Using arborescences to estimate hierarchicalness in directed complex networks

PubMed Central

2018-01-01

Complex networks are a useful tool for the understanding of complex systems. One of the emerging properties of such systems is their tendency to form hierarchies: networks can be organized in levels, with nodes in each level exerting control on the ones beneath them. In this paper, we focus on the problem of estimating how hierarchical a directed network is. We propose a structural argument: a network has a strong top-down organization if we need to delete only few edges to reduce it to a perfect hierarchy—an arborescence. In an arborescence, all edges point away from the root and there are no horizontal connections, both characteristics we desire in our idealization of what a perfect hierarchy requires. We test our arborescence score in synthetic and real-world directed networks against the current state of the art in hierarchy detection: agony, flow hierarchy and global reaching centrality. These tests highlight that our arborescence score is intuitive and we can visualize it; it is able to better distinguish between networks with and without a hierarchical structure; it agrees the most with the literature about the hierarchy of well-studied complex systems; and it is not just a score, but it provides an overall scheme of the underlying hierarchy of any directed complex network. PMID:29381761

Global network structure of dominance hierarchy of ant workers

PubMed Central

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

2014-01-01

Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e. all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e. number of workers that the focal worker attacks), not in-degree (i.e. number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks. PMID:25100318

Resolution of ranking hierarchies in directed networks.

PubMed

Letizia, Elisa; Barucca, Paolo; Lillo, Fabrizio

2018-01-01

Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit.

Resolution of ranking hierarchies in directed networks

PubMed Central

Barucca, Paolo; Lillo, Fabrizio

2018-01-01

Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit. PMID:29394278

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Generalized priority-queue network dynamics: Impact of team and hierarchy

NASA Astrophysics Data System (ADS)

Cho, Won-Kuk; Min, Byungjoon; Goh, K.-I.; Kim, I.-M.

2010-06-01

We study the effect of team and hierarchy on the waiting-time dynamics of priority-queue networks. To this end, we introduce generalized priority-queue network models incorporating interaction rules based on team-execution and hierarchy in decision making, respectively. It is numerically found that the waiting-time distribution exhibits a power law for long waiting times in both cases, yet with different exponents depending on the team size and the position of queue nodes in the hierarchy, respectively. The observed power-law behaviors have in many cases a corresponding single or pairwise-interacting queue dynamics, suggesting that the pairwise interaction may constitute a major dynamic consequence in the priority-queue networks. It is also found that the reciprocity of influence is a relevant factor for the priority-queue network dynamics.

Hierarchy in directed random networks.

PubMed

Mones, Enys

2013-02-01

In recent years, the theory and application of complex networks have been quickly developing in a markable way due to the increasing amount of data from real systems and the fruitful application of powerful methods used in statistical physics. Many important characteristics of social or biological systems can be described by the study of their underlying structure of interactions. Hierarchy is one of these features that can be formulated in the language of networks. In this paper we present some (qualitative) analytic results on the hierarchical properties of random network models with zero correlations and also investigate, mainly numerically, the effects of different types of correlations. The behavior of the hierarchy is different in the absence and the presence of giant components. We show that the hierarchical structure can be drastically different if there are one-point correlations in the network. We also show numerical results suggesting that the hierarchy does not change monotonically with the correlations and there is an optimal level of nonzero correlations maximizing the level of hierarchy.

TinkerCell: modular CAD tool for synthetic biology.

PubMed

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2009-10-29

Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks. An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at http://www.tinkercell.com. An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology.

TinkerCell: modular CAD tool for synthetic biology

PubMed Central

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2009-01-01

Background Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks. Results An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at . Conclusion An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology. PMID:19874625

Management of Large-Scale Wireless Sensor Networks Utilizing Multi-Parent Recursive Area Hierarchies

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

Cree, Johnathan V.; Delgado-Frias, Jose

2013-04-19

Autonomously configuring and self-healing a largescale wireless sensor network requires a light-weight maintenance protocol that is scalable. Further, in a battery powered wireless sensor network duty-cycling a node’s radio can reduce the power consumption of a device and extend the lifetime of a network. With duty-cycled nodes the power consumption of a node’s radio depends on the amount of communication is must perform and by reducing the communication the power consumption can also be reduced. Multi-parent hierarchies can be used to reduce the communication cost when constructing a recursive area clustering hierarchy when compared to singleparent solutions that utilize inefficientmore » communication methods such as flooding and information propagation via single-hop broadcasts. The multi-parent hierarchies remain scalable and provides a level of redundancy for the hierarchy.« less

Fiber fault location utilizing traffic signal in optical network.

