shortest loops through the node have to be taken into account. We propose approximations of the failure centrality which are valid for temporal-varying failures, and we dwell on the possibility of externally changing the relative importance of nodes in a given network by exploiting the interference between the loops of a node and the cycles of the temporal pattern of failures. In the limit of long failure cycles we show analytically that the escape in a node is larger than the one estimated from a stochastic failure with the same failure probability. We test our general formalism in two real-world networks (air-transportation and e-mail users) and show how communities lead to deviations from predictions for failures in hubs.

  1. Small-Scale Temporal and Spatial Variability in Regional-Scale CO2 Mixing Ratio Measurements

    NASA Astrophysics Data System (ADS)

    Crosson, E.; Corbin, K. D.; Davis, K. J.; Denning, S.; Lokupitiya, E. Y.; Miles, N.; Richardson, S.

    2009-05-01

    The study of regional-scale CO2 concentrations and fluxes lies between the detailed understanding of ecological processes that can be gathered via intensive local field study, and the overarching but mechanistically poor understanding of the global carbon cycle that is gained by analyzing the atmospheric CO2 budget. In addition to the importance of regional studies toward the fundamental goal of understanding the carbon balance of the continent, regional-scale studies are becoming increasingly important as the necessity of tracking progress in CO2 emissions reduction arises. This work is part of the NACP's Midcontinental Intensive (MCI) study. Specifically it adds a regional network of five communications-tower based atmospheric CO2 observations ("Ring 2") from April 2007 through October 2008 to the long-term atmospheric CO2 observing network (tall towers, aircraft profiles, and well- calibrated CO2 measurements on Ameriflux towers) in the mid-continent intensive region. The Ring 2 measurements are based on relatively new technology for CO2 measurement, wavelength-scanned cavity ring down spectroscopy (Picarro, Inc.), and the locations are regional in scale (roughly a 500-km diameter ring). We present results concerning data quality of the new instruments, including water vapor correction and uncertainties, as well as small-scale temporal and spatial variations that can be seen with this unique network. For example, the daily daytime average throughout the 2007 and 2008 growing seasons indicated a 50-ppm seasonal drawdown, with significant synoptic variability. The drawdown in this largely agricultural region (heavily influenced by corn) is significantly larger than the 20-30 ppm typically seen in forested regions. Also during the 2007 and 2008 growing seasons, the CO2 mixing ratio at the sites nearly always differs by more than 5 ppm, while at times the inter-site difference is as large as 30-50 ppm. While variability in the regional spatial gradients is expected

  2. The multilayer temporal network of public transport in Great Britain

    PubMed Central

    Gallotti, Riccardo; Barthelemy, Marc

    2015-01-01

    Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset. PMID:25977806

  3. The multilayer temporal network of public transport in Great Britain

    NASA Astrophysics Data System (ADS)

    Gallotti, Riccardo; Barthelemy, Marc

    2015-01-01

    Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.

  4. Levetiracetam reduces abnormal network activations in temporal lobe epilepsy

    PubMed Central

    Wandschneider, Britta; Stretton, Jason; Sidhu, Meneka; Centeno, Maria; Kozák, Lajos R.; Symms, Mark; Thompson, Pamela J.; Duncan, John S.

    2014-01-01

    Objective: We used functional MRI (fMRI) and a left-lateralizing verbal and a right-lateralizing visual-spatial working memory (WM) paradigm to investigate the effects of levetiracetam (LEV) on cognitive network activations in patients with drug-resistant temporal lobe epilepsy (TLE). Methods: In a retrospective study, we compared task-related fMRI activations and deactivations in 53 patients with left and 54 patients with right TLE treated with (59) or without (48) LEV. In patients on LEV, activation patterns were correlated with the daily LEV dose. Results: We isolated task- and syndrome-specific effects. Patients on LEV showed normalization of functional network deactivations in the right temporal lobe in right TLE during the right-lateralizing visual-spatial task and in the left temporal lobe in left TLE during the verbal task. In a post hoc analysis, a significant dose-dependent effect was demonstrated in right TLE during the visual-spatial WM task: the lower the LEV dose, the greater the abnormal right hippocampal activation. At a less stringent threshold (p < 0.05, uncorrected for multiple comparisons), a similar dose effect was observed in left TLE during the verbal task: both hippocampi were more abnormally activated in patients with lower doses, but more prominently on the left. Conclusions: Our findings suggest that LEV is associated with restoration of normal activation patterns. Longitudinal studies are necessary to establish whether the neural patterns translate to drug response. Classification of evidence: This study provides Class III evidence that in patients with drug-resistant TLE, levetiracetam has a dose-dependent facilitation of deactivation of mesial temporal structures. PMID:25253743

  5. Temporal Visualization of Social Network Dynamics: Prototypes for Nation of Neighbors

    NASA Astrophysics Data System (ADS)

    Ahn, Jae-Wook; Taieb-Maimon, Meirav; Sopan, Awalin; Plaisant, Catherine; Shneiderman, Ben

    Information visualization is a powerful tool for analyzing the dynamic nature of social communities. Using Nation of Neighbors community network as a testbed, we propose five principles of implementing temporal visualizations for social networks and present two research prototypes: NodeXL and TempoVis. Three different states are defined in order to visualize the temporal changes of social networks. We designed the prototypes to show the benefits of the proposed ideas by letting users interactively explore temporal changes of social networks.

  6. A mixing evolution model for bidirectional microblog user networks

    NASA Astrophysics Data System (ADS)

    Yuan, Wei-Guo; Liu, Yun

    2015-08-01

    Microblogs have been widely used as a new form of online social networking. Based on the user profile data collected from Sina Weibo, we find that the number of microblog user bidirectional friends approximately corresponds with the lognormal distribution. We then build two microblog user networks with real bidirectional relationships, both of which have not only small-world and scale-free but also some special properties, such as double power-law degree distribution, disassortative network, hierarchical and rich-club structure. Moreover, by detecting the community structures of the two real networks, we find both of their community scales follow an exponential distribution. Based on the empirical analysis, we present a novel evolution network model with mixed connection rules, including lognormal fitness preferential and random attachment, nearest neighbor interconnected in the same community, and global random associations in different communities. The simulation results show that our model is consistent with real network in many topology features.

  7. Cortical Networks Involved in Memory for Temporal Order.

    PubMed

    Manelis, Anna; Popov, Vencislav; Paynter, Christopher; Walsh, Matthew; Wheeler, Mark E; Vogt, Keith M; Reder, Lynne M

    2017-03-15

    We examined the neurobiological basis of temporal resetting, an aspect of temporal order memory, using a version of the delayed-match-to-multiple-sample task. While in an fMRI scanner, participants evaluated whether an item was novel or whether it had appeared before or after a reset event that signified the start of a new block of trials. Participants responded "old" to items that were repeated within the current block and "new" to both novel items and items that had last appeared before the reset event (pseudonew items). Medial-temporal, prefrontal, and occipital regions responded to absolute novelty of the stimulus-they differentiated between novel items and previously seen items, but not between old and pseudonew items. Activation for pseudonew items in the frontopolar and parietal regions, in contrast, was intermediate between old and new items. The posterior cingulate cortex extending to precuneus was the only region that showed complete temporal resetting, and its activation reflected whether an item was new or old according to the task instructions regardless of its familiarity. There was also a significant Condition (old/pseudonew) × Familiarity (second/third presentations) interaction effect on behavioral and neural measures. For pseudonew items, greater familiarity decreased response accuracy, increased RTs, increased ACC activation, and increased functional connectivity between ACC and the left frontal pole. The reverse was observed for old items. On the basis of these results, we propose a theoretical framework in which temporal resetting relies on an episodic retrieval network that is modulated by cognitive control and conflict resolution.

  8. Granger causality stock market networks: Temporal proximity and preferential attachment

    NASA Astrophysics Data System (ADS)

    Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard

    2015-06-01

    The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.

  9. Activity clocks: spreading dynamics on temporal networks of human contact

    NASA Astrophysics Data System (ADS)

    Gauvin, Laetitia; Panisson, André; Cattuto, Ciro; Barrat, Alain

    2013-10-01

    Dynamical processes on time-varying complex networks are key to understanding and modeling a broad variety of processes in socio-technical systems. Here we focus on empirical temporal networks of human proximity and we aim at understanding the factors that, in simulation, shape the arrival time distribution of simple spreading processes. Abandoning the notion of wall-clock time in favour of node-specific clocks based on activity exposes robust statistical patterns in the arrival times across different social contexts. Using randomization strategies and generative models constrained by data, we show that these patterns can be understood in terms of heterogeneous inter-event time distributions coupled with heterogeneous numbers of events per edge. We also show, both empirically and by using a synthetic dataset, that significant deviations from the above behavior can be caused by the presence of edge classes with strong activity correlations.

  10. Efficient stepwise detection of communities in temporal networks

    NASA Astrophysics Data System (ADS)

    He, Jialin; Chen, Duanbing; Sun, Chongjing; Fu, Yan; Li, Wenjun

    2017-03-01

    In temporal networks, dynamic community detection is composed of two separate stages: (i) community detection at each time step; (ii) community matching across time steps. In the traditional methods, the community matching across time steps is based on nodes, which is time consuming. In this paper, we suggest a simple method which takes advantage of historic community information to detect dynamic communities. After dividing each community at previous time step into a few modules, we cannot only use these modules to detect communities at current time step but also map communities across time steps. Results on synthetic and real networks demonstrate that our method cannot only maintain the quality of communities but also improve the efficiency of community matching significantly.

  11. Roles of clustering properties for degree-mixing pattern networks

    NASA Astrophysics Data System (ADS)

    Yu, Pei; Guo, Qiang; Li, Ren-De; Han, Jing-Ti; Liu, Jian-Guo

    The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely C3 and C4, are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients C3(k) and C4(k) are reported, one can find that the square structures would increase as the degree k of nodes increasing in disassortative networks. At the same time, the Pearson coefficient p between the clustering coefficients C3(k) and C4(k) is calculated for networks with different assortative coefficients. The Pearson coefficient p changes from ‑0.5 to 0.98 as the assortative coefficient r increasing from ‑0.5 to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients and for networks with tunable assortative coefficients and find that the clustering coefficient increases from 0.0038 to 0.5952 while the clustering coefficient increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.

  12. Temporal Evolution Of Information In Neural Networks With Feedback

    NASA Astrophysics Data System (ADS)

    Giahi Saravani, Aram; Pitkow, Xaq

    2015-03-01

    Recurrent neural networks are pivotal for information processing in the brain. Here we analyze how the information content of a neural population is altered by dynamic feedback of a stimulus estimated from the network activity. We find that the temporal evolution of the Fisher information in the model with feedback is bounded by the Fisher information in a network of pure integrators. The available information in the feedback model saturates with a time constant and to a final level both determined by the match between the estimator weights and the feedback weights. This network then encodes signals specifically from either the beginning or the end of the stimulus presentation, depending on this match. These results are relevant to recent experimental measurements of psychophysical kernels indicating that earlier stimuli have a stronger influence on perceptual discriminations than more recent stimuli. We discuss consequences of this description for choice correlations, a measure of how individual neuronal responses relate to perceptual estimates. McNair Foundation, Baylor College of Medicine, Rice University.

  13. An in-depth longitudinal analysis of mixing patterns in a small scientific collaboration network

    SciTech Connect

    Rodriguez, Marko A; Pepe, Alberto

    2009-01-01

    Many investigations of scientific collaboration are based on large-scale statistical analyses of networks constructed from bibliographic repositories. These investigations often rely on a wealth of bibliographic data, but very little or no other information about the individuals in the network, and thus, fail to illustate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a small-scale network of scientific collaboration (N = 291) constructed from the bibliographic record of a research center involved in the development and application of sensor network technologies. We perform a preliminary analysis of selected structural properties of the network, computing its range, configuration and topology. We then support our preliminary statistical analysis with an in-depth temporal investigation of the assortativity mixing of these node characteristics: academic department, affiliation, position, and country of origin of the individuals in the network. Our qualitative analysis of mixing patterns offers clues as to the nature of the scientific community being modeled in relation to its organizational, disciplinary, institutional, and international arrangements of collaboration.

  14. Understanding Charge Transport in Mixed Networks of Semiconducting Carbon Nanotubes

    PubMed Central

    2016-01-01

    The ability to select and enrich semiconducting single-walled carbon nanotubes (SWNT) with high purity has led to a fast rise of solution-processed nanotube network field-effect transistors (FETs) with high carrier mobilities and on/off current ratios. However, it remains an open question whether it is best to use a network of only one nanotube species (monochiral) or whether a mix of purely semiconducting nanotubes but with different bandgaps is sufficient for high performance FETs. For a range of different polymer-sorted semiconducting SWNT networks, we demonstrate that a very small amount of narrow bandgap nanotubes within a dense network of large bandgap nanotubes can dominate the transport and thus severely limit on-currents and effective carrier mobility. Using gate-voltage-dependent electroluminescence, we spatially and spectrally reveal preferential charge transport that does not depend on nominal network density but on the energy level distribution within the network and carrier density. On the basis of these results, we outline rational guidelines for the use of mixed SWNT networks to obtain high performance FETs while reducing the cost for purification. PMID:26867006

  15. Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

    ERIC Educational Resources Information Center

    Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

    2009-01-01

    To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

  16. Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

    ERIC Educational Resources Information Center

    Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

    2009-01-01

    To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

  17. Temporal variation of competition and facilitation in mixed species forests in Central Europe.

    PubMed

    del Río, M; Schütze, G; Pretzsch, H

    2014-01-01

    Facilitation, reduced competition or increased competition can arise in mixed stands and become essential to the performance of these stands when compared to pure stands. Facilitation and over-yielding are widely held to prevail on poor sites, whereas neutral interactions or competition, leading to under-yielding of mixed versus pure stands, can occur on fertile sites. While previous studies have focused on the spatial variation of mixing effects, we examine the temporal variation of facilitation and competition and its effect on growth. The study is based on tree ring measurement on cores from increment borings from 559 trees of Norway spruce (Picea abies [L.] Karst.), European beech (Fagus sylvatica [L.]) and sessile oak (Quercus petraea (Matt.) Liebl.) in southern Germany, half of which were in pure stands and half in adjacent mixed stands. Mean basal area growth indices were calculated from tree ring measurements for pure and mixed stands for every species and site. The temporal variation, with positive correlations between species-specific growth indices during periods of low growth and neutral or negative correlations during periods of high growth, is more distinct in mixed than in neighbouring pure stands. We provide evidence that years with low growth trigger over-yielding of trees in mixed as opposed to pure stands, while years with high growth lead to under-yielding. We discuss the relevance of the results in terms of advancing our understanding and modelling of mixed stands, extension of the stress gradient hypothesis, and the performance of mixed versus pure stands in the face of climate change. © 2013 German Botanical Society and The Royal Botanical Society of the Netherlands.

  18. Assortative mixing in functional brain networks during epileptic seizures

    NASA Astrophysics Data System (ADS)

    Bialonski, Stephan; Lehnertz, Klaus

    2013-09-01

    We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients; and from time-resolved estimates of the assortativity coefficient, we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.

  19. Adaption of the temporal correlation coefficient calculation for temporal networks (applied to a real-world pig trade network).

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-01-01

    The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.

  20. Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy.

    PubMed

    Bernhardt, Boris C; Bernasconi, Neda; Hong, Seok-Jun; Dery, Sebastian; Bernasconi, Andrea

    2016-07-01

    Temporal lobe epilepsy (TLE) is the most frequent drug-resistant epilepsy in adults and commonly associated with variable degrees of mesiotemporal atrophy on magnetic resonance imaging (MRI). Analyses of inter-regional connectivity have unveiled disruptions in large-scale cortico-cortical networks; little is known about the topological organization of the mesiotemporal lobe, the limbic subnetwork central to the disorder. We generated covariance networks based on high-resolution MRI surface-shape descriptors of the hippocampus, entorhinal cortex, and amygdala in 134 TLE patients and 45 age- and sex-matched controls. Graph-theoretical analysis revealed increased path length and clustering in patients, suggesting a shift toward a more regularized arrangement; findings were reproducible after split-half assessment and across 2 parcellation schemes. Analysis of inter-regional correlations and module participation showed increased within-structure covariance, but decreases between structures, particularly with regards to the hippocampus and amygdala. While higher clustering possibly reflects topological consequences of axonal sprouting, decreases in interstructure covariance may be a consequence of disconnection within limbic circuitry. Preoperative network parameters, specifically the segregation of the ipsilateral hippocampus, predicted long-term seizure freedom after surgery.

  1. Incorporation of varying types of temporal data in a neural network

    NASA Technical Reports Server (NTRS)

    Cohen, M. E.; Hudson, D. L.

    1992-01-01

    Most neural network models do not specifically deal with temporal data. Handling of these variables is complicated by the different uses to which temporal data are put, depending on the application. Even within the same application, temporal variables are often used in a number of different ways. In this paper, types of temporal data are discussed, along with their implications for approximate reasoning. Methods for integrating approximate temporal reasoning into existing neural network structures are presented. These methods are illustrated in a medical application for diagnosis of graft-versus-host disease which requires the use of several types of temporal data.

  2. Bridging Minds: A Mixed Methodology to Assess Networked Flow.

    PubMed

    Galimberti, Carlo; Chirico, Alice; Brivio, Eleonora; Mazzoni, Elvis; Riva, Giuseppe; Milani, Luca; Gaggioli, Andrea

    2015-01-01

    The main goal of this contribution is to present a methodological framework to study Networked Flow, a bio-psycho-social theory of collective creativity applying it on creative processes occurring via a computer network. First, we draw on the definition of Networked Flow to identify the key methodological requirements of this model. Next, we present the rationale of a mixed methodology, which aims at combining qualitative, quantitative and structural analysis of group dynamics to obtain a rich longitudinal dataset. We argue that this integrated strategy holds potential for describing the complex dynamics of creative collaboration, by linking the experiential features of collaborative experience (flow, social presence), with the structural features of collaboration dynamics (network indexes) and the collaboration outcome (the creative product). Finally, we report on our experience with using this methodology in blended collaboration settings (including both face-to-face and virtual meetings), to identify open issues and provide future research directions.

  3. Finding community structures in complex networks using mixed integer optimisation

    NASA Astrophysics Data System (ADS)

    Xu, G.; Tsoka, S.; Papageorgiou, L. G.

    2007-11-01

    The detection of community structure has been used to reveal the relationships between individual objects and their groupings in networks. This paper presents a mathematical programming approach to identify the optimal community structures in complex networks based on the maximisation of a network modularity metric for partitioning a network into modules. The overall problem is formulated as a mixed integer quadratic programming (MIQP) model, which can then be solved to global optimality using standard optimisation software. The solution procedure is further enhanced by developing special symmetry-breaking constraints to eliminate equivalent solutions. It is shown that additional features such as minimum/maximum module size and balancing among modules can easily be incorporated in the model. The applicability of the proposed optimisation-based approach is demonstrated by four examples. Comparative results with other approaches from the literature show that the proposed methodology has superior performance while global optimum is guaranteed.

  4. Mapping the convergent temporal epileptic network in left and right temporal lobe epilepsy.

    PubMed

    Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Qiu, Shijun; Hu, Dewen

    2017-02-03

    Left and right mesial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS) exhibits similar functional and clinical dysfunctions, such as depressive mood and emotional dysregulation, implying that the left and right mTLE may share a common network substrate. However, the convergent anatomical network disruption between the left and right HS remains largely uncharacterized. This study aimed to investigate whether the left and right mTLE share a similar anatomical network. We examined 43 (22 left, 21 right) mTLE patients with HS and 39 healthy controls using diffusion tensor imaging. Machine learning approaches were applied to extract the abnormal anatomical connectivity patterns in both the left and right mTLE. The left and right mTLE showed that 28 discriminating connections were exactly the same when compared to the controls. The same 28 connections showed high discriminating power in comparisons of the left mTLE versus controls (91.7%) and the right mTLE versus controls (90.0%); however, these connections failed to discriminate the left from the right mTLE. These discriminating connections, which were diminished both in the left and right mTLE, were primarily located in the limbic-frontal network, partially agreeing with the limbic-frontal dysregulation model of depression. These findings suggest that left and right mTLE share a convergent circuit, which may account for the mood and emotional deficits in mTLE and may suggest the neuropathological mechanisms underlying the comorbidity of depression and mTLE.

  5. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Cheung, Kar-Ming; Lee, Charles H.

    2012-01-01

    We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

  6. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Cheung, Kar-Ming; Lee, Charles H.

    2012-01-01

    We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

  7. Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

    PubMed

    Hill, Jonathon T; Demarest, Bradley; Gorsi, Bushra; Smith, Megan; Yost, H Joseph

    2017-10-01

    During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5, and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation. © 2017. Published by The Company of Biologists Ltd.

  8. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  9. The effects of temporal variability of mixed layer depth on primary productivity around Bermuda

    NASA Technical Reports Server (NTRS)

    Bissett, W. Paul; Meyers, Mark B.; Walsh, John J.; Mueller-Karger, Frank E.

    1994-01-01

    Temporal variations in primary production and surface chlorophyll concentrations, as measured by ship and satellite around Bermuda, were simulated with a numerical model. In the upper 450 m of the water column, population dynamics of a size-fractionated phytoplankton community were forced by daily changes of wind, light, grazing stress, and nutrient availability. The temporal variations of production and chlorophyll were driven by changes in nutrient introduction to the euphotic zone due to both high- and low-frequency changes of the mixed layer depth within 32 deg-34 deg N, 62 deg-64 deg W between 1979 and 1984. Results from the model derived from high-frequency (case 1) changes in the mixed layer depth showed variations in primary production and peak chlorophyll concentrations when compared with results from the model derived from low-frequency (case 2) mixed layer depth changes. Incorporation of size-fractionated plankton state variables in the model led to greater seasonal resolution of measured primary production and vertical chlorophyll profiles. The findings of this study highlight the possible inadequacy of estimating primary production in the sea from data of low-frequency temporal resolution and oversimplified biological simulations.

  10. The effects of temporal variability of mixed layer depth on primary productivity around Bermuda

    NASA Technical Reports Server (NTRS)

    Bissett, W. Paul; Meyers, Mark B.; Walsh, John J.; Mueller-Karger, Frank E.

    1994-01-01

    Temporal variations in primary production and surface chlorophyll concentrations, as measured by ship and satellite around Bermuda, were simulated with a numerical model. In the upper 450 m of the water column, population dynamics of a size-fractionated phytoplankton community were forced by daily changes of wind, light, grazing stress, and nutrient availability. The temporal variations of production and chlorophyll were driven by changes in nutrient introduction to the euphotic zone due to both high- and low-frequency changes of the mixed layer depth within 32 deg-34 deg N, 62 deg-64 deg W between 1979 and 1984. Results from the model derived from high-frequency (case 1) changes in the mixed layer depth showed variations in primary production and peak chlorophyll concentrations when compared with results from the model derived from low-frequency (case 2) mixed layer depth changes. Incorporation of size-fractionated plankton state variables in the model led to greater seasonal resolution of measured primary production and vertical chlorophyll profiles. The findings of this study highlight the possible inadequacy of estimating primary production in the sea from data of low-frequency temporal resolution and oversimplified biological simulations.

  11. Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy

    PubMed Central

    Vega-Zelaya, Lorena; Pastor, Jesús; de Sola, Rafael G.; Ortega, Guillermo J.

    2015-01-01

    Objective The current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE). Methods Scalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear–Pearson correlation–and non-linear–phase synchronization–measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy. Results Changes in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained. Conclusions In TLE patients, the transition to the ictal stage is accompanied by

  12. Exploring the structure and function of temporal networks with dynamic graphlets

    PubMed Central

    Hulovatyy, Y.; Chen, H.; Milenković, T.

    2015-01-01

    Motivation: With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships. Results: We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging. Availability and implementation: http://www.nd.edu/∼cone/DG. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26072480

  13. Selective frontal, parietal, and temporal networks in generalized seizures.

    PubMed

    Blumenfeld, Hal; Westerveld, Michael; Ostroff, Robert B; Vanderhill, Susan D; Freeman, Jason; Necochea, Alexandro; Uranga, Paula; Tanhehco, Tasha; Smith, Arien; Seibyl, John P; Stokking, Rik; Studholme, Colin; Spencer, Susan S; Zubal, I George

    2003-08-01

    Are "generalized" seizures truly generalized? Generalized tonic-clonic seizures are classified as either secondarily generalized with local onset or primarily generalized, without known focal onset. In both types of generalized seizures widespread regions of the nervous system engage in abnormally synchronous and high-frequency neuronal firing. However, emerging evidence suggests that all neurons are not homogeneously involved; specific nodes within the network may be crucial for the propagation and behavioral manifestations of generalized tonic-clonic seizures. Study of human tonic-clonic seizures has been limited by problems with patient movement and variable seizure types. To circumvent these problems, we imaged generalized tonic-clonic seizures during electroconvulsive therapy, in which seizure type and timing are well controlled. (99m)Tc-hexamethylpropylene amine oxime injections during seizures provide a "snapshot" of cerebral blood flow that can be imaged by single photon emission computed tomography (SPECT) after seizure termination. Here we show that focal regions of frontal and parietal association cortex show the greatest relative signal increases. Involvement of the higher-order association cortex may explain the profound impairment of consciousness seen in generalized seizures. In addition, focal involvement of the dominant temporal lobe was associated with impaired retrograde verbal memory. Similar focal increases were also seen in imaging of spontaneous secondarily generalized tonic-clonic seizures. Relative sparing of many brain regions during both spontaneous and induced seizures suggests that specific networks may be more important than others in so-called generalized seizures.

  14. Temporal prediction of epidemic patterns in community networks

    NASA Astrophysics Data System (ADS)

    Peng, Xiao-Long; Small, Michael; Xu, Xin-Jian; Fu, Xinchu

    2013-11-01

    Most previous studies of epidemic dynamics on complex networks suppose that the disease will eventually stabilize at either a disease-free state or an endemic one. In reality, however, some epidemics always exhibit sporadic and recurrent behaviour in one region because of the invasion from an endemic population elsewhere. In this paper we address this issue and study a susceptible-infected-susceptible epidemiological model on a network consisting of two communities, where the disease is endemic in one community but alternates between outbreaks and extinctions in the other. We provide a detailed characterization of the temporal dynamics of epidemic patterns in the latter community. In particular, we investigate the time duration of both outbreak and extinction, and the time interval between two consecutive inter-community infections, as well as their frequency distributions. Based on the mean-field theory, we theoretically analyse these three timescales and their dependence on the average node degree of each community, the transmission parameters and the number of inter-community links, which are in good agreement with simulations, except when the probability of overlaps between successive outbreaks is too large. These findings aid us in better understanding the bursty nature of disease spreading in a local community, and thereby suggesting effective time-dependent control strategies.

  15. Temporal variability of diapycnal mixing in the northern South China Sea

    NASA Astrophysics Data System (ADS)

    Sun, Hui; Yang, Qingxuan; Zhao, Wei; Liang, Xinfeng; Tian, Jiwei

    2016-12-01

    Temporal variability of diapycnal mixing over 7 months in the northern South China Sea was examined based on McLane Moored Profiler observations from 850 to 2200 m by employing a finescale parameterization. Intensified diffusivity exceeding the order of 10-3 m2/s in magnitude was found over the first half of October 2014, and from 2 December 2014 to 21 January 2015 (a typical wintertime). Strong internal tides and winds in winter were the likely candidates for the high-level diapycnal mixing in winter. As for the enhanced mixing during October 2014, we suspect the generation of near-bottom near-inertial waves through the interaction of mesoscale eddies and unique bottom topography was the cause.

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

    PubMed Central

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

    2014-01-01

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

  17. Mixed Transportation Network Design under a Sustainable Development Perspective

    PubMed Central

    Qin, Jin; Ni, Ling-lin; Shi, Feng

    2013-01-01

    A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%. PMID:23476142

  18. Mixed transportation network design under a sustainable development perspective.

    PubMed

    Qin, Jin; Ni, Ling-lin; Shi, Feng

    2013-01-01

    A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%.

  19. Isotopic constraints on water source mixing, network leakage and contamination in an urban groundwater system.

    PubMed

    Grimmeisen, F; Lehmann, M F; Liesch, T; Goeppert, N; Klinger, J; Zopfi, J; Goldscheider, N

    2017-04-01

    Water supply in developing countries is prone to large water losses due to leaky distribution networks and defective sewers, which may affect groundwater quality and quantity in urban areas and result in complex subsurface mixing dynamics. In this study, a multi-stable isotope approach was used to investigate spatiotemporal fluctuations of surface and sub-surface water source partitioning and mixing, and to assess nitrogen (N) contamination in the urban water cycle of As-Salt, Jordan. Water import from the King Abdullah Canal (KAC), mains waters from the network, and wastewater are characterized by distinct isotopic signatures, which allowed us to quantify city effluents into the groundwater. Temporal variations in isotopic signatures of polluted groundwater are explained by seasonally fluctuating inflow, and dilution by water that originates from Lake Tiberias and enters the urban water cycle via the KAC. Isotopic analysis (N and O) and comparison between groundwater nitrate and nitrate from mains water, water imports and wastewater confirmed that septic waste from leaky sewers is the main contributor of nitrate contamination. The nitrate of strongly contaminated groundwater was characterized by highest δ(15)NNO3 values (13.3±1.8‰), whereas lowest δ(15)NNO3 values were measured in unpolluted groundwater (6.9‰). Analogously, nitrate concentration and isotopic ratios were used for source partitioning and qualitatively confirmed δDH2O and δ(18)OH2O-based estimates. Dual water isotope endmember mixing calculations suggest that city effluents from leaky networks and sewers contribute 30-64% to the heavily polluted groundwater. Ternary mixing calculations including also chloride revealed that 5-18% of the polluted groundwater is wastewater. Up to two thirds of the groundwater originates from mains, indicating excessive water loss from the network, and calling for improved water supply management. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. The amygdalo-nigrostriatal network is critical for an optimal temporal performance

    PubMed Central

    Es-seddiqi, Mouna; El Massioui, Nicole; Samson, Nathalie; Brown, Bruce L.

    2016-01-01

    The amygdalo-nigrostriatal (ANS) network plays an essential role in enhanced attention to significant events. Interval timing requires attention to temporal cues. We assessed rats having a disconnected ANS network, due to contralateral lesions of the medial central nucleus of the amygdala (CEm) and dopaminergic afferents to the lateral striatum, as compared to controls (sham and ipsilateral lesions of CEm and dopaminergic afferents to LS) in a temporal bisection task. ANS disconnection induced poorer temporal precision and increased response latencies to a short duration. The present results reveal a role of the ANS network in temporal processing. PMID:26884227

  1. Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period.

    PubMed

    Dutta, Bhagat Lal; Ezanno, Pauline; Vergu, Elisabeta

    2014-11-01

    A good knowledge of the specificities of the animal trade network is highly valuable to better control pathogen spread on a large regional to transnational scale. Because of their temporal dynamical nature, studying multi-annual datasets is particularly needed to investigate whether structural patterns are stable over the years. In this study, we analysed the French cattle movement network from 2005 to 2009 for different spatial granularities and temporal windows, with the three-fold objective of exploring temporal variations of the main network characteristics, computing proxies for pathogen spread on this network, which accounts for its time-varying properties and identifying specificities related to the main types of animals and farms (dairy versus beef). Network properties did not qualitatively vary among different temporal and spatial granularities. About 40% of the holdings and 80% of the communes were directly interconnected. The width of the aggregation time window barely impacted normalised distributions of indicators. A period of 8-16 weeks would suffice for robust estimation of their main trends, whereas longer periods would provide more details on tails. The dynamic nature of the network could be seen through the small overlap between consecutive networks with 65% of common active nodes for only 3% of common links over 2005-2009. To control pathogen spread on such a network, by reducing the largest strongly connected component by more than 80%, movements should be prevented from 1 to 5% of the holdings with the highest centrality in the previous year network. The analysis of breed-wise and herd-wise subnetworks, dairy, beef and mixed, reveals similar trends in temporal variation of average indicators and their distributions. The link-based backbones of beef subnetworks seem to be more stable over time than those of other subnetworks. At a regional scale, node reachability accounting for time-respecting paths, as proxy of epidemic burden, is greater for

  2. A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

    NASA Astrophysics Data System (ADS)

    Li, Xiaodong; Ling, Feng; Du, Yun; Feng, Qi; Zhang, Yihang

    2014-07-01

    The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial-temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the

  3. Spatial-temporal modeling for electrical impedance imaging of a mixing process

    NASA Astrophysics Data System (ADS)

    West, R. M.; Meng, S.; Aykroyd, R. G.; Williams, R. A.

    2005-07-01

    The use of electrical tomography techniques for process visualization and investigation is a well-known example of a nonlinear, ill-posed, and underdetermined inverse problem. Hence stable and reliable solution is not possible using measured data alone, but requires regularization through prior information. The rôle of a Bayesian approach is therefore of fundamental importance, and when coupled with Markov chain Monte Carlo (MCMC) sampling, it can provide valuable statistical information about solution behavior and reliability, which is in contrast to most current approaches which provide only a single image reconstruction with unquantified errors. For many applications of dynamic electrical impedance imaging, some degree of both spatial and temporal smoothness is expected. Often temporal smoothness is ignored and only spatial smoothing is used. In the current application, the addition of an aliquot to a mixing vessel, smoothness is not appropriate prior information. Instead an aliquot prior is proposed, parameterized in terms of location, size, and resistivity. This approach leads to data-driven and adaptive smoothing, in contrast to the more usual global smoothing of standard regularization methods. Of further interest is the inclusion of temporal prior information: it is known that the aliquot moves and disperses in a specific manner. With this added temporal information, imaging is improved as are derived process parameters.

  4. Spatio-temporal study of non-degenerate two-wave mixing in bacteriorhodopsin films.

    PubMed

    Blaya, Salvador; González, Alejandro; Acebal, Pablo; Carretero, Luis

    2016-10-31

    A spatio-temporal analysis of non-degenerate two-wave mixing in a saturable absorber, such as bacteriorhodopsin (bR) film, is performed. To do this, a theoretical model describing the temporal variation of the intensities is developed by taking into account the dielectric constant as a function of bR population. A good agreement between theory and experimental measurements is obtained. Thus, the dependence of the optical gain and the main dielectric constant parameters are studied at different intensities and frequencies. As a result, the best intensity-frequency zones where higher coupling is reached are proposed, and it is also demonstrated that non-uniform patterns, which evolve over time as a function of frequency difference, can be observed.

  5. Temporal integration by stochastic recurrent network dynamics with bimodal neurons.

    PubMed

    Okamoto, Hiroshi; Isomura, Yoshikazu; Takada, Masahiko; Fukai, Tomoki

    2007-06-01

    Temporal integration of externally or internally driven information is required for a variety of cognitive processes. This computation is generally linked with graded rate changes in cortical neurons, which typically appear during a delay period of cognitive task in the prefrontal and other cortical areas. Here, we present a neural network model to produce graded (climbing or descending) neuronal activity. Model neurons are interconnected randomly by AMPA-receptor-mediated fast excitatory synapses and are subject to noisy background excitatory and inhibitory synaptic inputs. In each neuron, a prolonged afterdepolarizing potential follows every spike generation. Then, driven by an external input, the individual neurons display bimodal rate changes between a baseline state and an elevated firing state, with the latter being sustained by regenerated afterdepolarizing potentials. When the variance of background input and the uniform weight of recurrent synapses are adequately tuned, we show that stochastic noise and reverberating synaptic input organize these bimodal changes into a sequence that exhibits graded population activity with a nearly constant slope. To test the validity of the proposed mechanism, we analyzed the graded activity of anterior cingulate cortex neurons in monkeys performing delayed conditional Go/No-go discrimination tasks. The delay-period activities of cingulate neurons exhibited bimodal activity patterns and trial-to-trial variability that are similar to those predicted by the proposed model.

  6. An Algorithm for the Mixed Transportation Network Design Problem.

    PubMed

    Liu, Xinyu; Chen, Qun

    2016-01-01

    This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately.

  7. An Algorithm for the Mixed Transportation Network Design Problem

    PubMed Central

    Liu, Xinyu; Chen, Qun

    2016-01-01

    This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately. PMID:27626803

  8. Memory and betweenness preference in temporal networks induced from time series

    NASA Astrophysics Data System (ADS)

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-02-01

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

  9. Memory and betweenness preference in temporal networks induced from time series

    PubMed Central

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-01-01

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems. PMID:28157194

  10. Memory and betweenness preference in temporal networks induced from time series.

    PubMed

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-02-03

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

  11. Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution

    NASA Astrophysics Data System (ADS)

    Valdano, Eugenio; Poletto, Chiara; Colizza, Vittoria

    2015-12-01

    The epidemic threshold of a spreading process indicates the condition for the occurrence of the wide spreading regime, thus representing a predictor of the network vulnerability to the epidemic. Such threshold depends on the natural history of the disease and on the pattern of contacts of the network with its time variation. Based on the theoretical framework introduced in [E. Valdano, L. Ferreri, C. Poletto, V. Colizza, Phys. Rev. X 5, 21005 (2015)] for a susceptible-infectious-susceptible model, we formulate here an infection propagator approach to compute the epidemic threshold accounting for more realistic effects regarding a varying force of infection per contact, the presence of immunity, and a limited time resolution of the temporal network. We apply the approach to two temporal network models and an empirical dataset of school contacts. We find that permanent or temporary immunity do not affect the estimation of the epidemic threshold through the infection propagator approach. Comparisons with numerical results show the good agreement of the analytical predictions. Aggregating the temporal network rapidly deteriorates the predictions, except for slow diseases once the heterogeneity of the links is preserved. Weight-topology correlations are found to be the critical factor to be preserved to improve accuracy in the prediction.

  12. Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing.

    PubMed

    Geier, Christian; Lehnertz, Klaus; Bialonski, Stephan

    2015-01-01

    We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

  13. Impairment-constrained network design in mixed line rate and flexible-grid optical networks

    NASA Astrophysics Data System (ADS)

    Xie, Weisheng

    Mixed line rate (MLR) and flexible-grid optical networks are two promising network paradigms for next generation optical networks. In MLR optical networks, different optical channels may operate at different line rates and use the same amount of spectrum. In flexible-grid optical networks, besides different line rates, different optical channels can use different amount of spectrum. In both MLR and flexible-grid optical networks, the physical layer impairments will impact the signal reachability and will require regenerator placement to restore the signal quality. Different line rates and modulation formats suffer from different levels of impairments, and thus have different reachabilities. In this dissertation, we study multiple network design problems with impairment constraints for both MLR and flexible-grid optical networks. We first study regenerator site (RS) selection problems in MLR optical networks. Given a network topology, set of requests, and different line rates' reachabilities, the problem is to select the minimum number of nodes in the network as RSs. We divide the topic into two separate research problems depending on whether routing is fixed or flexible. Energy efficiency is an important factor that will impact the operational expenditure of a telecom network. When designing the routing and wavelength assignment approach for a set of connection requests, the placement of regenerators needs to be considered in order to increase energy efficiency. In this work, we study how to place the minimum number of regenerators in MLR optical networks, while satisfying all the requests. Virtual optical network (VON) mapping plays a vital role in optical network virtualization. When mapping VONs, it is necessary to provision backup resources to guarantee survivability. Thus, we consider how to map VONs that can survive single link failures in flexible-grid optical networks. The objective is to minimize network equipment cost, including regenerators. We also

  14. Temporal and spatial allocation of motor preparation during a mixed-strategy game.

    PubMed

    Mikulić, Areh; Dorris, Michael C

    2008-10-01

    Adopting a mixed response strategy in competitive situations can prevent opponents from exploiting predictable play. What drives stochastic action selection is unclear given that choice patterns suggest that, on average, players are indifferent to available options during mixed-strategy equilibria. To gain insight into this stochastic selection process, we examined how motor preparation was allocated during a mixed-strategy game. If selection processes on each trial reflect a global indifference between options, then there should be no bias in motor preparation (unbiased preparation hypothesis). If, however, differences exist in the desirability of options on each trial then motor preparation should be biased toward the preferred option (biased preparation hypothesis). We tested between these alternatives by examining how saccade preparation was allocated as human subjects competed against an adaptive computer opponent in an oculomotor version of the game "matching pennies." Subjects were free to choose between two visual targets using a saccadic eye movement. Saccade preparation was probed by occasionally flashing a visual distractor at a range of times preceding target presentation. The probability that a distractor would evoke a saccade error, and when it failed to do so, the probability of choosing each of the subsequent targets quantified the temporal and spatial evolution of saccade preparation, respectively. Our results show that saccade preparation became increasingly biased as the time of target presentation approached. Specifically, the spatial locus to which saccade preparation was directed varied from trial to trial, and its time course depended on task timing.

  15. Mixing as a driver of temporal variations in river hydrochemistry: 1. Insights from conservative tracers in the Andes-Amazon transition

    NASA Astrophysics Data System (ADS)

    Torres, Mark A.; Baronas, J. Jotautas; Clark, Kathryn E.; Feakins, Sarah J.; West, A. Joshua

    2017-04-01

    The response of hillslope processes to changes in precipitation may drive the observed changes in the solute geochemistry of rivers with discharge. This conjecture is most robust when variations in the key environmental factors that affect hillslope processes (e.g., lithology, erosion rate, and climate) are minimal across a river's catchment area. For rivers with heterogenous catchments, temporal variations in the relative contributions of different tributary subcatchments may modulate variations in solute geochemistry with runoff. In the absence of a dense network of hydrologic gauging stations, alternative approaches are required to distinguish between the different drivers of temporal variability in river solute concentrations. In this contribution, we apportion the water and solute fluxes of a reach of the Madre de Dios River (Peru) between its four major tributary subcatchments during two sampling campaigns (wet and dry seasons) using spatial variations in conservative tracers. Guided by the results of a mixing model, we identify temporal variations in solute concentrations of the main stem Madre de Dios that are due to changes in the relative contributions of each tributary. Our results suggest that variations in tributary mixing are, in part, responsible for the observed concentration-discharge (C-Q) relationships. The implications of these results are further explored by reanalyzing previously published C-Q data from this region, developing a theoretical model of tributary mixing, and, in a companion paper, comparing the C-Q behavior of a suite of major and trace elements in the Madre de Dios River system.

  16. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

    PubMed Central

    Carvalho, Fabiana M.; Chaim, Khallil T.; Sanchez, Tiago A.; de Araujo, Draulio B.

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  17. Mixed Criticality Scheduling for Industrial Wireless Sensor Networks

    PubMed Central

    Jin, Xi; Xia, Changqing; Xu, Huiting; Wang, Jintao; Zeng, Peng

    2016-01-01

    Wireless sensor networks (WSNs) have been widely used in industrial systems. Their real-time performance and reliability are fundamental to industrial production. Many works have studied the two aspects, but only focus on single criticality WSNs. Mixed criticality requirements exist in many advanced applications in which different data flows have different levels of importance (or criticality). In this paper, first, we propose a scheduling algorithm, which guarantees the real-time performance and reliability requirements of data flows with different levels of criticality. The algorithm supports centralized optimization and adaptive adjustment. It is able to improve both the scheduling performance and flexibility. Then, we provide the schedulability test through rigorous theoretical analysis. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones. PMID:27589741

  18. Detection and localization of change points in temporal networks with the aid of stochastic block models

    NASA Astrophysics Data System (ADS)

    De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

    2016-11-01

    A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

  19. Modelling temporal networks of human face-to-face contacts with public activity and individual reachability

    NASA Astrophysics Data System (ADS)

    Zhang, Yi-Qing; Cui, Jing; Zhang, Shu-Min; Zhang, Qi; Li, Xiang

    2016-02-01

    Modelling temporal networks of human face-to-face contacts is vital both for understanding the spread of airborne pathogens and word-of-mouth spreading of information. Although many efforts have been devoted to model these temporal networks, there are still two important social features, public activity and individual reachability, have been ignored in these models. Here we present a simple model that captures these two features and other typical properties of empirical face-to-face contact networks. The model describes agents which are characterized by an attractiveness to slow down the motion of nearby people, have event-triggered active probability and perform an activity-dependent biased random walk in a square box with periodic boundary. The model quantitatively reproduces two empirical temporal networks of human face-to-face contacts which are testified by their network properties and the epidemic spread dynamics on them.

  20. A study of the temporal robustness of the growing global container-shipping network

    PubMed Central

    Wang, Nuo; Wu, Nuan; Dong, Ling-ling; Yan, Hua-kun; Wu, Di

    2016-01-01

    Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately −0.51% for random errors and −12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies. PMID:27713549

  1. A study of the temporal robustness of the growing global container-shipping network

    NASA Astrophysics Data System (ADS)

    Wang, Nuo; Wu, Nuan; Dong, Ling-Ling; Yan, Hua-Kun; Wu, Di

    2016-10-01

    Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately ‑0.51% for random errors and ‑12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies.

  2. A study of the temporal robustness of the growing global container-shipping network.

    PubMed

    Wang, Nuo; Wu, Nuan; Dong, Ling-Ling; Yan, Hua-Kun; Wu, Di

    2016-10-07

    Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies' data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately -0.51% for random errors and -12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies.

  3. Mixed strategy and coevolution dynamics in social networks

    NASA Astrophysics Data System (ADS)

    Zhong, Weicai; Abbass, Hussein A.; Bender, Axel; Liu, Jing

    2011-01-01

    We investigate coevolution dynamics of both individual strategies and social ties as they adapt within the snowdrift game with mixed strategies. We propose a partner selection mechanism based on the concept of trust. Here trust is considered an instrument for an individual both selecting the right partners and being selected amongst other potential partners. Based on her local views of the system, the focal individual dismisses the link from the partner with the lowest trust and rewires to the partner’s partner with the highest trust. It is shown that such a trust-based partner switching mechanism favors the emergence of cooperators. Furthermore, when the number of an individual’s partners is restricted (which is a metaphor of limited capacities and capabilities of an individual in real environments), surprising assortative mixing patterns are formed in the emerging network and change the network’s degree distribution from a power-law distribution to an asymmetrically U-shaped distribution. This plays a leading role in preventing global avalanches triggered by perturbations acting on the state of the highly connected individuals.

  4. Statistical mechanics of structural and temporal credit assignment effects on learning in neural networks.

    PubMed

    Saito, Hiroshi; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato

    2011-05-01

    Neural networks can learn flexible input-output associations by changing their synaptic weights. The representational performance and learning dynamics of neural networks are intensively studied in several fields. Neural networks face the "credit assignment problem" in situations in which only incomplete performance evaluations are available. The credit assignment problem is that a network should assign credit or blame for its behaviors according to the contribution to the network performance. In reinforcement learning, a scalar evaluation signal is delivered to a network. The two main types of credit assignment problems in reinforcement learning are structural and temporal, that is, which parameters of the network (structural) and which past network activities (temporal) are related to an evaluation signal given from an environment. In this study, we apply statistical mechanical analysis to the learning processes in a simple neural network model to clarify the effects of two kinds of credit assignments and their interactions. Our model is based on node perturbation learning with eligibility trace. Node perturbation is a stochastic gradient learning method that can solve structural credit assignment problems by introducing a perturbation into the system output. The eligibility trace preserves the past network activities with a temporal credit to deal with the delay of an instruction signal. We show that both credit assignment effects mutually interact and the optimal time constant of the eligibility trace varies not only for the evaluation delay but also the network size.

  5. Estimating spatial and temporal components of variation in count data using negative binomial mixed models

    USGS Publications Warehouse

    Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.

    2013-01-01

    Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

  6. Fast network oscillations in vitro exhibit a slow decay of temporal auto-correlations.

    PubMed

    Poil, Simon-Shlomo; Jansen, Rick; van Aerde, Karlijn; Timmerman, Jaap; Brussaard, Arjen B; Mansvelder, Huibert D; Linkenkaer-Hansen, Klaus

    2011-08-01

    Ongoing neuronal oscillations in vivo exhibit non-random amplitude fluctuations as reflected in a slow decay of temporal auto-correlations that persist for tens of seconds. Interestingly, the decay of auto-correlations is altered in several brain-related disorders, including epilepsy, depression and Alzheimer's disease, suggesting that the temporal structure of oscillations depends on intact neuronal networks in the brain. Whether structured amplitude modulation occurs only in the intact brain or whether isolated neuronal networks can also give rise to amplitude modulation with a slow decay is not known. Here, we examined the temporal structure of cholinergic fast network oscillations in acute hippocampal slices. For the first time, we show that a slow decay of temporal correlations can emerge from synchronized activity in isolated hippocampal networks from mice, and is maximal at intermediate concentrations of the cholinergic agonist carbachol. Using zolpidem, a positive allosteric modulator of GABA(A) receptor function, we found that increased inhibition leads to longer oscillation bursts and more persistent temporal correlations. In addition, we asked if these findings were unique for mouse hippocampus, and we therefore analysed cholinergic fast network oscillations in rat prefrontal cortex slices. We observed significant temporal correlations, which were similar in strength to those found in mouse hippocampus and human cortex. Taken together, our data indicate that fast network oscillations with temporal correlations can be induced in isolated networks in vitro in different species and brain areas, and therefore may serve as model systems to investigate how altered temporal correlations in disease may be rescued with pharmacology. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  7. Research on mixed network architecture collaborative application model

    NASA Astrophysics Data System (ADS)

    Jing, Changfeng; Zhao, Xi'an; Liang, Song

    2009-10-01

    When facing complex requirements of city development, ever-growing spatial data, rapid development of geographical business and increasing business complexity, collaboration between multiple users and departments is needed urgently, however conventional GIS software (such as Client/Server model or Browser/Server model) are not support this well. Collaborative application is one of the good resolutions. Collaborative application has four main problems to resolve: consistency and co-edit conflict, real-time responsiveness, unconstrained operation, spatial data recoverability. In paper, application model called AMCM is put forward based on agent and multi-level cache. AMCM can be used in mixed network structure and supports distributed collaborative. Agent is an autonomous, interactive, initiative and reactive computing entity in a distributed environment. Agent has been used in many fields such as compute science and automation. Agent brings new methods for cooperation and the access for spatial data. Multi-level cache is a part of full data. It reduces the network load and improves the access and handle of spatial data, especially, in editing the spatial data. With agent technology, we make full use of its characteristics of intelligent for managing the cache and cooperative editing that brings a new method for distributed cooperation and improves the efficiency.

  8. Mapping the neuropsychological profile of temporal lobe epilepsy using cognitive network topology and graph theory.

    PubMed

    Kellermann, Tanja S; Bonilha, Leonardo; Eskandari, Ramin; Garcia-Ramos, Camille; Lin, Jack J; Hermann, Bruce P

    2016-10-01

    Normal cognitive function is defined by harmonious interaction among multiple neuropsychological domains. Epilepsy has a disruptive effect on cognition, but how diverse cognitive abilities differentially interact with one another compared with healthy controls (HC) is unclear. This study used graph theory to analyze the community structure of cognitive networks in adults with temporal lobe epilepsy (TLE) compared with that in HC. Neuropsychological assessment was performed in 100 patients with TLE and 82 HC. For each group, an adjacency matrix was constructed representing pair-wise correlation coefficients between raw scores obtained in each possible test combination. For each cognitive network, each node corresponded to a cognitive test; each link corresponded to the correlation coefficient between tests. Global network structure, community structure, and node-wise graph theory properties were qualitatively assessed. The community structure in patients with TLE was composed of fewer, larger, more mixed modules, characterizing three main modules representing close relationships between the following: 1) aspects of executive function (EF), verbal and visual memory, 2) speed and fluency, and 3) speed, EF, perception, language, intelligence, and nonverbal memory. Conversely, controls exhibited a relative division between cognitive functions, segregating into more numerous, smaller modules consisting of the following: 1) verbal memory, 2) language, perception, and intelligence, 3) speed and fluency, and 4) visual memory and EF. Overall node-wise clustering coefficient and efficiency were increased in TLE. Adults with TLE demonstrate a less clear and poorly structured segregation between multiple cognitive domains. This panorama suggests a higher degree of interdependency across multiple cognitive domains in TLE, possibly indicating compensatory mechanisms to overcome functional impairments. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.

    PubMed

    Strohmeier, Daniel; Bekhti, Yousra; Haueisen, Jens; Gramfort, Alexandre

    2016-10-01

    Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach based on two MEG data sets. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support recovery, and stability.

  10. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    NASA Astrophysics Data System (ADS)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  11. The Amygdalo-Nigrostriatal Network Is Critical for an Optimal Temporal Performance

    ERIC Educational Resources Information Center

    Es-seddiqi, Mouna; El Massioui, Nicole; Samson, Nathalie; Brown, Bruce L.; Doyère, Valérie

    2016-01-01

    The amygdalo-nigrostriatal (ANS) network plays an essential role in enhanced attention to significant events. Interval timing requires attention to temporal cues. We assessed rats having a disconnected ANS network, due to contralateral lesions of the medial central nucleus of the amygdala (CEm) and dopaminergic afferents to the lateral striatum,…

  12. The Amygdalo-Nigrostriatal Network Is Critical for an Optimal Temporal Performance

    ERIC Educational Resources Information Center

    Es-seddiqi, Mouna; El Massioui, Nicole; Samson, Nathalie; Brown, Bruce L.; Doyère, Valérie

    2016-01-01

    The amygdalo-nigrostriatal (ANS) network plays an essential role in enhanced attention to significant events. Interval timing requires attention to temporal cues. We assessed rats having a disconnected ANS network, due to contralateral lesions of the medial central nucleus of the amygdala (CEm) and dopaminergic afferents to the lateral striatum,…

  13. Temporal Reliability and Lateralization of the Resting-State Language Network

    PubMed Central

    Zou, Qihong; Wang, Jue; Gao, Jia-Hong; Niu, Zhendong

    2014-01-01

    The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR) on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability. PMID:24475058

  14. Temporal reliability and lateralization of the resting-state language network.

    PubMed

    Zhu, Linlin; Fan, Yang; Zou, Qihong; Wang, Jue; Gao, Jia-Hong; Niu, Zhendong

    2014-01-01

    The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR) on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability.

  15. Reconstruction of missing data in social networks based on temporal patterns of interactions

    NASA Astrophysics Data System (ADS)

    Stomakhin, Alexey; Short, Martin B.; Bertozzi, Andrea L.

    2011-11-01

    We discuss a mathematical framework based on a self-exciting point process aimed at analyzing temporal patterns in the series of interaction events between agents in a social network. We then develop a reconstruction model that allows one to predict the unknown participants in a portion of those events. Finally, we apply our results to the Los Angeles gang network.

  16. Evaluating the Spatio-Temporal Factors that Structure Network Parameters of Plant-Herbivore Interactions

    PubMed Central

    López-Carretero, Antonio; Díaz-Castelazo, Cecilia; Boege, Karina; Rico-Gray, Víctor

    2014-01-01

    Despite the dynamic nature of ecological interactions, most studies on species networks offer static representations of their structure, constraining our understanding of the ecological mechanisms involved in their spatio-temporal stability. This is the first study to evaluate plant-herbivore interaction networks on a small spatio-temporal scale. Specifically, we simultaneously assessed the effect of host plant availability, habitat complexity and seasonality on the structure of plant-herbivore networks in a coastal tropical ecosystem. Our results revealed that changes in the host plant community resulting from seasonality and habitat structure are reflected not only in the herbivore community, but also in the emergent properties (network parameters) of the plant-herbivore interaction network such as connectance, selectiveness and modularity. Habitat conditions and periods that are most stressful favored the presence of less selective and susceptible herbivore species, resulting in increased connectance within networks. In contrast, the high degree of selectivennes (i.e. interaction specialization) and modularity of the networks under less stressful conditions was promoted by the diversification in resource use by herbivores. By analyzing networks at a small spatio-temporal scale we identified the ecological factors structuring this network such as habitat complexity and seasonality. Our research offers new evidence on the role of abiotic and biotic factors in the variation of the properties of species interaction networks. PMID:25340790

  17. Aerosol black carbon characteristics over Central India: Temporal variation and its dependence on mixed layer height

    NASA Astrophysics Data System (ADS)

    Kompalli, Sobhan Kumar; Babu, S. Suresh; Moorthy, K. Krishna; Manoj, M. R.; Kumar, N. V. P. Kiran; Shaeb, K. Hareef Baba; Joshi, Ashok Kumar

    2014-10-01

    In a first of its kind study over the Indian region, concurrent and extensive measurements of black carbon (BC) concentration and atmospheric boundary layer parameters are used to quantify the role of atmospheric boundary layer in producing temporal changes in BC. During this study, 18 months (2011-12) data of continuous measurements of BC aerosols, made over a semi-urban location, Nagpur, in Central India are used along with concurrent measurements of vertical profiles of atmospheric thermodynamics, made using weekly ascents of GPS aided Radiosonde for a period of 1 year. From the balloon data, mixed layer heights and ventilation coefficients are estimated, and the monthly and seasonal changes in BC mass concentration are examined in the light of the boundary layer changes. Seasonally, the BC mass concentration was highest (~ 4573 ± 1293 ng m- 3) in winter (December-February), and lowest (~ 1588 ± 897 ng m- 3) in monsoon (June-September), while remained moderate (~ 3137 ± 1446 ng m- 3) in pre-monsoon (March-May), and post-monsoon (~ 3634 ± 813 ng m- 3) (October-November) seasons. During the dry seasons, when the rainfall is scanty or insignificantly small, the seasonal variations in BC concentrations have a strong inverse relationship with mixed layer height and ventilation coefficient. However, the lowest BC concentrations do not occur during the season when the mixed layer height (MLH) is highest or the ventilation coefficient is the highest; rather it occurs when the rainfall is strong (during summer monsoon season) and airmass changes to primarily of marine origin.

  18. The Role of Temporal Trends in Growing Networks

    PubMed Central

    Ruppin, Eytan; Shavitt, Yuval

    2016-01-01

    The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment. PMID:27486847

  19. Absolute versus temporal anomaly and percent of saturation soil moisture spatial variability for six networks worldwide

    NASA Astrophysics Data System (ADS)

    Brocca, L.; Zucco, G.; Mittelbach, H.; Moramarco, T.; Seneviratne, S. I.

    2014-07-01

    The analysis of the spatial-temporal variability of soil moisture can be carried out considering the absolute (original) soil moisture values or relative values, such as the percent of saturation or temporal anomalies. Over large areas, soil moisture data measured at different sites can be characterized by large differences in their minimum, mean, and maximum absolute values, even though in relative terms their temporal patterns are very similar. In these cases, the analysis considering absolute compared with percent of saturation or temporal anomaly soil moisture values can provide very different results with significant consequences for their use in hydrological applications and climate science. In this study, in situ observations from six soil moisture networks in Italy, Spain, France, Switzerland, Australia, and United States are collected and analyzed to investigate the spatial soil moisture variability over large areas (250-150,000 km2). Specifically, the statistical and temporal stability analyses of soil moisture have been carried out for absolute, temporal anomaly, and percent of saturation values (using two different formulations for temporal anomalies). The results highlight that the spatial variability of the soil moisture dynamic (i.e., temporal anomalies) is significantly lower than that of the absolute soil moisture values. The spatial variance of the time-invariant component (temporal mean of each site) is the predominant contribution to the total spatial variance of absolute soil moisture data. Moreover, half of the networks show a minimum in the spatial variability for intermediate conditions when the temporal anomalies are considered, in contrast with the widely recognized behavior of absolute soil moisture data. The analyses with percent saturation data show qualitatively similar results as those for the temporal anomalies because of the applied normalization which reduces spatial variability induced by differences in mean absolute soil moisture

  20. A functional magnetic resonance imaging study mapping the episodic memory encoding network in temporal lobe epilepsy

    PubMed Central

    Sidhu, Meneka K.; Stretton, Jason; Winston, Gavin P.; Bonelli, Silvia; Centeno, Maria; Vollmar, Christian; Symms, Mark; Thompson, Pamela J.; Koepp, Matthias J.

    2013-01-01

    Functional magnetic resonance imaging has demonstrated reorganization of memory encoding networks within the temporal lobe in temporal lobe epilepsy, but little is known of the extra-temporal networks in these patients. We investigated the temporal and extra-temporal reorganization of memory encoding networks in refractory temporal lobe epilepsy and the neural correlates of successful subsequent memory formation. We studied 44 patients with unilateral temporal lobe epilepsy and hippocampal sclerosis (24 left) and 26 healthy control subjects. All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words with subsequent out-of-scanner recognition assessments. A blocked analysis was used to investigate activations during encoding and neural correlates of subsequent memory were investigated using an event-related analysis. Event-related activations were then correlated with out-of-scanner verbal and visual memory scores. During word encoding, control subjects activated the left prefrontal cortex and left hippocampus whereas patients with left hippocampal sclerosis showed significant additional right temporal and extra-temporal activations. Control subjects displayed subsequent verbal memory effects within left parahippocampal gyrus, left orbitofrontal cortex and fusiform gyrus whereas patients with left hippocampal sclerosis activated only right posterior hippocampus, parahippocampus and fusiform gyrus. Correlational analysis showed that patients with left hippocampal sclerosis with better verbal memory additionally activated left orbitofrontal cortex, anterior cingulate cortex and left posterior hippocampus. During face encoding, control subjects showed right lateralized prefrontal cortex and bilateral hippocampal activations. Patients with right hippocampal sclerosis showed increased temporal activations within the superior temporal gyri bilaterally and no increased extra-temporal areas of activation compared with

  1. Spatial Connectivity and Temporal Response of Variable Source Areas (VSAs): Implications for Catchment Scale Water and Solute Mixing

    NASA Astrophysics Data System (ADS)

    Inamdar, S.; Mitchell, M.; McDonnell, J.; McGlynn, B.; Shanley, J.

    2001-05-01

    The significance of variable source areas (VSAs) in storm runoff generation and as loci for mixing of event and pre-event waters has long been recognized. Recent research suggests that VSAs may also play an important role in regulating the export of C and N solutes from catchments. We hypothesize that the spatial distribution of VSAs in the catchment and their connectedness with the stream network is a first order control on the temporal dynamics and expression of water and solutes from the catchment. We examined two contrasting scenarios of VSA distribution: (1) VSAs located lower in the catchment and well connected to the stream network, versus, (2) discrete VSAs located in the upper portions of the catchment and disconnected from the stream network. We evaluated the potential impact of these scenarios on: (a) the timing and peak of event water contributions, and (b) the timing and peak of solute signatures. We hypothesized that if VSAs are well connected to the stream network (Scenario 1), then event water contributions would be distinct and would predominate early on during the rising limb of the hydrograph of stream discharge. In contrast, if VSAs are isolated and disconnected (Scenario 2), then event water contributions would be damped and delayed and possibly continue to be observed through hydrograph recession. We believe solutes such as dissolved organic carbon (DOC), which are primarily flushed from near surface soil horizons, will follow an event water trajectory. We tested these hypotheses for a 135 ha forested headwater catchment in the Adirondack Mountains of New York. Detailed storm runoff and solute data for the catchment are available since 1994. A two-component separation model using base cations (Na, Mg, Ca, and K) was used to partition stormflow discharge into pre-event and event components. Event water contributions were small on the rising limb of the hydrograph, reached their maximum just after the discharge peak, and continued through the

  2. Distinct Patterns of Temporal and Directional Connectivity among Intrinsic Networks in the Human Brain.

    PubMed

    Shine, James M; Kucyi, Aaron; Foster, Brett L; Bickel, Stephan; Wang, Danhong; Liu, Hesheng; Poldrack, Russell A; Hsieh, Liang-Tien; Hsiang, Jen Chun; Parvizi, Josef

    2017-10-04

    To determine the spatiotemporal relationships among intrinsic networks of the human brain, we recruited seven neurosurgical patients (four males and three females) who were implanted with intracranial depth electrodes. We first identified canonical resting-state networks at the individual subject level using an iterative matching procedure on each subject's resting-state fMRI data. We then introduced single electrical pulses to fMRI pre-identified nodes of the default network (DN), frontoparietal network (FPN), and salience network (SN) while recording evoked responses in other recording sites within the same networks. We found bidirectional signal flow across the three networks, albeit with distinct patterns of evoked responses within different time windows. We used a data-driven clustering approach to show that stimulation of the FPN and SN evoked a rapid (<70 ms) response that was predominantly higher within the SN sites, whereas stimulation of the DN led to sustained responses in later time windows (85-200 ms). Stimulations in the medial temporal lobe components of the DN evoked relatively late effects (>130 ms) in other nodes of the DN, as well as FPN and SN. Our results provide temporal information about the patterns of signal flow between intrinsic networks that provide insights into the spatiotemporal dynamics that are likely to constrain the architecture of the brain networks supporting human cognition and behavior.SIGNIFICANCE STATEMENT Despite great progress in the functional neuroimaging of the human brain, we still do not know the precise set of rules that define the patterns of temporal organization between large-scale networks of the brain. In this study, we stimulated and then recorded electrical evoked potentials within and between three large-scale networks of the brain, the default network (DN), frontoparietal network (FPN), and salience network (SN), in seven subjects undergoing invasive neurosurgery. Using a data-driven clustering approach, we

  3. Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

    PubMed Central

    Ponce-Alvarez, Adrián; Deco, Gustavo; Hagmann, Patric; Romani, Gian Luca; Mantini, Dante; Corbetta, Maurizio

    2015-01-01

    Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous. PMID:25692996

  4. Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

    2017-03-01

    Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

  5. Temporal solar irradiance variability analysis using neural networks

    NASA Astrophysics Data System (ADS)

    Tebabal, Ambelu; Damtie, Baylie; Nigussie, Melessew

    A feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

  6. The temporal dynamics of resource use by frugivorous birds: a network approach.

    PubMed

    Carnicer, Jofre; Jordano, Pedro; Melián, Carlos J

    2009-07-01

    Ecological network patterns are influenced by diverse processes that operate at different temporal rates. Here we analyzed whether the coupled effect of local abundance variation, seasonally phenotypic plastic responses, and species evolutionary adaptations might act in concert to shape network patterns. We studied the temporal variation in three interaction properties of bird species (number of interactions per species, interaction strength, and interaction asymmetry) in a temporal sequence of 28 plant-frugivore interaction networks spanning two years in a Mediterranean shrubland community. Three main hypotheses dealing with the temporal variation of network properties were tested, examining the effects of abundance, switching behavior between alternative food resources, and morphological traits in determining consumer interaction patterns. Our results demonstrate that temporal variation in consumer interaction patterns is explained by short-term variation in resource and bird abundances and seasonal dietary switches between alternative resources (fleshy fruits and insects). Moreover, differences in beak morphology are associated with differences in switching behavior between resources, suggesting an important role of foraging adaptations in determining network patterns. We argue that beak shape adaptations might determine generalist and specialist feeding behaviors and thus the positions of consumer species within the network. Finally, we provide a preliminary framework to interpret phylogenetic signal in plant-animal networks. Indeed, we show that the strength of the phylogenetic signal in networks depends on the relative importance of abundance, behavioral, and morphological variables. We show that these variables strongly differ in their phylogenetic signal. Consequently, we suggest that moderate and significant phylogenetic effects should be commonly observed in networks of species interactions.

  7. Mixed care networks of community-dwelling older adults with physical health impairments in the Netherlands.

    PubMed

    Broese van Groenou, Marjolein; Jacobs, Marianne; Zwart-Olde, Ilse; Deeg, Dorly J H

    2016-01-01

    As part of long-term care reforms, home-care organisations in the Netherlands are required to strengthen the linkage between formal and informal caregivers of home-dwelling older adults. Information on the variety in mixed care networks may help home-care organisations to develop network type-dependent strategies to connect with informal caregivers. This study first explores how structural (size, composition) and functional features (contact and task overlap between formal and informal caregivers) contribute to different types of mixed care networks. Second, it examines to what degree these network types are associated with the care recipients' characteristics. Through home-care organisations in Amsterdam, the Netherlands, we selected 74 frail home-dwelling clients who were receiving care in 2011-2012 from both informal and formal caregivers. The care networks of these older adults were identified by listing all persons providing help with five different types of tasks. This resulted in care networks comprising an average of 9.7 caregivers, of whom 67% were formal caregivers. On average, there was contact between caregivers within 34% of the formal-informal dyads, and both caregivers carried out at least one similar type of task in 29% of these dyads. A principal component analysis of size, composition, contact and task overlap showed two distinct network dimensions from which four network types were constructed: a small mixed care network, a small formal network, a large mixed network and a large formal network. Bivariate analyses showed that the care recipients' activities of daily living level, memory problems, social network, perceived control of care and level of mastery differed significantly between these four types. The results imply that different network types require different actions from formal home-care organisations, such as mobilising the social network in small formal networks, decreasing task differentiation in large formal networks and assigning

  8. Impact of degree mixing pattern on consensus formation in social networks

    NASA Astrophysics Data System (ADS)

    Liu, Xiao Fan; Tse, Chi Kong

    The consensus formation process in a social network is affected by a number of factors. This paper studies how the degree mixing pattern of a social network affects the consensus formation process. A social network of more than 50,000 nodes was sampled from the online social services website Twitter. Nodes in the Twitter user network are grouped by their in-degrees and out-degrees. A degree mixing correlation is proposed to measure the randomness of the mixing pattern for each degree group. The DeGroot model is used to simulate the consensus formation processes in the network. Simulation suggests that the non-random degree mixing pattern of social networks can slow down the rate of consensus.

  9. Working memory network plasticity after anterior temporal lobe resection: a longitudinal functional magnetic resonance imaging study

    PubMed Central

    Stretton, Jason; Sidhu, Meneka K.; Winston, Gavin P.; Bartlett, Philippa; McEvoy, Andrew W.; Symms, Mark R.; Koepp, Matthias J.; Thompson, Pamela J.

    2014-01-01

    Working memory is a crucial cognitive function that is disrupted in temporal lobe epilepsy. It is unclear whether this impairment is a consequence of temporal lobe involvement in working memory processes or due to seizure spread to extratemporal eloquent cortex. Anterior temporal lobe resection controls seizures in 50–80% of patients with drug-resistant temporal lobe epilepsy and the effect of surgery on working memory are poorly understood both at a behavioural and neural level. We investigated the impact of temporal lobe resection on the efficiency and functional anatomy of working memory networks. We studied 33 patients with unilateral medial temporal lobe epilepsy (16 left) before, 3 and 12 months after anterior temporal lobe resection. Fifteen healthy control subjects were also assessed in parallel. All subjects had neuropsychological testing and performed a visuospatial working memory functional magnetic resonance imaging paradigm on these three separate occasions. Changes in activation and deactivation patterns were modelled individually and compared between groups. Changes in task performance were included as regressors of interest to assess the efficiency of changes in the networks. Left and right temporal lobe epilepsy patients were impaired on preoperative measures of working memory compared to controls. Working memory performance did not decline following left or right temporal lobe resection, but improved at 3 and 12 months following left and, to a lesser extent, following right anterior temporal lobe resection. After left anterior temporal lobe resection, improved performance correlated with greater deactivation of the left hippocampal remnant and the contralateral right hippocampus. There was a failure of increased deactivation of the left hippocampal remnant at 3 months after left temporal lobe resection compared to control subjects, which had normalized 12 months after surgery. Following right anterior temporal lobe resection there was a

  10. Effects of temporal fluctuations, fluid density effects and heterogeneity on mixing of two fluids for a stable stratification

    NASA Astrophysics Data System (ADS)

    Pool, Maria; Dentz, Marco; Post, Vincent E. A.

    2017-04-01

    Mixing and dispersion in coastal aquifers are controlled by density variations, which are influenced by temporal fluctuations on multiple time-scales ranging from days (tides), seasons (pumping and recharge) to glacial cycles (regression and transgressions). Transient forcing effects lead to a complex space and time dependent flow response which induces enhanced spreading and mixing of dissolved substances. We study effective mixing and solute transport in temporally fluctuating flow for a stable stratification of two fluids of different density using detailed numerical simulation as well as accurate column experiments. For the homogeneous case, we quantify the observed transport behaviors and interface evolution by a time-averaged model that is obtained from a two-scale expansion of the full transport problem, and derive explicit expressions for the center of mass and width of the mixing zone between the two fluids (Pool et al., 2016). We find that the magnitude of transient-driven mixing is mainly controlled by the hydraulic diffusivity, the period, and the initial interface location. For the heterogeneous case, transient forcing and density-dependent transport is investigated considering multigaussian random log conductivity fields and more complex heterogeneous fields characterized by connected patterns of high and low conductivity. We find that the mixing potential and 'hot spots' are directly related to the deformation properties and topology of the flow field, specifically its stretching behavior in response to temporal fluctuations. We also find that gravity forces due to density variations cause smoother concentration distribution leading to a decrease in the width of the transition zone. However the mixing potential is similar as the one obtained with constant density. Reference: Pool, M., M. Dentz, and V.E.A. Post (2016), Transient forcing effects on mixing of two fluids for a stable stratification, Water Resour. Res., 52, 7178-7197, doi:10.1002/2016WR

  11. Effect of chemical heat release in a temporally evolving mixing layer

    NASA Technical Reports Server (NTRS)

    Higuera, F. J.; Moser, R. D.

    1994-01-01

    Two-dimensional numerical simulations of a temporally evolving mixing layer with an exothermic infinitely fast diffusion flame between two unmixed reactants have been carried out in the limit of zero Mach number to study the effect of the heat release on the early stages of the evolution of the flow. Attention has been directed to relatively large values of the oxidizer-to-fuel mass stoichiometric ratio typical of hydrocarbon flames, and initial vorticity distributions thicker than the temperature and species distributions have been chosen to mimic the situation at the outlet of a jet. The results show that, during the stages of the evolution covered by the present simulations, enhancement of combustion occurs by local stretching of the flame without much augmentation of its area. The rate of product generation depends strongly on the initial conditions, which suggests the possibility of controlling the combustion by acting on the flow. Rollup and vortex amalgamation still occur in these reacting flows but are very much affected by the production of new vorticity by baroclinic torques. These torques lead to counter rotating vortex pairs around the flame and, more importantly, in thin layers of light fluid that leave the vicinity of the flame when the Kelvin-Helmholtz instability begins to develop. Propelled by the vortex pairs, these layers wind around, split on reaching high pressure regions, and originate new vortex pairs in a process that ends up building large-scale vortices with a vorticity distribution more complex than for a constant density fluid.

  12. Ultrafast temporal pulse shaping via phase-sensitive three-wave mixing.

    PubMed

    Yin, Y C; French, D; Jovanovic, I

    2010-08-16

    It is well-known that the process of optical parametric amplification (OPA) can be sensitive to the phases of the incident waves. In OPA realized by three-wave mixing, injection of all three waves into the same mode with appropriate phase relationship results in amplification of the signal phase, with an associated deamplification of the signal energy. Prospects for the use of this technique in the temporal domain for shaping ultrashort laser pulses are analyzed using a numerical model. Several representative pulse shaping capabilities of this technique are identified, which can significantly augment the performance of common passive pulse shaping methods operating in the Fourier domain. It is found that the use of phase-sensitive OPA shows a potential for significant compression of approximately 100 fs pulses, steepening of the rise time of ultrashort pulses, and production of pulse doublets and pulse trains. It is also shown that the group velocity mismatch can assist the shaping process. Such pulse shaping capabilities are found to be within reach of this technique in common nonlinear optical crystals pumped by pulses available from compact femtosecond chirped-pulse amplification laser systems.

  13. Comparative Study of Three High Order Schemes for LES of Temporally Evolving Mixing Layers

    NASA Technical Reports Server (NTRS)

    Yee, Helen M. C.; Sjogreen, Biorn Axel; Hadjadj, C.

    2012-01-01

    Three high order shock-capturing schemes are compared for large eddy simulations (LES) of temporally evolving mixing layers (TML) for different convective Mach numbers (Mc) ranging from the quasi-incompressible regime to highly compressible supersonic regime. The considered high order schemes are fifth-order WENO (WENO5), seventh-order WENO (WENO7) and the associated eighth-order central spatial base scheme with the dissipative portion of WENO7 as a nonlinear post-processing filter step (WENO7fi). This high order nonlinear filter method (H.C. Yee and B. Sjogreen, Proceedings of ICOSAHOM09, June 22-26, 2009, Trondheim, Norway) is designed for accurate and efficient simulations of shock-free compressible turbulence, turbulence with shocklets and turbulence with strong shocks with minimum tuning of scheme parameters. The LES results by WENO7fi using the same scheme parameter agree well with experimental results of Barone et al. (2006), and published direct numerical simulations (DNS) work of Rogers & Moser (1994) and Pantano & Sarkar (2002), whereas results by WENO5 and WENO7 compare poorly with experimental data and DNS computations.

  14. LES of Temporally Evolving Mixing Layers by an Eighth-Order Filter Scheme

    NASA Technical Reports Server (NTRS)

    Hadjadj, A; Yee, H. C.; Sjogreen, B.

    2011-01-01

    An eighth-order filter method for a wide range of compressible flow speeds (H.C. Yee and B. Sjogreen, Proceedings of ICOSAHOM09, June 22-26, 2009, Trondheim, Norway) are employed for large eddy simulations (LES) of temporally evolving mixing layers (TML) for different convective Mach numbers (Mc) and Reynolds numbers. The high order filter method is designed for accurate and efficient simulations of shock-free compressible turbulence, turbulence with shocklets and turbulence with strong shocks with minimum tuning of scheme parameters. The value of Mc considered is for the TML range from the quasi-incompressible regime to the highly compressible supersonic regime. The three main characteristics of compressible TML (the self similarity property, compressibility effects and the presence of large-scale structure with shocklets for high Mc) are considered for the LES study. The LES results using the same scheme parameters for all studied cases agree well with experimental results of Barone et al. (2006), and published direct numerical simulations (DNS) work of Rogers & Moser (1994) and Pantano & Sarkar (2002).