PubMed

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

2013-10-07

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

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

PubMed Central

Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Silva, Filipi Nascimento; da Fontoura Costa, Luciano; Ferreira, Leandro Rodrigues; Furlanetto, Glaucio; Chacur, Paulo; Zerbini, Maria Claudia Nogueira; Carneiro-Sampaio, Magda

2016-01-01

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue - obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT) - and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the “canonical” way of thymus functioning. Conversely, DS networks represent a “non-canonical” way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes. PMID:26848775

A neural model of the temporal dynamics of figure-ground segregation in motion perception.

PubMed

Raudies, Florian; Neumann, Heiko

2010-03-01

How does the visual system manage to segment a visual scene into surfaces and objects and manage to attend to a target object? Based on psychological and physiological investigations, it has been proposed that the perceptual organization and segmentation of a scene is achieved by the processing at different levels of the visual cortical hierarchy. According to this, motion onset detection, motion-defined shape segregation, and target selection are accomplished by processes which bind together simple features into fragments of increasingly complex configurations at different levels in the processing hierarchy. As an alternative to this hierarchical processing hypothesis, it has been proposed that the processing stages for feature detection and segregation are reflected in different temporal episodes in the response patterns of individual neurons. Such temporal epochs have been observed in the activation pattern of neurons as low as in area V1. Here, we present a neural network model of motion detection, figure-ground segregation and attentive selection which explains these response patterns in an unifying framework. Based on known principles of functional architecture of the visual cortex, we propose that initial motion and motion boundaries are detected at different and hierarchically organized stages in the dorsal pathway. Visual shapes that are defined by boundaries, which were generated from juxtaposed opponent motions, are represented at different stages in the ventral pathway. Model areas in the different pathways interact through feedforward and modulating feedback, while mutual interactions enable the communication between motion and form representations. Selective attention is devoted to shape representations by sending modulating feedback signals from higher levels (working memory) to intermediate levels to enhance their responses. Areas in the motion and form pathway are coupled through top-down feedback with V1 cells at the bottom end of the hierarchy. We propose that the different temporal episodes in the response pattern of V1 cells, as recorded in recent experiments, reflect the strength of modulating feedback signals. This feedback results from the consolidated shape representations from coherent motion patterns and the attentive modulation of responses along the cortical hierarchy. The model makes testable predictions concerning the duration and delay of the temporal episodes of V1 cell responses as well as their response variations that were caused by modulating feedback signals. Copyright 2009 Elsevier Ltd. All rights reserved.

Cortical Hubs Form a Module for Multisensory Integration on Top of the Hierarchy of Cortical Networks

PubMed Central

Zamora-López, Gorka; Zhou, Changsong; Kurths, Jürgen

2009-01-01

Sensory stimuli entering the nervous system follow particular paths of processing, typically separated (segregated) from the paths of other modal information. However, sensory perception, awareness and cognition emerge from the combination of information (integration). The corticocortical networks of cats and macaque monkeys display three prominent characteristics: (i) modular organisation (facilitating the segregation), (ii) abundant alternative processing paths and (iii) the presence of highly connected hubs. Here, we study in detail the organisation and potential function of the cortical hubs by graph analysis and information theoretical methods. We find that the cortical hubs form a spatially delocalised, but topologically central module with the capacity to integrate multisensory information in a collaborative manner. With this, we resolve the underlying anatomical substrate that supports the simultaneous capacity of the cortex to segregate and to integrate multisensory information. PMID:20428515

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

PubMed

Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael

2017-06-15

Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A tribal abstraction network for SNOMED CT target hierarchies without attribute relationships.

PubMed

Ochs, Christopher; Geller, James; Perl, Yehoshua; Chen, Yan; Agrawal, Ankur; Case, James T; Hripcsak, George

2015-05-01

Large and complex terminologies, such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), are prone to errors and inconsistencies. Abstraction networks are compact summarizations of the content and structure of a terminology. Abstraction networks have been shown to support terminology quality assurance. In this paper, we introduce an abstraction network derivation methodology which can be applied to SNOMED CT target hierarchies whose classes are defined using only hierarchical relationships (ie, without attribute relationships) and similar description-logic-based terminologies. We introduce the tribal abstraction network (TAN), based on the notion of a tribe-a subhierarchy rooted at a child of a hierarchy root, assuming only the existence of concepts with multiple parents. The TAN summarizes a hierarchy that does not have attribute relationships using sets of concepts, called tribal units that belong to exactly the same multiple tribes. Tribal units are further divided into refined tribal units which contain closely related concepts. A quality assurance methodology that utilizes TAN summarizations is introduced. A TAN is derived for the Observable entity hierarchy of SNOMED CT, summarizing its content. A TAN-based quality assurance review of the concepts of the hierarchy is performed, and erroneous concepts are shown to appear more frequently in large refined tribal units than in small refined tribal units. Furthermore, more erroneous concepts appear in large refined tribal units of more tribes than of fewer tribes. In this paper we introduce the TAN for summarizing SNOMED CT target hierarchies. A TAN was derived for the Observable entity hierarchy of SNOMED CT. A quality assurance methodology utilizing the TAN was introduced and demonstrated. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Attention Induced Gain Stabilization in Broad and Narrow-Spiking Cells in the Frontal Eye-Field of Macaque Monkeys

PubMed Central

Brandt, Christian; Dasilva, Miguel; Gotthardt, Sascha; Chicharro, Daniel; Panzeri, Stefano; Distler, Claudia

2016-01-01

Top-down attention increases coding abilities by altering firing rates and rate variability. In the frontal eye field (FEF), a key area enabling top-down attention, attention induced firing rate changes are profound, but its effect on different cell types is unknown. Moreover, FEF is the only cortical area investigated in which attention does not affect rate variability, as assessed by the Fano factor, suggesting that task engagement affects cortical state nonuniformly. We show that putative interneurons in FEF of Macaca mulatta show stronger attentional rate modulation than putative pyramidal cells. Partitioning rate variability reveals that both cell types reduce rate variability with attention, but more strongly so in narrow-spiking cells. The effects are captured by a model in which attention stabilizes neuronal excitability, thereby reducing the expansive nonlinearity that links firing rate and variance. These results show that the effect of attention on different cell classes and different coding properties are consistent across the cortical hierarchy, acting through increased and stabilized neuronal excitability. SIGNIFICANCE STATEMENT Cortical processing is critically modulated by attention. A key feature of this influence is a modulation of “cortical state,” resulting in increased neuronal excitability and resilience of the network against perturbations, lower rate variability, and an increased signal-to-noise ratio. In the frontal eye field (FEF), an area assumed to control spatial attention in human and nonhuman primates, firing rate changes with attention occur, but rate variability, quantified by the Fano factor, appears to be unaffected by attention. Using recently developed analysis tools and models to quantify attention effects on narrow- and broad-spiking cell activity, we show that attention alters cortical state strongly in the FEF, demonstrating that its effect on the neuronal network is consistent across the cortical hierarchy. PMID:27445139

Hierarchy Bayesian model based services awareness of high-speed optical access networks

NASA Astrophysics Data System (ADS)

Bai, Hui-feng

2018-03-01

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit (ONU) and to perform complex services awareness from the whole view of system in optical line terminal (OLT). Simulation results show that the proposed scheme is able to achieve better quality of services (QoS), in terms of packet loss rate and time delay.

NOA: A Scalable Multi-Parent Clustering Hierarchy for WSNs

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

Cree, Johnathan V.; Delgado-Frias, Jose; Hughes, Michael A.

2012-08-10

NOA is a multi-parent, N-tiered, hierarchical clustering algorithm that provides a scalable, robust and reliable solution to autonomous configuration of large-scale wireless sensor networks. The novel clustering hierarchy's inherent benefits can be utilized by in-network data processing techniques to provide equally robust, reliable and scalable in-network data processing solutions capable of reducing the amount of data sent to sinks. Utilizing a multi-parent framework, NOA reduces the cost of network setup when compared to hierarchical beaconing solutions by removing the expense of r-hop broadcasting (r is the radius of the cluster) needed to build the network and instead passes network topologymore » information among shared children. NOA2, a two-parent clustering hierarchy solution, and NOA3, the three-parent variant, saw up to an 83% and 72% reduction in overhead, respectively, when compared to performing one round of a one-parent hierarchical beaconing, as well as 92% and 88% less overhead when compared to one round of two- and three-parent hierarchical beaconing hierarchy.« less

Social network and dominance hierarchy analyses at Chimpanzee Sanctuary Northwest

PubMed Central

Mayhew, Jessica A.; Mulcahy, John B.