  15. The structural plasticity of white matter networks following anterior temporal lobe resection

    PubMed Central

    Yogarajah, Mahinda; Focke, Niels K.; Bonelli, Silvia B.; Thompson, Pamela; Vollmar, Christian; McEvoy, Andrew W.; Alexander, Daniel C.; Symms, Mark R.; Koepp, Matthias J.

    2010-01-01

    Anterior temporal lobe resection is an effective treatment for refractory temporal lobe epilepsy. The structural consequences of such surgery in the white matter, and how these relate to language function after surgery remain unknown. We carried out a longitudinal study with diffusion tensor imaging in 26 left and 20 right temporal lobe epilepsy patients before and a mean of 4.5 months after anterior temporal lobe resection. The whole-brain analysis technique tract-based spatial statistics was used to compare pre- and postoperative data in the left and right temporal lobe epilepsy groups separately. We observed widespread, significant, mean 7%, decreases in fractional anisotropy in white matter networks connected to the area of resection, following both left and right temporal lobe resections. However, we also observed a widespread, mean 8%, increase in fractional anisotropy after left anterior temporal lobe resection in the ipsilateral external capsule and posterior limb of the internal capsule, and corona radiata. These findings were confirmed on analysis of the native clusters and hand drawn regions of interest. Postoperative tractography seeded from this area suggests that this cluster is part of the ventro-medial language network. The mean pre- and postoperative fractional anisotropy and parallel diffusivity in this cluster were significantly correlated with postoperative verbal fluency and naming test scores. In addition, the percentage change in parallel diffusivity in this cluster was correlated with the percentage change in verbal fluency after anterior temporal lobe resection, such that the bigger the increase in parallel diffusivity, the smaller the fall in language proficiency after surgery. We suggest that the findings of increased fractional anisotropy in this ventro-medial language network represent structural reorganization in response to the anterior temporal lobe resection, which may damage the more susceptible dorso-lateral language pathway

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

    PubMed Central

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

    2015-01-01

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

  17. Habituation-based mechanism for encoding temporal information in artificial neural networks

    NASA Astrophysics Data System (ADS)

    Stiles, Bryan W.; Ghosh, Joydeep

    1995-04-01

    A novel neural network is proposed for the dynamic classification of spatio-temporal signals. The network is designed to classify signals of different durations, taking into account correlations among different signal segments. Such a network is applicable to SONAR and speech signal classification problems, among others. Network parameters are adapted based on the biologically observed habituation mechanism. This allows the storage of contextual information, without a substantial increase in network complexity. Experiments on classification of high dimensional feature vectors obtained from Banzhaf sonograms, demonstrate that the proposed network performs better than time delay neural networks while using a less complex structure. A mathematical justification of the capabilities of the habituation based mechanism is also provided.

  18. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    PubMed Central

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-01-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. PMID:27874024

  19. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  20. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks.

    PubMed

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-22

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  1. Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.

    PubMed

    Vestergaard, Christian L; Génois, Mathieu

    2015-10-01

    Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.

  2. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis.

    PubMed

    Xiang, Zuoshuang; Minter, Rebecca M; Bi, Xiaoming; Woolf, Peter J; He, Yongqun

    2007-09-15

    Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference, such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data. miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA is available at http://www.minituba.org.

  3. Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks

    PubMed Central

    Vestergaard, Christian L.; Génois, Mathieu

    2015-01-01

    Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling. PMID:26517860

  4. Towards a temporal network analysis of interactive WiFi users

    NASA Astrophysics Data System (ADS)

    Zhang, Yan; Wang, Lin; Zhang, Yi-Qing; Li, Xiang

    2012-06-01

    Complex networks are used to depict topological features of complex systems. The structure of a network characterizes the interactions among elements of the system, and facilitates the study of many dynamical processes taking place on it. In previous investigations, the topological infrastructure underlying dynamical systems is simplified as a static and invariable skeleton. However, this assumption cannot cover the temporal features of many time-evolution networks, whose components are evolving and mutating. In this letter, utilizing the log data of WiFi users in a Chinese university campus, we infuse the temporal dimension into the construction of dynamical human contact network. By quantitative comparison with the traditional aggregation approach, we find that the temporal contact network differs in many features, e.g., the reachability, the path length distribution. We conclude that the correlation between temporal path length and duration is not only determined by their definitions, but also influenced by the micro-dynamical features of human activities under certain social circumstance as well. The time order of individuals' interaction events plays a critical role in understanding many dynamical processes via human close proximity interactions studied in this letter. Besides, our study also provides a promising measure to identify the potential superspreaders by distinguishing the nodes functioning as the relay hub. The first two authors contributed equally to this paper.

  5. Inference of causality in epidemics on temporal contact networks

    NASA Astrophysics Data System (ADS)

    Braunstein, Alfredo; Ingrosso, Alessandro

    2016-06-01

    Investigating into the past history of an epidemic outbreak is a paramount problem in epidemiology. Based on observations about the state of individuals, on the knowledge of the network of contacts and on a mathematical model for the epidemic process, the problem consists in describing some features of the posterior distribution of unobserved past events, such as the source, potential transmissions, and undetected positive cases. Several methods have been proposed for the study of these inference problems on discrete-time, synchronous epidemic models on networks, including naive Bayes, centrality measures, accelerated Monte-Carlo approaches and Belief Propagation. However, most traced real networks consist of short-time contacts on continuous time. A possibility that has been adopted is to discretize time line into identical intervals, a method that becomes more and more precise as the length of the intervals vanishes. Unfortunately, the computational time of the inference methods increase with the number of intervals, turning a sufficiently precise inference procedure often impractical. We show here an extension of the Belief Propagation method that is able to deal with a model of continuous-time events, without resorting to time discretization. We also investigate the effect of time discretization on the quality of the inference.

  6. Activity Changes Induced by Spatio-Temporally Correlated Stimuli in Cultured Cortical Networks

    NASA Astrophysics Data System (ADS)

    Takayama, Yuzo; Moriguchi, Hiroyuki; Jimbo, Yasuhiko

    Activity-dependent plasticity probably plays a key role in learning and memory in biological information processing systems. Though long-term potentiation and depression have been extensively studied in the filed of neuroscience, little is known on the mechanisms for integrating these modifications on network-wide activity changes. In this report, we studied effects of spatio-temporally correlated stimuli on the neuronal network activity. Rat cortical neurons were cultured on substrates with 64 embedded micro-electrodes and the evoked responses were extracellularly recorded and analyzed. We compared spatio-temporal patterns of the responses between before and after repetitive application of correlated stimuli. After the correlated stimuli, the networks showed significantly different responses from those in the initial states. The modified activity reflected structures of the repeatedly applied correlated stimuli. The results suggested that spatiotemporally correlated inputs systematically induced modification of synaptic strengths in neuronal networks, which could serve as an underlying mechanism of associative memory.

  7. Aging and percolation dynamics in a Non-Poissonian temporal network model

    NASA Astrophysics Data System (ADS)

    Moinet, Antoine; Starnini, Michele; Pastor-Satorras, Romualdo

    2016-08-01

    We present an exhaustive mathematical analysis of the recently proposed Non-Poissonian Activity Driven (NoPAD) model [Moinet et al., Phys. Rev. Lett. 114, 108701 (2015), 10.1103/PhysRevLett.114.108701], a temporal network model incorporating the empirically observed bursty nature of social interactions. We focus on the aging effects emerging from the non-Poissonian dynamics of link activation, and on their effects on the topological properties of time-integrated networks, such as the degree distribution. Analytic expressions for the degree distribution of integrated networks as a function of time are derived, exploring both limits of vanishing and strong aging. We also address the percolation process occurring on these temporal networks, by computing the threshold for the emergence of a giant connected component, highlighting the aging dependence. Our analytic predictions are checked by means of extensive numerical simulations of the NoPAD model.

  8. Deciphering the connectivity structure of biological networks using MixNet

    PubMed Central

    Picard, Franck; Miele, Vincent; Daudin, Jean-Jacques; Cottret, Ludovic; Robin, Stéphane

    2009-01-01

    Background As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles. Results We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering. Conclusion We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks. PMID:19534742

  9. Reactive mixing in heterogeneous porous media flows: concentration gradient distribution, spatial intermittency and temporal scaling of effective reaction kinetics

    NASA Astrophysics Data System (ADS)

    Le Borgne, T.; Dentz, M.; Ginn, T. R.; Villermaux, E.

    2015-12-01

    Reactive mixing processes play a central role in a range of porous media systems, including CO2 sequestration operations, reactive geothermal dipoles, biofilms, or flow-through reactors. Many of these reactions are limited by fluid mixing processes that bring the reactants into contact. Hence, the temporal dynamics of effective global reactivity is determined by the creation of concentration gradients by fluid stretching and their dissipation by diffusion [1,2]. From the analysis of the elongation and aggregation of lamellar structures formed in the transported scalar fields, we derive analytical predictions for the probability density functions of concentration gradients in heterogeneous Darcy flows over a large range of Péclet numbers and permeability field variances. In this framework, we show that heterogeneous Darcy fields generate highly intermittent concentration fields, as manifested by the spatial scaling of structure functions. The resulting effective reaction rates display a range of temporal behaviors that depend on the degree of heterogeneity. In the large Damköhler limit, we derive analytical expressions for these temporal scalings in the different regimes that arise when exploring the Péclet number space. We generalize these results for different random flows, including turbulent flows. References:[1] Le Borgne, T., M. Dentz, E. Villermaux, The lamellar description of mixing in porous media, J. of Fluid Mech., vol. 770, pp. 458-498 [2] Le Borgne, T., M. Dentz, E. Villermaux, Stretching, coalescence and mixing in porous media, Phys. Rev. Lett., 110, 204501 (2013)

  10. Effect of changing spatio-temporal precipitation patterns on river network dynamics

    NASA Astrophysics Data System (ADS)

    Abed-Elmdoust, A.; Singh, A.

    2015-12-01

    Quantifying the impacts of climate change on landscape evolution has been a subject of intense research for past many years. Among the various impacts of climate change in landscapes is the influence of changing precipitation patterns on the re-organization of river network across the basin. Here, we investigate the effect of non-uniform spatio-temporal patterns of precipitations on the evolution of river networks. For this, we simulate river networks dynamics based on optimal channel network approach, on a prescribed two-dimensional lattice, under constant and varying spatial and temporal patterns of precipitation. For a given precipitation rate, the steady state river network (the optimal channel network) is obtained by finding a drainage pattern that minimizes the total energy of the network. This steady state river network is further perturbed with non-uniform rainfall patterns and its dynamics are recorded. We show that under non-uniform rainfall conditions, river networks significantly re-organize themselves towards new steady states with different total energies. Although the reorganized river networks statistically follow similar bifurcation rules (Horton's laws), they exhibit different geomorphological signatures. In particular, the re-organization on the landscape is mainly seen in the form of order based channel migration, basin capture and formation of new channels and basins. Based on an empirical slope-area relationship coupled with optimal channel network, we generate three-dimensional digital elevation models of the evolved landscape for further exploring the hillslope-channels interactions. Our results show the potential use of optimal channel networks as a simple and yet versatile method for exploring the effects of climate change on river networks.

  11. A modularity-based method reveals mixed modules from chemical-gene heterogeneous network.

    PubMed

    Song, Jianglong; Tang, Shihuan; Liu, Xi; Gao, Yibo; Yang, Hongjun; Lu, Peng

    2015-01-01

    For a multicomponent therapy, molecular network is essential to uncover its specific mode of action from a holistic perspective. The molecular system of a Traditional Chinese Medicine (TCM) formula can be represented by a 2-class heterogeneous network (2-HN), which typically includes chemical similarities, chemical-target interactions and gene interactions. An important premise of uncovering the molecular mechanism is to identify mixed modules from complex chemical-gene heterogeneous network of a TCM formula. We thus proposed a novel method (MixMod) based on mixed modularity to detect accurate mixed modules from 2-HNs. At first, we compared MixMod with Clauset-Newman-Moore algorithm (CNM), Markov Cluster algorithm (MCL), Infomap and Louvain on benchmark 2-HNs with known module structure. Results showed that MixMod was superior to other methods when 2-HNs had promiscuous module structure. Then these methods were tested on a real drug-target network, in which 88 disease clusters were regarded as real modules. MixMod could identify the most accurate mixed modules from the drug-target 2-HN (normalized mutual information 0.62 and classification accuracy 0.4524). In the end, MixMod was applied to the 2-HN of Buchang naoxintong capsule (BNC) and detected 49 mixed modules. By using enrichment analysis, we investigated five mixed modules that contained primary constituents of BNC intestinal absorption liquid. As a matter of fact, the findings of in vitro experiments using BNC intestinal absorption liquid were found to highly accord with previous analysis. Therefore, MixMod is an effective method to detect accurate mixed modules from chemical-gene heterogeneous networks and further uncover the molecular mechanism of multicomponent therapies, especially TCM formulae.

  12. Spatial-Temporal Reasoning Applications of Computational Intelligence in the Game of Go and Computer Networks

    DTIC Science & Technology

    2012-01-01

    coevolution learning (CEL) and a self-play feedforward neural network with TD learning (TDL) [85]. A neural network needed to be learned, for example, a...because the opponent is a random player, a WPC, and the 22 performance is compared among the methods given in the paper, i.e. pure TD, pure coevolution ...34 Coevolution versus self-play temporal difference learning for acquiring position evaluation in small-board Go," IEEE Transactions on Evolutionary

  13. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    PubMed Central

    2009-01-01

    Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism. PMID:19300527

  14. Neural network for processing both spatial and temporal data with time based back-propagation

    NASA Technical Reports Server (NTRS)

    Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

    1993-01-01

    Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

  15. Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems.

    PubMed

    Marwan, Norbert; Kurths, Jürgen

    2015-09-01

    We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.

  16. Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification.

    PubMed

    Wei, Hua-Liang; Billings, Stephen A; Zhao, Yifan; Guo, Lingzhong

    2009-01-01

    In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.

  17. An adaptive wavelet neural network for spatio-temporal system identification.

    PubMed

    Wei, H L; Billings, S A; Zhao, Y F; Guo, L Z

    2010-12-01

    Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework.

  18. Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

    PubMed

    Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W

    2016-11-15

    There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects.

  19. A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition.

    PubMed

    Juang, Chia-Feng; Ku, Ksuan-Chun

    2005-08-01

    A fuzzified Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (FTRFN) for handling fuzzy temporal information is proposed in this paper. The FTRFN extends our previously proposed network, TRFN, to deal with fuzzy temporal signals represented by Gaussian or triangular fuzzy numbers. In the precondition part of FTRFN, matching degrees between input fuzzy variables and fuzzy antecedent sets is performed by similarity measure. In the TSK-type consequence, a linear combination of fuzzy variables is computed, where two sets of combination coefficients, one for the center and the other for the width of each fuzzy number, are used. Derivation of the linear combination results and final network output is based on left-right fuzzy number operation. There are no rules in FTRFN initially; they are constructed online by concurrent structure and parameter learning, where all free parameters in the precondition/consequence of FTRFN are all tunable. FTRFN can be applied on a variety of domains related to fuzzy temporal information processing. In this paper, it has been applied on one-dimensional and two-dimensional fuzzy temporal sequence prediction and CCD-based temporal gesture recognition. The performance of FTRFN is verified from these examples.

  20. A low cost sensor network approach to investigate spatio-temporal patterns of stream temperatures and electrical conductivity

    NASA Astrophysics Data System (ADS)

    Lieder, Ernestine; Weiler, Markus; Blume, Theresa

    2016-04-01

    Understanding water and energy fluxes at the stream and catchment scale remains a challenging task. Within the CAOS-project-framework it is our aim to investigate spatiotemporal patterns of stream temperature and to deduce understanding about the underlying hydrological system. A low cost sensor network was installed in summer 2015 to monitor stream temperature and EC patterns in time and space. 90 HOBO temperature sensors, which were modified to additionally measure EC, were installed at 30 confluences across the Attert catchment (288 km²) in Luxembourg. The design of the sensor network allows for the investigation of three research questions: a) spatial patterns of stream temperatures and EC and their dynamics across the region b) estimation of relative streamflow contributions and their temporal dynamics by using simple mixing models and c) estimation of heat transport. The data will thus provide valuable insight in runoff contributions from different sub-catchments, and a combined analysis with distributed measurements of soil moisture and shallow groundwater will improve our process understanding by linking hillslope scale processes with stream responses. First results indicate that streams in different geologies show distinct temperature and EC patterns throughout the observation period. Differences are also found with respect to temporal dynamics both for longer periods as well as diurnal fluctuations. These differences are likely to be caused by differences in flow paths on the one hand (e.g. amount of groundwater contribution) and exposure to direct radiation on the other hand.

  1. Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

    NASA Astrophysics Data System (ADS)

    Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin

    Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.

  2. The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions.

    PubMed

    Jackson, Rebecca L; Hoffman, Paul; Pobric, Gorana; Lambon Ralph, Matthew A

    2016-02-03

    The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these

  3. The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions

    PubMed Central

    Jackson, Rebecca L.; Hoffman, Paul; Pobric, Gorana

    2016-01-01

    The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. SIGNIFICANCE STATEMENT Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions

  4. Separating temporal and topological effects in walk-based network centrality

    NASA Astrophysics Data System (ADS)

    Colman, Ewan R.; Charlton, Nathaniel

    2016-07-01

    The recently introduced concept of dynamic communicability is a valuable tool for ranking the importance of nodes in a temporal network. Two metrics, broadcast score and receive score, were introduced to measure the centrality of a node with respect to a model of contagion based on time-respecting walks. This article examines the temporal and structural factors influencing these metrics by considering a versatile stochastic temporal network model. We analytically derive formulas to accurately predict the expectation of the broadcast and receive scores when one or more columns in a temporal edge-list are shuffled. These methods are then applied to two publicly available data sets and we quantify how much the centrality of each individual depends on structural or temporal influences. From our analysis, we highlight two practical contributions: a way to control for temporal variation when computing dynamic communicability and the conclusion that the broadcast and receive scores can, under a range of circumstances, be replaced by the row and column sums of the matrix exponential of a weighted adjacency matrix given by the data.

  5. Spatial and temporal variation of water temperature regimes on the Snoqualmie River network

    Treesearch

    Ashley E. Steel; Colin Sowder; Erin E. Peterson

    2016-01-01

    Although mean temperatures change annually and are highly correlated with elevation, the entire thermal regime on the Snoqualmie River, Washington, USA does not simply shift with elevation or season. Particular facets of the thermal regime have unique spatial patterns on the river network and at particular times of the year. We used a spatially and temporally dense...

  6. Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    PubMed Central

    Sanz-García, Ancor; Vega-Zelaya, Lorena; Pastor, Jesús; Torres, Cristina V.; Sola, Rafael G.; Ortega, Guillermo J.

    2016-01-01

    Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control seizures. However, a significant minority of patients continues to exhibit post-operative seizures, even in those cases in which the suspected source of seizures has been correctly localized and resected. The protocol presented here combines a clinical procedure routinely employed during the pre-operative evaluation of temporal lobe epilepsy (TLE) patients with a novel technique for network analysis. The method allows for the evaluation of the temporal evolution of mesial network parameters. The bilateral insertion of foramen ovale electrodes (FOE) into the ambient cistern simultaneously records electrocortical activity at several mesial areas in the temporal lobe. Furthermore, network methodology applied to the recorded time series tracks the temporal evolution of the mesial networks both interictally and during the seizures. In this way, the presented protocol offers a unique way to visualize and quantify measures that considers the relationships between several mesial areas instead of a single area. PMID:28060326

  7. Network Alterations Supporting Word Retrieval in Patients with Medial Temporal Lobe Epilepsy

    ERIC Educational Resources Information Center

    Protzner, Andrea B.; McAndrews, Mary Pat

    2011-01-01

    Although the hippocampus is not considered a key structure in semantic memory, patients with medial-temporal lobe epilepsy (mTLE) have deficits in semantic access on some word retrieval tasks. We hypothesized that these deficits reflect the negative impact of focal epilepsy on remote cerebral structures. Thus, we expected that the networks that…

  8. Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients.

    PubMed

    Sanz-García, Ancor; Vega-Zelaya, Lorena; Pastor, Jesús; Torres, Cristina V; Sola, Rafael G; Ortega, Guillermo J

    2016-12-18

    Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control seizures. However, a significant minority of patients continues to exhibit post-operative seizures, even in those cases in which the suspected source of seizures has been correctly localized and resected. The protocol presented here combines a clinical procedure routinely employed during the pre-operative evaluation of temporal lobe epilepsy (TLE) patients with a novel technique for network analysis. The method allows for the evaluation of the temporal evolution of mesial network parameters. The bilateral insertion of foramen ovale electrodes (FOE) into the ambient cistern simultaneously records electrocortical activity at several mesial areas in the temporal lobe. Furthermore, network methodology applied to the recorded time series tracks the temporal evolution of the mesial networks both interictally and during the seizures. In this way, the presented protocol offers a unique way to visualize and quantify measures that considers the relationships between several mesial areas instead of a single area.

  9. Network Alterations Supporting Word Retrieval in Patients with Medial Temporal Lobe Epilepsy

    ERIC Educational Resources Information Center

    Protzner, Andrea B.; McAndrews, Mary Pat

    2011-01-01

    Although the hippocampus is not considered a key structure in semantic memory, patients with medial-temporal lobe epilepsy (mTLE) have deficits in semantic access on some word retrieval tasks. We hypothesized that these deficits reflect the negative impact of focal epilepsy on remote cerebral structures. Thus, we expected that the networks that…

  10. Synchronization and information transmission in spatio-temporal networks of deformable units

    NASA Astrophysics Data System (ADS)

    Moukam Kakmeni, F. M.; Baptista, M. S.

    2008-06-01

    We study the relationship between synchronization and the rate with which information is exchanged between nodes in a spatio-temporal network that describes the dynamics of classical particles under a substrate Remoissenet-Peyrard potential. We also show how phase and complete synchronization can be detected in this network. The difficulty in detecting phase synchronization in such a network appears due to the highly non-coherent character of the particle dynamics which unables a proper definition of the phase dynamics. The difficulty in detecting complete synchronization appears due to the spatio character of the potential which results in an asymptotic state highly dependent on the initial state.

  11. Influence of Resting-State Network on Lateralization of Functional Connectivity in Mesial Temporal Lobe Epilepsy.

    PubMed

    Su, L; An, J; Ma, Q; Qiu, S; Hu, D

    2015-08-01

    Although most studies on epilepsy have focused on the epileptogenic zone, epilepsy is a system-level disease characterized by aberrant neuronal synchronization among groups of neurons. Increasingly, studies have indicated that mesial temporal lobe epilepsy may be a network-level disease; however, few investigations have examined resting-state functional connectivity of the entire brain, particularly in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. This study primarily investigated whole-brain resting-state functional connectivity abnormality in patients with mesial temporal lobe epilepsy and right hippocampal sclerosis during the interictal period. We investigated resting-state functional connectivity of 21 patients with mesial temporal lobe epilepsy with right hippocampal sclerosis and 21 neurologically healthy controls. A multivariate pattern analysis was used to identify the functional connections that most clearly differentiated patients with mesial temporal lobe epilepsy with right hippocampal sclerosis from controls. Discriminative analysis of functional connections indicated that the patients with mesial temporal lobe epilepsy with right hippocampal sclerosis exhibited decreased resting-state functional connectivity within the right hemisphere and increased resting-state functional connectivity within the left hemisphere. Resting-state network analysis suggested that the internetwork connections typically obey the hemispheric lateralization trend and most of the functional connections that disturb the lateralization trend are the intranetwork ones. The current findings suggest that weakening of the resting-state functional connectivity associated with the right hemisphere appears to strengthen resting-state functional connectivity on the contralateral side, which may be related to the seizure-induced damage and underlying compensatory mechanisms. Resting-state network-based analysis indicated that the compensatory mechanism among

  12. Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

    PubMed Central

    Tagliazucchi, Enzo; von Wegner, Frederic; Morzelewski, Astrid; Brodbeck, Verena; Jahnke, Kolja; Laufs, Helmut

    2013-01-01

    The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as long-term memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest. PMID:24003146

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

    PubMed

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

    2015-07-01

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

  14. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks.

    PubMed

    Calhoun, Vince D; Kiehl, Kent A; Pearlson, Godfrey D

    2008-07-01

    Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail.

  15. Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting

    PubMed Central

    Chang, Chi-Fu; Liang, Wei-Kuang; Lai, Chiou-Lian; Hung, Daisy L.; Juan, Chi-Hung

    2016-01-01

    In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session. PMID:27375459

  16. Understanding structure of urban traffic network based on spatial-temporal correlation analysis

    NASA Astrophysics Data System (ADS)

    Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

    2017-08-01

    Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

  17. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    SciTech Connect

    Song, Mingzhou; Lewis, Chris K.; Lance, Eric; Chesler, Elissa J; Kirova, Roumyana; Langston, Michael A; Bergeson, Susan

    2009-01-01

    The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  18. A spatio-temporal model of wrinkling in photopolymerised networks

    NASA Astrophysics Data System (ADS)

    Hennessy, Matthew; Vitale, Alessandra; Stavrinou, Paul; Matar, Omar; Cabral, Joao

    2015-03-01

    Photopolymerisation is a common solidification process whereby crosslinked polymer networks are created by illuminating a monomer-rich bath with collimated light. In addition, photopolymerisation is extensively employed industrially and shows exceptional promise for advanced three-dimensional patterning of functional surfaces. Under conditions of strong optical attenuation and limited mass and thermal diffusion, polymerisation occurs in a localised region which propagates from the illuminated surface into the bulk as a travelling wave with a planar wavefront. Under specific conditions that we set out to map, this planar wavefront may become unstable and the surface of the resulting gel can acquire a wrinkled morphology. We believe this instability is mechanical in nature and arises from compressive stresses that are generated during frontal photopolymerization. In this talk, we will present a novel mathematical model that captures both the photopolymerisation with wrinkling processes. We show that by coupling photopolymerisation with wrinkling in a controlled manner, a number of interesting and industrially relevant patterns can be achieved.

  19. Temporal analysis of social networks using three-way DEDICOM.

    SciTech Connect

    Bader, Brett William; Harshman, Richard A. (University of Ontario, London, Ontario, Canada); Kolda, Tamara Gibson

    2006-06-01

    DEDICOM is an algebraic model for analyzing intrinsically asymmetric relationships, such as the balance of trade among nations or the flow of information among organizations or individuals. It provides information on latent components in the data that can be regarded as ''properties'' or ''aspects'' of the objects, and it finds a few patterns that can be combined to describe many relationships among these components. When we apply this technique to adjacency matrices arising from directed graphs, we obtain a smaller graph that gives an idealized description of its patterns. Three-way DEDICOM is a higher-order extension of the model that has certain uniqueness properties. It allows for a third mode of the data, such as time, and permits the analysis of semantic graphs. We present an improved algorithm for computing three-way DEDICOM on sparse data and demonstrate it by applying it to the adjacency tensor of a semantic graph with time-labeled edges. Our application uses the Enron email corpus, from which we construct a semantic graph corresponding to email exchanges among Enron personnel over a series of 44 months. Meaningful patterns are recovered in which the representation of asymmetries adds insight into the social networks at Enron.

  20. Diagnosis of Autism Spectrum Disorders Using Temporally-Distinct Resting-State Functional Connectivity Networks

    PubMed Central

    Wee, Chong-Yaw; Yap, Pew-Thian; Shen, Dinggang

    2016-01-01

    Introduction Resting-state functional magnetic resonance imaging (R-fMRI) is dynamic in nature since neural activities constantly change over the time and are dominated by repeating brief activations and deactivations involving many brain regions. Each region participates in multiple brain functions and is part of various functionally distinct but spatially overlapping networks. Functional connectivity computed as correlations over the entire time series always overlooks inter-region interactions that often occur repeatedly and dynamically in time, limiting its application to disease diagnosis. Aims We develop a novel framework that uses short-time activation patterns of brain connectivity to better detect subtle disease-induced disruptions of brain connectivity. A clustering algorithm is first used to temporally decompose R-fMRI time series into distinct clusters with similar spatial distribution of neural activity based on the assumption that functionally distinct networks should be largely temporally distinct since brain states do not simultaneously coexist in general. A Pearson correlation-based functional connectivity network is then constructed for each cluster to allow for better exploration of spatiotemporal dynamics of individual neural activity. To reduce significant inter-subject variability and to remove possible spurious connections, we use a group-constrained sparse regression model to construct a backbone sparse network for each cluster and use it to weight the corresponding Pearson correlation network. Results The proposed method outperforms the conventional static, temporally-dependent fully-connected correlation-based networks by at least 7% on a publicly available autism dataset. We were able to reproduce similar results using data from other centers. Conclusions By combining the advantages of temporal independence and group-constrained sparse regression, our method improves autism diagnosis. PMID:26821773

  1. Distinct temporal recruitment of Plasmodium alveolins to the subpellicular network.

    PubMed

    Tremp, Annie Z; Al-Khattaf, Fatimah S; Dessens, Johannes T

    2014-11-01

    The zoite stages of malaria parasites (merozoite, ookinete and sporozoite) possess a distinctive cortical structure termed the pellicle, which is defined by a double membrane layer named the inner membrane complex (IMC). The IMC is supported by a cytoskeleton of intermediate filaments, termed the subpellicular network (SPN). Plasmodium IMC1 proteins, or alveolins, make up a conserved family of structurally related proteins that comprise building blocks of the SPN. Here, using green fluorescent protein (GFP) tagging in P. berghei, we show that the alveolins PbIMC1c and PbIMC1e are expressed in all three zoite stages. Our data reveal that PbIMC1e is assembled into the SPN concurrent with pellicle development, while PbIMC1c is assembled after pellicle formation. In the sexual stages, these processes are accompanied by different gene expressions from maternal and paternal alleles: PbIMC1e is expressed uniquely from the maternal allele, while PbIMC1c is expressed from the maternal allele in gametocytes, but from both parental alleles during ookinete development. These findings establish biogenesis of the cortical cytoskeleton in Plasmodium to be a complex and dynamic process, involving distinct parental gene expression and chronological recruitment of its protein constituents. While allelic replacement of the pbimc1c and pbimc1e genes with GFP-tagged versions was readily achieved using double crossover homologous recombination, attempts to disrupt these genes by this strategy only resulted in the integration of the selectable marker and GFP reporter into non-specific genomic locations. The recurrent inability to disrupt these genes provides the first genetic evidence that alveolins are necessary for asexual blood-stage parasite development in Plasmodium.

  2. Topological fractal networks introduced by mixed degree distribution

    NASA Astrophysics Data System (ADS)

    Zou, Liuhua; Pei, Wenjiang; Li, Tao; He, Zhenya; Cheung, Yiuming

    2007-07-01

    Several fundamental properties of real complex networks, such as the small-world effect, the scale-free degree distribution, and recently discovered topological fractal structure, have presented the possibility of a unique growth mechanism and allow for uncovering universal origins of collective behaviors. However, highly clustered scale-free network, with power-law degree distribution, or small-world network models, with exponential degree distribution, are not self-similarity. We investigate networks growth mechanism of the branching-deactivated geographical attachment preference that learned from certain empirical evidence of social behaviors. It yields high clustering and spectrums of degree distribution ranging from algebraic to exponential, average shortest path length ranging from linear to logarithmic. We observe that the present networks fit well with small-world graphs and scale-free networks in both limit cases (exponential and algebraic degree distribution, respectively), obviously lacking self-similar property under a length-scale transformation. Interestingly, we find perfect topological fractal structure emerges by a mixture of both algebraic and exponential degree distributions in a wide range of parameter values. The results present a reliable connection among small-world graphs, scale-free networks and topological fractal networks, and promise a natural way to investigate universal origins of collective behaviors.

  3. Synchronization properties of heterogeneous neuronal networks with mixed excitability type.

    PubMed

    Leone, Michael J; Schurter, Brandon N; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G

    2015-03-01

    We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

  4. Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany

    PubMed Central

    Lentz, Hartmut H. K.; Koher, Andreas; Hövel, Philipp; Gethmann, Jörn; Sauter-Louis, Carola; Selhorst, Thomas; Conraths, Franz J.

    2016-01-01

    Background Animal trade plays an important role for the spread of infectious diseases in livestock populations. The central question of this work is how infectious diseases can potentially spread via trade in such a livestock population. We address this question by analyzing the underlying network of animal movements. In particular, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. Methodology The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume do barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size. PMID:27152712

  5. What does diffusion tensor imaging (DTI) tell us about cognitive networks in temporal lobe epilepsy?

    PubMed Central

    Kucukboyaci, N. Erkut; Puckett, Olivia K.; Lee, Davis; Loi, Richard Q.; Paul, Brianna; McDonald, Carrie R.

    2015-01-01

    Diffusion tensor imaging (DTI) has provided considerable insight into our understanding of epilepsy as a network disorder, revealing subtle alterations in white matter microstructure both proximal and distal to the epileptic focus. These white matter changes have been shown to assist with lateralizing the seizure focus, as well as delineating the location/anatomy of key white matter tracts (i.e., optic radiations) for surgical planning. However, only recently have studies emerged describing the utility of DTI for probing cognitive networks in patients with epilepsy and for examining the structural plasticity within these networks both before and after epilepsy surgery. Here, we review the current literature describing the use of DTI for understanding language and memory networks in patients with temporal lobe epilepsy (TLE), as well as the extant literature on networks associated with executive functioning and global intelligence. Studies of memory and language reveal a complex network of frontotemporal fibers that contribute to naming and fluency performance in TLE, and demonstrate that these networks appear to undergo adaptive changes in response to surgical intervention. Although studies of executive functioning and global intelligence have been less conclusive, there is accumulating evidence that aberrant communication between frontoparietal and medial temporal networks may underlie working memory impairment in TLE. More recently, multimodal imaging studies have provided evidence that disruptions within these white matter networks co-localize with functional changes observed on functional MRI. However, structure-function associations are not entirely coherent and may breakdown in patients with TLE, especially those with a left-sided seizure focus. Although the reasons for discordant findings are unclear, small sample sizes, heterogeneity within patient populations and limitations of the current tensor model may account for contradictory and null findings

  6. Spatial-temporal data model and fractal analysis of transportation network in GIS environment

    NASA Astrophysics Data System (ADS)

    Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

    2008-10-01

    How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

  7. Temporal motifs reveal collaboration patterns in online task-oriented networks

    NASA Astrophysics Data System (ADS)

    Xuan, Qi; Fang, Huiting; Fu, Chenbo; Filkov, Vladimir

    2015-05-01

    Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

  8. Altered organization of face-processing networks in temporal lobe epilepsy.

    PubMed

    Riley, Jeffrey D; Fling, Brett W; Cramer, Steven C; Lin, Jack J

    2015-05-01

    Deficits in social cognition are common and significant in people with temporal lobe epilepsy (TLE), but the functional and structural underpinnings remain unclear. The present study investigated how the side of seizure focus impacts face-processing networks in temporal lobe epilepsy. We used functional magnetic resonance imaging (fMRI) of a face-processing paradigm to identify face-responsive regions in 24 individuals with unilateral temporal lobe epilepsy (left = 15; right = 9) and 19 healthy controls. fMRI signals of face-responsive regions ipsilateral and contralateral to the side of seizure onset were delineated in TLE and compared to the healthy controls with right and left sides combined. Diffusion tensor images were acquired to investigate structural connectivity between face regions that differed in fMRI signals between the two groups. In TLE, activation of the cortical face-processing networks varied according to side of seizure onset. In temporal lobe epilepsy, the laterality of amygdala activation was shifted to the side contralateral to the seizure focus, whereas controls showed no significant asymmetry. Furthermore, compared to controls, patients with TLE showed decreased activation of the occipital face-responsive region on the ipsilateral side and an increased activity of the anterior temporal lobe in the side contralateral to the seizure focus. Probabilistic tractography revealed that the occipital face area and anterior temporal lobe are connected via the inferior longitudinal fasciculus, which in individuals with TLE showed reduced integrity. Taken together, these findings suggest that brain function and white matter integrity of networks subserving face processing are impaired on the side of seizure onset, accompanied by altered responses on the side contralateral to the seizure. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

  9. A Mixed-Methods Social Networks Study Design for Research on Transnational Families

    ERIC Educational Resources Information Center

    Bernardi, Laura

    2011-01-01

    This paper advocates the adoption of a mixed-methods research design to describe and analyze ego-centered social networks in transnational family research. Drawing on the experience of the "Social Networks Influences on Family Formation" project (2004-2005; see Bernardi, Keim, & von der Lippe, 2007a, 2007b), I show how the combined…

  10. Temporal changes in the structure of a plant-frugivore network are influenced by bird migration and fruit availability

    PubMed Central

    Andresen, Ellen; Díaz-Castelazo, Cecilia

    2016-01-01

    Background. Ecological communities are dynamic collections whose composition and structure change over time, making up complex interspecific interaction networks. Mutualistic plant–animal networks can be approached through complex network analysis; these networks are characterized by a nested structure consisting of a core of generalist species, which endows the network with stability and robustness against disturbance. Those mutualistic network structures can vary as a consequence of seasonal fluctuations and food availability, as well as the arrival of new species into the system that might disorder the mutualistic network structure (e.g., a decrease in nested pattern). However, there is no assessment on how the arrival of migratory species into seasonal tropical systems can modify such patterns. Emergent and fine structural temporal patterns are adressed here for the first time for plant-frugivorous bird networks in a highly seasonal tropical environment. Methods. In a plant-frugivorous bird community, we analyzed the temporal turnover of bird species comprising the network core and periphery of ten temporal interaction networks resulting from different bird migration periods. Additionally, we evaluated how fruit abundance and richness, as well as the arrival of migratory birds into the system, explained the temporal changes in network parameters such as network size, connectance, nestedness, specialization, interaction strength asymmetry and niche overlap. The analysis included data from 10 quantitative plant-frugivorous bird networks registered from November 2013 to November 2014. Results. We registered a total of 319 interactions between 42 plant species and 44 frugivorous bird species; only ten bird species were part of the network core. We witnessed a noteworthy turnover of the species comprising the network periphery during migration periods, as opposed to the network core, which did not show significant temporal changes in species composition. Our

  11. Temporal changes in the structure of a plant-frugivore network are influenced by bird migration and fruit availability.

    PubMed

    Ramos-Robles, Michelle; Andresen, Ellen; Díaz-Castelazo, Cecilia

    2016-01-01

    Background. Ecological communities are dynamic collections whose composition and structure change over time, making up complex interspecific interaction networks. Mutualistic plant-animal networks can be approached through complex network analysis; these networks are characterized by a nested structure consisting of a core of generalist species, which endows the network with stability and robustness against disturbance. Those mutualistic network structures can vary as a consequence of seasonal fluctuations and food availability, as well as the arrival of new species into the system that might disorder the mutualistic network structure (e.g., a decrease in nested pattern). However, there is no assessment on how the arrival of migratory species into seasonal tropical systems can modify such patterns. Emergent and fine structural temporal patterns are adressed here for the first time for plant-frugivorous bird networks in a highly seasonal tropical environment. Methods. In a plant-frugivorous bird community, we analyzed the temporal turnover of bird species comprising the network core and periphery of ten temporal interaction networks resulting from different bird migration periods. Additionally, we evaluated how fruit abundance and richness, as well as the arrival of migratory birds into the system, explained the temporal changes in network parameters such as network size, connectance, nestedness, specialization, interaction strength asymmetry and niche overlap. The analysis included data from 10 quantitative plant-frugivorous bird networks registered from November 2013 to November 2014. Results. We registered a total of 319 interactions between 42 plant species and 44 frugivorous bird species; only ten bird species were part of the network core. We witnessed a noteworthy turnover of the species comprising the network periphery during migration periods, as opposed to the network core, which did not show significant temporal changes in species composition. Our results

  12. Temporal Dynamics of the Default Mode Network Characterize Meditation-Induced Alterations in Consciousness.

    PubMed

    Panda, Rajanikant; Bharath, Rose D; Upadhyay, Neeraj; Mangalore, Sandhya; Chennu, Srivas; Rao, Shobini L

    2016-01-01

    Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network.

  13. Temporal Dynamics of the Default Mode Network Characterize Meditation-Induced Alterations in Consciousness

    PubMed Central

    Panda, Rajanikant; Bharath, Rose D.; Upadhyay, Neeraj; Mangalore, Sandhya; Chennu, Srivas; Rao, Shobini L.

    2016-01-01

    Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network. PMID:27499738

  14. Exploiting temporal network structures of human interaction to effectively immunize populations.

    PubMed

    Lee, Sungmin; Rocha, Luis E C; Liljeros, Fredrik; Holme, Petter

    2012-01-01

    Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading (of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies--we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure--future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these.

  15. Exploiting Temporal Network Structures of Human Interaction to Effectively Immunize Populations

    PubMed Central

    Lee, Sungmin; Rocha, Luis E. C.; Liljeros, Fredrik; Holme, Petter

    2012-01-01

    Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading (of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies—we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure—future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these. PMID

  16. Spatio-Temporal Patterns of the International Merger and Acquisition Network.

    PubMed

    Dueñas, Marco; Mastrandrea, Rossana; Barigozzi, Matteo; Fagiolo, Giorgio

    2017-09-07

    This paper analyses the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of M&As to build a temporal sequence of binary and weighted-directed networks for the period 1995-2010 and 224 countries (nodes) connected according to their M&As flows (links). We study different geographical and temporal aspects of the international M&A network (IMAN), building sequences of filtered sub-networks whose links belong to specific intervals of distance or time. Given that M&As and trade are complementary ways of reaching foreign markets, we perform our analysis using statistics employed for the study of the international trade network (ITN), highlighting the similarities and differences between the ITN and the IMAN. In contrast to the ITN, the IMAN is a low density network characterized by a persistent giant component with many external nodes and low reciprocity. Clustering patterns are very heterogeneous and dynamic. High-income economies are the main acquirers and are characterized by high connectivity, implying that most countries are targets of a few acquirers. Like in the ITN, geographical distance strongly impacts the structure of the IMAN: link-weights and node degrees have a non-linear relation with distance, and an assortative pattern is present at short distances.

  17. On the robustness of in- and out-components in a temporal network.

    PubMed

    Konschake, Mario; Lentz, Hartmut H K; Conraths, Franz J; Hövel, Philipp; Selhorst, Thomas

    2013-01-01

    Many networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation properties of infectious diseases in time-dependent networks. In particular, we analyze a dataset containing livestock trade movements. The corresponding networks are known to be a major route for the spread of animal diseases. In this context chronology is crucial. A disease can only spread if the temporal sequence of trade contacts forms a chain of causality. Therefore, the identification of relevant nodes under time-varying network topologies is of great interest for the implementation of counteractions. We find that a time-aggregated approach might fail to identify epidemiologically relevant nodes. Hence, we explore the adaptability of the concept of centrality of nodes to temporal networks using a data-driven approach on the example of animal trade. We utilize the size of the in- and out-component of nodes as centrality measures. Both measures are refined to gain full awareness of the time-dependent topology and finite infectious periods. We show that the size of the components exhibit strong temporal heterogeneities. In particular, we find that the size of the components is overestimated in time-aggregated networks. For disease control, however, a risk assessment independent of time and specific disease properties is usually favored. We therefore explore the disease parameter range, in which a time-independent identification of central nodes remains possible. We find a ranking of nodes according to their component sizes reasonably stable for a wide range of infectious periods. Samples based on this ranking are robust enough against varying disease parameters and hence are promising tools for disease control.

  18. Temporal Classification Error Compensation of Convolutional Neural Network for Traffic Sign Recognition

    NASA Astrophysics Data System (ADS)

    Yoon, Seungjong; Kim, Eungtae

    2017-02-01

    In this paper, we propose the method that classifies the traffic signs by using Convolutional Neural Network(CNN) and compensates the error rate of CNN using the temporal correlation between adjacent successive frames. Instead of applying a conventional CNN architecture with more layers, Temporal Classification Error Compensation(TCEC) is proposed to improve the error rate in the architecture which has less nodes and layers than a conventional CNN. Experimental results show that the complexity of the proposed method could be reduced by 50% compared with that of the conventional CNN with same layers, and the error rate could be improved by about 3%.

  19. Memory network plasticity after temporal lobe resection: a longitudinal functional imaging study

    PubMed Central

    Sidhu, Meneka K.; Stretton, Jason; Winston, Gavin P.; McEvoy, Andrew W.; Symms, Mark; Thompson, Pamela J.; Koepp, Matthias J.

    2016-01-01

    Anterior temporal lobe resection can control seizures in up to 80% of patients with temporal lobe epilepsy. Memory decrements are the main neurocognitive complication. Preoperative functional reorganization has been described in memory networks, but less is known of postoperative reorganization. We investigated reorganization of memory-encoding networks preoperatively and 3 and 12 months after surgery. We studied 36 patients with unilateral medial temporal lobe epilepsy (19 right) before and 3 and 12 months after anterior temporal lobe resection. Fifteen healthy control subjects were studied at three equivalent time points. All subjects had neuropsychological testing at each of the three time points. A functional magnetic resonance imaging memory-encoding paradigm of words and faces was performed with subsequent out-of-scanner recognition assessments. Changes in activations across the time points in each patient group were compared to changes in the control group in a single flexible factorial analysis. Postoperative change in memory across the time points was correlated with postoperative activations to investigate the efficiency of reorganized networks. Left temporal lobe epilepsy patients showed increased right anterior hippocampal and frontal activation at both 3 and 12 months after surgery relative to preoperatively, for word and face encoding, with a concomitant reduction in left frontal activation 12 months postoperatively. Right anterior hippocampal activation 12 months postoperatively correlated significantly with improved verbal learning in patients with left temporal lobe epilepsy from preoperatively to 12 months postoperatively. Preoperatively, there was significant left posterior hippocampal activation that was sustained 3 months postoperatively at word encoding, and increased at face encoding. For both word and face encoding this was significantly reduced from 3 to 12 months postoperatively. Patients with right temporal lobe epilepsy showed increased

  20. A mixed-methods study of research dissemination across practice-based research networks.

    PubMed

    Lipman, Paula Darby; Lange, Carol J; Cohen, Rachel A; Peterson, Kevin A

    2014-01-01

    Practice-based research networks may be expanding beyond research into rapid learning systems. This mixed-methods study uses Agency for Healthcare Research and Quality registry data to identify networks currently engaged in dissemination of research findings and to select a sample to participate in qualitative semistructured interviews. An adapted Diffusion of Innovations framework was used to organize concepts by characteristics of networks, dissemination activities, and mechanisms for rapid learning. Six regional networks provided detailed information about dissemination strategies, organizational context, role of practice-based research network, member involvement, and practice incentives. Strategies compatible with current practices and learning innovations that generate observable improvements may increase effectiveness of rapid learning approaches.

  1. Steady state and mean recurrence time for random walks on stochastic temporal networks

    NASA Astrophysics Data System (ADS)

    Speidel, Leo; Lambiotte, Renaud; Aihara, Kazuyuki; Masuda, Naoki

    2015-01-01

    Random walks are basic diffusion processes on networks and have applications in, for example, searching, navigation, ranking, and community detection. Recent recognition of the importance of temporal aspects on networks spurred studies of random walks on temporal networks. Here we theoretically study two types of event-driven random walks on a stochastic temporal network model that produces arbitrary distributions of interevent times. In the so-called active random walk, the interevent time is reinitialized on all links upon each movement of the walker. In the so-called passive random walk, the interevent time is reinitialized only on the link that has been used the last time, and it is a type of correlated random walk. We find that the steady state is always the uniform density for the passive random walk. In contrast, for the active random walk, it increases or decreases with the node's degree depending on the distribution of interevent times. The mean recurrence time of a node is inversely proportional to the degree for both active and passive random walks. Furthermore, the mean recurrence time does or does not depend on the distribution of interevent times for the active and passive random walks, respectively.

  2. Temporal stability of network centrality in control and default mode networks: Specific associations with externalizing psychopathology in children and adolescents.

    PubMed

    Sato, João Ricardo; Biazoli, Claudinei Eduardo; Salum, Giovanni Abrahão; Gadelha, Ary; Crossley, Nicolas; Satterthwaite, Theodore D; Vieira, Gilson; Zugman, André; Picon, Felipe Almeida; Pan, Pedro Mario; Hoexter, Marcelo Queiroz; Anés, Mauricio; Moura, Luciana Monteiro; Del'aquilla, Marco Antonio Gomes; Amaro, Edson; McGuire, Philip; Lacerda, Acioly L T; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin; Bressan, Rodrigo Affonseca

    2015-12-01

    Abnormal connectivity patterns have frequently been reported as involved in pathological mental states. However, most studies focus on "static," stationary patterns of connectivity, which may miss crucial biological information. Recent methodological advances have allowed the investigation of dynamic functional connectivity patterns that describe non-stationary properties of brain networks. Here, we introduce a novel graphical measure of dynamic connectivity, called time-varying eigenvector centrality (tv-EVC). In a sample 655 children and adolescents (7-15 years old) from the Brazilian "High Risk Cohort Study for Psychiatric Disorders" who were imaged using resting-state fMRI, we used this measure to investigate age effects in the temporal in control and default-mode networks (CN/DMN). Using support vector regression, we propose a network maturation index based on the temporal stability of tv-EVC. Moreover, we investigated whether the network maturation is associated with the overall presence of behavioral and emotional problems with the Child Behavior Checklist. As hypothesized, we found that the tv-EVC at each node of CN/DMN become more stable with increasing age (P < 0.001 for all nodes). In addition, the maturity index for this particular network is indeed associated with general psychopathology in children assessed by the total score of Child Behavior Checklist (P = 0.027). Moreover, immaturity of the network was mainly correlated with externalizing behavior dimensions. Taken together, these results suggest that changes in functional network dynamics during neurodevelopment may provide unique insights regarding pathophysiology. © 2015 Wiley Periodicals, Inc.

  3. Temporal Information Partition Networks (TIPNets): A Process Network Framework to Reveal Eco-Hydrologic Feedbacks, Responses and Shifts

    NASA Astrophysics Data System (ADS)

    Goodwell, A. E.; Kumar, P.

    2016-12-01

    Within an ecosystem, components of the atmosphere, vegetation, and the root-soil system participate in forcing and feedback reactions at varying time scales and intensities. These interactions constitute a complex network that exhibits behavioral shifts due to perturbations ranging from weather events to long-term drought or land use change. However, it is challenging to characterize this shifting network due to multiple drivers, non-linear interactions, and feedback-induced synchronization. To overcome these issues, we implement a process network approach where eco-hydrologic time-series variables are nodes and information measures are links. We introduce a Temporal Information Partition Network (TIPNet) framework in which multivariate lagged mutual information between pairs of source nodes and target nodes is decomposed into synergistic, redundant, and unique components, each of which reveals different aspects of interactions within the network. We use methods to compute information measures given as few as 200 data points to construct TIPNets based on 1-minute weather station data (radiation Rg, air temperature Ta, wind speed WS, relative humidity RH, precipitation PPT, and leaf wetness LWet) from the Intensively Managed Critical Zone Observatory (IML-CZO) during the growing season of 2015. We find that the source node pairs (Ta, Rg) and (Ta, RH) tend to be synchronized sources that provide dominantly redundant information to target nodes during the day and night, respectively. Meanwhile, source node pairs that include WS often provide synergistic information, indicating the presence of multiple non-redundant influences. We assess temporal shifts in network behavior for weather conditions including rainy periods, dew, and accumulating drought to show how process networks reveal ecosystem behaviors and responses.

  4. Network-oriented massive spatio-temporal data model and its applications

    NASA Astrophysics Data System (ADS)

    Liu, Renyi; Liu, Nan; Bao, Weizheng; Zhu, Yan

    2006-10-01

    It is foundation and key of developing GIS platforms of new generation to study the network-oriented massive spatial and spatio-temporal data model. But the research has met many difficulties. The paper combines two models of massive spatial data and spatio-temporal data seemed to be independent to study together in theory and technique. On the base of analyzing the limitations of present geographical spatial data model and spatio-temporal data model, a new model with characteristics of new generation's GIS platform, that is, Feature-Oriented Massive Spatio-temporal Object Tree (FOMSOT) with four-tier architectures is presented. The FOMSOT breaks down the constraint of map layer. It can deal with the massive spatio-temporal data better. The dynamic multi-base state with amendment (DMSA), fast index of base state with amendment in section, storage factors of variable granularity (SFVG) are used in FOMSOT which can manage the massive spatio-temporal data in high efficiency. A prototype "LyranMap" of new generation's GIS platform with the theory and technical method of FOMSOT has been realized, and it has been used in some application systems, for example, the land planning system "LandPlanner", land investigation system "LandExplorer" and land cadastral system "LandReGIS". These verify the correctness and effectiveness of the FOMSOT.

  5. Impact of Partial Time Delay on Temporal Dynamics of Watts-Strogatz Small-World Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Yan, Hao; Sun, Xiaojuan

    2017-06-01

    In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts-Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay τ and the other is the probability of partial time delay pdelay. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay τ, the probability pdelay could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay τ, temporal coherence and mean firing rate do not have great changes with respect to pdelay. Time delay τ always has great influence on both temporal coherence and mean firing rate no matter what is the value of pdelay. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay τ. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

  6. Spatio-temporal propagation of cascading overload failures in spatially embedded networks.

    PubMed

    Zhao, Jichang; Li, Daqing; Sanhedrai, Hillel; Cohen, Reuven; Havlin, Shlomo

    2016-01-12

    Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems.

  7. Spatio-temporal propagation of cascading overload failures in spatially embedded networks

    PubMed Central

    Zhao, Jichang; Li, Daqing; Sanhedrai, Hillel; Cohen, Reuven; Havlin, Shlomo

    2016-01-01

    Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems. PMID:26754065

  8. The Compact Network RTK Method: An Effective Solution to Reduce GNSS Temporal and Spatial Decorrelation Error

    NASA Astrophysics Data System (ADS)

    Park, Byungwoon; Kee, Changdon

    This paper proposes a method that combines compact real-time kinematic (RTK) and reference station (RS) networking techniques, and shows that this approach can reduce both the temporal and spatial decorrelation error. The compact RTK method compatibility with all the conventional network RTK systems, i.e., Master-Auxiliary Concept (MAC), Virtual Reference Stations (VRS), and Fl40 s and determines position with 68 cm vertical error (95%) in a 100 by 100 km region. Moreover, the Compact Network RTK approach enables network RTK service providers to reduce the data-link bandwidth for correction messages to 5-700 bps (bit/s) down from several thousand bps, currently 9600 bps of GPRS/GSM, without a severe degradation of accuracy.

  9. Effect of node attributes on the temporal dynamics of network structure

    NASA Astrophysics Data System (ADS)

    Momeni, Naghmeh; Fotouhi, Babak

    2017-03-01

    Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

  10. Reactive mixing in heterogeneous porous media flows: scalar gradient distribution, spatial intermittency and temporal scaling of effective reaction kinetics

    NASA Astrophysics Data System (ADS)

    Le Borgne, Tanguy; Dentz, Marco; Villermaux, Emmanuel

    2015-11-01

    Reactive mixing processes play a central role in a range of porous media systems, including CO2 sequestration operations, reactive geothermal dipoles, biofilms, or flow-through reactors. Many of these reactions are limited by fluid mixing processes that bring the reactants into contact. Hence, the temporal dynamics of effective global reactivity is determined by the creation of concentration gradients by fluid stretching and their dissipation by diffusion. From the analysis of the elongation and aggregation of lamellar structures formed in the transported scalar fields, we derive analytical predictions for the probability density functions of scalar gradients in heterogeneous Darcy flows over a large range of Péclet numbers and permeability field variances. In this framework, we show that heterogeneous Darcy fields generate highly intermittent concentration fields, as manifested by the spatial scaling of structure functions. The resulting effective reaction rates display a range of temporal behaviors that depend on the degree of heterogeneity. In the large Damköhler limit, we derive analytical expressions for these temporal scalings in the different regimes that arise when exploring the Péclet number space. We generalize these results for different random flows.

  11. Mixed methods analysis of urban environmental stewardship networks

    Treesearch

    James J.T. Connolly; Erika S. Svendsen; Dana R. Fisher; Lindsay K. Campbell

    2015-01-01

    While mixed methods approaches to research have been accepted practice within the social sciences for several decades (Tashakkori and Teddlie 2003), the rising demand for cross-disciplinary analyses of socio-environmental processes has necessitated a renewed examination of this approach within environmental studies. Urban environmental stewardship is one area where it...

  12. Temporal variation in end-member chemistry and its influence on runoff mixing patterns in a forested, Piedmont catchment

    NASA Astrophysics Data System (ADS)

    Inamdar, Shreeram; Dhillon, Gurbir; Singh, Shatrughan; Dutta, Sudarshan; Levia, Delphis; Scott, Durelle; Mitchell, Myron; Van Stan, John; McHale, Patrick

    2013-04-01

    Runoff mixing patterns for base flow and 42 storm events were investigated for a 3 year period (2008-2010) in a 12 ha forested catchment in the mid-Atlantic, Piedmont region of the USA. Eleven distinct runoff sources were sampled independently and included: precipitation, throughfall, stemflow, litter leachate, wetland soil water, tension soil water, shallow groundwater, groundwater seeps, hyporheic water, riparian groundwater, and deep groundwater. A rigorous end-member mixing analysis (EMMA) was implemented and all base flow, storm-flow, and end-member chemistries were evaluated in a two-dimensional mixing space. End-members enclosed stream water chemistry and displayed a systematic continuum in EMMA space. Base-flow chemistry of stream waters was similar to groundwater seeps. Storm-event runoff was attributed to contributions from surficial sources (precipitation, throughfall, stemflow, and litter leachate) on the rising limb of the discharge hydrograph that was followed by soil and shallow groundwater sources on the recession limb of the hydrograph. The shapes of the storm-event hysteresis loops (wide versus tight, linear patterns) varied with hydrologic conditions from wet, hydrologically well-connected conditions to a dry, disconnected state. Detailed temporal data on end-member chemistry allowed us to explain the changes in stream water hysteresis patterns and runoff mixing space to shifts in end-member chemistry that occurred as the catchment became hydrologically disconnected. These results highlight the need to recognize the temporal variation in end-member chemistry as a function of catchment wetness and the need to collect high-frequency data on both-stream water as well as potential runoff end-members to better characterize catchment flow paths and mixing responses.

  13. Temporal Information Partitioning Networks (TIPNets): Characterizing emergent behavior in complex ecohydrologic systems

    NASA Astrophysics Data System (ADS)

    Goodwell, Allison; Kumar, Praveen

    2017-04-01

    Within an ecosystem, components of the atmosphere, vegetation, and the root-soil system participate in forcing and feedback reactions at varying time scales and intensities. These interactions constitute a complex network that exhibits behavioral shifts due to perturbations ranging from weather events to long-term drought or land use change. However, it is challenging to characterize this shifting network due to multiple drivers, non-linear interactions, and synchronization due to feedback. To overcome these issues, we implement a process network approach where eco-hydrologic time-series variables are nodes and information measures are links. We introduce a Temporal Information Partition Network (TIPNet) framework in which multivariate lagged mutual information between source and target nodes is decomposed into synergistic, redundant, and unique components, each of which reveals different aspects of interactions within the network. We use methods to compute information measures given as few as 200 data points to construct TIPNets based on 1-minute weather station data (radiation Rg, air temperature Ta, wind speed WS, relative humidity RH, precipitation PPT, and leaf wetness LWet) from Central Illinois during the growing season of 2015. We assess temporal shifts in network behavior for various weather conditions and over the growing season. We find that wet time periods are associated with complex and synergistic network structures compared to dry conditions, and that seasonal network patterns reveal responses to vegetation growth and rainfall trends. This framework is applicable to study a broad range of complex systems composed of multiple interacting components, and may aid process understanding, model improvement, and resilience and vulnerability assessments.

  14. Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations

    NASA Astrophysics Data System (ADS)

    Antulov-Fantulin, Nino; Lančić, Alen; Šmuc, Tomislav; Štefančić, Hrvoje; Šikić, Mile

    2015-06-01

    Detection of patient zero can give new insights to epidemiologists about the nature of first transmissions into a population. In this Letter, we study the statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure. By using exact analytic calculations and Monte Carlo estimators, we demonstrate the detectability limits for the susceptible-infected-recovered model, which primarily depend on the spreading process characteristics. Finally, we demonstrate the applicability of the approach in a case of a simulated sexually transmitted infection spreading over an empirical temporal network of sexual interactions.

  15. Knowledge engineering for temporal dependency networks as operations procedures. [in space communication

    NASA Technical Reports Server (NTRS)

    Fayyad, Kristina E.; Hill, Randall W., Jr.; Wyatt, E. J.

    1993-01-01

    This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

  16. Power-law distributed temporal heterogeneity of human activities promotes cooperation on complex networks

    NASA Astrophysics Data System (ADS)

    Liu, Chao; Li, Rong

    2016-09-01

    An evolutionary prisoner's dilemma game (PDG) with players located on Barabási-Albert scale-free networks is studied. The impact of players' heterogeneous temporal activity pattern on the evolution of cooperation is investigated. To this end, the normal procedure that players update their strategies immediately after a round of game is discarded. Instead, players update strategies according to their assigned reproduction time, which follows a power-law distribution. We find that the temporal heterogeneity of players' activities facilitates the prosperity of cooperation, indicating the important role of hubs in the maintenance of cooperation on scale-free networks. When the reproduction time is assigned to individuals negatively related to their degrees, a fluctuation of the cooperation level with the increase of the exponent β is observed.

  17. A Markov model for the temporal dynamics of balanced random networks of finite size

    PubMed Central

    Lagzi, Fereshteh; Rotter, Stefan

    2014-01-01

    The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between

  18. Stability Analysis of Attractor Neural Network Model of Inferior Temporal Cortex —Relationship between Attractor Stability and Learning Order—

    NASA Astrophysics Data System (ADS)

    Tomoyuki Kimoto,; Tatsuya Uezu,; Masato Okada,

    2010-06-01

    Miyashita found that the long-term memory of visual stimuli is stored in the monkey’s inferior temporal cortex and that the temporal correlation in terms of the learning order of visual stimuli is converted into spatial correlation in terms of the firing rate patterns of the neuron group. To explain Miyashita’s findings, Griniasty et al. [Neural Comput. 5 (1993) 1] and Amit et al. [J. Neurosci. 14 (1994) 6435] proposed the attractor neural network model, and the Amit model has been examined only for the stable state acquired by storing memory patterns in a fixed sequence. In the real world, however, the learning order has statistical continuity but it also has randomness, and the stability of the state changes depending on the statistical properties of learning order when memory patterns are stored randomly. In addition, it is preferable for the stable state to become an appropriate attractor that reflects the relationship between memory patterns by the statistical properties of the learning order. In this study, we examined the dependence of the stable state on the statistical properties of the learning order without modifying the Amit model. The stable state was found to change from the correlated attractor to the Hopfield or Mp attractor, which is the mixed state with all memory patterns when the rate of random learning increases. Furthermore, we found that if the statistical properties of the learning order change, the stable state can change to an appropriate attractor reflecting the relationship between memory patterns.

  19. Comparison of mixed and lamellar coculture spatial arrangements for tissue engineering capillary networks in vitro.

    PubMed

    Peters, Erica B; Christoforou, Nicolas; Leong, Kam W; Truskey, George A

    2013-03-01

    Coculture of endothelial cells (ECs) and smooth muscle cells (SMCs) in vitro can yield confluent monolayers or EC networks. Factors influencing this transition are not known. In this study, we examined whether the spatial arrangement of EC-SMC cocultures affected EC migration, network morphology, and angiogenic protein secretion. Human umbilical cord blood-derived ECs (hCB-ECs) were grown in coculture with human aortic SMCs in either a mixed or lamellar spatial geometry and analyzed over a culture period of 12 days. The hCB-ECs cultured on SMCs in a mixed system had higher cell speeds, shorter persistence times, and lower random motility coefficients than ECs in a lamellar system. By day 12 of coculture, mixed systems demonstrated greater anastomoses and capillary loop formation than lamellar systems as evidenced by a higher number of branch points, angle of curvature between branch points, and percentage of imaged area covered by networks. The network morphology was more uniform in the mixed systems than the lamellar systems with fewer EC clusters present after several days in culture. Proliferation of hCB-ECs was higher for mixed cocultures during the first 24 h of coculture, and then declined dramatically suggesting that proliferation only contributed to network formation during the early stages of coculture. Proteome assay results show reduced solution levels, but no change in intracellular levels of angiogenic proteins in lamellar systems compared to mixed systems. These data suggest that mixing ECs and SMCs together favors the formation of EC networks to a greater extent than a lamellar arrangement in which ECs form a cell layer above a confluent, quiescent layer of SMCs.

  20. Assessing the spatial and temporal variability of fAPAR 2-flux estimates in a temperate mixed coniferous forest

    NASA Astrophysics Data System (ADS)

    Putzenlechner, Birgitta; Sanchez-Azofeifa, Arturo; Ludwig, Ralf

    2016-04-01

    The fraction of absorbed photosynthetically active radiation (fAPAR) is recognized as one of the essential climate variables as it characterizes activity and dynamics of the Earth's terrestrial biosphere (GCOS, 2010). By linking photosynthetic active radiation (PAR) to the absorption of plants, fAPAR represents a crucial variable for describing land surface and atmosphere interactions considered in global circulation models as well as in production efficiency models for estimating terrestrial carbon balances. Recent studies report discrepancies between global fAPAR satellite products regarding both absolute values and uncertainty representation, thereby stressing the need for independent ground measurements (D'Odrico et al., 2014; Picket-Heaps et al., 2014; Tao et al., 2015). However, there is a lack of basic information to better understand the spatial and temporal bias of PAR field observations, particularly in forest ecosystems. In theory, it is known that fAPAR estimates are affected by e.g. illumination conditions, leaf area index, leaf color, background brightness, which in turn may lead to considerable bias of field measurements. However, theoretical findings lack validation in the field as well as practical recommendations for field protocols. In this study, the variability of two-flux fAPAR estimates with regards to different illumination conditions (solar zenith angles, diffuse radiation conditions) are investigated. Measurements of PAR are carried out at Graswang environmental monitoring site in Southern Germany within a temperate mixed coniferous forest. A relatively new environmental monitoring technology based on Wireless Sensor Networks (WSN) is applied, allowing for permanent synchronized measurements of transmitted PAR, thereby reducing temporal sampling bias. Transmitted PAR is obtained from 16 photon flux sensors, 1.3 m above the surface. With a reference sensor outside the forest measuring incoming PAR, a two-flux estimate based on the ratio of

  1. Long-term temporal variation in the organization of an ant–plant network

    PubMed Central

    Díaz-Castelazo, Cecilia; Sánchez-Galván, Ingrid R.; Guimarães, Paulo R.; Raimundo, Rafael L. Galdini; Rico-Gray, Víctor

    2013-01-01

    Background and Aims Functional groups of species interact and coevolve in space and time, forming complex networks of interacting species. A long-term study of temporal variation of an ant–plant network is presented with the aims of: (1) depicting its structural changes over a 20-year period; (2) detailing temporal variation in network topology, as revealed by nestedness and modularity analysis and other parameters (i.e. connectance, niche overlap); and (3) identifying long-term turnover in taxonomic structure (i.e. switches in ant resource use or plant visitor assemblages according to taxa). Methods Fieldwork was carried out at La Mancha, Mexico, and ant–plant interactions were observed between 1989 and 1991, between 1998 and 2000, and between May 2010 and 2011. Occurrences of ants on extrafloral nectaries (EFNs) were recorded. The resulting ant–plant networks were constructed from qualitative presence–absence data determined by a species–species matrix defined by the frequency of occurrence of each pairwise ant–plant interaction. Key Results Network variation across time was stable and a persistent nested structure may have contributed to the maintenance of resilient and species-rich communities. Modularity was lower than expected, especially in the most recent networks, indicating that the community exhibited high overlap among interacting species (e.g. few species were hubs in the more recent network, being partly responsible for the nested pattern). Structurally, the connections created among modules by super-generalists gave cohesion to subsets of species that otherwise would remain unconnected. This may have allowed an increasing cascade-effect of evolutionary events among modules. Mutualistic ant–plant interactions were structured 20 years ago mainly by the subdominant nectarivorous ant species Camponotus planatus and Crematogaster brevispinosa, which monopolized the best extrafloral nectar resources and out-competed other species with broader

  2. Long-term temporal variation in the organization of an ant-plant network.

    PubMed

    Díaz-Castelazo, Cecilia; Sánchez-Galván, Ingrid R; Guimarães, Paulo R; Raimundo, Rafael L Galdini; Rico-Gray, Víctor

    2013-06-01

    Functional groups of species interact and coevolve in space and time, forming complex networks of interacting species. A long-term study of temporal variation of an ant-plant network is presented with the aims of: (1) depicting its structural changes over a 20-year period; (2) detailing temporal variation in network topology, as revealed by nestedness and modularity analysis and other parameters (i.e. connectance, niche overlap); and (3) identifying long-term turnover in taxonomic structure (i.e. switches in ant resource use or plant visitor assemblages according to taxa). Fieldwork was carried out at La Mancha, Mexico, and ant-plant interactions were observed between 1989 and 1991, between 1998 and 2000, and between May 2010 and 2011. Occurrences of ants on extrafloral nectaries (EFNs) were recorded. The resulting ant-plant networks were constructed from qualitative presence-absence data determined by a species-species matrix defined by the frequency of occurrence of each pairwise ant-plant interaction. Network variation across time was stable and a persistent nested structure may have contributed to the maintenance of resilient and species-rich communities. Modularity was lower than expected, especially in the most recent networks, indicating that the community exhibited high overlap among interacting species (e.g. few species were hubs in the more recent network, being partly responsible for the nested pattern). Structurally, the connections created among modules by super-generalists gave cohesion to subsets of species that otherwise would remain unconnected. This may have allowed an increasing cascade-effect of evolutionary events among modules. Mutualistic ant-plant interactions were structured 20 years ago mainly by the subdominant nectarivorous ant species Camponotus planatus and Crematogaster brevispinosa, which monopolized the best extrafloral nectar resources and out-competed other species with broader feeding habits. Through time, these ants, which are

  3. Mixing as a Driver of Temporal Variations in River Hydrochemistry: Concentration-runoff Dynamics in the Andes-Amazon

    NASA Astrophysics Data System (ADS)

    Baronas, J. J.; Torres, M.; West, A. J.; Clark, K. E.

    2016-12-01

    Mixing as a Driver of Temporal Variations in River Hydrochemistry: Concentration-runoff Dynamics in the Andes-Amazon Variations in riverine solute concentrations and ratios with changing runoff are increasingly used to probe the fundamental relationship between weathering and hydrology. While immensely useful and insightful, this approach relies on the assumption that riverine chemistry is an integrated, spatially and temporally robust representation of weathering reactions throughout the whole catchment. In this study, we investigate the concentration-runoff (C-Q) dynamics of a suite of major (Na, Mg, Ca, Si, K, and SO4) and trace (Al, Ba, Cd, Co, Cr, Cu, Fe, Ge, Li, Mn, Mo, Nd, Ni, Rb, Sr, U, V, and Zn) elements in nested catchments of variable size and spanning the Andes mountains - Amazon foreland floodplain geomorphic gradient. The data indicate that, with several exceptions, trace metal C-Q behavior differs from that of major ions. The major elements exhibit various degrees of dilution with increasing runoff at all sites, whereas the concentrations of most trace elements either increase or show no relationship with increasing runoff in the three larger catchments (160 to 28,000 km2 area). We show that the observed C-Q relationships are influenced by changing tributary mixing ratios, which in turn are controlled by spatio-temporal variations in precipitation that scale with catchment area. Certain trace metals (most notably Fe, Mn, Nd, and Co) are non-conservatively lost from solution during in-channel processes, which may be related to colloidal size-partitioning, and could potentially exert additional control on C-Q dynamics of these elements. Overall, our findings suggest that spatial heterogeneity and variable mixing effects need to be assessed in catchments of 100 km2 (and perhaps even smaller) before C-Q or ratio-Q relationships can be interpreted as changes in hillslope-scale fluid flow paths or variations in weathering reaction rates.

  4. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs

    SciTech Connect

    William C. McLendon III; Brost, Randy C.

    2016-05-01

    Remote sensing systems produce large volumes of high-resolution images that are difficult to search. The GeoGraphy (pronounced Geo-Graph-y) framework [2, 20] encodes remote sensing imagery into a geospatial-temporal semantic graph representation to enable high level semantic searches to be performed. Typically scene objects such as buildings and trees tend to be shaped like blocks with few holes, but other shapes generated from path networks tend to have a large number of holes and can span a large geographic region due to their connectedness. For example, we have a dataset covering the city of Philadelphia in which there is a single road network node spanning a 6 mile x 8 mile region. Even a simple question such as "find two houses near the same street" might give unexpected results. More generally, nodes arising from networks of paths (roads, sidewalks, trails, etc.) require additional processing to make them useful for searches in GeoGraphy. We have assigned the term Path Network Recovery to this process. Path Network Recovery is a three-step process involving (1) partitioning the network node into segments, (2) repairing broken path segments interrupted by occlusions or sensor noise, and (3) adding path-aware search semantics into GeoQuestions. This report covers the path network recovery process, how it is used, and some example use cases of the current capabilities.

  5. Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

    PubMed

    Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

    2015-01-01

    Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

  6. Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

    PubMed Central

    Chen, Shi; Ilany, Amiyaal; White, Brad J.; Sanderson, Michael W.; Lanzas, Cristina

    2015-01-01

    Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways. PMID:26107251

  7. Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity.

    PubMed

    Brookes, Matthew J; O'Neill, George C; Hall, Emma L; Woolrich, Mark W; Baker, Adam; Palazzo Corner, Sofia; Robson, Siân E; Morris, Peter G; Barnes, Gareth R

    2014-05-01

    The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time-frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time-frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Capturing complexity: Mixing methods in the analysis of a European tobacco control policy network

    PubMed Central

    Weishaar, Heide; Amos, Amanda; Collin, Jeff

    2015-01-01

    Social network analysis (SNA), a method which can be used to explore networks in various contexts, has received increasing attention. Drawing on the development of European smoke-free policy, this paper explores how a mixed method approach to SNA can be utilised to investigate a complex policy network. Textual data from public documents, consultation submissions and websites were extracted, converted and analysed using plagiarism detection software and quantitative network analysis, and qualitative data from public documents and 35 interviews were thematically analysed. While the quantitative analysis enabled understanding of the network's structure and components, the qualitative analysis provided in-depth information about specific actors' positions, relationships and interactions. The paper establishes that SNA is suited to empirically testing and analysing networks in EU policymaking. It contributes to methodological debates about the antagonism between qualitative and quantitative approaches and demonstrates that qualitative and quantitative network analysis can offer a powerful tool for policy analysis. PMID:26185482

  9. Capturing complexity: Mixing methods in the analysis of a European tobacco control policy network.

    PubMed

    Weishaar, Heide; Amos, Amanda; Collin, Jeff

    Social network analysis (SNA), a method which can be used to explore networks in various contexts, has received increasing attention. Drawing on the development of European smoke-free policy, this paper explores how a mixed method approach to SNA can be utilised to investigate a complex policy network. Textual data from public documents, consultation submissions and websites were extracted, converted and analysed using plagiarism detection software and quantitative network analysis, and qualitative data from public documents and 35 interviews were thematically analysed. While the quantitative analysis enabled understanding of the network's structure and components, the qualitative analysis provided in-depth information about specific actors' positions, relationships and interactions. The paper establishes that SNA is suited to empirically testing and analysing networks in EU policymaking. It contributes to methodological debates about the antagonism between qualitative and quantitative approaches and demonstrates that qualitative and quantitative network analysis can offer a powerful tool for policy analysis.

  10. Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy.