2018-01-01

Different aspects of sociality bear considerable weight on the individual- and group-level welfare of captive nonhuman primates. Social Network Analysis (SNA) is a useful tool for gaining a holistic understanding of the dynamic social relationships of captive primate groups. Gaining a greater understanding of captive chimpanzees through investigations of centrality, preferred and avoided relationships, dominance hierarchy, and social network diagrams can be useful in advising current management practices in sanctuaries and other captive settings. In this study, we investigated the dyadic social relationships, group-level social networks, and dominance hierarchy of seven chimpanzees (Pan troglodytes) at Chimpanzee Sanctuary Northwest. We used focal-animal and instantaneous scan sampling to collect 106.75 total hours of associative, affiliative, and agonistic data from June to September 2016. We analyzed our data using SOCPROG to derive dominance hierarchies and network statistics, and we diagrammed the group’s social networks in NetDraw. Three individuals were most central in the grooming network, while two others had little connection. Through agonistic networks, we found that group members reciprocally exhibited agonism, and the group’s dominance hierarchy was statistically non-linear. One chimpanzee emerged as the most dominant through agonism but was least connected to other group members across affiliative networks. Our results indicate that the conventional methods used to calculate individuals’ dominance rank may be inadequate to wholly depict a group’s social relationships in captive sanctuary populations. Our results have an applied component that can aid sanctuary staff in a variety of ways to best ensure the improvement of group welfare. PMID:29444112

Social network and dominance hierarchy analyses at Chimpanzee Sanctuary Northwest.

PubMed

Funkhouser, Jake A; Mayhew, Jessica A; Mulcahy, John B

2018-01-01

Different aspects of sociality bear considerable weight on the individual- and group-level welfare of captive nonhuman primates. Social Network Analysis (SNA) is a useful tool for gaining a holistic understanding of the dynamic social relationships of captive primate groups. Gaining a greater understanding of captive chimpanzees through investigations of centrality, preferred and avoided relationships, dominance hierarchy, and social network diagrams can be useful in advising current management practices in sanctuaries and other captive settings. In this study, we investigated the dyadic social relationships, group-level social networks, and dominance hierarchy of seven chimpanzees (Pan troglodytes) at Chimpanzee Sanctuary Northwest. We used focal-animal and instantaneous scan sampling to collect 106.75 total hours of associative, affiliative, and agonistic data from June to September 2016. We analyzed our data using SOCPROG to derive dominance hierarchies and network statistics, and we diagrammed the group's social networks in NetDraw. Three individuals were most central in the grooming network, while two others had little connection. Through agonistic networks, we found that group members reciprocally exhibited agonism, and the group's dominance hierarchy was statistically non-linear. One chimpanzee emerged as the most dominant through agonism but was least connected to other group members across affiliative networks. Our results indicate that the conventional methods used to calculate individuals' dominance rank may be inadequate to wholly depict a group's social relationships in captive sanctuary populations. Our results have an applied component that can aid sanctuary staff in a variety of ways to best ensure the improvement of group welfare.

Hierarchy Measure for Complex Networks

PubMed Central

Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

2012-01-01

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure. PMID:22470477

Computing chemical organizations in biological networks.

PubMed

Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter

2008-07-15

Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

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

NASA Astrophysics Data System (ADS)

Zhang, Wei; Rao, Qiaomeng

2018-01-01

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

Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

PubMed Central

Czégel, Dániel; Palla, Gergely

2015-01-01

Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology. PMID:26657012

Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

NASA Astrophysics Data System (ADS)

Czégel, Dániel; Palla, Gergely

2015-12-01

Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.

Random walk hierarchy measure: What is more hierarchical, a chain, a tree or a star?

PubMed

Czégel, Dániel; Palla, Gergely

2015-12-10

Signs of hierarchy are prevalent in a wide range of systems in nature and society. One of the key problems is quantifying the importance of hierarchical organisation in the structure of the network representing the interactions or connections between the fundamental units of the studied system. Although a number of notable methods are already available, their vast majority is treating all directed acyclic graphs as already maximally hierarchical. Here we propose a hierarchy measure based on random walks on the network. The novelty of our approach is that directed trees corresponding to multi level pyramidal structures obtain higher hierarchy scores compared to directed chains and directed stars. Furthermore, in the thermodynamic limit the hierarchy measure of regular trees is converging to a well defined limit depending only on the branching number. When applied to real networks, our method is computationally very effective, as the result can be evaluated with arbitrary precision by subsequent multiplications of the transition matrix describing the random walk process. In addition, the tests on real world networks provided very intuitive results, e.g., the trophic levels obtained from our approach on a food web were highly consistent with former results from ecology.