    PubMed

    de Campos, Brunno Machado; Coan, Ana Carolina; Lin Yasuda, Clarissa; Casseb, Raphael Fernandes; Cendes, Fernando

    2016-09-01

    Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large-scale RSNs is differently affected in patients with right- and left-MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting-state functional-MRIs of 99 subjects (52 controls, 26 right- and 21 left-MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF(2) C-toolbox, which provided ROI-wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right-MTLE. Both groups showed abnormal correlation between the dorsal-DMN and the posterior salience, as well as between the dorsal-DMN and the executive-control network. Patients with left-MTLE also showed reduced correlation between the dorsal-DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left-MTLE expressed a low cluster coefficient, whereas the altered connections on right-MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right- and left-MTLE patients with HS have widespread abnormal interactions of large-scale brain networks; however, all parameters evaluated indicate that left-MTLE has a more intricate bihemispheric dysfunction compared to right-MTLE. Hum Brain Mapp 37:3137-3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  11. Investigating the relationship between subjective drug craving and temporal dynamics of the default mode network, executive control network, and salience network in methamphetamine dependents using rsfMRI

    NASA Astrophysics Data System (ADS)

    Soltanian-Zadeh, Somayyeh; Hossein-Zadeh, Gholam-Ali; Shahbabaie, Alireza; Ekhtiari, Hamed

    2016-03-01

    Resting state functional connectivity (rsFC) studies using fMRI provides a great deal of knowledge on the spatiotemporal organization of the brain. The relationships between and within a number of resting state functional networks, namely the default mode network (DMN), salience network (SN) and executive control network (ECN) have been intensely studied in basic and clinical cognitive neuroscience [1]. However, the presumption of spatial and temporal stationarity has mostly restricted the assessment of rsFC [1]. In this study, sliding window correlation analysis and k-means clustering were exploited to examine the temporal dynamics of rsFC of these three networks in 24 abstinent methamphetamine dependents. Afterwards, using canonical correlation analysis (CCA) the possible relationship between the level of self-reported craving and the temporal dynamics was examined. Results indicate that the rsFC transits between 6 discrete "FC states" in the meth dependents. CCA results show that higher levels of craving are associated with higher probability of transiting from state 4 to 6 (positive FC of DMN-ECN getting weak and negative FC of DMN-SN appearing) and staying in state 4 (positive FC of DMN-ECN), lower probability of staying in state 2 (negative FC of DMN-ECN), transiting from state 4 to 2 (change of positive FC of DMN-ECN to negative FC), and transiting from state 3 to 5 (appearance of negative FC of DMN-SN and positive FC of DMN-ECN with the presence of negative FC of SN-ECN). Quantitative measures of temporal dynamics in large-scale brain networks could bring new added values to increase potentials for applications of rsfMRI in addiction medicine.

  12. Using spatio-temporal asymmetry to enhance mixing in chaotic flows: From maps to stirred tanks

    NASA Astrophysics Data System (ADS)

    Alvarez, Mario Moises

    Under laminar flow conditions, chaos is the only route to achieve effective mixing. Indeed, industrially relevant devices such as static mixers, stirred tanks, and roller bottles work because they create chaotic flows. However, they are generally operated and designed in a symmetric fashion (e.g. symmetric construction, periodic operation). Under such circumstances, chaotic and nonchaotic regions always co-exist, often hindering mixing performance. The introduction of asymmetries (in space or time) has been proposed as a means to improve mixing performance by generating globally chaotic systems in which the entire flow domain is subject to the action of exponential stretching and repeated folding, key features of chaotic flows capable of good mixing. Here we compare mixing performance of symmetric and asymmetric mixing flows from the point of view of the properties of the structure that they generate. In particular, we analyze two classes of systems: We use computer simulations to follow the process of elongation and deformation of interfaces as they are advected by time-periodic and aperiodic protocols in an idealized 2-D flow (the sine flow). The distribution of length scales characteristic of the partially mixed structures in this flow is calculated and their statistical properties are investigated. As the main conclusion, we find that the distribution of length scales is universal (independently on the periodic or aperiodic nature of the flow), and predictable (based on stretching calculations) for any globally chaotic flow. Subsequently, mixing structures and flow patterns in stirred tank systems of geometries encountered in engineering practice and operated in the laminar regime are investigated experimentally using UV visualization techniques, Particle Image Velocimetry (PIV) and Planar Laser Induced Fluorescence (p-LIF). It is experimentally demonstrated that concentric stirred tank configurations achieve partial chaos only by virtue of the small

  13. Bayesian Mixed-Membership Models of Complex and Evolving Networks

    DTIC Science & Technology

    2006-12-01

    in human dynamics. Nature, 435:207–211, 2005a. A. L. Barabasi. Network theory—the emergence of the creative enterprise. Science , 308:639–641, 2005b. A...Kidd, L. A. Zhivotovsky, and M. W. Feldman. Genetic structure of human populations. Science , 298:2381–2385, 2002. S. T. Roweis and L. K. Saul...Software Research Program in Computation, Organizations and Society School of Computer Science Carnegie Mellon University 5000 Forbes Avenue Pittsburgh

  14. A diffusion perspective on temporal networks: A case study on a supermarket

    NASA Astrophysics Data System (ADS)

    Deng, Shiguo; Qiu, Lu; Yang, Yue; Yang, Huijie

    2016-01-01

    From a large amount of records, one can extract behavioral characteristics of a social system at different scales. Theoretically, it can help us to know how the global behavior of a social system is formed from individual activities. Practically, it can be used to optimize and even to control the social system. Complicated relationships between the individuals form a network, which evolves with time. The behavior of the system can be accordingly understood in the framework of temporal network. In the present paper, instead of focusing on microscopic structures, we develop a framework to investigate temporal networks from the viewpoint of diffusion process, in which each snapshot network is divided into groups and the ID number of the group a node belongs to is used to measure its state. By this way trajectories of the nodes form an ensemble of realizations of a stochastic process. As an illustration, we investigate the diffusion behavior of a supermarket. One can find that with the increase of time the customers cluster and separate into different groups. Meanwhile, the system evolves in a significant order way, instead of a complete random one.

  15. Factors affecting reorganisation of memory encoding networks in temporal lobe epilepsy

    PubMed Central

    Sidhu, M.K.; Stretton, J.; Winston, G.P.; Symms, M.; Thompson, P.J.; Koepp, M.J.; Duncan, J.S.

    2015-01-01

    Summary Aims In temporal lobe epilepsy (TLE) due to hippocampal sclerosis reorganisation in the memory encoding network has been consistently described. Distinct areas of reorganisation have been shown to be efficient when associated with successful subsequent memory formation or inefficient when not associated with successful subsequent memory. We investigated the effect of clinical parameters that modulate memory functions: age at onset of epilepsy, epilepsy duration and seizure frequency in a large cohort of patients. Methods We studied 53 patients with unilateral TLE and hippocampal sclerosis (29 left). All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words. A continuous regression analysis was used to investigate the effects of age at onset of epilepsy, epilepsy duration and seizure frequency on the activation patterns in the memory encoding network. Results Earlier age at onset of epilepsy was associated with left posterior hippocampus activations that were involved in successful subsequent memory formation in left hippocampal sclerosis patients. No association of age at onset of epilepsy was seen with face encoding in right hippocampal sclerosis patients. In both left hippocampal sclerosis patients during word encoding and right hippocampal sclerosis patients during face encoding, shorter duration of epilepsy and lower seizure frequency were associated with medial temporal lobe activations that were involved in successful memory formation. Longer epilepsy duration and higher seizure frequency were associated with contralateral extra-temporal activations that were not associated with successful memory formation. Conclusion Age at onset of epilepsy influenced verbal memory encoding in patients with TLE due to hippocampal sclerosis in the speech-dominant hemisphere. Shorter duration of epilepsy and lower seizure frequency were associated with less disruption of the efficient memory encoding network whilst

  16. Factors affecting reorganisation of memory encoding networks in temporal lobe epilepsy.

    PubMed

    Sidhu, M K; Stretton, J; Winston, G P; Symms, M; Thompson, P J; Koepp, M J; Duncan, J S

    2015-02-01

    In temporal lobe epilepsy (TLE) due to hippocampal sclerosis reorganisation in the memory encoding network has been consistently described. Distinct areas of reorganisation have been shown to be efficient when associated with successful subsequent memory formation or inefficient when not associated with successful subsequent memory. We investigated the effect of clinical parameters that modulate memory functions: age at onset of epilepsy, epilepsy duration and seizure frequency in a large cohort of patients. We studied 53 patients with unilateral TLE and hippocampal sclerosis (29 left). All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words. A continuous regression analysis was used to investigate the effects of age at onset of epilepsy, epilepsy duration and seizure frequency on the activation patterns in the memory encoding network. Earlier age at onset of epilepsy was associated with left posterior hippocampus activations that were involved in successful subsequent memory formation in left hippocampal sclerosis patients. No association of age at onset of epilepsy was seen with face encoding in right hippocampal sclerosis patients. In both left hippocampal sclerosis patients during word encoding and right hippocampal sclerosis patients during face encoding, shorter duration of epilepsy and lower seizure frequency were associated with medial temporal lobe activations that were involved in successful memory formation. Longer epilepsy duration and higher seizure frequency were associated with contralateral extra-temporal activations that were not associated with successful memory formation. Age at onset of epilepsy influenced verbal memory encoding in patients with TLE due to hippocampal sclerosis in the speech-dominant hemisphere. Shorter duration of epilepsy and lower seizure frequency were associated with less disruption of the efficient memory encoding network whilst longer duration and higher seizure

  17. Whole network, temporal and parietal lobe contributions to the earliest phases of language production.

    PubMed

    Principe, Alessandro; Calabria, Marco; Campo, Adrià Tauste; Cruzat, Josephine; Conesa, Gerardo; Costa, Albert; Rocamora, Rodrigo

    2017-10-01

    We investigated whether it is possible to study the network dynamics and the anatomical regions involved in the earliest moments of picture naming by using invasive electroencephalogram (EEG) traces to predict naming errors. Four right-handed participants with focal epilepsy explored with extensive stereotactic implant montages that recorded temporal, parietal and occipital regions -in two patients of both hemispheres-named a total of 228 black and white pictures in three different sessions recorded in different days. The subjects made errors that involved anomia and semantic dysphasia, which related to word frequency and not to visual complexity. Using different modalities of spectrum analysis and classification with a support vector machine (SVM) we could predict errors with rates that ranged from slightly above chance level to 100%, even in the preconscious phase, i.e., 100 msec after stimulus presentation. The highest rates were obtained using the gamma bands of all contact spectra without averaging, which implies a fine modulation of the neuronal activity at a network level. Despite no subset of nodes could match the whole set, rates close to the best prediction scores were obtained through the spectra of the temporal-parietal and temporal-occipital junction along with the temporal pole and hippocampus. When both hemispheres were explored nodes from the left side dominated in the best subsets. We argue that posterior temporal regions, especially of the dominant side, are involved very early, even in the preconscious phase (100 msec), in language production. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. A network of RNA and protein interactions in Fronto Temporal Dementia

    PubMed Central

    Fontana, Francesca; Siva, Kavitha; Denti, Michela A.

    2015-01-01

    Frontotemporal 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. PMID:25852467

  19. Modelling and temporal performances evaluation of networked control systems using (max, +) algebra

    NASA Astrophysics Data System (ADS)

    Ammour, R.; Amari, S.

    2015-01-01

    In this paper, we address the problem of temporal performances evaluation of producer/consumer networked control systems. The aim is to develop a formal method for evaluating the response time of this type of control systems. Our approach consists on modelling, using Petri nets classes, the behaviour of the whole architecture including the switches that support multicast communications used by this protocol. (max, +) algebra formalism is then exploited to obtain analytical formulas of the response time and the maximal and minimal bounds. The main novelty is that our approach takes into account all delays experienced at the different stages of networked automation systems. Finally, we show how to apply the obtained results through an example of networked control system.

  20. A network of RNA and protein interactions in Fronto Temporal Dementia.

    PubMed

    Fontana, Francesca; Siva, Kavitha; Denti, Michela A

    2015-01-01

    Frontotemporal 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.

  1. ViSiBooL-visualization and simulation of Boolean networks with temporal constraints.

    PubMed

    Schwab, Julian; Burkovski, Andre; Siegle, Lea; Müssel, Christoph; Kestler, Hans A

    2016-10-22

    : Mathematical models and their simulation are increasingly used to gain insights into cellular pathways and regulatory networks. Dynamics of regulatory factors can be modeled using Boolean networks (BNs), among others. Text-based representations of models are precise descriptions, but hard to understand and interpret. ViSiBooL aims at providing a graphical way of modeling and simulating networks. By providing visualizations of static and dynamic network properties simultaneously, it is possible to directly observe the effects of changes in the network structure on the behavior. In order to address the challenges of clear design and a user-friendly graphical user interface (GUI), ViSiBooL implements visual representations of BNs. Additionally temporal extensions of the BNs for the modeling of regulatory time delays are incorporated. The GUI of ViSiBooL allows to model, organize, simulate and visualize BNs as well as corresponding simulation results such as attractors. Attractor searches are performed in parallel to the modeling process. Hence, changes in the network behavior are visualized at the same time.

  2. Network analysis of temporal effects of intermittent and sustained hypoxia on rat lungs

    PubMed Central

    Wu, Wei; Dave, Nilesh B.; Yu, Guoying; Strollo, Patrick J.; Kovkarova-Naumovski, Elizabeta; Ryter, Stefan W.; Reeves, Stephen R.; Dayyat, Ehab; Wang, Yang; Choi, Augustine M. K.; Gozal, David; Kaminski, Naftali

    2008-01-01

    The molecular networks underlying the lung response to hypoxia are not fully understood. We employed systems biology approaches to study temporal effects of intermittent or sustained hypoxia on gene expression in rat lungs. We obtained gene expression profiles from rats exposed to intermittent or sustained hypoxia lasting 0–30 days and identified differentially expressed genes, their patterns, biological processes, and regulatory networks critical for lung response to intermittent or sustained hypoxia. We validated selected genes with quantitative real-time PCR. Intermittent and sustained hypoxia induced two distinct sets of genes in rat lungs that displayed different temporal expression patterns. Intermittent hypoxia induced genes mostly involved in ion transport and homeostasis, neurological processes, and steroid hormone receptor activity, while sustained hypoxia induced genes principally participating in immune responses. The intermittent hypoxia-activated network suggested a role for cross talk between estrogen receptor 1 (ESR1) and other key proteins in hypoxic responses. The sustained hypoxia-activated network was indicative of vascular remodeling and pulmonary hypertension. We confirmed the temporal expression changes of 12 genes (including the Esr1 gene and 4 ESR1 target genes) in intermittent hypoxia and 8 genes in sustained hypoxia with quantitative real-time PCR. Conclusions: intermittent and sustained hypoxia induced distinct gene expression patterns in rat lungs. The functional characteristics of genes activated by these two distinct perturbations suggest their roles in the downstream physiological effects of intermittent and sustained hypoxia. Our results demonstrate the discovery potential of applying systems biology approaches to the understanding of mechanisms underlying hypoxic lung response. PMID:18826996

  3. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Lee, Charles H.; Cheung, Kar-Ming

    2012-01-01

    In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

  4. A network approach to mixing delegates at meetings.

    PubMed

    Vaggi, Federico; Schiavinotto, Tommaso; Lawson, Jonathan Ld; Chessel, Anatole; Dodgson, James; Geymonat, Marco; Sato, Masamitsu; Carazo Salas, Rafael Edgardo; Csikász-Nagy, Attila

    2014-01-01

    Delegates at scientific meetings can come from diverse backgrounds and use very different methods in their research. Promoting interactions between these 'distant' delegates is challenging but such interactions could lead to novel interdisciplinary collaborations and unexpected breakthroughs. We have developed a network-based 'speed dating' approach that allows us to initiate such distant interactions by pairing every delegate with another delegate who might be of interest to them, but whom they might never have encountered otherwise. Here we describe our approach and its algorithmic implementation.

  5. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Lee, Charles H.; Cheung, Kar-Ming

    2012-01-01

    In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

  6. Dissecting apple tree architecture into genetic, ontogenetic and environmental effects: mixed linear modelling of repeated spatial and temporal measures.

    PubMed

    Segura, Vincent; Cilas, Christian; Costes, Evelyne

    2008-01-01

    The present study aimed to dissect tree architectural plasticity into genetic, ontogenetic and environmental effects over the first 4 yr of growth of an apple (Malus x domestica) F1 progeny by means of mixed linear modelling of repeated data. Traits related to both growth and branching processes were annually assessed on different axes of the trees planted in a staggered-start design. Both spatial repetitions, (i.e. different axis types) and temporal repetitions (i.e. successive ages of trees) were considered in a mixed linear model of repeated data. A significant genotype effect was found for most studied traits and interactions between genotype and year and/or age were also detected. The analysis of repeated temporal measures highlighted that the magnitude of the decrease in primary growth is mainly determined by the first year of growth, and the decrease in bottom diameter increment is concomitant with the first fruiting occurrence. This approach allowed us to distinguish among the traits that were under genetic control, those for which this control is exerted differentially throughout tree life or depending on climatic conditions or an axis type. Mapping quantitative trait loci (QTL) that are specific to these different effects will constitute the next step in the research.

  7. Emergence of disassortative mixing from pruning nodes in growing scale-free networks

    PubMed Central

    Wang, Sheng-Jun; Wang, Zhen; Jin, Tao; Boccaletti, Stefano

    2014-01-01

    Disassortative mixing is ubiquitously found in technological and biological networks, while the corresponding interpretation of its origin remains almost virgin. We here give evidence that pruning the largest-degree nodes of a growing scale-free network has the effect of decreasing the degree correlation coefficient in a controllable and tunable way, while keeping both the trait of a power-law degree distribution and the main properties of network's resilience and robustness under failures or attacks. The essence of these observations can be attributed to the fact the deletion of large-degree nodes affects the delicate balance of positive and negative contributions to degree correlation in growing scale-free networks, eventually leading to the emergence of disassortativity. Moreover, these theoretical prediction will get further validation in the empirical networks. We support our claims via numerical results and mathematical analysis, and we propose a generative model for disassortative growing scale-free networks. PMID:25520244

  8. Emergence of disassortative mixing from pruning nodes in growing scale-free networks.

    PubMed

    Wang, Sheng-Jun; Wang, Zhen; Jin, Tao; Boccaletti, Stefano

    2014-12-18

    Disassortative mixing is ubiquitously found in technological and biological networks, while the corresponding interpretation of its origin remains almost virgin. We here give evidence that pruning the largest-degree nodes of a growing scale-free network has the effect of decreasing the degree correlation coefficient in a controllable and tunable way, while keeping both the trait of a power-law degree distribution and the main properties of network's resilience and robustness under failures or attacks. The essence of these observations can be attributed to the fact the deletion of large-degree nodes affects the delicate balance of positive and negative contributions to degree correlation in growing scale-free networks, eventually leading to the emergence of disassortativity. Moreover, these theoretical prediction will get further validation in the empirical networks. We support our claims via numerical results and mathematical analysis, and we propose a generative model for disassortative growing scale-free networks.

  9. Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method.

    PubMed

    Zhao, Jianjun; Wang, Yanying; Zhang, Hongyan; Zhang, Zhengxiang; Guo, Xiaoyi; Yu, Shan; Du, Wala

    2016-01-01

    The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

  10. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study.

    PubMed

    Lenters, Lindsey M; Cole, Donald C; Godoy-Ruiz, Paula

    2014-01-25

    Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers' careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world.

  11. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study

    PubMed Central

    2014-01-01

    Background Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers’ careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. Methods A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Results Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Conclusions Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world. PMID:24460819

  12. Disclosing Sexual Assault Within Social Networks: A Mixed-Method Investigation.

    PubMed

    Dworkin, Emily R; Pittenger, Samantha L; Allen, Nicole E

    2016-03-01

    Most survivors of sexual assault disclose their experiences within their social networks, and these disclosure decisions can have important implications for their entry into formal systems and well-being, but no research has directly examined these networks as a strategy to understand disclosure decisions. Using a mixed-method approach that combined survey data, social network analysis, and interview data, we investigate whom, among potential informal responders in the social networks of college students who have experienced sexual assault, survivors contact regarding their assault, and how survivors narrate the role of networks in their decisions about whom to contact. Quantitative results suggest that characteristics of survivors, their social networks, and members of these networks are associated with disclosure decisions. Using data from social network analysis, we identified that survivors tended to disclose to a smaller proportion of their network when many network members had relationships with each other or when the network had more subgroups. Our qualitative analysis helps to contextualize these findings. © Society for Community Research and Action 2016.

  13. Widespread changes in network activity allow non-invasive detection of mesial temporal lobe seizures.

    PubMed

    Lam, Alice D; Zepeda, Rodrigo; Cole, Andrew J; Cash, Sydney S

    2016-10-01

    Decades of experience with intracranial recordings in patients with epilepsy have demonstrated that seizures can occur in deep cortical regions such as the mesial temporal lobes without showing any obvious signs of seizure activity on scalp electroencephalogram. Predicated on the idea that these seizures are purely focal, currently, the only way to detect these 'scalp-negative seizures' is with intracranial recordings. However, intracranial recordings are only rarely performed in patients with epilepsy, and are almost never performed outside of the context of epilepsy. As such, little is known about scalp-negative seizures and their role in the natural history of epilepsy, their effect on cognitive function, and their association with other neurological diseases. Here, we developed a novel approach to non-invasively identify scalp-negative seizures arising from the mesial temporal lobe based on scalp electroencephalogram network connectivity measures. We identified 25 scalp-negative mesial temporal lobe seizures in 10 patients and obtained control records from an additional 13 patients, all of whom underwent recordings with foramen ovale electrodes and scalp electroencephalogram. Scalp data from these records were used to train a scalp-negative seizure detector, which consisted of a pair of logistic regression classifiers that used scalp electroencephalogram coherence properties as input features. On cross-validation performance, this detector correctly identified scalp-negative seizures in 40% of patients, and correctly identified the side of seizure onset for each seizure detected. In comparison, routine clinical interpretation of these scalp electroencephalograms failed to identify any of the scalp-negative seizures. Among the patients in whom the detector raised seizure alarms, 80% had scalp-negative mesial temporal lobe seizures. The detector had a false alarm rate of only 0.31 per day and a positive predictive value of 75%. Of the 13 control patients, false

  14. Controllability and Synchronization Analysis of Identical-Hierarchy Mixed-Valued Logical Control Networks.

    PubMed

    Zhong, Jie; Lu, Jianquan; Huang, Tingwen; Ho, Daniel W C

    2016-06-14

    This paper investigates the controllability and synchronization problems for identical-hierarchy mixed-valued logical control networks. The logical network considered is hierarchical, and Boolean network is a special case of logical network. Here, identical-hierarchy means that there are identical number of nodes in each layer of logical network and corresponding nodes have the same dimension for any two layers of logical networks. Meanwhile, in each layer of logical networks, the dimensions of nodes are distinct, and it is called a mixed-valued logical network. First, the controllability problem is investigated and two notions of controllability are presented, i.e., group-controllability and simultaneously-controllability. By resorting to Perron-Frobenius theorem, some necessary and sufficient criteria are obtained to guarantee group-controllability and simultaneously-controllability, respectively. Second, based on the algebraic representation of the studied model, synchronization problems are analytically discussed for two types of controls, i.e., free control sequences and state-output feedback control. Finally, two numerical examples are presented to show the validness of our main results.

  15. Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks.

    PubMed

    Scholtes, Ingo; Wider, Nicolas; Pfitzner, René; Garas, Antonios; Tessone, Claudio J; Schweitzer, Frank

    2014-09-24

    Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

  16. Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons.

    PubMed

    König, Jochem; Krahn, Ulrike; Binder, Harald

    2013-12-30

    Network meta-analysis techniques allow for pooling evidence from different studies with only partially overlapping designs for getting a broader basis for decision support. The results are network-based effect estimates that take indirect evidence into account for all pairs of treatments. The results critically depend on homogeneity and consistency assumptions, which are sometimes difficult to investigate. To support such evaluation, we propose a display of the flow of evidence and introduce new measures that characterize the structure of a mixed treatment comparison. Specifically, a linear fixed effects model for network meta-analysis is considered, where the network estimates for two treatments are linear combinations of direct effect estimates comparing these or other treatments. The linear coefficients can be seen as the generalization of weights known from classical meta-analysis. We summarize properties of these coefficients and display them as a weighted directed acyclic graph, representing the flow of evidence. Furthermore, measures are introduced that quantify the direct evidence proportion, the mean path length, and the minimal parallelism of mixed treatment comparisons. The graphical display and the measures are illustrated for two published network meta-analyses. In these applications, the proposed methods are seen to render transparent the process of data pooling in mixed treatment comparisons. They can be expected to be more generally useful for guiding and facilitating the validity assessment in network meta-analysis.

  17. Spatial and temporal variability of the refractivity over Tahiti from a coarse network of GPS stations

    NASA Astrophysics Data System (ADS)

    Serafini, J.; Fadil, A.; Sichoix, L.; Barriot, J.

    2010-12-01

    Slant wet delays (SWD) caused by the presence of water vapor in the atmosphere are routinely obtained from GPS measurements. Powerful tomography techniques have been developed to derive from them the refractivity of the atmosphere, which could be subject to strong spatial and temporal variations, especially over tropical zones. In this poster we model the spatial and temporal variability of the refractivity over the Tahiti Island. In a first study, we model the spatial part of the variability. For this purpose, GPS data spanning a four months period (June-Sept 2010) from a coarse network of nine stations are analyzed using the GAMIT software package. In particular, we found that the SWD variability is more important at the South East of the Island. In a second study we reconstruct the temporal part of the variability. For this purpose, ten years of GPS data from the IGS station THTI, located on the Punaauia suburb of Papeete are processed with respect to the precise point positioning (PPP) mode of the GIPSY-OASIS II software package. The derived SWD allow us to reconstruct, through a regularized inverse process, time series of the ZWD and North / East gradients of the refractivity. We show that the main components of the SWD are relative to semi-diurnal and seasonal terms. Finally, this spatio-temporal model of the refractivity permits us to build a robust estimate of the covariance matrix of the underlying stochastic process.

  18. Recursive neural network rule extraction for data with mixed attributes.

    PubMed

    Setiono, R; Baesens, B; Mues, C

    2008-02-01

    In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.

  19. Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models

    PubMed Central

    Abdelnour, Farras; Mueller, Susanne; Raj, Ashish

    2015-01-01

    Mesial temporal lobe epilepsy (TLE) is characterized by stereotyped origination and spread pattern of epileptogenic activity, which is reflected in stereotyped topographic distribution of neuronal atrophy on magnetic resonance imaging (MRI). Both epileptogenic activity and atrophy spread appear to follow white matter connections. We model the networked spread of activity and atrophy in TLE from first principles via two simple first order network diffusion models. Atrophy distribution is modeled as a simple consequence of the propagation of epileptogenic activity in one model, and as a progressive degenerative process in the other. We show that the network models closely reproduce the regional volumetric gray matter atrophy distribution of two epilepsy cohorts: 29 TLE subjects with medial temporal sclerosis (TLE-MTS), and 50 TLE subjects with normal appearance on MRI (TLE-no). Statistical validation at the group level suggests high correlation with measured atrophy (R = 0.586 for TLE-MTS, R = 0.283 for TLE-no). We conclude that atrophy spread model out-performs the hyperactivity spread model. These results pave the way for future clinical application of the proposed model on individual patients, including estimating future spread of atrophy, identification of seizure onset zones and surgical planning. PMID:26513579

  20. Large-Scale Brain Networks of the Human Left Temporal Pole: A Functional Connectivity MRI Study

    PubMed Central

    Pascual, Belen; Masdeu, Joseph C.; Hollenbeck, Mark; Makris, Nikos; Insausti, Ricardo; Ding, Song-Lin; Dickerson, Bradford C.

    2015-01-01

    The most rostral portion of the human temporal cortex, the temporal pole (TP), has been described as “enigmatic” because its functional neuroanatomy remains unclear. Comparative anatomy studies are only partially helpful, because the human TP is larger and cytoarchitectonically more complex than in nonhuman primates. Considered by Brodmann as a single area (BA 38), the human TP has been recently parceled into an array of cytoarchitectonic subfields. In order to clarify the functional connectivity of subregions of the TP, we undertook a study of 172 healthy adults using resting-state functional connectivity MRI. Remarkably, a hierarchical cluster analysis performed to group the seeds into distinct subsystems according to their large-scale functional connectivity grouped 87.5% of the seeds according to the recently described cytoarchitectonic subregions of the TP. Based on large-scale functional connectivity, there appear to be 4 major subregions of the TP: 1) dorsal, with predominant connectivity to auditory/somatosensory and language networks; 2) ventromedial, predominantly connected to visual networks; 3) medial, connected to paralimbic structures; and 4) anterolateral, connected to the default-semantic network. The functional connectivity of the human TP, far more complex than its known anatomic connectivity in monkey, is concordant with its hypothesized role as a cortical convergence zone. PMID:24068551

  1. Dynamic design of ecological monitoring networks for non-Gaussian spatio-temporal data

    USGS Publications Warehouse

    Wikle, C.K.; Royle, J. Andrew

    2005-01-01

    Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatio-temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

  2. Multiple Stability of a Sparsely Encoded Attractor Neural Network Model for the Inferior Temporal Cortex

    NASA Astrophysics Data System (ADS)

    Kimoto, Tomoyuki; Uezu, Tatsuya; Okada, Masato

    2008-12-01

    We study a neural network model for the inferior temporal cortex, in terms of finite memory loading and sparse coding. We show that an uncorrelated Hopfield-type attractor and some correlated attractors have multiple stability, and examine the retrieval dynamics for these attractors when the initial state is set to a noise-degraded memory pattern. Then, we show that there is a critical initial overlap: that is, the system converges to the correlated attractor when the noise level is large, and otherwise to the Hopfield-type attractor. Furthermore, we study the time course of the correlation between the correlated attractors in the retrieval dynamics. On the basis of these theoretical results, we resolve the controversy regarding previous physiologic experimental findings regarding neuron properties in the inferior temporal cortex and propose a new experimental paradigm.

  3. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention

    PubMed Central

    Shine, James M.; Koyejo, Oluwasanmi; Poldrack, Russell A.

    2016-01-01

    Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience. PMID:27528672

  4. Reliable Attention Network Scores and Mutually Inhibited Inter-network Relationships Revealed by Mixed Design and Non-orthogonal Method.

    PubMed

    Wang, Yi-Feng; Jing, Xiu-Juan; Liu, Feng; Li, Mei-Ling; Long, Zhi-Liang; Yan, Jin H; Chen, Hua-Fu

    2015-05-21

    The attention system can be divided into alerting, orienting, and executive control networks. The efficiency and independence of attention networks have been widely tested with the attention network test (ANT) and its revised versions. However, many studies have failed to find effects of attention network scores (ANSs) and inter-network relationships (INRs). Moreover, the low reliability of ANSs can not meet the demands of theoretical and empirical investigations. Two methodological factors (the inter-trial influence in the event-related design and the inter-network interference in orthogonal contrast) may be responsible for the unreliability of ANT. In this study, we combined the mixed design and non-orthogonal method to explore ANSs and directional INRs. With a small number of trials, we obtained reliable and independent ANSs (split-half reliability of alerting: 0.684; orienting: 0.588; and executive control: 0.616), suggesting an individual and specific attention system. Furthermore, mutual inhibition was observed when two networks were operated simultaneously, indicating a differentiated but integrated attention system. Overall, the reliable and individual specific ANSs and mutually inhibited INRs provide novel insight into the understanding of the developmental, physiological and pathological mechanisms of attention networks, and can benefit future experimental and clinical investigations of attention using ANT.

  5. Reorganization of anterior and posterior hippocampal networks associated with memory performance in mesial temporal lobe epilepsy.

    PubMed

    Li, Hong; Ji, Caihong; Zhu, Lujia; Huang, Peiyu; Jiang, Biao; Xu, Xiaojun; Sun, Jianzhong; Chen, Zhong; Ding, Meiping; Zhang, Minming; Wang, Shuang

    2017-05-01

    To investigate the pattern of functional demarcation of hippocampal network and its relationship with memory performance in mesial temporal lobe epilepsy (mTLE) with unilateral hippocampal sclerosis. Resting state fMRI data were acquired from fifteen left mTLE patients, fourteen right mTLE patients and twenty healthy subjects. We explore the hippocampal-cortical alterations and corresponding inter-hemispheric functional connectivity (FC) across anterior and posterior hippocampal networks. The association between FC and memory performance was assessed. Left mTLE showed increased intra-hemispheric FC in anterior hippocampal networks, including left anterior hippocampal-entorhinal cortex and right anterior hippocampal-orbitofrontal cortex, and decreased inter-hemispheric FC between anterior hippocampus, entorhinal cortex and posterior cingulate cortex. Right mTLE was associated with extensive reduction in inter-hemispheric FC along the areas of anterior and posterior hippocampal networks. Intra-hemispheric FC between left anterior hippocampus and entorhinal cortex was positively correlated with verbal memory in left mTLE. Inter-hemispheric FC between posterior parahippocampal gyrus was negatively correlated with verbal memory in right mTLE. Our findings suggested that left and right mTLE exhibit different neural reorganization patterns of anterior and posterior hippocampal networks associated with verbal memory. The findings may facilitate the characterization of mTLE associated with memory deficit. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  6. Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia.

    PubMed

    Collins, Jessica A; Montal, Victor; Hochberg, Daisy; Quimby, Megan; Mandelli, Maria Luisa; Makris, Nikos; Seeley, William W; Gorno-Tempini, Maria Luisa; Dickerson, Bradford C

    2017-02-01

    A wealth of neuroimaging research has associated semantic variant primary progressive aphasia with distributed cortical atrophy that is most prominent in the left anterior temporal cortex; however, there is little consensus regarding which region within the anterior temporal cortex is most prominently damaged, which may indicate the putative origin of neurodegeneration. In this study, we localized the most prominent and consistent region of atrophy in semantic variant primary progressive aphasia using cortical thickness analysis in two independent patient samples (n = 16 and 28, respectively) relative to age-matched controls (n = 30). Across both samples the point of maximal atrophy was located in the same region of the left temporal pole. This same region was the point of maximal atrophy in 100% of individual patients in both semantic variant primary progressive aphasia samples. Using resting state functional connectivity in healthy young adults (n = 89), we showed that the seed region derived from the semantic variant primary progressive aphasia analysis was strongly connected with a large-scale network that closely resembled the distributed atrophy pattern in semantic variant primary progressive aphasia. In both patient samples, the magnitude of atrophy within a brain region was predicted by that region's strength of functional connectivity to the temporopolar seed region in healthy adults. These findings suggest that cortical atrophy in semantic variant primary progressive aphasia may follow connectional pathways within a large-scale network that converges on the temporal pole. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

    PubMed

    Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

    2013-05-01

    On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

  8. A network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption

    NASA Astrophysics Data System (ADS)

    Zhu, Lijuan; Liu, Jingao

    2013-07-01

    This paper describes a network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption. This protocol can provide every bank user a safe and effective way to manage his own bank account, and also can effectively prevent the hacker attacks and bank clerk crime, so that it is absolute to guarantee the legitimate rights and interests of bank users.

  9. Using Mixed-Method Design and Network Analysis to Measure Development of Interagency Collaboration

    ERIC Educational Resources Information Center

    Cross, Jennifer Eileen; Dickmann, Ellyn; Newman-Gonchar, Rebecca; Fagan, Jesse Michael

    2009-01-01

    In recent years, there has been increasing attention to the importance of interagency collaboration for improving community well-being, environmental and public health, and educational outcomes. This article uses a mixed-methods approach including network analysis to examine the changes in interagency collaboration in one site funded by the Safe…

  10. Temporal Traffic Dynamics Improve the Connectivity of Ad Hoc Cognitive Radio Networks

    DTIC Science & Technology

    2014-02-12

    for comparison . We consider temporal dynamics in the primary traffic that could be caused by mobility and/or changes in the traffic load and pattern...Proximity vs . opportunity,” in Proc. ACM MobiCom Workshop Cogn. Radio Netw., Sep. 2009, pp. 37–42. [5] A. Abbagnale, F. Cuomo, and E. Cipollone... Appl . Probab., vol. 43, no. 2, pp. 552–562, 2006. [12] Z. N. Kong and E. M. Yeh, “Connectivity and latency in large-scale wireless networks with

  11. Distributed Configuration of Sensor Network for Fault Detection in Spatio-Temporal Systems

    NASA Astrophysics Data System (ADS)

    Patan, Maciej; Kowalów, Damian

    2017-01-01

    The problem of fault detection in spatio-temporal systems is formulated as that of maximizing the power of a parametric hypothesis test verifying the nominal state of the process under consideration. Then, adopting a pairwise communication schemes, a computational procedure is developed for the spatial configuration of the observation locations for sensor network which monitor changes in the underlying parameters of a distributed parameter system. As a result, the problem of planning the percentage of experimental effort spent at given sensor locations can be solved in a fully decentralized fashion. The approach is verified on a numerical example involving sensor selection for a convective diffusion process.

  12. Modified Penna bit-string network evolution model for scale-free networks with assortative mixing

    NASA Astrophysics Data System (ADS)

    Kim, Yup; Choi, Woosik; Yook, Soon-Hyung

    2012-02-01

    Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 < γ š 3, which is consistent with the values for real social networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.

  13. Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: a diffusion tensor imaging study.

    PubMed

    Xu, Y; Qiu, S; Wang, J; Liu, Z; Zhang, R; Li, S; Cheng, L; Liu, Z; Wang, W; Huang, R

    2014-10-24

    Mesial temporal lobe epilepsy (mTLE) is the most common drug-refractory focal epilepsy in adults. Although previous functional and morphological studies have revealed abnormalities in the brain networks of mTLE, the topological organization of the brain white matter (WM) networks in mTLE patients is still ambiguous. In this study, we constructed brain WM networks for 14 left mTLE patients and 22 age- and gender-matched normal controls using diffusion tensor tractography and estimated the alterations of network properties in the mTLE brain networks using graph theoretical analysis. We found that networks for both the mTLE patients and the controls exhibited prominent small-world properties, suggesting a balanced topology of integration and segregation. However, the brain WM networks of mTLE patients showed a significant increased characteristic path length but significant decreased global efficiency, which indicate a disruption in the organization of the brain WM networks in mTLE patients. Moreover, we found significant between-group differences in the nodal properties in several brain regions, such as the left superior temporal gyrus, left hippocampus, the right occipital and right temporal cortices. The robustness analysis showed that the results were likely to be consistent for the networks constructed with different definitions of node and edge weight. Taken together, our findings may suggest an adverse effect of epileptic seizures on the organization of large-scale brain WM networks in mTLE patients.

  14. Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study.

    PubMed

    Jiang, Wenyu; Li, Jianping; Chen, Xuemei; Ye, Wei; Zheng, Jinou

    2017-01-01

    Previous studies have shown that temporal lobe epilepsy (TLE) involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE) patients and 19 healthy controls. Global network properties were computed by graph theoretical analysis, and correlations were assessed between global network properties and alertness. The results from these analyses showed that rTLE patients exhibit abnormal small-world attributes in structural and functional networks. Structural networks shifted toward more regular attributes, but functional networks trended toward more random attributes. After controlling for the influence of the disease duration, negative correlations were found between alertness, small-worldness, and the cluster coefficient. However, alertness did not correlate with either the characteristic path length or global efficiency in rTLE patients. Our findings show that disruptions of the topological construction of brain structural and functional networks as well as small-world property bias are associated with deficits in alertness in rTLE patients. These data suggest that reorganization of brain networks develops as a mechanism to compensate for altered structural and functional brain function during disease progression.

  15. The feasibility program with security constraints and network rearrangement costs - Application to mixed /analogue and digital/ transmission networks

    NASA Astrophysics Data System (ADS)

    Serreault, J.-Y.; Minoux, M.

    1980-02-01

    This paper presents a second version of the 'feasibility program' (SECURAD) developed by the CNET. This program enables the routing at minimal costs of several thousand of requirements (expressed in circuits or groups of circuits) on very large mixed (analogue and digital) transmission networks, while respecting given capacities and taking into account security constraints and network-rearrangement costs. Consideration is given to a procedure for determining lower bounds for the cost of the optimal solution by solving a dual problem, and thus checking the quality of the approximate solutions obtained.

  16. Voluntary Vaccination through Self-organizing Behaviors on Locally-mixed Social Networks.

    PubMed

    Shi, Benyun; Qiu, Hongjun; Niu, Wenfang; Ren, Yizhi; Ding, Hong; Chen, Dan

    2017-06-01

    Voluntary vaccination reflects how individuals weigh the risk of infection and the cost of vaccination against the spread of vaccine-preventable diseases, such as smallpox and measles. In a homogeneously mixing population, the infection risk of an individual depends largely on the proportion of vaccinated individuals due to the effects of herd immunity. While in a structured population, the infection risk can also be affected by the structure of individuals' social network. In this paper, we focus on studying individuals' self-organizing behaviors under the circumstance of voluntary vaccination in different types of social networks. Specifically, we assume that each individual together with his/her neighbors forms a local well-mixed environment, where individuals meet equally often as long as they have a common neighbor. We carry out simulations on four types of locally-mixed social networks to investigate the network effects on voluntary vaccination. Furthermore, we also evaluate individuals' vaccinating decisions through interacting with their "neighbors of neighbors". The results and findings of this paper provide a new perspective for vaccination policy-making by taking into consideration human responses in complex social networks.

  17. Combining social and genetic networks to study HIV transmission in mixing risk groups

    NASA Astrophysics Data System (ADS)

    Zarrabi, Narges; Prosperi, Mattia C. F.; Belleman, Robbert G.; Di Giambenedetto, Simona; Fabbiani, Massimiliano; De Luca, Andrea; Sloot, Peter M. A.

    2013-09-01

    Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the research focuses on the transmission within HIV risk groups, while transmission between different risk groups has been less studied. We use a proposed filter-reduction method to infer hypothetical transmission networks of HIV by combining data from social and genetic scales. We modified the filtering process in order to include mixing risk groups in the model. For this, we use the information on phylogenetic clusters obtained through phylogenetic analysis. A probability matrix is also defined to specify contact rates between individuals form the same and different risk groups. The method converts the data form each scale into network forms and combines them by overlaying and computing their intersection. We apply this method to reconstruct networks of HIV infected patients in central Italy, including mixing between risk groups. Our results suggests that bisexual behavior among Italian MSM and IDU partnership are relatively important in heterosexual transmission of HIV in central Italy.

  18. Mental visual synthesis is originated in the fronto-temporal network of the left hemisphere.

    PubMed

    Yomogida, Yukihito; Sugiura, Motoaki; Watanabe, Jobu; Akitsuki, Yuko; Sassa, Yuko; Sato, Teruyuki; Matsue, Yoshihiko; Kawashima, Ryuta

    2004-12-01

    Mental visual synthesis is the capacity for experiencing, constructing, or manipulating 'mental imagery'. To investigate brain networks involved in mental visual synthesis, brain activity was measured in right-handed healthy volunteers during mental imagery tasks, in which the subjects were instructed to imagine a novel object, that does not exist in the real world, by composing it from two visually presented words associated with a real object or two achromatic line drawings of a real object, using functional magnetic resonance imaging (fMRI). Both tasks activated the same areas in the inferior frontal and inferior temporal cortices of the left hemisphere. Our results indicate that the source of mental visual synthesis may be formed by activity of a brain network consisting of these areas, which are also involved in semantic operations and visual imagery.

  19. Semi-degradable poly(β-amino ester) networks with temporally controlled enhancement of mechanical properties.

    PubMed

    Safranski, David L; Weiss, Daiana; Clark, J Brian; Taylor, W Robert; Gall, Ken

    2014-08-01

    Biodegradable polymers are clinically used in numerous biomedical applications, and classically show a loss of mechanical properties within weeks of implantation. This work demonstrates a new class of semi-degradable polymers that show an increase in mechanical properties through degradation via a controlled shift in a thermal transition. Semi-degradable polymer networks, poly(β-amino ester)-co-methyl methacrylate, were formed from a low glass transition temperature crosslinker, poly(β-amino ester), and high glass transition temperature monomer, methyl methacrylate, which degraded in a manner dependent upon the crosslinker chemical structure. In vitro and in vivo degradation revealed changes in mechanical behavior due to the degradation of the crosslinker from the polymer network. This novel polymer system demonstrates a strategy to temporally control the mechanical behavior of polymers and to enhance the initial performance of smart biomedical devices.

  20. Direct numerical simulations of a temporally evolving mixing layer subject to forcing

    NASA Technical Reports Server (NTRS)

    Claus, Russell W.

    1986-01-01

    The vortical evolution of mixing layers subject to various types of forcing is numerically simulated using pseudospectral methods. The effect of harmonic forcing and random noise in the initial conditions is examined with some results compared to experimental data. Spanwise forcing is found to enhance streamwise vorticity in a nonlinear process leading to a slow, secondary growth of the shear layer. The effect of forcing on a chemical reaction is favorably compared with experimental data at low Reynolds numbers. Combining harmonic and subharmonic forcing is shown to both augment and later destroy streamwise vorticity.

  1. Quality assessment of static aggregation compared to the temporal approach based on a pig trade network in Northern Germany.

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-07-01

    Recent analyses of animal movement networks focused on the static aggregation of trade contacts over different time windows, which neglects the system's temporal variation. In terms of disease spread, ignoring the temporal dynamics can lead to an over- or underestimation of an outbreak's speed and extent. This becomes particularly evident, if the static aggregation allows for the existence of more paths compared to the number of time-respecting paths (i.e. paths in the right chronological order). Therefore, the aim of this study was to reveal differences between static and temporal representations of an animal trade network and to assess the quality of the static aggregation in comparison to the temporal counterpart. Contact data from a pig trade network (2006-2009) of a producer community in Northern Germany were analysed. The results show that a median value of 8.7 % (4.6-14.1%) of the nodes and 3.1% (1.6-5.5%) of the edges were active on a weekly resolution. No fluctuations in the activity patterns were obvious. Furthermore, 50% of the nodes already had one trade contact after approximately six months. For an accumulation window with increasing size (one day each), the accumulation rate, i.e. the relative increase in the number of nodes or edges, stayed relatively constant below 0.07% for the nodes and 0.12 % for the edges. The temporal distances had a much wider distribution than the topological distances. 84% of the temporal distances were smaller than 90 days. The maximum temporal distance was 1000 days, which corresponds to the temporal diameter of the present network. The median temporal correlation coefficient, which measures the probability for an edge to persist across two consecutive time steps, was 0.47, with a maximum value of 0.63 at the accumulation window of 88 days. The causal fidelity measures the fraction of the number of static paths which can also be taken in the temporal network. For the whole observation period relatively high values

  2. Nearest neighbor imputation using spatial-temporal correlations in wireless sensor networks.

    PubMed

    Li, YuanYuan; Parker, Lynne E

    2014-01-01

    Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network's performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a kd-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for kd-tree construction, and Euclidean distance for kd-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental results

  3. ePRISM: A case study in multiple proxy and mixed temporal resolution integration

    USGS Publications Warehouse

    Robinson, Marci M.; Dowsett, Harry J.

    2010-01-01

    As part of the Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project, we present the ePRISM experiment designed I) to provide climate modelers with a reconstruction of an early Pliocene warm period that was warmer than the PRISM interval (similar to 3.3 to 3.0 Ma), yet still similar in many ways to modern conditions and 2) to provide an example of how best to integrate multiple-proxy sea surface temperature (SST) data from time series with varying degrees of temporal resolution and age control as we begin to build the next generation of PRISM, the PRISM4 reconstruction, spanning a constricted time interval. While it is possible to tie individual SST estimates to a single light (warm) oxygen isotope event, we find that the warm peak average of SST estimates over a narrowed time interval is preferential for paleoclimate reconstruction as it allows for the inclusion of more records of multiple paleotemperature proxies.

  4. Spatial and temporal variations of sediment size on a mixed sand and gravel beach

    NASA Astrophysics Data System (ADS)

    Horn, Diane P.; Walton, Susan M.

    2007-12-01

    Mixed sand and gravel beaches have been the subject of comparatively few studies in the UK. This paper describes the sediment distribution before, during and after a programme of beach nourishment along a section of mixed sand and gravel beach forming part of the Pevensey Bay Coastal Defences, in East Sussex, UK. The beach was recharged in September 2002, and beach profiles were measured along three cross-shore transects from August 2002 to February 2003. Sediment samples were taken along the transects between August and November 2002, and a total of 147 sediment samples were analysed, 40 before nourishment and 107 after nourishment. The majority of the sediment samples were strongly bimodal, with mean sizes varying between a minimum of 0.18 mm (2.48 ϕ) for the sand fraction and a maximum of 27 mm (- 4.74 ϕ) for the gravel. The recharge material was also bimodal but contained more fine sediment than the natural beach material, particularly on the upper beach. The recharge sediment had grain sizes and sorting similar to some of the natural material but lower bimodality parameters than any of the natural sediment. The sediment distributions after recharge contained significantly more fine sediment, particularly on the upper beach. Over time, the beach profile lowered and fine sediment appeared to be selectively transported seawards from the beachface.

  5. Imaging and spatio-temporal analysis of turbulent mixing of hydrothermal water discharging into a river (Breitenbush Hot Springs, Oregon)

    NASA Astrophysics Data System (ADS)

    Andrews, B. J.; Cardenas, M. B.; Bennett, P.

    2009-12-01

    High-frequency (16 Hz), high-resolution (1-2 mm pixels) thermal infrared images show the effects of jet entry conditions on spatial and temporal scales of mixing between a discharging plume of hot spring water (~60 °C) and a small stream (~10 °C) at Breitenbush Hot Springs, Oregon. Images of thermal plumes showing eddy cascades through space and time are analyzed with correlation analyses to obtain timescales and length-scales of mixing. Optical flow velocimetry of the images provides insight to the transient two-dimensional flow fields of the plumes. The 3-inch diameter discharge pipe was positioned such that the jet is at the surface, partially submerged, or at the bottom of the 15-cm deep stream. The three jet entry conditions are hereafter referred to as “shallow”, “middle”, and “deep”. In the shallow and middle positions, the jet is apparent on the stream surface as a hot region extending downstream from the pipe. Turbulent mixing between the jet and the stream occurs along the jet margins, such that the discharge plume broadens and cools downstream. In the deep position, the jet reaches the surface as a broad plume ~7.5 cm downstream from the pipe; the highest measured temperatures are not directly above the pipe mouth, but displaced downstream. Streamwise spatial autocorrelation analysis of the temperature field under shallow and middle entry conditions show correlation length scales of ~30 cm for a transect along the center ~7.5 cm of the jet; the correlation length scale abruptly reduces to <10 cm on either side of the jet. Under deep conditions, the streamwise correlation length scale is ~20 cm along the middle ~10 cm of the plume and ~10 cm on either side of the plume. Temporal autocorrelation analysis of the temperature fields shows quasi-periodicity for all three pipe positions and decrease in frequency with distance from the pipe (shallow and middle) or center of the upwelling plume (deep). Correlation analyses of the velocity fields

  6. Dynamic analysis of traffic time series at different temporal scales: A complex networks approach

    NASA Astrophysics Data System (ADS)

    Tang, Jinjun; Wang, Yinhai; Wang, Hua; Zhang, Shen; Liu, Fang

    2014-07-01

    The analysis of dynamics in traffic flow is an important step to achieve advanced traffic management and control in Intelligent Transportation System (ITS). Complexity and periodicity are definitely two fundamental properties in traffic dynamics. In this study, we first measure the complexity of traffic flow data by Lempel-Ziv algorithm at different temporal scales, and the data are collected from loop detectors on freeway. Second, to obtain more insight into the complexity and periodicity in traffic time series, we then construct complex networks from traffic time series by considering each day as a cycle and each cycle as a single node. The optimal threshold value of complex networks is estimated by the distribution of density and its derivative. In addition, the complex networks are subsequently analyzed in terms of some statistical properties, such as average path length, clustering coefficient, density, average degree and betweenness. Finally, take 2 min aggregation data as example, we use the correlation coefficient matrix, adjacent matrix and closeness to exploit the periodicity of weekdays and weekends in traffic flow data. The findings in this paper indicate that complex network is a practical tool for exploring dynamics in traffic time series.

  7. Extensive excitatory network interactions shape temporal processing of communication signals in a model sensory system.

    PubMed

    Ma, Xiaofeng; Kohashi, Tsunehiko; Carlson, Bruce A

    2013-07-01

    Many sensory brain regions are characterized by extensive local network interactions. However, we know relatively little about the contribution of this microcircuitry to sensory coding. Detailed analyses of neuronal microcircuitry are usually performed in vitro, whereas sensory processing is typically studied by recording from individual neurons in vivo. The electrosensory pathway of mormyrid fish provides a unique opportunity to link in vitro studies of synaptic physiology with in vivo studies of sensory processing. These fish communicate by actively varying the intervals between pulses of electricity. Within the midbrain posterior exterolateral nucleus (ELp), the temporal filtering of afferent spike trains establishes interval tuning by single neurons. We characterized pairwise neuronal connectivity among ELp neurons with dual whole cell recording in an in vitro whole brain preparation. We found a densely connected network in which single neurons influenced the responses of other neurons throughout the network. Similarly tuned neurons were more likely to share an excitatory synaptic connection than differently tuned neurons, and synaptic connections between similarly tuned neurons were stronger than connections between differently tuned neurons. We propose a general model for excitatory network interactions in which strong excitatory connections both reinforce and adjust tuning and weak excitatory connections make smaller modifications to tuning. The diversity of interval tuning observed among this population of neurons can be explained, in part, by each individual neuron receiving a different complement of local excitatory inputs.

  8. Temporally sequenced intelligent block-matching and motion-segmentation using locally coupled networks.

    PubMed

    Zhang, Xiaofu; Minai, A A

    2004-09-01

    Motion-based segmentation is a very important capability for computer vision and video analysis. It depends fundamentally on the system's ability to estimate optic flow using temporally proximate image frames. This is often done using block-matching. However, block-matching is sensitive to the presence of observational noise, which is inevitable in real images. Also, images often include regions of homogeneous intensity, where block-matching is problematic. A better method in this case is to estimate motion at the region level. In the approach described in this paper, we have attempted to address the noise-sensitivity and texture-insufficiency problems using a two-pathway system. The pixel-level pathway is a multilayer pulse-coupled neural network (PCNN)-like locally coupled network used to correct outliers in the block-matching motion estimates and produce improved estimates in regions with sufficient texture. In contrast, the region-level pathway is used to estimate the motion for regions with little intensity variation. In this pathway, a PCNN network first partitions intensity images into homogeneous regions, and a motion vector is then determined for the whole region. The optic flows from both pathways are fused together based on the estimated intensity variation. The fused optic flow is then segmented by a one-layer PCNN network. Results on synthetic and real images are presented to demonstrate that the accuracy of segmentation is improved significantly by taking advantage of the complementary strengths and weaknesses of the two pathways.

  9. Zombie projects, negative networks, and multigenerational science: The temporality of the International Map of the World.

    PubMed

    Rankin, William

    2017-06-01

    The International Map of the World was a hugely ambitious scheme to create standardized maps of the entire world. It was first proposed in 1891 and remained a going concern until 1986. Over the course of the project's official life, nearly every country in the world took part, and map sheets were published showing all but a few areas of the planet. But the project ended quite unceremoniously, repudiated by cartographers and mapping institutions alike, and it is now remembered as a 'sad story' of network failure. How can we evaluate this kind of sprawling, multigenerational project? In order to move beyond practitioners' (and historians') habit of summarizing the entire endeavor using the blunt categories of success and failure, I propose a more temporally aware reading, one that both disaggregates the (persistent) project from the (always changing) network and sees project and network as invertible, with the possibility of zombie projects and negative networks that can remain robust even when disconnected from their original goals. I therefore see the abandonment of the International Map of the World as resulting from vigorous collaboration and new norms in cartography, not from lack of cooperation or other resources. New categories are required for analyzing science over the long durée.

  10. A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data.

    PubMed

    Gui, Shupeng; Rice, Andrew P; Chen, Rui; Wu, Liang; Liu, Ji; Miao, Hongyu

    2017-01-31

    Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher. Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 10(4), and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate the application of our algorithm in practice using the time-course gene expression data from a study on human respiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on transcription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that the biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial cell infection by IAV. The proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB (Ⓡ) , and the source code is

  11. Linear mixed model approach to network meta-analysis for continuous outcomes in periodontal research.

    PubMed

    Tu, Yu-Kang

    2015-02-01

    Analysing continuous outcomes for network meta-analysis by means of linear mixed models is a great challenge, as it requires statistical software packages to specify special patterns of model error variance and covariance structure. This article demonstrates a non-Bayesian approach to network meta-analysis for continuous outcomes in periodontal research with a special focus on the adjustment of data dependency. Seventeen studies on guided tissue regeneration were used to illustrate how the proposed linear mixed models for network meta-analysis of continuous outcomes. Arm-based network meta-analysis use treatment arms from each study as the unit of analysis; when patients are randomly assigned to each arm, data are deemed independent and therefore no adjustment is required for multi-arm trials. Trial-based network meta-analysis use treatment contrasts as the unit of analysis, and therefore treatment contrasts within a multi-arm trial are not independent. This data dependency occurs also in split-mouth studies, and adjustments for data dependency are therefore required. Arm-based analysis is the preferred approach to network meta-analysis, when all included studies use the parallel group design and some compare more than two treatment arms. When included studies used designs that yield dependent data, the trial-based analysis is the preferred approach. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

    PubMed Central

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-01-01

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults. PMID:28282936

  13. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

    PubMed

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-03-09

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

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

    PubMed Central

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

    2016-01-01

    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. PMID:27808267

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

    NASA Astrophysics Data System (ADS)

    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.

  16. Spatial and temporal variations of microbial community in a mixed plug-flow loop reactor fed with dairy manure

    PubMed Central

    Li, Yueh-Fen; Chen, Po-Hsu; Yu, Zhongtang

    2014-01-01

    Mixed plug-flow loop reactor (MPFLR) has been widely adopted by the US dairy farms to convert cattle manure to biogas. However, the microbiome in MPFLR digesters remains unexplored. In this study, the microbiome in a MPFLR digester operated on a mega-dairy farm was examined thrice over a 2 month period. Within 23 days of retention time, 55–70% of total manure solid was digested. Except for a few minor volatile fatty acids (VFAs), total VFA concentration and pH remained similar along the course of the digester and over time. Metagenomic analysis showed that although with some temporal variations, the bacterial community was rather stable spatially in the digester. The methanogenic community was also stable both spatially and temporally in the digester. Among methanogens, genus Methanosaeta dominated in the digester. Quantitative polymerase chain reaction (qPCR) analysis and metagenomic analysis yielded different relative abundance of individual genera of methanogens, especially for Methanobacterium, which was predominant based on qPCR analysis but undetectable by metagenomics. Collectively, the results showed that only small microbial and chemical gradients existed within the digester, and the digestion process occurred similarly throughout the MPFLR digester. The findings of this study may help improve the operation and design of this type of manure digesters. PMID:24690147

  17. Spatial and temporal variations of microbial community in a mixed plug-flow loop reactor fed with dairy manure.

    PubMed

    Li, Yueh-Fen; Chen, Po-Hsu; Yu, Zhongtang

    2014-07-01

    Mixed plug-flow loop reactor (MPFLR) has been widely adopted by the US dairy farms to convert cattle manure to biogas. However, the microbiome in MPFLR digesters remains unexplored. In this study, the microbiome in a MPFLR digester operated on a mega-dairy farm was examined thrice over a 2 month period. Within 23 days of retention time, 55-70% of total manure solid was digested. Except for a few minor volatile fatty acids (VFAs), total VFA concentration and pH remained similar along the course of the digester and over time. Metagenomic analysis showed that although with some temporal variations, the bacterial community was rather stable spatially in the digester. The methanogenic community was also stable both spatially and temporally in the digester. Among methanogens, genus Methanosaeta dominated in the digester. Quantitative polymerase chain reaction (qPCR) analysis and metagenomic analysis yielded different relative abundance of individual genera of methanogens, especially for Methanobacterium, which was predominant based on qPCR analysis but undetectable by metagenomics. Collectively, the results showed that only small microbial and chemical gradients existed within the digester, and the digestion process occurred similarly throughout the MPFLR digester. The findings of this study may help improve the operation and design of this type of manure digesters.

  18. Nearest neighbor imputation using spatial–temporal correlations in wireless sensor networks

    PubMed Central

    Li, YuanYuan; Parker, Lynne E.

    2016-01-01

    Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network’s performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a kd-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for kd-tree construction, and Euclidean distance for kd-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental results

  19. Temporal Evolution and Scaling of Mixing in Two-dimensional Rayleigh-Taylor Turbulence

    NASA Astrophysics Data System (ADS)

    Zhou, Quan; Quan Zhou Team

    2013-11-01

    We report a high-resolution numerical study of two-dimensional (2D) miscible Rayleigh-Taylor (RT) incompressible turbulence with the Boussinesq approximation. We present results from an ensemble of 100 independent realizations performed at unit Prandtl number and small Atwood number with a spatial resolution of 2048 × 8193 grid points and Rayleigh number up to Ra ~1011 . Our main focus is on the temporal evolution and the scaling behavior of global quantities and of small-scale turbulence properties. Our results show that the buoyancy force balances the inertial force at all scales below the integral length scale and thus validate the basic force-balance assumption of the Bolgiano-Obukhov scenario in 2D RT turbulence. It is further found that the Kolmogorov dissipation scale η (t) ~t 1 / 8 , the kinetic-energy dissipation rate ɛu (t) ~t - 1 / 2 , and the thermal dissipation rate ɛθ (t) ~t-1 . All of these scaling properties are in excellent agreement with the theoretical predictions of the Chertkov model [Phys. Rev. Lett. 91, 115001 (2003)]. This work was supported by the Natural Science Foundation of China (NSFC) under Grant Nos. 11222222 and 11161160554 and Innovation Program of Shanghai Municipal Education Commission under Grant No. 13YZ008.

  20. CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

    PubMed Central

    Weiss, Scott T.

    2014-01-01

    Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

  1. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    PubMed

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  2. Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups.

    PubMed

    Zachariadis, Markos; Oborn, Eivor; Barrett, Michael; Zollinger-Read, Paul

    2013-01-01

    To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation. Mixed-method, multisite and case study research. Six clinical commissioning groups and local clusters in the East of England area, covering in total 208 GPs and 1 662 000 population. Semistructured interviews with 56 lead GPs, practice managers and staff from the local health authorities (primary care trusts, PCT) as well as various healthcare professionals; 21 observations of clinical commissioning group (CCG) board and executive meetings; electronic survey of 58 CCG board members (these included GPs, practice managers, PCT employees, nurses and patient representatives) and subsequent social network analysis. Collaborative relationships between CCG board members and stakeholders from their healthcare network; clarifying the role of GPs as network leaders; strengths and areas for development of CCGs. Drawing upon innovation network theory provides unique insights of the CCG leaders' activities in establishing best practices and introducing new clinical pathways. In this context we identified three network leadership roles: managing knowledge flows, managing network coherence and managing network stability. Knowledge sharing and effective collaboration among GPs enable network stability and the alignment of CCG objectives with those of the wider health system (network coherence). Even though activities varied between commissioning groups, collaborative initiatives were common. However, there was significant variation among CCGs around the level of engagement with providers, patients and local authorities. Locality (sub) groups played an important role because they linked commissioning decisions with patient needs and brought the leaders closer to frontline stakeholders

  3. Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups

    PubMed Central

    Zachariadis, Markos; Oborn, Eivor; Barrett, Michael; Zollinger-Read, Paul

    2013-01-01

    Objective To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation. Design Mixed-method, multisite and case study research. Setting Six clinical commissioning groups and local clusters in the East of England area, covering in total 208 GPs and 1 662 000 population. Methods Semistructured interviews with 56 lead GPs, practice managers and staff from the local health authorities (primary care trusts, PCT) as well as various healthcare professionals; 21 observations of clinical commissioning group (CCG) board and executive meetings; electronic survey of 58 CCG board members (these included GPs, practice managers, PCT employees, nurses and patient representatives) and subsequent social network analysis. Main outcome measures Collaborative relationships between CCG board members and stakeholders from their healthcare network; clarifying the role of GPs as network leaders; strengths and areas for development of CCGs. Results Drawing upon innovation network theory provides unique insights of the CCG leaders’ activities in establishing best practices and introducing new clinical pathways. In this context we identified three network leadership roles: managing knowledge flows, managing network coherence and managing network stability. Knowledge sharing and effective collaboration among GPs enable network stability and the alignment of CCG objectives with those of the wider health system (network coherence). Even though activities varied between commissioning groups, collaborative initiatives were common. However, there was significant variation among CCGs around the level of engagement with providers, patients and local authorities. Locality (sub) groups played an important role because they linked commissioning decisions with

  4. Mapping U.S. cattle shipment networks: Spatial and temporal patterns of trade communities from 2009 to 2011.

    PubMed

    Gorsich, Erin E; Luis, Angela D; Buhnerkempe, Michael G; Grear, Daniel A; Portacci, Katie; Miller, Ryan S; Webb, Colleen T

    2016-11-01

    The application of network analysis to cattle shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole. Such a quantitative description of cattle shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies. Here, we analyze a longitudinal dataset of beef and dairy cattle shipments from 2009 to 2011 in the United States to characterize communities within the broader cattle shipment network, which are groups of counties that ship mostly to each other. Because shipments occur over time, we aggregate the data at various temporal scales to examine the consistency of network and community structure over time. Our results identified nine large (>50 counties) communities based on shipments of beef cattle in 2009 aggregated into an annual network and nine large communities based on shipments of dairy cattle. The size and connectance of the shipment network was highly dynamic; monthly networks were smaller than yearly networks and revealed seasonal shipment patterns consistent across years. Comparison of the shipment network over time showed largely consistent shipping patterns, such that communities identified on annual networks of beef and diary shipments from 2009 still represented 41-95% of shipments in monthly networks from 2009 and 41-66% of shipments from networks in 2010 and 2011. The temporal aspects of cattle shipments suggest that future applications of the U.S. cattle shipment network should consider seasonal shipment patterns. However, the consistent within-community shipping patterns indicate that yearly communities could provide a reasonable way to group regions for management. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

    PubMed

    Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

    2015-03-01

    Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula, in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

  6. Large Eddy Simulation (LES) of Particle-Laden Temporal Mixing Layers

    NASA Technical Reports Server (NTRS)

    Bellan, Josette; Radhakrishnan, Senthilkumaran

    2012-01-01

    High-fidelity models of plume-regolith interaction are difficult to develop because of the widely disparate flow conditions that exist in this process. The gas in the core of a rocket plume can often be modeled as a time-dependent, high-temperature, turbulent, reacting continuum flow. However, due to the vacuum conditions on the lunar surface, the mean molecular path in the outer parts of the plume is too long for the continuum assumption to remain valid. Molecular methods are better suited to model this region of the flow. Finally, granular and multiphase flow models must be employed to describe the dust and debris that are displaced from the surface, as well as how a crater is formed in the regolith. At present, standard commercial CFD (computational fluid dynamics) software is not capable of coupling each of these flow regimes to provide an accurate representation of this flow process, necessitating the development of custom software. This software solves the fluid-flow-governing equations in an Eulerian framework, coupled with the particle transport equations that are solved in a Lagrangian framework. It uses a fourth-order explicit Runge-Kutta scheme for temporal integration, an eighth-order central finite differencing scheme for spatial discretization. The non-linear terms in the governing equations are recast in cubic skew symmetric form to reduce aliasing error. The second derivative viscous terms are computed using eighth-order narrow stencils that provide better diffusion for the highest resolved wave numbers. A fourth-order Lagrange interpolation procedure is used to obtain gas-phase variable values at the particle locations.

  7. Spatial and temporal structure of the clinical research based on mesenchymal stromal cells: A network analysis.

    PubMed

    Monsarrat, Paul; Kemoun, Philippe; Vergnes, Jean-Noel; Sensebe, Luc; Casteilla, Louis; Planat-Benard, Valerie

    2017-01-01

    Using innovative tools derived from social network analysis, the aims of this study were (i) to decipher the spatial and temporal structure of the research centers network dedicated to the therapeutic uses of mesenchymal stromal cells (MSCs) and (ii) to measure the influence of fields of applications, cellular sources and industry funding on network topography. From each trial using MSCs reported on ClinicalTrials.gov, all research centers were extracted. Networks were generated using Cytoscape 3.2.2, where each center was assimilated to a node, and one trial to an edge connecting two nodes. The analysis included 563 studies. An independent segregation was obvious between continents. Asian, South American and African centers were significantly more isolated than other centers. Isolated centers had fewer advanced phases (P <0.001), completed studies (P = 0.01) and industry-supported studies (P <0.001). Various thematic priorities among continents were identified: the cardiovascular, digestive and nervous system diseases were strongly studied by North America, Europe and Asia, respectively. The choice of cellular sources also affected the network topography; North America was primarily involved in bone-marrow-derived MSC research, whereas Europe and Asia dominated the use of adipose-derived MSCs. Industrial funding was the highest for North American centers (90.5%). Strengthening of international standards and statements with institutional, federal and industrial partners is necessary. More connections would facilitate the transfer of knowledge, sharing of resources, mobility of researchers and advancement of trials. Developing partnerships between industry and academic centers seems beneficial to the advancement of trials across different phases and would facilitate the translation of research discoveries. Copyright © 2017 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

  8. Spatio-temporal filtering for determination of common mode error in regional GNSS networks

    NASA Astrophysics Data System (ADS)

    Bogusz, Janusz; Gruszczynski, Maciej; Figurski, Mariusz; Klos, Anna

    2015-04-01

    The spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually estimated with the regional spatial filtering, such as the "stacking". In this paper, we show the stacking approach for the set of ASG-EUPOS permanent stations, assuming that spatial distribution of the CME is uniform over the whole region of Poland (more than 600 km extent). The ASG-EUPOS is a multifunctional precise positioning system based on the reference network designed for Poland. We used a 5- year span time series (2008-2012) of daily solutions in the ITRF2008 from Bernese 5.0 processed by the Military University of Technology EPN Local Analysis Centre (MUT LAC). At the beginning of our analyses concerning spatial dependencies, the correlation coefficients between each pair of the stations in the GNSS network were calculated. This analysis shows that spatio-temporal behaviour of the GPS-derived time series is not purely random, but there is the evident uniform spatial response. In order to quantify the influence of filtering using CME, the norms L1 and L2 were determined. The values of these norms were calculated for the North, East and Up components twice: before performing the filtration and after stacking. The observed reduction of the L1 and L2 norms was up to 30% depending on the dimension of the network. However, the question how to define an optimal size of CME-analysed subnetwork remains unanswered in this research, due to the fact that our network is not extended enough.

  9. Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach.

    PubMed

    Sone, Daichi; Matsuda, Hiroshi; Ota, Miho; Maikusa, Norihide; Kimura, Yukio; Sumida, Kaoru; Yokoyama, Kota; Imabayashi, Etsuko; Watanabe, Masako; Watanabe, Yutaka; Okazaki, Mitsutoshi; Sato, Noriko

    2016-09-01

    Graph theory is an emerging method to investigate brain networks. Altered cerebral blood flow (CBF) has frequently been reported in temporal lobe epilepsy (TLE), but graph theoretical findings of CBF are poorly understood. Here, we explored graph theoretical networks of CBF in TLE using arterial spin labeling imaging. We recruited patients with TLE and unilateral hippocampal sclerosis (HS) (19 patients with left TLE, and 21 with right TLE) and 20 gender- and age-matched healthy control subjects. We obtained all participants' CBF maps using pseudo-continuous arterial spin labeling and analyzed them using the Graph Analysis Toolbox (GAT) software program. As a result, compared to the controls, the patients with left TLE showed a significantly low clustering coefficient (p=0.024), local efficiency (p=0.001), global efficiency (p=0.010), and high transitivity (p=0.015), whereas the patients with right TLE showed significantly high assortativity (p=0.046) and transitivity (p=0.011). The group with right TLE also had high characteristic path length values (p=0.085), low global efficiency (p=0.078), and low resilience to targeted attack (p=0.101) at a trend level. Lower normalized clustering coefficient (p=0.081) in the left TLE and higher normalized characteristic path length (p=0.089) in the right TLE were found also at a trend level. Both the patients with left and right TLE showed significantly decreased clustering in similar areas, i.e., the cingulate gyri, precuneus, and occipital lobe. Our findings revealed differing left-right network metrics in which an inefficient CBF network in left TLE and vulnerability to irritation in right TLE are suggested. The left-right common finding of regional decreased clustering might reflect impaired default-mode networks in TLE.

  10. Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device

    PubMed Central

    McKinstry, Jeffrey L.; Edelman, Gerald M.

    2013-01-01

    Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions. PMID:23760804

  11. Mixed outer synchronization of coupled complex networks with time-varying coupling delay.

    PubMed

    Wang, Jun-Wei; Ma, Qinghua; Zeng, Li; Abd-Elouahab, Mohammed Salah

    2011-03-01

    In this paper, the problem of outer synchronization between two complex networks with the same topological structure and time-varying coupling delay is investigated. In particular, we introduce a new type of outer synchronization behavior, i.e., mixed outer synchronization (MOS), in which different state variables of the corresponding nodes can evolve into complete synchronization, antisynchronization, and even amplitude death simultaneously for an appropriate choice of the scaling matrix. A novel nonfragile linear state feedback controller is designed to realize the MOS between two networks and proved analytically by using Lyapunov-Krasovskii stability theory. Finally, numerical simulations are provided to demonstrate the feasibility and efficacy of our proposed control approach.

  12. Temporal encoding of multispectral satellite imagery for segmentation using pulsed coupled neural networks

    NASA Astrophysics Data System (ADS)

    Tarr, Gregory L.; Carreras, Richard A.; Fender, Janet S.; Clastres, Xavier; Freyss, Laurent; Samuelides, Manuel

    1995-11-01

    Unlike biological vision, most techniques for computer image processing are not robust over large samples of imagery. Natural systems seem unaffected by variation in local illumination and textures which interfere with conventional analysis. While change detection algorithms have been partially successful, many important tasks like extraction of roads and communication lines remain unsolved. The solution to these problems may lie in examining architectures and algorithms used by biological imaging systems. Pulsed oscillatory neural network design, based on biomemetics, seem to solve some of these problems. Pulsed oscillatory neural networks are examined for application to image analysis and segmentation of multispectral imagery from the Satellite Pour l'Observation de la Terre. Using biological systems as a model for image analysis of complex data, a pulsed coupled networks using an integrate and fire mechanism is developed. This architecture, based on layers of pulsed coupled neurons is tested against common image segmentation problems. Using a reset activation pulse similar to that generated by sacatic motor commands, an algorithm is developed which demonstrates the biological vision could be based on adaptive histogram techniques. This architecture is demonstrated to be both biologically plausible and more effective than conventional techniques. Using the pulse time-of-arrival as the information carrier, the image is reduced to a time signal, temporal encoding of imagery, which allows an intelligent filtering based on expectation. This technique is uniquely suited to multispectral/multisensor imagery and other sensor fusion problems.

  13. Spatial-temporal assessment and redesign of groundwater quality monitoring network: a case study.

    PubMed

    Owlia, Rashid Reza; Abrishamchi, Ahmad; Tajrishy, Massoud

    2011-01-01

    Assessment of groundwater quality monitoring networks requires methods to determine the potential efficiency and cost-effectiveness of the current monitoring programs. To this end, the concept of entropy has been considered as a promising method in previous studies since it quantitatively measures the information produced by a network. In this study, the measure of transinformation in the discrete entropy theory and the transinformation-distance (T-D) curves, which are used frequently by other researchers, are used to quantify the efficiency of a monitoring network. This paper introduces a new approach to decrease dispersion in results by performing cluster analysis that uses fuzzy equivalence relations. As a result, the sampling (temporal) frequency determination method also recommends the future sampling frequencies for each location based on certain criteria such as direction, magnitude, correlation with neighboring stations, and uncertainty of the concentration trend derived from representative historical concentration data. The proposed methodology is applied to groundwater resources in the Tehran-Karadj aquifer, Tehran, Iran.

  14. Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa

    PubMed Central

    Gehan, Malia A; Mockler, Todd C; Weinig, Cynthia; Ewers, Brent E

    2017-01-01

    The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought. PMID:28826479

  15. Spatio-temporal waves and targeted vaccination in recurrent epidemic network models.

    PubMed

    Litvak-Hinenzon, Anna; Stone, Lewi

    2009-09-06

    The success of an infectious disease to invade a population is strongly controlled by the population's specific connectivity structure. Here, a network model is presented as an aid in understanding the role of social behaviour and heterogeneous connectivity in determining the spatio-temporal patterns of disease dynamics. We explore the controversial origins of long-term recurrent oscillations believed to be characteristic of diseases that have a period of temporary immunity after infection. In particular, we focus on sexually transmitted diseases such as syphilis, where this controversy is currently under review. Although temporary immunity plays a key role, it is found that, in realistic small-world networks, the social and sexual behaviour of individuals also has a great influence in generating long-term cycles. The model generates circular waves of infection with unusual spatial dynamics that depend on focal areas that act as pacemakers in the population. Eradication of the disease can be efficiently achieved by eliminating the pacemakers with a targeted vaccination scheme. A simple difference equation model is derived, which captures the infection dynamics of the network model and gives insights into their origins and their eradication through vaccination. Illustrative videos may be found in the electronic supplementary material.

  16. Spatial and temporal variability of nitrous oxide emissions in a mixed farming landscape of Denmark

    NASA Astrophysics Data System (ADS)

    Schelde, K.; Cellier, P.; Bertolini, T.; Dalgaard, T.; Weidinger, T.; Theobald, M. R.; Olesen, J. E.