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.

PubMed

Reinharz, Vladimir; Soulé, Antoine; Westhof, Eric; Waldispühl, Jérôme; Denise, Alain

2018-05-04

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage.

PubMed

Freyre-González, Julio A; Tauch, Andreas

2017-09-10

Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as l-glutamic acid and l-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage=73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage=71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Biomedical Wireless Ambulatory Crew Monitor

NASA Technical Reports Server (NTRS)

Chmiel, Alan; Humphreys, Brad

2009-01-01

A compact, ambulatory biometric data acquisition system has been developed for space and commercial terrestrial use. BioWATCH (Bio medical Wireless and Ambulatory Telemetry for Crew Health) acquires signals from biomedical sensors using acquisition modules attached to a common data and power bus. Several slots allow the user to configure the unit by inserting sensor-specific modules. The data are then sent real-time from the unit over any commercially implemented wireless network including 802.11b/g, WCDMA, 3G. This system has a distributed computing hierarchy and has a common data controller on each sensor module. This allows for the modularity of the device along with the tailored ability to control the cards using a relatively small master processor. The distributed nature of this system affords the modularity, size, and power consumption that betters the current state of the art in medical ambulatory data acquisition. A new company was created to market this technology.

Selective attention modulates high-frequency activity in the face-processing network.

PubMed

Müsch, Kathrin; Hamamé, Carlos M; Perrone-Bertolotti, Marcela; Minotti, Lorella; Kahane, Philippe; Engel, Andreas K; Lachaux, Jean-Philippe; Schneider, Till R

2014-11-01

Face processing depends on the orchestrated activity of a large-scale neuronal network. Its activity can be modulated by attention as a function of task demands. However, it remains largely unknown whether voluntary, endogenous attention and reflexive, exogenous attention to facial expressions equally affect all regions of the face-processing network, and whether such effects primarily modify the strength of the neuronal response, the latency, the duration, or the spectral characteristics. We exploited the good temporal and spatial resolution of intracranial electroencephalography (iEEG) and recorded from depth electrodes to uncover the fast dynamics of emotional face processing. We investigated frequency-specific responses and event-related potentials (ERP) in the ventral occipito-temporal cortex (VOTC), ventral temporal cortex (VTC), anterior insula, orbitofrontal cortex (OFC), and amygdala when facial expressions were task-relevant or task-irrelevant. All investigated regions of interest (ROI) were clearly modulated by task demands and exhibited stronger changes in stimulus-induced gamma band activity (50-150 Hz) when facial expressions were task-relevant. Observed latencies demonstrate that the activation is temporally coordinated across the network, rather than serially proceeding along a processing hierarchy. Early and sustained responses to task-relevant faces in VOTC and VTC corroborate their role for the core system of face processing, but they also occurred in the anterior insula. Strong attentional modulation in the OFC and amygdala (300 msec) suggests that the extended system of the face-processing network is only recruited if the task demands active face processing. Contrary to our expectation, we rarely observed differences between fearful and neutral faces. Our results demonstrate that activity in the face-processing network is susceptible to the deployment of selective attention. Moreover, we show that endogenous attention operates along the whole face-processing network, and that these effects are reflected in frequency-specific changes in the gamma band. Copyright © 2014 Elsevier Ltd. All rights reserved.

Auditing SNOMED Relationships Using a Converse Abstraction Network

PubMed Central

Wei, Duo; Halper, Michael; Elhanan, Gai; Chen, Yan; Perl, Yehoshua; Geller, James; Spackman, Kent A.

2009-01-01

In SNOMED CT, a given kind of attribute relationship is defined between two hierarchies, a source and a target. Certain hierarchies (or subhierarchies) serve only as targets, with no outgoing relationships of their own. However, converse relationships—those pointing in a direction opposite to the defined relationships—while not explicitly represented in SNOMED’s inferred view, can be utilized in forming an alternative view of a source. In particular, they can help shed light on a source hierarchy’s overall relationship structure. Toward this end, an abstraction network, called the converse abstraction network (CAN), derived automatically from a given SNOMED hierarchy is presented. An auditing methodology based on the CAN is formulated. The methodology is applied to SNOMED’s Device subhierarchy and the related device relationships of the Procedure hierarchy. The results indicate that the CAN is useful in finding opportunities for refining and improving SNOMED. PMID:20351941

Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

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

Cree, Johnathan Vee; Delgado-Frias, Jose

Large scale wireless sensor networks have been proposed for applications ranging from anomaly detection in an environment to vehicle tracking. Many of these applications require the networks to be distributed across a large geographic area while supporting three to five year network lifetimes. In order to support these requirements large scale wireless sensor networks of duty-cycled devices need a method of efficient and effective autonomous configuration/maintenance. This method should gracefully handle the synchronization tasks duty-cycled networks. Further, an effective configuration solution needs to recognize that in-network data aggregation and analysis presents significant benefits to wireless sensor network and should configuremore » the network in a way such that said higher level functions benefit from the logically imposed structure. NOA, the proposed configuration and maintenance protocol, provides a multi-parent hierarchical logical structure for the network that reduces the synchronization workload. It also provides higher level functions with significant inherent benefits such as but not limited to: removing network divisions that are created by single-parent hierarchies, guarantees for when data will be compared in the hierarchy, and redundancies for communication as well as in-network data aggregation/analysis/storage.« less

Stability of glassy hierarchical networks

NASA Astrophysics Data System (ADS)

Zamani, M.; Camargo-Forero, L.; Vicsek, T.

2018-02-01

The structure of interactions in most animal and human societies can be best represented by complex hierarchical networks. In order to maintain close-to-optimal function both stability and adaptability are necessary. Here we investigate the stability of hierarchical networks that emerge from the simulations of an organization type with an efficiency function reminiscent of the Hamiltonian of spin glasses. Using this quantitative approach we find a number of expected (from everyday observations) and highly non-trivial results for the obtained locally optimal networks, including, for example: (i) stability increases with growing efficiency and level of hierarchy; (ii) the same perturbation results in a larger change for more efficient states; (iii) networks with a lower level of hierarchy become more efficient after perturbation; (iv) due to the huge number of possible optimal states only a small fraction of them exhibit resilience and, finally, (v) ‘attacks’ targeting the nodes selectively (regarding their position in the hierarchy) can result in paradoxical outcomes.

A class Hierarchical, object-oriented approach to virtual memory management

NASA Technical Reports Server (NTRS)

Russo, Vincent F.; Campbell, Roy H.; Johnston, Gary M.

1989-01-01

The Choices family of operating systems exploits class hierarchies and object-oriented programming to facilitate the construction of customized operating systems for shared memory and networked multiprocessors. The software is being used in the Tapestry laboratory to study the performance of algorithms, mechanisms, and policies for parallel systems. Described here are the architectural design and class hierarchy of the Choices virtual memory management system. The software and hardware mechanisms and policies of a virtual memory system implement a memory hierarchy that exploits the trade-off between response times and storage capacities. In Choices, the notion of a memory hierarchy is captured by abstract classes. Concrete subclasses of those abstractions implement a virtual address space, segmentation, paging, physical memory management, secondary storage, and remote (that is, networked) storage. Captured in the notion of a memory hierarchy are classes that represent memory objects. These classes provide a storage mechanism that contains encapsulated data and have methods to read or write the memory object. Each of these classes provides specializations to represent the memory hierarchy.

Genomic analysis of the hierarchical structure of regulatory networks

PubMed Central

Yu, Haiyuan; Gerstein, Mark

2006-01-01

A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

Systemic risk and hierarchical transitions of financial networks

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Lee, Jae Woo

2017-06-01

In this paper, the change in topological hierarchy, which is measured by the minimum spanning tree constructed from the cross-correlations between the stock indices from the S & P 500 for 1998-2012 in a one year moving time window, was used to analyze a financial crisis. The hierarchy increased in all minor crises in the observation time window except for the sharp crisis of 2007-2008 when the global financial crisis occurred. The sudden increase in hierarchy just before the global financial crisis can be used for the early detection of an upcoming crisis. Clearly, the higher the hierarchy, the higher the threats to financial stability. The scaling relations were developed to observe the changes in hierarchy with the network topology. These scaling relations can also identify and quantify the financial crisis periods, and appear to contain the predictive power of an upcoming crisis.

Systemic risk and hierarchical transitions of financial networks.