    2012-08-01

    Nitrous oxide (N2O) emissions from agricultural land are variable at the landscape scale due to variability in land use, management, soil type, and topography. A field experiment was carried out in a typical mixed farming landscape in Denmark, to investigate the main drivers of variations in N2O emissions, measured using static chambers. Measurements were made over a period of 20 months, and sampling was intensified during two weeks in spring 2009 when chambers were installed at ten locations or fields to cover different crops and topography and slurry was applied to three of the fields. N2O emissions during spring 2009 were relatively low, with maximum values below 20 ng N m-2 s-1. This applied to all land use types including winter grain crops, grasslands, meadows, and wetlands. Slurry application to wheat fields resulted in short-lived two-fold increases in emissions. The moderate N2O fluxes and their moderate response to slurry application were attributed to dry soil conditions due to the absence of rain during the four previous weeks. Cumulative annual emissions from two arable fields that were both fertilized with mineral fertilizer and manure were large (17 kg N2O-N ha-1 yr-1 and 5.5 kg N2O-N ha-1 yr-1) during the previous year when soil water conditions were favourable for N2O production during the first month following fertilizer application. Our findings confirm the importance of weather conditions as well as nitrogen management on N2O fluxes.

  17. Ozone deposition in a mixed forest ecosystem - temporal variation and removal processes

    NASA Astrophysics Data System (ADS)

    Pokorska, Olga; Gruening, Carsten; Goded, Ignacio

    2016-04-01

    Forests are a major sink for tropospheric ozone, however the fate of ozone within the forest canopy is still not well understood. In this study we will present results of 3 years ozone flux measurements at eddy covariance flux tower in Ispra (Northern Italy) over mixed forest ecosystem. The main tree species in this ecosystem are Quercus robur (80%), Alnus glutinosa (10%), Populus alba (5%) and Carpinus betulus (3%). The measurements were carried out continuously from January 2013 till December 2015. Flux measurements at the canopy level with the eddy covariance technique were complemented with measurements of meteorological parameters and measurements of VOCs (Volatile Organic Compounds) using PTR-MS (Proton Transfer Reaction - Mass Spectrometry) during an intensive observation period. Continuous measurements produced a big dataset which allowed us to investigate the controls on ozone fluxes and to do multiyear comparisons. Patterns of ozone concentration, ozone fluxes and ozone deposition velocity over forest canopy will be presented in relation to physiological activity of the trees and time of the year. Current research is mainly aimed at better understanding of the contribution of destruction processes within the canopy. Therefore, the collected data were used to calculate the partitioning of total ozone fluxes between stomatal and non-stomatal sinks. We found that the stomatal uptake contributed less that non-stomatal uptake to the total ozone flux during the growing seasons. In particular, non-stomatal ozone removal by reactions with isoprene and other VOCs will be discussed. We will present contribution and change of individual ozone deposition sinks over the years in response to environmental parameters.

  18. Temporal dynamics of instream wood in headwater streams draining mixed Carpathian forests

    NASA Astrophysics Data System (ADS)

    Galia, Tomáš; Šilhán, Karel; Ruiz-Villanueva, Virginia; Tichavský, Radek; Stoffel, Markus

    2017-09-01

    Instream wood can reside in fluvial systems over varying periods depending on its geographical context, instream position, tree species, piece size, and fluvial environment. In this paper, we investigate the residence time of two typical species representing a majority of instream wood in steep headwaters of the Carpathians and located under mixed forest canopy. Residence times of individual logs were then confronted with other wood parameters (i.e., wood dimensions, mean annual increment rate, tree age, class of wood stabilisation and decay, geomorphic function of wood pieces, and the proportion of the log length within the active channel). Norway spruce (Picea abies (L.) Karst.) samples indicated more than two times longer mean and maximal residence times as compared to European beech (Fagus sylvatica L.) based on the successful cross-dating of 127 logs. Maximum residence time in the headwaters was 128 years for P. abies and 59 years for F. sylvatica. We demonstrate that log age and log diameter played an important role in the preservation of wood in the fluvial system, especially in the case of F. sylvatica instream wood. By contrast, we did not observe any significant trends between wood residence time and total wood length. Instream wood with geomorphic functions (i.e., formation of steps and jams) did not show any differences in residence time as compared to nonfunctional wood. Nevertheless, we found shorter residence times for hillslope-stabilised pieces when compared to pieces located entirely in the channel (either unattached or stabilised by other wood or bed sediments). We also observed changes of instream wood orientation with respect to wood residence time. This suggests some movement of instream wood (i.e., its turning or short-distance transport), including pieces longer than channel width in the steep headwaters studied here (1.5 ≤ W ≤ 3.5 m), over the past few decades.

  19. Social Network Decay as Potential Recovery from Homelessness: A Mixed Methods Study in Housing First Programming

    PubMed Central

    Golembiewski, Elizabeth; Watson, Dennis P.; Robison, Lisa; Coberg, John W.

    2017-01-01

    The positive relationship between social support and mental health has been well documented, but individuals experiencing chronic homelessness face serious disruptions to their social networks. Housing First (HF) programming has been shown to improve health and stability of formerly chronically homeless individuals. However, researchers are only just starting to understand the impact HF has on residents’ individual social integration. The purpose of the current study was to describe and understand changes in social networks of residents living in a HF program. Researchers employed a longitudinal, convergent parallel mixed method design, collecting quantitative social network data through structured interviews (n = 13) and qualitative data through semi-structured interviews (n = 20). Quantitative results demonstrated a reduction in network size over the course of one year. However, increases in both network density and frequency of contact with network members increased. Qualitative interviews demonstrated a strengthening in the quality of relationships with family and housing providers and a shedding of burdensome and abusive relationships. These results suggest network decay is a possible indicator of participants’ recovery process as they discontinued negative relationships and strengthened positive ones. PMID:28890807

  20. Realization of couplings in a polynomial type mixed-mode cellular neural network.

    PubMed

    Laiho, Mika; Paasio, Ari; Kananen, Asko; Halonen, Kari

    2003-12-01

    In this paper realization of couplings between cells in a polynomial type mixed-mode cellular neural network (CNN) is analyzed. The choice of the multiplier is discussed and two multiplier types are analyzed. Also, two circuits for generating the second and third order polynomial terms of cell output are described. The accuracy of the multipliers and polynomial circuits at the presence of device mismatch is analyzed.

  1. Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA.

    PubMed

    Guclu, Hasan; Read, Jonathan; Vukotich, Charles J; Galloway, David D; Gao, Hongjiang; Rainey, Jeanette J; Uzicanin, Amra; Zimmer, Shanta M; Cummings, Derek A T

    2016-01-01

    Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.

  2. Use of generalized linear mixed models for network meta-analysis.

    PubMed

    Tu, Yu-Kang

    2014-10-01

    In the past decade, a new statistical method-network meta-analysis-has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of multiple treatments. Bayesian network meta-analysis, as proposed by Lu and Ades, also known as "mixed treatments comparisons," provides a flexible modeling framework to take into account complexity in the data structure. This article shows how to implement the Lu and Ades model in the frequentist generalized linear mixed model. Two examples are provided to demonstrate how centering the covariates for random effects estimation within each trial can yield correct estimation of random effects. Moreover, under the correct specification for random effects estimation, the dummy coding and contrast basic parameter coding schemes will yield the same results. It is straightforward to incorporate covariates, such as moderators and confounders, into the generalized linear mixed model to conduct meta-regression for multiple treatment comparisons. Moreover, this approach may be extended easily to other types of outcome variables, such as continuous, counts, and multinomial. © The Author(s) 2014.

  3. Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

    PubMed Central

    Guclu, Hasan; Read, Jonathan; Vukotich, Charles J.; Galloway, David D.; Gao, Hongjiang; Rainey, Jeanette J.; Uzicanin, Amra; Zimmer, Shanta M.; Cummings, Derek A. T.

    2016-01-01

    Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools. PMID:26978780

  4. A Regulated Double-Negative Feedback Decodes the Temporal Gradient of Input Stimulation in a Cell Signaling Network

    PubMed Central

    Park, Sang-Min; Shin, Sung-Young; Cho, Kwang-Hyun

    2016-01-01

    Revealing the hidden mechanism of how cells sense and react to environmental signals has been a central question in cell biology. We focused on the rate of increase of stimulation, or temporal gradient, known to cause different responses of cells. We have investigated all possible three-node enzymatic networks and identified a network motif that robustly generates a transient or sustained response by acute or gradual stimulation, respectively. We also found that a regulated double-negative feedback within the motif is essential for the temporal gradient-sensitive switching. Our analysis highlights the essential structure and mechanism enabling cells to properly respond to dynamic environmental changes. PMID:27584002

  5. Cooperation-induced temporal complexity in networks of pulse-coupled units

    NASA Astrophysics Data System (ADS)

    Geneston, Elvis; Grigolini, Paolo

    2012-02-01

    We study a network of stochastic pulse-coupled units generating bursts with the same size distribution as the neuronal avalanches in mature cultured neurons, recently revealed by the experimental observation. We prove that in addition to this form of complexity this model yields a form of phase transition generating also temporal complexity. This means that the distance from two consecutive bursts fits the prescription of a Mittag-Leffler (ML) function renewal theory. There exists a critical value of the cooperation parameter at which this description applies to the whole time regime. By increasing the cooperation parameter the ML theory breaks down and the sequence of bursts tend to become periodic with the same intensity. We make the conjecture that the analysis of this model may shed light into the theoretical foundation of neuronal burst leaders and that the recently discovered principle of complexity management may be conveniently applied to the neuro-physiological processes that are properly described by this model.

  6. Temporal organization of GABAergic interneurons in the intermediate CA1 hippocampus during network oscillations.

    PubMed

    Forro, Thomas; Valenti, Ornella; Lasztoczi, Balint; Klausberger, Thomas

    2015-05-01

    Travelling theta oscillations and sharp wave-associated ripples (SWRs) provide temporal structures to neural activity in the CA1 hippocampus. The contribution of rhythm-generating GABAergic interneurons to network timing across the septotemporal CA1 axis remains unknown. We recorded the spike-timing of identified parvalbumin (PV)-expressing basket, axo-axonic, oriens-lacunosum moleculare (O-LM) interneurons, and pyramidal cells in the intermediate CA1 (iCA1) of anesthetized rats in relation to simultaneously detected network oscillations in iCA1 and dorsal CA1 (dCA1). Distinct interneuron types were coupled differentially to SWR, and the majority of iCA1 SWR events occurred simultaneously with dCA1 SWR events. In contrast, iCA1 theta oscillations were shifted in time relative to dCA1 theta oscillations. During theta cycles, the highest firing of iCA1 axo-axonic cells was followed by PV-expressing basket cells and subsequently by O-LM together with pyramidal cells, similar to the firing sequence of dCA1 cell types reported previously. However, we observed that this temporal organization of cell types is shifted in time between dCA1 and iCA1, together with the respective shift in theta oscillations. We show that GABAergic activity can be synchronized during SWR but is shifted in time from dCA1 to iCA1 during theta oscillations, highlighting the flexible inhibitory control of excitatory activity across a brain structure.

  7. Evaluating complementary networks of restoration plantings for landscape-scale occurrence of temporally dynamic species.

    PubMed

    Ikin, Karen; Tulloch, Ayesha; Gibbons, Philip; Ansell, Dean; Seddon, Julian; Lindenmayer, David

    2016-10-01

    Multibillion dollar investments in land restoration make it critical that conservation goals are achieved cost-effectively. Approaches developed for systematic conservation planning offer opportunities to evaluate landscape-scale, temporally dynamic biodiversity outcomes from restoration and improve on traditional approaches that focus on the most species-rich plantings. We investigated whether it is possible to apply a complementarity-based approach to evaluate the extent to which an existing network of restoration plantings meets representation targets. Using a case study of woodland birds of conservation concern in southeastern Australia, we compared complementarity-based selections of plantings based on temporally dynamic species occurrences with selections based on static species occurrences and selections based on ranking plantings by species richness. The dynamic complementarity approach, which incorporated species occurrences over 5 years, resulted in higher species occurrences and proportion of targets met compared with the static complementarity approach, in which species occurrences were taken at a single point in time. For equivalent cost, the dynamic complementarity approach also always resulted in higher average minimum percent occurrence of species maintained through time and a higher proportion of the bird community meeting representation targets compared with the species-richness approach. Plantings selected under the complementarity approaches represented the full range of planting attributes, whereas those selected under the species-richness approach were larger in size. Our results suggest that future restoration policy should not attempt to achieve all conservation goals within individual plantings, but should instead capitalize on restoration opportunities as they arise to achieve collective value of multiple plantings across the landscape. Networks of restoration plantings with complementary attributes of age, size, vegetation structure, and

  8. A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2014-11-01

    Artificial Neural Networks (ANNs) and Kriging have both been used for hydraulic head simulation. In this study, the two methodologies were combined in order to simulate the spatial and temporal distribution of hydraulic head in a study area. In order to achieve that, a fuzzy logic inference system can also be used. Different ANN architectures and variogram models were tested, together with the use or not of a fuzzy logic system. The developed algorithm was implemented and applied for predicting, spatially and temporally, the hydraulic head in an area located in Bavaria, Germany. The performance of the algorithm was evaluated using leave one out cross validation and various performance indicators were derived. The best results were achieved by using ANNs with two hidden layers, with the use of the fuzzy logic system and by utilizing the power-law variogram. The results obtained from this procedure can be characterized as favorable, since the RMSE of the method is in the order of magnitude of 10-2 m. Therefore this method can be used successfully in aquifers where geological characteristics are obscure, but a variety of other, easily accessible data, such as meteorological data can be easily found.

  9. Temporal and spatial evolution of brain network topology during the first two years of life.

    PubMed

    Gao, Wei; Gilmore, John H; Giovanello, Kelly S; Smith, Jeffery Keith; Shen, Dinggang; Zhu, Hongtu; Lin, Weili

    2011-01-01

    The mature brain features high wiring efficiency for information transfer. However, the emerging process of such an efficient topology remains elusive. With resting state functional MRI and a large cohort of normal pediatric subjects (n = 147) imaged during a critical time period of brain development, 3 wk- to 2 yr-old, the temporal and spatial evolution of brain network topology is revealed. The brain possesses the small world topology immediately after birth, followed by a remarkable improvement in whole brain wiring efficiency in 1 yr olds and becomes more stable in 2 yr olds. Regional developments of brain wiring efficiency and the evolution of functional hubs suggest differential development trend for primary and higher order cognitive functions during the first two years of life. Simulations of random errors and targeted attacks reveal an age-dependent improvement of resilience. The lower resilience to targeted attack observed in 3 wk old group is likely due to the fact that there are fewer well-established long-distance functional connections at this age whose elimination might have more profound implications in the overall efficiency of information transfer. Overall, our results offer new insights into the temporal and spatial evolution of brain topology during early brain development.

  10. Optimal design of mixed-media packet-switching networks - Routing and capacity assignment

    NASA Technical Reports Server (NTRS)

    Huynh, D.; Kuo, F. F.; Kobayashi, H.

    1977-01-01

    This paper considers a mixed-media packet-switched computer communication network which consists of a low-delay terrestrial store-and-forward subnet combined with a low-cost high-bandwidth satellite subnet. We show how to route traffic via ground and/or satellite links by means of static, deterministic procedures and assign capacities to channels subject to a given linear cost such that the network average delay is minimized. Two operational schemes for this network model are investigated: one is a scheme in which the satellite channel is used as a slotted ALOHA channel; the other is a new multiaccess scheme we propose in which whenever a channel collision occurs, retransmission of the involved packets will route through ground links to their destinations. The performance of both schemes is evaluated and compared in terms of cost and average packet delay tradeoffs for some examples. The results offer guidelines for the design and optimal utilization of mixed-media networks.

  11. Hierarchical Organization and Disassortative Mixing of Correlation-Based Weighted Financial Networks

    NASA Astrophysics Data System (ADS)

    Cai, Shi-Min; Zhou, Yan-Bo; Zhou, Tao; Zhou, Pei-Ling

    Correlation-based weighted financial networks are analyzed to present cumulative distribution of strength with a power-law tail, which suggests that a small number of hub-like stocks have greater influence on the whole fluctuation of financial market than others. The relationship between clustering and connectivity of vertices emphasizes hierarchical organization, which has been depicted by minimal span tree in previous work. These results urge us to further study the mixing patter of financial network to understand the tendency for vertices to be connected to vertices that are like (or unlike) them in some way. The measurement of average nearest-neighbor degree running over classes of vertices with degree k shows a descending trend when k increases. This interesting result is first uncovered in our work, and suggests the disassortative mixing of financial network which refers to a bias in favor of connections between dissimilar vertices. All the results in weighted complex network aspect may provide some insights to deeper understand the underlying mechanism of financial market and model the evolution of financial market.

  12. Optimal design of mixed-media packet-switching networks - Routing and capacity assignment

    NASA Technical Reports Server (NTRS)

    Huynh, D.; Kuo, F. F.; Kobayashi, H.

    1977-01-01

    This paper considers a mixed-media packet-switched computer communication network which consists of a low-delay terrestrial store-and-forward subnet combined with a low-cost high-bandwidth satellite subnet. We show how to route traffic via ground and/or satellite links by means of static, deterministic procedures and assign capacities to channels subject to a given linear cost such that the network average delay is minimized. Two operational schemes for this network model are investigated: one is a scheme in which the satellite channel is used as a slotted ALOHA channel; the other is a new multiaccess scheme we propose in which whenever a channel collision occurs, retransmission of the involved packets will route through ground links to their destinations. The performance of both schemes is evaluated and compared in terms of cost and average packet delay tradeoffs for some examples. The results offer guidelines for the design and optimal utilization of mixed-media networks.

  13. Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

    NASA Astrophysics Data System (ADS)

    Gollas, Frank; Tetzlaff, Ronald

    2009-05-01

    Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal

  14. Temporal Pattern Recognition: A Network Architecture For Multi-Sensor Fusion

    NASA Astrophysics Data System (ADS)

    Priebe, C. E.; Marchette, D. J.

    1989-03-01

    A self-organizing network architecture for the learning and recognition of temporal patterns is proposed. This multi-layered architecture has as its focal point a layer of multi-dimensional Gaussian classification nodes, and the learning scheme employed is based on standard statistical moving mean and moving covariance calculations. The nodes are implemented in the network architecture by using a Gaussian, rather than sigmoidal, transfer function acting on the input from numerous connections. Each connection is analogous to a separate dimension for the Gaussian function. The learning scheme is a one-pass method, eliminating the need for repetitive presentation of the teaching stimuli. The Gaussian classes developed are representative of the statistics of the teaching data and act as templates in classifying novel inputs. The input layer employs a time-based decay to develop a time-ordered representation of the input stimuli. This temporal pattern recognition architecture is used to perform multi-sensor fusion and scene analysis for ROBART II, an autonomous sentry robot employing heterogeneous and homogeneous binary (on / off) sensors. The system receives sensor packets from ROBART indicating which sensors are active. The packets from various sensors are integrated in the input layer. As time progresses these sensor outputs become ordered, allowing the system to recognize activities which are dependent, not only on the individual events which make up the activity, but also on the order in which these events occur and their relative spacing throughout time. Each Gaussian classification node, representing a learned activity as an ordered sequence of sensor outputs, calculates its activation value independently, based on the activity in the input layer. These Gaussian activation values are then used to determine which, if any, of the learned sequences are present and with what confidence. The classification system is capable of recognizing activities despite missing

  15. Bi–Mn mixed metal organic oxide: A novel 3d-6p mixed metal coordination network

    SciTech Connect

    Shi, Fa-Nian; Rosa Silva, Ana; Bian, Liang

    2015-05-15

    A new terminology of metal organic oxide (MOO) was given a definition as a type of coordination polymers which possess the feature of inorganic connectivity between metals and the direct bonded atoms and show 1D, 2D or 3D inorganic sub-networks. One such compound was shown as an example. A 3d-6p (Mn–Bi. Named MOOMnBi) mixed metals coordination network has been synthesized via hydrothermal method. The new compound with the molecular formula of [MnBi{sub 2}O(1,3,5-BTC){sub 2}]{sub n} (1,3,5-BTC stands for benzene-1,3,5-tricarboxylate) was characterized via single crystal X-ray diffraction technique that revealed a very interesting 3-dimensional (3D) framework with Bi{sub 4}O{sub 2}(COO){sub 12} clusters which are further connected to Mn(COO){sub 6} fragments into a 2D MOO. The topology study indicates an unprecedented topological type with the net point group of (4{sup 13}.6{sup 2})(4{sup 13}.6{sup 8})(4{sup 16}.6{sup 5})(4{sup 18}.6{sup 10})(4{sup 22}.6{sup 14})(4{sup 3}) corresponding to 3,6,7,7,8,9-c hexa-nodal net. MOOMnBi shows catalytic activity in the synthesis of (E)-α,β-unsaturated ketones. - Graphical abstract: This metal organic framework (MOF) is the essence of a 2D metal organic oxide (MOO). - Highlights: • New concept of metal organic oxide (MOO) was defined and made difference from metal organic framework. • New MOO of MOOMnBi was synthesized by hydrothermal method. • Crystal structure of MOOMnBi was determined by single crystal X-ray analysis. • The catalytic activity of MOOMnBi was studied showing reusable after 2 cycles.

  16. A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis.

    PubMed

    Akutekwe, Arinze; Seker, Huseyin

    2014-01-01

    Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied. However, these methods lack the ability to detect temporal relationships among biomarker genes that will aid better understanding of the mechanism of hypertension disease. In this paper we apply a proposed two-stage bio-network construction approach that combines the power and computational efficiency of classification methods with the well-established predictive ability of Dynamic Bayesian Network. We demonstrate our method using the analysis of male young-onset hypertension microarray dataset. Four key genes were identified by the Least Angle Shrinkage and Selection Operator (LASSO) and three Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. Results show that cell regulation FOXQ1 may inhibit the expression of focusyltransferase-6 (FUT6) and that ABCG1 ATP-binding cassette sub-family G may also play inhibitory role against NR2E3 nuclear receptor sub-family 2 and CGB2 Chromatin Gonadotrophin.

  17. Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

    PubMed Central

    Wallach, Thomas; Schellenberg, Katja; Maier, Bert; Kalathur, Ravi Kiran Reddy; Porras, Pablo; Wanker, Erich E.; Futschik, Matthias E.; Kramer, Achim

    2013-01-01

    Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner. PMID:23555304

  18. Temporal evolution of a drainage fracture network into an elastic medium with internal fluid generation

    NASA Astrophysics Data System (ADS)

    Kobchenko, Maya; Hafver, Andreas; Dysthe, Dag Kristian; Renard, Francois

    2013-04-01

    Escape of internally generated fluids from low permeability rocks plays an important role in several geological systems. Primary migration of hydrocarbons, dehydration of sediments and hydrated mantellic rocks in subduction zones in the Earth's crust are geological examples where the existing permeability cannot accommodate transport of generated fluids in low permeability rocks and fluid pressure build-up may alter the permeability by fracturing. Fractures form and propagate in the rock due to internal pressure build-up. We develop an easy and reproducible analog experiment to simulate fracture formation in low permeability rock during internal fluid/gas production. This work aims to describe the physical mechanism of fracture network growth and temporal evolution of created fractures. A tight elastic gelatin matrix is used as a rock analog. The nucleation, propagation and coalescence of fractures within the solid matrix occurs due to CO2 production by yeast consuming sugar and is followed using optical means. We quantify first how an equilibrium fracture network self-develop, and then how the intermittent fluid transport is controlled by the dynamics of opening and closing of fractures, with a well-defined time frequency.

  19. Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks

    PubMed Central

    Sun, Shao-Yan; Liu, Zhi-Ping; Zeng, Tao; Wang, Yong; Chen, Luonan

    2013-01-01

    T2DM is complex in its dynamical dependence on multiple tissues, disease states, and factors' interactions. However, most existing work devoted to characterizing its pathophysiology from one static tissue, individual factors, or single state. Here we perform a spatio-temporal analysis on T2DM by developing a new form of molecular network, i.e. ‘differential expression network’ (DEN), which can reflect phenotype differences at network level. Static DENs show that three tissues (white adipose, skeletal muscle, and liver) all suffer from severe inflammation and perturbed metabolism, among which metabolic functions are seriously affected in liver. Dynamical analysis on DENs reveals metabolic function changes in adipose and liver are consistent with insulin resistance (IR) deterioration. Close investigation on IR pathway identifies ‘disease interactions’, revealing that IR deterioration is earlier than that on SlC2A4 in adipose and muscle. Our analysis also provides evidence that rising of insulin secretion is the root cause of IR in diabetes. PMID:23881262

  20. Understanding spatial and temporal patterning of astrocyte calcium transients via interactions between network transport and extracellular diffusion

    NASA Astrophysics Data System (ADS)

    Shtrahman, E.; Maruyama, D.; Olariu, E.; Fink, C. G.; Zochowski, M.

    2017-02-01

    Astrocytes form interconnected networks in the brain and communicate via calcium signaling. We investigate how modes of coupling between astrocytes influence the spatio-temporal patterns of calcium signaling within astrocyte networks and specifically how these network interactions promote coordination within this group of cells. To investigate these complex phenomena, we study reduced cultured networks of astrocytes and neurons. We image the spatial temporal patterns of astrocyte calcium activity and quantify how perturbing the coupling between astrocytes influences astrocyte activity patterns. To gain insight into the pattern formation observed in these cultured networks, we compare the experimentally observed calcium activity patterns to the patterns produced by a reduced computational model, where we represent astrocytes as simple units that integrate input through two mechanisms: gap junction coupling (network transport) and chemical release (extracellular diffusion). We examine the activity patterns in the simulated astrocyte network and their dependence upon these two coupling mechanisms. We find that gap junctions and extracellular chemical release interact in astrocyte networks to modulate the spatiotemporal patterns of their calcium dynamics. We show agreement between the computational and experimental findings, which suggests that the complex global patterns can be understood as a result of simple local coupling mechanisms.

  1. A new implantable middle ear hearing device for mixed hearing loss: A feasibility study in human temporal bones.

    PubMed

    Huber, Alexander M; Ball, Geoffrey R; Veraguth, Dorothe; Dillier, Norbert; Bodmer, Daniel; Sequeira, Damien

    2006-12-01

    To assess the feasibility of a new, active middle ear device in temporal bones (TB). This device is designed for patients with mixed hearing loss subsequent to chronic middle ear infection, surgery, or trauma. This Bell-Vibroplasty is built from a VIBRANT MED-EL Vibrant Soundbridge and a Kurz Bell titanium partial ossicular replacement prosthesis. In three fresh TBs, healthy and reconstructed middle ears were analyzed by means of laser Doppler interferometry. The sound transmission properties of a partial ossicular replacement prosthesis and a passive and an active Bell-Vibroplasty were compared with healthy middle ear function. The measurements provided reliable results with small standard deviations and good signal-to-noise ratios. The performance levels of the partial ossicular replacement prosthesis and of the passive Bell-Vibroplasty were comparable with that of healthy middle ear function. The activated Bell-Vibroplasty provided linear function and a flat frequency response within the measured frequency range (500 Hz-8 kHz), with peak deviations of less than 10 dB. The maximum output of the Bell-Vibroplasty was equivalent to 125-dB sound pressure level. Bell-Vibroplasty is feasible in TBs. Bell-Vibroplasty performance in TBs is sufficient to allow for a clinical trial as a next step.

  2. Temporal context in floristic classification

    NASA Astrophysics Data System (ADS)

    Fitzgerald, R. W.; Lees, B. G.

    1996-11-01

    Multi-temporal remote sensing data present a number of significant problems for the statistical and spatial competence of a classifier. Ideally, a classifier of multi-temporal data should be temporally invariant. It must have the capacity to account for the variations in season, growth cycle, radiometric, and atmospheric conditions at any point in time when classifying the land cover. This paper tests two methods of creating a temporally invariant classifier based on the pattern recognition capabilities of a neural network. A suite of twelve multi-temporal datasets spread over 5 yr along with a comprehensive mix of environmental variables are fused into floristic classification images by the neural network. Uncertainties in the classifications are addressed explicitly with a confidence mask generated from the fuzzy membership value's output by the neural network. These confidence masks are used to produce constrained classification images. The overall accuracy percentage achieved from a study site containing highly disturbed undulating terrain averages 60%. The first method of training, sequential learning of temporal context, is tested by an examination of the step-by-step evolution of the sequential training process. This reveals that the sequential classifier may not have learned about time, because time was constant during each network training session. It also suggests that there are optimal times during the annual cycle to train the classifier for particular floristic classes. The second method of training the classifier is randomised exposure to the entire temporal training suite. Time was now a fluctuating input variable during the network training process. This method produced the best spatially accurate results. The performance of this classifier as a temporally invariant classifier is tested amongst four multi-temporal datasets with encouraging results. The classifier consistently achieved an overall accuracy percentage of 60%. The pairwise predicted

  3. Identification of Pre-Spike Network in Patients with Mesial Temporal Lobe Epilepsy

    PubMed Central

    Faizo, Nahla L.; Burianová, Hana; Gray, Marcus; Hocking, Julia; Galloway, Graham; Reutens, David

    2014-01-01

    Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation. PMID

  4. Left fronto-temporal dysconnectivity within the language network in schizophrenia: an fMRI and DTI study.

    PubMed

    Leroux, Elise; Delcroix, Nicolas; Dollfus, Sonia

    2014-09-30

    Schizophrenia is a mental disorder characterized by language disorders. Studies reveal that both a functional dysconnectivity and a disturbance in the integrity of white matter fibers are implicated in the language process in patients with schizophrenia. Here, we investigate the relationship between functional connectivity within a language-comprehension network and anatomical connectivity using fiber tracking in schizophrenia. We hypothesized that patients would present an impaired functional connectivity in the language network due to anatomical dysconnectivity. Participants comprised 20 patients with DSM-IV schizophrenia and 20 healthy controls who were studied with functional magnetic resonance imaging and diffusion tensor imaging. The temporal correlation coefficient and diffusion values between the left frontal and temporal clusters, belonging to the language network, were individually extracted, in order to study the relationships of anatomo-functional connectivity. In patients, functional connectivity was positively correlated with fractional anisotropy, but was negatively correlated with radial diffusivity and/or mean diffusivity, in the left arcuate fasciculus and part of the inferior occipitofrontal fasciculus, determined as the fronto-temporal tracts. Our findings indicate a close relationship between functional and anatomical dysconnectivity in patients with schizophrenia. The disturbance in the integrity of the left fronto-temporal tracts might be one origin of the functional dysconnectivity in the language-comprehension network in schizophrenia.

  5. Identify the diversity of mesoscopic structures in networks: A mixed random walk approach

    NASA Astrophysics Data System (ADS)

    Ma, Yifang; Jiang, Xin; Li, Meng; Shen, Xin; Guo, Quantong; Lei, Yanjun; Zheng, Zhiming

    2013-10-01

    Community or cluster structure, which can provide insight into the natural partitions and inner connections of a network, is a key feature in studying the mesoscopic structure of complex systems. Although numerous methods for community detection have been proposed ever since, there is still a lack of understanding on how to quantify the diversity of pre-divided community structures, or rank the roles of communities in participating in specific dynamic processes. Inspired by the Law of Mass Action in chemical kinetics, we introduce here the community random walk energy (CRWE), which reflects a potential based on the diffusion phase of a mixed random walk process taking place on the network, to identify the configuration of community structures. The difference of CRWE allows us to distinguish the intrinsic topological diversity between individual communities, on condition that all the communities are pre-arranged in the network. We illustrate our method by performing numerical simulations on constructive community networks and a real social network with distinct community structures. As an application, we apply our method to characterize the diversity of human genome communities, which provides a possible use of our method in inferring the genetic similarity between human populations.

  6. Efficient and Secure Temporal Credential-Based Authenticated Key Agreement Using Extended Chaotic Maps for Wireless Sensor Networks

    PubMed Central

    Lee, Tian-Fu

    2015-01-01

    A secure temporal credential-based authenticated key agreement scheme for Wireless Sensor Networks (WSNs) enables a user, a sensor node and a gateway node to realize mutual authentication using temporal credentials. The user and the sensor node then negotiate a common secret key with the help of the gateway node, and establish a secure and authenticated channel using this common secret key. To increase efficiency, recent temporal credential-based authenticated key agreement schemes for WSNs have been designed to involve few computational operations, such as hash and exclusive-or operations. However, these schemes cannot protect the privacy of users and withstand possible attacks. This work develops a novel temporal credential-based authenticated key agreement scheme for WSNs using extended chaotic maps, in which operations are more efficient than modular exponential computations and scalar multiplications on an elliptic curve. The proposed scheme not only provides higher security and efficiency than related schemes, but also resolves their weaknesses. PMID:26121612

  7. Efficient and Secure Temporal Credential-Based Authenticated Key Agreement Using Extended Chaotic Maps for Wireless Sensor Networks.

    PubMed

    Lee, Tian-Fu

    2015-06-25

    A secure temporal credential-based authenticated key agreement scheme for Wireless Sensor Networks (WSNs) enables a user, a sensor node and a gateway node to realize mutual authentication using temporal credentials. The user and the sensor node then negotiate a common secret key with the help of the gateway node, and establish a secure and authenticated channel using this common secret key. To increase efficiency, recent temporal credential-based authenticated key agreement schemes for WSNs have been designed to involve few computational operations, such as hash and exclusive-or operations. However, these schemes cannot protect the privacy of users and withstand possible attacks. This work develops a novel temporal credential-based authenticated key agreement scheme for WSNs using extended chaotic maps, in which operations are more efficient than modular exponential computations and scalar multiplications on an elliptic curve. The proposed scheme not only provides higher security and efficiency than related schemes, but also resolves their weaknesses.

  8. Understanding cancer networks better to implement them more effectively: a mixed methods multi-case study.

    PubMed

    Tremblay, Dominique; Touati, Nassera; Roberge, Danièle; Breton, Mylaine; Roch, Geneviève; Denis, Jean-Louis; Candas, Bernard; Francoeur, Danièle

    2016-03-21

    Managed cancer networks are widely promoted in national cancer control programs as an organizational form that enables integrated care as well as enhanced patient outcomes. While national programs are set by policy-makers, the detailed implementation of networks is delegated at the service delivery and institutional levels. It is likely that the capacity to ensure more integrated cancer services requires multi-level governance processes responsive to the strengths and limitations of the contexts and capable of supporting network-based working. Based on an empirical case, this study aims to analyze the implementation of a mandated cancer network, focusing on governance and health services integration as core concepts in the study. This nested multi-case study uses mixed methods to explore the implementation of a mandated cancer network in Quebec, a province of Canada. The case is the National Cancer Network (NCN) subdivided into three micro-cases, each defined by the geographic territory of a health and social services region. For each region, two local health services centers (LHSCs) are selected based on their differences with respect to determining characteristics. Qualitative data will be collected from various sources using three strategies: review of documents, focus groups, and semi-directed interviews with stakeholders. The qualitative data will be supplemented with a survey that will measure the degree of integration as a proxy for implementation of the NCN. A score will be constructed, and then triangulated with the qualitative data, which will have been subjected to content analysis. Qualitative, quantitative, and mixed methods data will be interpreted within and across cases in order to identify governance patterns similarities and differences and degree of integration in contexts. This study is designed to inform decision-making to develop more effective network implementation strategies by thoroughly describing multi-level governance processes of a

  9. What Determines the Temporal Changes of Species Degree and Strength in an Oceanic Island Plant-Disperser Network?

    PubMed Central

    González-Castro, Aarón; Yang, Suann; Nogales, Manuel; Carlo, Tomás A.

    2012-01-01

    Network models of frugivory and seed dispersal are usually static. To date, most studies on mutualistic networks assert that interaction properties such as species' degree (k) and strength (s) are strongly influenced by species abundances. We evaluated how species' degree and strength change as a function of temporal variation not only in species abundance, but also in species persistence (i.e., phenology length). In a two-year study, we collected community-wide data on seed dispersal by birds and examined the seasonal dynamics of the above-mentioned interaction properties. Our analyses revealed that species abundance is an important predictor for plant strength within a given sub-network. However, our analyses also reveal that species' degree can often be best explained by the length of fruiting phenology (for plants degree) or by the number of fruiting species (for dispersers degree), which are factors that can be decoupled from the relative abundance of the species participating in the network. Moreover, our results suggest that generalist dispersers (when total study period is considered) act as temporal generalists, with degree constrained by the number of plant species displaying fruits in each span. Along with species identity, our findings underscore the need for a temporal perspective, given that seasonality is an inherent property of many mutualistic networks. PMID:22844470

  10. Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

    NASA Astrophysics Data System (ADS)

    Lotero, Laura; Hurtado, Rafael G.; Floría, Luis Mario; Gómez-Gardeñes, Jesús

    2016-10-01

    We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized.

  11. Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes.

    PubMed

    Lotero, Laura; Hurtado, Rafael G; Floría, Luis Mario; Gómez-Gardeñes, Jesús

    2016-10-01

    We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized.