PubMed

Nobi, Ashadun; Lee, Jae Woo

2017-06-01

In this paper, the change in topological hierarchy, which is measured by the minimum spanning tree constructed from the cross-correlations between the stock indices from the S & P 500 for 1998-2012 in a one year moving time window, was used to analyze a financial crisis. The hierarchy increased in all minor crises in the observation time window except for the sharp crisis of 2007-2008 when the global financial crisis occurred. The sudden increase in hierarchy just before the global financial crisis can be used for the early detection of an upcoming crisis. Clearly, the higher the hierarchy, the higher the threats to financial stability. The scaling relations were developed to observe the changes in hierarchy with the network topology. These scaling relations can also identify and quantify the financial crisis periods, and appear to contain the predictive power of an upcoming crisis.

Prolactin Alters the Mammary Epithelial Hierarchy, Increasing Progenitors and Facilitating Ovarian Steroid Action.

PubMed

O'Leary, Kathleen A; Shea, Michael P; Salituro, Stephanie; Blohm, Courtney E; Schuler, Linda A

2017-10-10

Hormones drive mammary development and function and play critical roles in breast cancer. Epidemiologic studies link prolactin (PRL) to increased risk for aggressive cancers that express estrogen receptor α (ERα). However, in contrast to ovarian steroids, PRL actions on the mammary gland outside of pregnancy are poorly understood. We employed the transgenic NRL-PRL model to examine the effects of PRL alone and with defined estrogen/progesterone exposure on stem/progenitor activity and regulatory networks that drive epithelial differentiation. PRL increased progenitors and modulated transcriptional programs, even without ovarian steroids, and with steroids further raised stem cell activity associated with elevated canonical Wnt signaling. However, despite facilitating some steroid actions, PRL opposed steroid-driven luminal maturation and increased CD61 + luminal cells. Our findings demonstrate that PRL can powerfully influence the epithelial hierarchy alone and temper the actions of ovarian steroids, which may underlie its role in the development of breast cancer. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

The Fragility of Individual-Based Explanations of Social Hierarchies: A Test Using Animal Pecking Orders

PubMed Central

2016-01-01

The standard approach in accounting for hierarchical differentiation in biology and the social sciences considers a hierarchy as a static distribution of individuals possessing differing amounts of some valued commodity, assumes that the hierarchy is generated by micro-level processes involving individuals, and attempts to reverse engineer the processes that produced the hierarchy. However, sufficient experimental and analytical results are available to evaluate this standard approach in the case of animal dominance hierarchies (pecking orders). Our evaluation using evidence from hierarchy formation in small groups of both hens and cichlid fish reveals significant deficiencies in the three tenets of the standard approach in accounting for the organization of dominance hierarchies. In consequence, we suggest that a new approach is needed to explain the organization of pecking orders and, very possibly, by implication, for other kinds of social hierarchies. We develop an example of such an approach that considers dominance hierarchies to be dynamic networks, uses dynamic sequences of interaction (dynamic network motifs) to explain the organization of dominance hierarchies, and derives these dynamic sequences directly from observation of hierarchy formation. We test this dynamical explanation using computer simulation and find a good fit with actual dynamics of hierarchy formation in small groups of hens. We hypothesize that the same dynamic sequences are used in small groups of many other animal species forming pecking orders, and we discuss the data required to evaluate our hypothesis. Finally, we briefly consider how our dynamic approach may be generalized to other kinds of social hierarchies using the example of the distribution of empty gastropod (snail) shells occupied in populations of hermit crabs. PMID:27410230

Understanding Informal Feedback Seeking in the Workplace: The Impact of the Position in the Organizational Hierarchy

ERIC Educational Resources Information Center

van der Rijt, Janine; Van den Bossche, Piet; Segers, Mien S. R.

2013-01-01

Purpose: The purpose of this study is to investigate whether the position of employees in the organizational hierarchy is important in explaining their feedback seeking behaviour. Design/methodology/approach: This study takes a social network perspective by using an ego-centric network survey to investigate employees' feedback seeking behaviour…

Do Convolutional Neural Networks Learn Class Hierarchy?

PubMed

Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu

2018-01-01

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks.