  12. Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

    PubMed Central

    Hurtado, Rafael G.; Floría, Luis Mario

    2016-01-01

    We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized. PMID:27853531

  13. The spatial structure and temporal synchrony of water quality in stream networks

    NASA Astrophysics Data System (ADS)

    Abbott, Benjamin; Gruau, Gerard; Zarneske, Jay; Barbe, Lou; Gu, Sen; Kolbe, Tamara; Thomas, Zahra; Jaffrezic, Anne; Moatar, Florentina; Pinay, Gilles

    2017-04-01

    To feed nine billion people in 2050 while maintaining viable aquatic ecosystems will require an understanding of nutrient pollution dynamics throughout stream networks. Most regulatory frameworks such as the European Water Framework Directive and U.S. Clean Water Act, focus on nutrient concentrations in medium to large rivers. This strategy is appealing because large rivers integrate many small catchments and total nutrient loads drive eutrophication in estuarine and oceanic ecosystems. However, there is growing evidence that to understand and reduce downstream nutrient fluxes we need to look upstream. While headwater streams receive the bulk of nutrients in river networks, the relationship between land cover and nutrient flux often breaks down for small catchments, representing an important ecological unknown since 90% of global stream length occurs in catchments smaller than 15 km2. Though continuous monitoring of thousands of small streams is not feasible, what if we could learn what we needed about where and when to implement monitoring and conservation efforts with periodic sampling of headwater catchments? To address this question we performed repeat synoptic sampling of 56 nested catchments ranging in size from 1 to 370 km2 in western France. Spatial variability in carbon and nutrient concentrations decreased non-linearly as catchment size increased, with thresholds in variance for organic carbon and nutrients occurring between 36 and 68 km2. While it is widely held that temporal variance is higher in smaller streams, we observed consistent temporal variance across spatial scales and the ranking of catchments based on water quality showed strong synchrony in the water chemistry response to seasonal variation and hydrological events. We used these observations to develop two simple management frameworks. The subcatchment leverage concept proposes that mitigation and restoration efforts are more likely to succeed when implemented at spatial scales expressing

  14. Consistent Large-Eddy Simulation of a Temporal Mixing Layer Laden with Evaporating Drops. Part 2; A Posteriori Modelling

    NASA Technical Reports Server (NTRS)

    Leboissertier, Anthony; Okong'O, Nora; Bellan, Josette

    2005-01-01

    Large-eddy simulation (LES) is conducted of a three-dimensional temporal mixing layer whose lower stream is initially laden with liquid drops which may evaporate during the simulation. The gas-phase equations are written in an Eulerian frame for two perfect gas species (carrier gas and vapour emanating from the drops), while the liquid-phase equations are written in a Lagrangian frame. The effect of drop evaporation on the gas phase is considered through mass, species, momentum and energy source terms. The drop evolution is modelled using physical drops, or using computational drops to represent the physical drops. Simulations are performed using various LES models previously assessed on a database obtained from direct numerical simulations (DNS). These LES models are for: (i) the subgrid-scale (SGS) fluxes and (ii) the filtered source terms (FSTs) based on computational drops. The LES, which are compared to filtered-and-coarsened (FC) DNS results at the coarser LES grid, are conducted with 64 times fewer grid points than the DNS, and up to 64 times fewer computational than physical drops. It is found that both constant-coefficient and dynamic Smagorinsky SGS-flux models, though numerically stable, are overly dissipative and damp generated small-resolved-scale (SRS) turbulent structures. Although the global growth and mixing predictions of LES using Smagorinsky models are in good agreement with the FC-DNS, the spatial distributions of the drops differ significantly. In contrast, the constant-coefficient scale-similarity model and the dynamic gradient model perform well in predicting most flow features, with the latter model having the advantage of not requiring a priori calibration of the model coefficient. The ability of the dynamic models to determine the model coefficient during LES is found to be essential since the constant-coefficient gradient model, although more accurate than the Smagorinsky model, is not consistently numerically stable despite using DNS

  15. Consistent Large-Eddy Simulation of a Temporal Mixing Layer Laden with Evaporating Drops. Part 2; A Posteriori Modelling

    NASA Technical Reports Server (NTRS)

    Leboissertier, Anthony; Okong'O, Nora; Bellan, Josette

    2005-01-01

    Large-eddy simulation (LES) is conducted of a three-dimensional temporal mixing layer whose lower stream is initially laden with liquid drops which may evaporate during the simulation. The gas-phase equations are written in an Eulerian frame for two perfect gas species (carrier gas and vapour emanating from the drops), while the liquid-phase equations are written in a Lagrangian frame. The effect of drop evaporation on the gas phase is considered through mass, species, momentum and energy source terms. The drop evolution is modelled using physical drops, or using computational drops to represent the physical drops. Simulations are performed using various LES models previously assessed on a database obtained from direct numerical simulations (DNS). These LES models are for: (i) the subgrid-scale (SGS) fluxes and (ii) the filtered source terms (FSTs) based on computational drops. The LES, which are compared to filtered-and-coarsened (FC) DNS results at the coarser LES grid, are conducted with 64 times fewer grid points than the DNS, and up to 64 times fewer computational than physical drops. It is found that both constant-coefficient and dynamic Smagorinsky SGS-flux models, though numerically stable, are overly dissipative and damp generated small-resolved-scale (SRS) turbulent structures. Although the global growth and mixing predictions of LES using Smagorinsky models are in good agreement with the FC-DNS, the spatial distributions of the drops differ significantly. In contrast, the constant-coefficient scale-similarity model and the dynamic gradient model perform well in predicting most flow features, with the latter model having the advantage of not requiring a priori calibration of the model coefficient. The ability of the dynamic models to determine the model coefficient during LES is found to be essential since the constant-coefficient gradient model, although more accurate than the Smagorinsky model, is not consistently numerically stable despite using DNS

  16. Development of Polymer Network of Phenolic and Epoxies Resins Mixed with Linseed Oil: Pilot Study

    NASA Astrophysics Data System (ADS)

    Ku, H.; Cardona, F.; Rogers, D.; Munoz, J.-C.

    2010-08-01

    Epoxy resin was mixed with phenolic resins in different percentages by weight. Composite 40/60 means the proportion by weight of epoxy resin is 40%. It was found that only composites 50/50 and 40/60 could be cured in ambient conditions. Dynamic mechanical analysis showed that only these two composites form interpenetrating polymer network. The addition of linseed oil to the two resins results also in the formation of interpenetrating network irrespective of proportion by weight of the resins; the mechanical properties will only be better when the percentage by weight of epoxy resin is higher; the aim of reducing cost and at the same time maintaining the mechanical properties cannot be fully achieved because epoxy resin is much more expensive than its counterpart.

  17. Assessing the performance of the International Monitoring System's infrasound network: Geographical coverage and temporal variabilities

    NASA Astrophysics Data System (ADS)

    Le Pichon, A.; Vergoz, J.; Blanc, E.; Guilbert, J.; Ceranna, L.; Evers, L.; Brachet, N.

    2009-04-01

    A global-scale analysis of detections made at all 36 currently operating International Monitoring System (IMS) infrasound arrays confirms that the primary factor controlling signal detectability is the seasonal variability of the stratospheric zonal wind. At most arrays, ˜80% of the detections in the 0.2- to 2-Hz bandpass are associated with propagation downwind of the dominant stratospheric wind direction. Previous IMS infrasound network performance models neglect the time- and site-dependent effects of both stratospheric meteorological variability and ambient noise models. In this study both effects are incorporated; we compare empirical and improved specifications of the stratospheric wind and include station-dependent wind noise models. Using a deterministic approach, the influence of individual model parameters on the network performance is systematically assessed. At frequencies of interest for detecting atmospheric explosions (0.2-2 Hz), the simulations predict that explosions equivalent to ˜500 t of TNT would be detected by at least two stations at any time of the year. The detection capability is best around January and July when stratospheric winds are strongest, compared to the equinox periods when zonal winds reduce and reverse. The model predicts that temporal fluctuations in the ground-to-stratosphere meteorological variables generate detection threshold variations on daily and seasonal timescales of ˜50 and ˜500 t, respectively. While the strong zonal winds lead to an improvement in detection capability, their highly directional nature leads to an increase in the location uncertainty owing to the decreased azimuthal separation of the detecting stations.

  18. Ice nucleating particles from a large-scale sampling network: insight into geographic and temporal variability

    NASA Astrophysics Data System (ADS)

    Schrod, Jann; Weber, Daniel; Thomson, Erik S.; Pöhlker, Christopher; Saturno, Jorge; Artaxo, Paulo; Curtius, Joachim; Bingemer, Heinz

    2017-04-01

    The number concentration of ice nucleating particles (INP) is an important, yet under quantified atmospheric parameter. The temporal and geographic extent of observations worldwide remains relatively small, with many regions of the world (even whole continents and oceans), almost completely unrepresented by observational data. Measurements at pristine sites are particularly rare, but all the more valuable because such observations are necessary to estimate the pre-industrial baseline of aerosol and cloud related parameters that are needed to better understand the climate system and forecast future scenarios. As a partner of BACCHUS we began in September 2014 to operate an INP measurement network of four sampling stations, with a global geographic distribution. The stations are located at unique sites reaching from the Arctic to the equator: the Amazonian Tall Tower Observatory ATTO in Brazil, the Observatoire Volcanologique et Sismologique on the island of Martinique in the Caribbean Sea, the Zeppelin Observatory at Svalbard in the Norwegian Arctic and the Taunus Observatory near Frankfurt, Germany. Since 2014 samples were collected regularly by electrostatic precipitation of aerosol particles onto silicon substrates. The INP on the substrate are activated and analyzed in the isothermal static diffusion chamber FRIDGE at temperatures between -20°C and -30°C and relative humidity with respect to ice from 115 to 135%. Here we present data from the years 2015 and 2016 from this novel INP network and from selected campaign-based measurements from remote sites, including the Mt. Kenya GAW station. Acknowledgements The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) project BACCHUS under grant agreement No 603445 and the Deutsche Forschungsgemeinschaft (DFG) under the Research Unit FOR 1525 (INUIT).

  19. Increased interictal cerebral glucose metabolism in a cortical-subcortical network in drug naive patients with cryptogenic temporal lobe epilepsy.

    PubMed Central

    Franceschi, M; Lucignani, G; Del Sole, A; Grana, C; Bressi, S; Minicucci, F; Messa, C; Canevini, M P; Fazio, F

    1995-01-01

    Positron emission tomography with [18F]-2-fluoro-2-deoxy-D-glucose ([18F]FDG) has been used to assess the pattern of cerebral metabolism in different types of epilepsies. However, PET with [18F]FDG has never been used to evaluate drug naive patients with cryptogenic temporal lobe epilepsy, in whom the mechanism of origin and diffusion of the epileptic discharge may differ from that underlying other epilepsies. In a group of patients with cryptogenic temporal lobe epilepsy, never treated with antiepileptic drugs, evidence has been found of significant interictal glucose hypermetabolism in a bilateral neural network including the temporal lobes, thalami, basal ganglia, and cingular cortices. The metabolism in these areas and frontal lateral cortex enables the correct classification of all patients with temporal lobe epilepsy and controls by discriminant function analysis. Other cortical areas--namely, frontal basal and lateral, temporal mesial, and cerebellar cortices--had bilateral increases of glucose metabolism ranging from 10 to 15% of normal controls, although lacking stringent statistical significance. This metabolic pattern could represent a pathophysiological state of hyperactivity predisposing to epileptic discharge generation or diffusion, or else a network of inhibitory circuits activated to prevent the diffusion of the epileptic discharge. PMID:7561924

  20. Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization

    PubMed Central

    2013-01-01

    Background Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. Results Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. Conclusion The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters. PMID:24176044

  1. PAX: A mixed hardware/software simulation platform for spiking neural networks.

    PubMed

    Renaud, S; Tomas, J; Lewis, N; Bornat, Y; Daouzli, A; Rudolph, M; Destexhe, A; Saïghi, S

    2010-09-01

    Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulators generally combine digital and analog forms of computation. In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing-Dependent Plasticity). The neurons and networks are configurable, and are computed in 'biological real time' by which we mean that the difference between simulated time and simulation time is guaranteed lower than 50 mus. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organization and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners. Copyright 2010 Elsevier Ltd. All rights reserved.

  2. A Temporal Credential-Based Mutual Authentication with Multiple-Password Scheme for Wireless Sensor Networks.

    PubMed

    Liu, Xin; Zhang, Ruisheng; Liu, Qidong

    2017-01-01

    Wireless sensor networks (WSNs), which consist of a large number of sensor nodes, have become among the most important technologies in numerous fields, such as environmental monitoring, military surveillance, control systems in nuclear reactors, vehicle safety systems, and medical monitoring. The most serious drawback for the widespread application of WSNs is the lack of security. Given the resource limitation of WSNs, traditional security schemes are unsuitable. Approaches toward withstanding related attacks with small overhead have thus recently been studied by many researchers. Numerous studies have focused on the authentication scheme for WSNs, but most of these works cannot achieve the security performance and overhead perfectly. Nam et al. proposed a two-factor authentication scheme with lightweight sensor computation for WSNs. In this paper, we review this scheme, emphasize its drawbacks, and propose a temporal credential-based mutual authentication with a multiple-password scheme for WSNs. Our scheme uses multiple passwords to achieve three-factor security performance and generate a session key between user and sensor nodes. The security analysis phase shows that our scheme can withstand related attacks, including a lost password threat, and the comparison phase shows that our scheme involves a relatively small overhead. In the comparison of the overhead phase, the result indicates that more than 95% of the overhead is composed of communication and not computation overhead. Therefore, the result motivates us to pay further attention to communication overhead than computation overhead in future research.

  3. Synaptic Reorganization of the Perisomatic Inhibitory Network in Hippocampi of Temporal Lobe Epileptic Patients

    PubMed Central

    Wittner, Lucia

    2017-01-01

    GABAergic inhibition and particularly perisomatic inhibition play a crucial role in controlling the firing properties of large principal cell populations. Furthermore, GABAergic network is a key element in the therapy attempting to reduce epileptic activity. Here, we present a review showing the synaptic changes of perisomatic inhibitory neuronal subtypes in the hippocampus of temporal lobe epileptic patients, including parvalbumin- (PV-) containing and cannabinoid Type 1 (CB1) receptor-expressing (and mainly cholecystokinin-positive) perisomatic inhibitory cells, known to control hippocampal synchronies. We have examined the synaptic input of principal cells in the dentate gyrus and Cornu Ammonis region in human control and epileptic hippocampi. Perisomatic inhibitory terminals establishing symmetric synapses were found to be sprouted in the dentate gyrus. Preservation of perisomatic input was found in the Cornu Ammonis 1 and Cornu Ammonis 2 regions, as long as pyramidal cells are present. Higher density of CB1-immunostained terminals was found in the epileptic hippocampus of sclerotic patients, especially in the dentate gyrus. We concluded that both types of (PV- and GABAergic CB1-containing) perisomatic inhibitory cells are mainly preserved or showed sprouting in epileptic samples. The enhanced perisomatic inhibitory signaling may increase principal cell synchronization and contribute to generation of epileptic seizures and interictal spikes. PMID:28116310

  4. Spatio-temporal Remodeling of Functional Membrane Microdomains Organizes the Signaling Networks of a Bacterium

    PubMed Central

    Schneider, Johannes; Klein, Teresa; Mielich-Süss, Benjamin; Koch, Gudrun; Franke, Christian; Kuipers, Oscar P.; Kovács, Ákos T.; Sauer, Markus; Lopez, Daniel

    2015-01-01

    Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs) that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium. PMID:25909364

  5. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter

    PubMed Central

    Dodds, Peter Sheridan; Harris, Kameron Decker; Kloumann, Isabel M.; Bliss, Catherine A.; Danforth, Christopher M.

    2011-01-01

    Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust and tunable metric can be constructed and defended. PMID:22163266

  6. A Temporal Credential-Based Mutual Authentication with Multiple-Password Scheme for Wireless Sensor Networks

    PubMed Central

    Zhang, Ruisheng; Liu, Qidong

    2017-01-01

    Wireless sensor networks (WSNs), which consist of a large number of sensor nodes, have become among the most important technologies in numerous fields, such as environmental monitoring, military surveillance, control systems in nuclear reactors, vehicle safety systems, and medical monitoring. The most serious drawback for the widespread application of WSNs is the lack of security. Given the resource limitation of WSNs, traditional security schemes are unsuitable. Approaches toward withstanding related attacks with small overhead have thus recently been studied by many researchers. Numerous studies have focused on the authentication scheme for WSNs, but most of these works cannot achieve the security performance and overhead perfectly. Nam et al. proposed a two-factor authentication scheme with lightweight sensor computation for WSNs. In this paper, we review this scheme, emphasize its drawbacks, and propose a temporal credential-based mutual authentication with a multiple-password scheme for WSNs. Our scheme uses multiple passwords to achieve three-factor security performance and generate a session key between user and sensor nodes. The security analysis phase shows that our scheme can withstand related attacks, including a lost password threat, and the comparison phase shows that our scheme involves a relatively small overhead. In the comparison of the overhead phase, the result indicates that more than 95% of the overhead is composed of communication and not computation overhead. Therefore, the result motivates us to pay further attention to communication overhead than computation overhead in future research. PMID:28135288

  7. Temporal regulation of EGF signaling networks by the scaffold protein Shc1

    PubMed Central

    Zheng, Yong; Zhang, Cunjie; Croucher, David R.; Soliman, Mohamed A.; St-Denis, Nicole; Pasculescu, Adrian; Taylor, Lorne; Tate, Stephen A.; Hardy, Rod W.; Colwill, Karen; Dai, Anna Yue; Bagshaw, Rick; Dennis, James W.; Gingras, Anne-Claude; Daly, Roger J.; Pawson, Tony

    2016-01-01

    Cell-surface receptors frequently employ scaffold proteins to recruit cytoplasmic targets, but the rationale for this is uncertain. Activated receptor tyrosine kinases, for example, engage scaffolds such as Shc1 that contain phosphotyrosine (pTyr) binding (PTB) domains. Using quantitative mass spectrometry, we find that Shc1 responds to epidermal growth factor (EGF) stimulation through multiple waves of distinct phosphorylation events and protein interactions. Following stimulation, Shc1 rapidly binds a group of proteins that activate pro-mitogenic/survival pathways dependent on recruitment of the Grb2 adaptor to Shc1 pTyr sites. Akt-mediated feedback phosphorylation of Shc1 Ser29 then recruits the Ptpn12 tyrosine phosphatase. This is followed by a sub-network of proteins involved in cytoskeletal reorganization, trafficking and signal termination that binds Shc1 with delayed kinetics, largely through the SgK269 pseudokinase/adaptor protein. Ptpn12 acts as a switch to convert Shc1 from pTyr/Grb2-based signaling to SgK269-mediated pathways that regulate cell invasion and morphogenesis. The Shc1 scaffold therefore directs the temporal flow of signaling information following EGF stimulation. PMID:23846654

  8. Learning invariant object recognition from temporal correlation in a hierarchical network.

    PubMed

    Lessmann, Markus; Würtz, Rolf P

    2014-06-01

    Invariant object recognition, which means the recognition of object categories independent of conditions like viewing angle, scale and illumination, is a task of great interest that humans can fulfill much better than artificial systems. During the last years several basic principles were derived from neurophysiological observations and careful consideration: (1) Developing invariance to possible transformations of the object by learning temporal sequences of visual features that occur during the respective alterations. (2) Learning in a hierarchical structure, so basic level (visual) knowledge can be reused for different kinds of objects. (3) Using feedback to compare predicted input with the current one for choosing an interpretation in the case of ambiguous signals. In this paper we propose a network which implements all of these concepts in a computationally efficient manner which gives very good results on standard object datasets. By dynamically switching off weakly active neurons and pruning weights computation is sped up and thus handling of large databases with several thousands of images and a number of categories in a similar order becomes possible. The involved parameters allow flexible adaptation to the information content of training data and allow tuning to different databases relatively easily. Precondition for successful learning is that training images are presented in an order assuring that images of the same object under similar viewing conditions follow each other. Through an implementation with sparse data structures the system has moderate memory demands and still yields very good recognition rates.

  9. Selective Attention to Semantic and Syntactic Features Modulates Sentence Processing Networks in Anterior Temporal Cortex

    PubMed Central

    Rogalsky, Corianne

    2009-01-01

    Numerous studies have identified an anterior temporal lobe (ATL) region that responds preferentially to sentence-level stimuli. It is unclear, however, whether this activity reflects a response to syntactic computations or some form of semantic integration. This distinction is difficult to investigate with the stimulus manipulations and anomaly detection paradigms traditionally implemented. The present functional magnetic resonance imaging study addresses this question via a selective attention paradigm. Subjects monitored for occasional semantic anomalies or occasional syntactic errors, thus directing their attention to semantic integration, or syntactic properties of the sentences. The hemodynamic response in the sentence-selective ATL region (defined with a localizer scan) was examined during anomaly/error-free sentences only, to avoid confounds due to error detection. The majority of the sentence-specific region of interest was equally modulated by attention to syntactic or compositional semantic features, whereas a smaller subregion was only modulated by the semantic task. We suggest that the sentence-specific ATL region is sensitive to both syntactic and integrative semantic functions during sentence processing, with a smaller portion of this area preferentially involved in the later. This study also suggests that selective attention paradigms may be effective tools to investigate the functional diversity of networks involved in sentence processing. PMID:18669589

  10. Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter.

    PubMed

    Dodds, Peter Sheridan; Harris, Kameron Decker; Kloumann, Isabel M; Bliss, Catherine A; Danforth, Christopher M

    2011-01-01

    Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust and tunable metric can be constructed and defended.

  11. Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain.

    PubMed

    Martínez-López, B; Perez, A M; Sánchez-Vizcaíno, J M

    2009-09-01

    Social network analysis was used in combination with techniques for detection of temporal-spatial clusters to identify operations at high risk of receiving or dispatching pigs, from January through December 2005, in the Spanish province of Salamanca. The temporal-spatial structure of the network was explicitly analyzed to estimate the statistical significance of observed clusters. Significant (P<0.01) temporal-spatial clusters identified were grouped into two compartments based on the nature and extent of the contacts among operations within the clusters. One of the compartments was identified from January through April, included a high proportion of extensive farms (0.39), and was likely to be related with the production and trade of Iberian pigs. The other compartment encompassed a smaller proportion of extensive farms (0.11: P<0.01), took place from May through December, and was probably related to intensive production systems. Analysis of a sub-section of the network, which was selected based on the administrative division of Spain, yielded to the identification of a different set of clusters, showing that results of social network analysis may be sensitive to the extension of the information used in the analysis. The approach presented here will be useful for the implementation of differential surveillance, prevention, and control strategies at specific times and locations, which will aid in the optimization of human and financial resources.

  12. Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks

    PubMed Central

    Karahanoğlu, Fikret Işik; Van De Ville, Dimitri

    2015-01-01

    Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art signal processing techniques, we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs' maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial; typically, three to four iCAPs occur simultaneously in combinations that are consistent with their behaviour profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, we instead find evidence for two DMN-related iCAPs consisting the posterior cingulate cortex that differentially interact with the attention network. These findings demonstrate how the fMRI resting state can be functionally decomposed into spatially and temporally overlapping building blocks using iCAPs. PMID:26178017

  13. Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

    PubMed Central

    Li, Xia; Rao, Shaoqi; Jiang, Wei; Li, Chuanxing; Xiao, Yun; Guo, Zheng; Zhang, Qingpu; Wang, Lihong; Du, Lei; Li, Jing; Li, Li; Zhang, Tianwen; Wang, Qing K

    2006-01-01

    Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to

  14. The Structural Plasticity of White Matter Networks Following Anterior Temporal Lobe Resection

    ERIC Educational Resources Information Center

    Yogarajah, Mahinda; Focke, Niels K.; Bonelli, Silvia B.; Thompson, Pamela; Vollmar, Christian; McEvoy, Andrew W.; Alexander, Daniel C.; Symms, Mark R.; Koepp, Matthias J.; Duncan, John S.

    2010-01-01

    Anterior temporal lobe resection is an effective treatment for refractory temporal lobe epilepsy. The structural consequences of such surgery in the white matter, and how these relate to language function after surgery remain unknown. We carried out a longitudinal study with diffusion tensor imaging in 26 left and 20 right temporal lobe epilepsy…

  15. The Structural Plasticity of White Matter Networks Following Anterior Temporal Lobe Resection

    ERIC Educational Resources Information Center

    Yogarajah, Mahinda; Focke, Niels K.; Bonelli, Silvia B.; Thompson, Pamela; Vollmar, Christian; McEvoy, Andrew W.; Alexander, Daniel C.; Symms, Mark R.; Koepp, Matthias J.; Duncan, John S.

    2010-01-01

    Anterior temporal lobe resection is an effective treatment for refractory temporal lobe epilepsy. The structural consequences of such surgery in the white matter, and how these relate to language function after surgery remain unknown. We carried out a longitudinal study with diffusion tensor imaging in 26 left and 20 right temporal lobe epilepsy…

  16. Isolated right temporal lobe stroke patients present with Geschwind Gastaut syndrome, frontal network syndrome and delusional misidentification syndromes.

    PubMed

    Hoffmann, Michael

    2008-01-01

    Right temporal lobe lesion syndrome elicitation presents a clinical challenge. Aside from occasional covert quadrantanopias, heralding elementary neurological deficits are absent. Isolated right and left temporal lobe stroke patients were analyzed for the panoply of known temporal and frontal cognitive and neuropsychiatric syndromes. Temporal lobe stroke patients were analyzed, derived from a dedicated cognitive stroke registry. Patients were screened by a validated bedside cognitive battery and a neuropsychological test battery, including the Bear Fedio Inventory for diagnosis of the Geschwind Gastaut (GG) syndrome, frontal network syndrome testing (FNS), emotional intelligence testing and delusional misidentification syndromes (DMIS). NIH stroke scores were documented and lesion location identified with the 3 dimensional digitized Cerefy coxial brain atlas. Exclusions were coma, encephalopathy and medication related effects. Of 2389 patients analyzed, in patients with isolated right temporal lobe (IRT) stroke (n = 5, infarcts n = 3, hemorrhage n = 2), the GG syndrome and FNS were present in all five. Other relatively frequent syndromes included DMIS in 4, visuospatial dysfunction in 2 and amusia in 2. No patient had a NIHSS greater than 1. The only elementary neurological sign was quadrantanopia in 3 patients. Lesion location was mid and lateral temporal lobe (n = 2), middle and mesial temporal lobe (n = 1) middle temporal lobe (n = 1) and lateral temporal lobe (n = 1). Comparison with isolated left temporal lobe (ILT) stroke revealed syndromes of aphasia (n = 4), alexia (n = 2), acalculia (n = 2), agnosia (n = 2), verbal amnesia (n = 1), none of which occurred in the IRT patients. The mean NIHSS scores of IRT (0.6) and ILT strokes (4.2) was different (t = 2.23, p = 0.04). The 2 x 8 Fisher Exact Test revealed significant differences for the clusters of syndromes occurring in the right and left isolated temporal lobe lesions (p = 0.00002). The GG syndrome, FNS and

  17. Temporal and Spatial prediction of groundwater levels using Artificial Neural Networks, Fuzzy logic and Kriging interpolation.

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2014-05-01

    The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of

  18. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    PubMed Central

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  19. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

    PubMed

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-06-10

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.

  20. Success of Anomia Treatment in Aphasia Is Associated With Preserved Architecture of Global and Left Temporal Lobe Structural Networks.

    PubMed

    Bonilha, Leonardo; Gleichgerrcht, Ezequiel; Nesland, Travis; Rorden, Chris; Fridriksson, Julius

    2016-03-01

    Targeted speech therapy can lead to substantial naming improvement in some subjects with anomia following dominant-hemisphere stroke. We investigated whether treatment-induced improvement in naming is associated with poststroke preservation of structural neural network architecture. Twenty-four patients with poststroke chronic aphasia underwent 30 hours of speech therapy over a 2-week period and were assessed at baseline and after therapy. Whole brain maps of neural architecture were constructed from pretreatment diffusion tensor magnetic resonance imaging to derive measures of global brain network architecture (network small-worldness) and regional network influence (nodal betweenness centrality). Their relationship with naming recovery was evaluated with multiple linear regressions. Treatment-induced improvement in correct naming was associated with poststroke preservation of global network small worldness and of betweenness centrality in temporal lobe cortical regions. Together with baseline aphasia severity, these measures explained 78% of the variability in treatment response. Preservation of global and left temporal structural connectivity broadly explains the variability in treatment-related naming improvement in aphasia. These findings corroborate and expand on previous classical lesion-symptom mapping studies by elucidating some of the mechanisms by which brain damage may relate to treated aphasia recovery. Favorable naming outcomes may result from the intact connections between spared cortical areas that are functionally responsive to treatment. © The Author(s) 2015.

  1. Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

    SciTech Connect

    Zheng, B. |; Qian, W.; Clarke, L.P.

    1996-10-01

    A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC`s) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC`s and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP`s MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed.

  2. Mixed Reversible Covalent Crosslink Kinetics Enable Precise, Hierarchical Mechanical Tuning of Hydrogel Networks.

    PubMed

    Yesilyurt, Volkan; Ayoob, Andrew M; Appel, Eric A; Borenstein, Jeffrey T; Langer, Robert; Anderson, Daniel G

    2017-05-01

    Hydrogels play a central role in a number of medical applications and new research aims to engineer their mechanical properties to improve their capacity to mimic the functional dynamics of native tissues. This study shows hierarchical mechanical tuning of hydrogel networks by utilizing mixtures of kinetically distinct reversible covalent crosslinks. A methodology is described to precisely tune stress relaxation in PEG networks formed from mixtures of two different phenylboronic acid derivatives with unique diol complexation rates, 4-carboxyphenylboronic acid, and o-aminomethylphenylboronic acid. Gel relaxation time and the mechanical response to dynamic shear are exquisitely controlled by the relative concentrations of the phenylboronic acid derivatives. The differences observed in the crossover frequencies corresponding to pKa differences in the phenylboronic acid derivatives directly connect the molecular kinetics of the reversible crosslinks to the macroscopic dynamic mechanical behavior. Mechanical tuning by mixing reversible covalent crosslinking kinetics is found to be independent of other attributes of network architecture, such as molecular weight between crosslinks. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Diminished default mode network recruitment of the hippocampus and parahippocampus in temporal lobe epilepsy

    PubMed Central

    James, G. Andrew; Tripathi, Shanti Prakash; Ojemann, Jeffrey G.; Gross, Robert E.; Drane, Daniel L.

    2014-01-01

    Object Functional neuroimaging has shown that the brain organizes into several independent networks of spontaneously coactivated regions during wakeful rest (resting state). Previous research has suggested that 1 such network, the default mode network (DMN), shows diminished recruitment of the hippocampus with temporal lobe epilepsy (TLE). This work seeks to elucidate how hippocampal recruitment into the DMN varies by hemisphere of epileptogenic focus. Methods The authors addressed this issue using functional MRI to assess resting-state DMN connectivity in 38 participants (23 control participants, 7 patients with TLE and left-sided epileptogenic foci, and 8 patients with TLE and right-sided foci). Independent component analysis was conducted to identify resting-state brain networks from control participants’ data. The DMN was identified and deconstructed into its individual regions of interest (ROIs). The functional connectivity of these ROIs was analyzed both by hemisphere (left vs right) and by laterality to the epileptogenic focus (ipsilateral vs contralateral). Results This attempt to replicate previously published methods with this data set showed that patients with left-sided TLE had reduced connectivity between the posterior cingulate (PCC) and both the left (p = 0.012) and right (p < 0.002) hippocampus, while patients with right-sided TLE showed reduced connectivity between the PCC and right hippocampus (p < 0.004). After recoding ROIs by laterality, significantly diminished functional connectivity was observed between the PCC and hippocampus of both hemispheres (ipsilateral hippocampus, p < 0.001; contralateral hippocampus, p = 0.017) in patients with TLE compared with control participants. Regression analyses showed the reduced DMN recruitment of the ipsilateral hippocampus and parahippocampal gyrus (PHG) to be independent of clinical variables including hippocampal sclerosis, seizure frequency, and duration of illness. The graph theory metric of

  4. The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks.

    PubMed

    Constantinou, Anthony C; Fenton, Norman

    2017-01-01

    In 2015 the British government announced a number of major tax reforms for individual landlords. To give landlords time to adjust, some of these tax measures are being introduced gradually from April 2017, with full effect in tax year 2020/21. The changes in taxation have received much media attention since there has been widespread belief that the new measures were sufficiently skewed against landlords that they could signal the end of the Buy-To-Let (BTL) investment era in the UK. This paper assesses the prospective performance of BTL investments in London from the investor's perspective, and examines the impact of incoming tax reforms using a novel Temporal Bayesian Network model. The model captures uncertainties of interest by simulating the impact of changing circumstances and the interventions available to an investor at various time-steps of a BTL investment portfolio. The simulation results suggest that the new tax reforms are likely to have a detrimental effect on net profits from rental income, and this hits risk-seeking investors who favour leverage much harder than risk-averse investors who do not seek to expand their property portfolio. The impact on net profits also poses substantial risks for lossmaking returns excluding capital gains, especially in the case of rising interest rates. While this makes it less desirable or even non-viable for some to continue being a landlord, based on the current status of all factors taken into consideration for simulation, investment prospects are still likely to remain good within a reasonable range of interest rate and capital growth rate variations. The results also suggest that the recent trend of property prices in London increasing faster than rents will not continue for much longer; either capital growth rates will have to decrease, rental growth rates will have to increase, or we shall observe a combination of the two events.

  5. The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks

    PubMed Central

    Fenton, Norman

    2017-01-01

    In 2015 the British government announced a number of major tax reforms for individual landlords. To give landlords time to adjust, some of these tax measures are being introduced gradually from April 2017, with full effect in tax year 2020/21. The changes in taxation have received much media attention since there has been widespread belief that the new measures were sufficiently skewed against landlords that they could signal the end of the Buy-To-Let (BTL) investment era in the UK. This paper assesses the prospective performance of BTL investments in London from the investor’s perspective, and examines the impact of incoming tax reforms using a novel Temporal Bayesian Network model. The model captures uncertainties of interest by simulating the impact of changing circumstances and the interventions available to an investor at various time-steps of a BTL investment portfolio. The simulation results suggest that the new tax reforms are likely to have a detrimental effect on net profits from rental income, and this hits risk-seeking investors who favour leverage much harder than risk-averse investors who do not seek to expand their property portfolio. The impact on net profits also poses substantial risks for lossmaking returns excluding capital gains, especially in the case of rising interest rates. While this makes it less desirable or even non-viable for some to continue being a landlord, based on the current status of all factors taken into consideration for simulation, investment prospects are still likely to remain good within a reasonable range of interest rate and capital growth rate variations. The results also suggest that the recent trend of property prices in London increasing faster than rents will not continue for much longer; either capital growth rates will have to decrease, rental growth rates will have to increase, or we shall observe a combination of the two events. PMID:28654698

  6. Recurrent temporal networks and language acquisition-from corticostriatal neurophysiology to reservoir computing.

    PubMed

    Dominey, Peter F

    2013-01-01

    One of the most paradoxical aspects of human language is that it is so unlike any other form of behavior in the animal world, yet at the same time, it has developed in a species that is not far removed from ancestral species that do not possess language. While aspects of non-human primate and avian interaction clearly constitute communication, this communication appears distinct from the rich, combinatorial and abstract quality of human language. So how does the human primate brain allow for language? In an effort to answer this question, a line of research has been developed that attempts to build a language processing capability based in part on the gross neuroanatomy of the corticostriatal system of the human brain. This paper situates this research program in its historical context, that begins with the primate oculomotor system and sensorimotor sequencing, and passes, via recent advances in reservoir computing to provide insight into the open questions, and possible approaches, for future research that attempts to model language processing. One novel and useful idea from this research is that the overlap of cortical projections onto common regions in the striatum allows for adaptive binding of cortical signals from distinct circuits, under the control of dopamine, which has a strong adaptive advantage. A second idea is that recurrent cortical networks with fixed connections can represent arbitrary sequential and temporal structure, which is the basis of the reservoir computing framework. Finally, bringing these notions together, a relatively simple mechanism can be built for learning the grammatical constructions, as the mappings from surface structure of sentences to their meaning. This research suggests that the components of language that link conceptual structure to grammatical structure may be much simpler that has been proposed in other research programs. It also suggests that part of the residual complexity is in the conceptual system itself.

  7. Network Anisotropy Trumps Noise for Efficient Object Coding in Macaque Inferior Temporal Cortex.

    PubMed

    Chen, Yueh-Peng; Lin, Chia-Pei; Hsu, Yu-Chun; Hung, Chou P

    2015-07-08

    How neuronal ensembles compute information is actively studied in early visual cortex. Much less is known about how local ensembles function in inferior temporal (IT) cortex, the last stage of the ventral visual pathway that supports visual recognition. Previous reports suggested that nearby neurons carry information mostly independently, supporting efficient processing (Barlow, 1961). However, others postulate that noise covariation effects may depend on network anisotropy/homogeneity and on how the covariation relates to representation. Do slow trial-by-trial noise covariations increase or decrease IT's object coding capability, how does encoding capability relate to correlational structure (i.e., the spatial pattern of signal and noise redundancy/homogeneity across neurons), and does knowledge of correlational structure matter for decoding? We recorded simultaneously from ∼80 spiking neurons in ∼1 mm(3) of macaque IT under light neurolept anesthesia. Noise correlations were stronger for neurons with correlated tuning, and noise covariations reduced object encoding capability, including generalization across object pose and illumination. Knowledge of noise covariations did not lead to better decoding performance. However, knowledge of anisotropy/homogeneity improved encoding and decoding efficiency by reducing the number of neurons needed to reach a given performance level. Such correlated neurons were found mostly in supragranular and infragranular layers, supporting theories that link recurrent circuitry to manifold representation. These results suggest that redundancy benefits manifold learning of complex high-dimensional information and that subsets of neurons may be more immune to noise covariation than others. How noise affects neuronal population coding is poorly understood. By sampling densely from local populations supporting visual object recognition, we show that recurrent circuitry supports useful representations and that subsets of neurons may be

  8. A mixed connection recovery strategy for surviving dual link failure in WDM networks

    NASA Astrophysics Data System (ADS)

    Yadav, Dharmendra Singh; Rana, Santosh; Prakash, Shashi

    2013-03-01

    As the size and complexity of a network increases, the probability of a dual link failure also increases. For recovering the dual link failures, two strategies have been presented in past. As per the first strategy, SPP-MAS (Shared Path Protection-Maximum Allowable Sharing), the sharing of backup lightpaths in SPP (Shared Path Protection) has been reduced, and in the second strategy TBPS (Two Backup Path Shared), the reservation of two backup lightpaths for each primary lightpath has been undertaken. The main flaw of these strategies is the requirement of redundant network resources towards the establishment of backup lightpaths, and the occurrence of trap problem after the second link fails. To minimize the redundant backup resources and the trap problem, a mixed connection recovery algorithm namely Adaptive Backup Routing over Reserved Resources (ABRRR) has been proposed. The design of ABRRR takes leverage of both, the pre-planned, and the post-failure connection recovery mechanisms. In ABRRR, the failed connections are re-provisioned adaptively over the pre-allocated backup network resources. Adaptive re-provisioning of the failed connection minimizes the trap problem. Using simulation experiments, we undertake a comparative study of the proposed strategy with the existing strategies (i.e. SPP-MAS and TBPS) under the network parameters of Blocking Probability, Dual Restorability, and Resource Utilization Ratio (RUR). Detailed investigations establish that the use of ABRRR leads to lower Blocking Probability, higher Dual Restorability, and minimized RUR compared to the existing strategies. Results also show that the proposed strategy not only survives more connections but also utilizes fewer numbers of resources compared to the existing strategies.

  9. Use of a mobile social networking intervention for weight management: a mixed-methods study protocol.

    PubMed

    Laranjo, Liliana; Lau, Annie Y S; Martin, Paige; Tong, Huong Ly; Coiera, Enrico

    2017-07-12

    Obesity and physical inactivity are major societal challenges and significant contributors to the global burden of disease and healthcare costs. Information and communication technologies are increasingly being used in interventions to promote behaviour change in diet and physical activity. In particular, social networking platforms seem promising for the delivery of weight control interventions.We intend to pilot test an intervention involving the use of a social networking mobile application and tracking devices (Fitbit Flex 2 and Fitbit Aria scale) to promote the social comparison of weight and physical activity, in order to evaluate whether mechanisms of social influence lead to changes in those outcomes over the course of the study. Mixed-methods study involving semi-structured interviews and a pre-post quasi-experimental pilot with one arm, where healthy participants in different body mass index (BMI) categories, aged between 19 and 35 years old, will be subjected to a social networking intervention over a 6-month period. The primary outcome is the average difference in weight before and after the intervention. Secondary outcomes include BMI, number of steps per day, engagement with the intervention, social support and system usability. Semi-structured interviews will assess participants' expectations and perceptions regarding the intervention. Ethics approval was granted by Macquarie University's Human Research