PubMed

Filho, Humberto A; Machicao, Jeaneth; Bruno, Odemir M

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks

PubMed Central

Filho, Humberto A.; Machicao, Jeaneth

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology. PMID:29734359

Solving moment hierarchies for chemical reaction networks

NASA Astrophysics Data System (ADS)

Krishnamurthy, Supriya; Smith, Eric

2017-10-01

The study of chemical reaction networks (CRN’s) is a very active field. Earlier well-known results (Feinberg 1987 Chem. Enc. Sci. 42 2229, Anderson et al 2010 Bull. Math. Biol. 72 1947) identify a topological quantity called deficiency, for any CRN, which, when exactly equal to zero, leads to a unique factorized steady-state for these networks. No results exist however for the steady states of non-zero-deficiency networks. In this paper, we show how to write the full moment-hierarchy for any non-zero-deficiency CRN obeying mass-action kinetics, in terms of equations for the factorial moments. Using these, we can recursively predict values for lower moments from higher moments, reversing the procedure usually used to solve moment hierarchies. We show, for non-trivial examples, that in this manner we can predict any moment of interest, for CRN’s with non-zero deficiency and non-factorizable steady states.

Hierarchy in gene expression is predictive of risk, progression, and outcome in adult acute myeloid leukemia

NASA Astrophysics Data System (ADS)

Tripathi, Shubham; Deem, Michael W.

2015-02-01

Cancer progresses with a change in the structure of the gene network in normal cells. We define a measure of organizational hierarchy in gene networks of affected cells in adult acute myeloid leukemia (AML) patients. With a retrospective cohort analysis based on the gene expression profiles of 116 AML patients, we find that the likelihood of future cancer relapse and the level of clinical risk are directly correlated with the level of organization in the cancer related gene network. We also explore the variation of the level of organization in the gene network with cancer progression. We find that this variation is non-monotonic, which implies the fitness landscape in the evolution of AML cancer cells is non-trivial. We further find that the hierarchy in gene expression at the time of diagnosis may be a useful biomarker in AML prognosis.

The attracting power of the gaze of politicians is modulated by the personality and ideological attitude of their voters: a functional magnetic resonance imaging study.

PubMed

Cazzato, Valentina; Liuzza, Marco Tullio; Caprara, Gian Vittorio; Macaluso, Emiliano; Aglioti, Salvatore Maria

2015-10-01

Observing someone rapidly moving their eyes induces reflexive shifts of overt and covert attention in the onlooker. Previous studies have shown that this process can be modulated by the onlooker's personality, as well as by the social features of the person depicted in the cued face. Here, we investigated whether an individual's preference for social dominance orientation, in-group perceived similarity (PS), and political affiliation of the cued-face modulated neural activity within specific nodes of the social attention network. During functional magnetic resonance imaging, participants were requested to perform a gaze-following task to investigate whether the directional gaze of various Italian political personages might influence the oculomotor behaviour of in-group or out-group voters. After scanning, we acquired measures of PS in personality traits with each political personage and preference for social dominance orientation. Behavioural data showed that higher gaze interference for in-group than out-group political personages was predicted by a higher preference for social hierarchy. Higher blood oxygenation level-dependent activity in incongruent vs. congruent conditions was found in areas associated with orienting to socially salient events and monitoring response conflict, namely the left frontal eye field, right supramarginal gyrus, mid-cingulate cortex and left anterior insula. Interestingly, higher ratings of PS with the in-group and less preference for social hierarchy predicted increased activity in the left frontal eye field during distracting gaze movements of in-group as compared with out-group political personages. Our results suggest that neural activity in the social orienting circuit is modulated by higher-order social dimensions, such as in-group PS and individual differences in ideological attitudes. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Social hierarchy modulates neural responses of empathy for pain

PubMed Central

Feng, Chunliang; Li, Zhihao; Feng, Xue; Wang, Lili; Tian, Tengxiang

2016-01-01

Recent evidence indicates that empathic responses to others’ pain are modulated by various situational and individual factors. However, few studies have examined how empathy and underlying brain functions are modulated by social hierarchies, which permeate human society with an enormous impact on social behavior and cognition. In this study, social hierarchies were established based on incidental skill in a perceptual task in which all participants were mediumly ranked. Afterwards, participants were scanned with functional magnetic resonance imaging while watching inferior-status or superior-status targets receiving painful or non-painful stimulation. The results revealed that painful stimulation applied to inferior-status targets induced higher activations in the anterior insula (AI) and anterior medial cingulate cortex (aMCC), whereas these empathic brain activations were significantly attenuated in response to superior-status targets’ pain. Further, this neural empathic bias to inferior-status targets was accompanied by stronger functional couplings of AI with brain regions important in emotional processing (i.e. thalamus) and cognitive control (i.e. middle frontal gyrus). Our findings indicate that emotional sharing with others’ pain is shaped by relative positions in a social hierarchy such that underlying empathic neural responses are biased toward inferior-status compared with superior-status individuals. PMID:26516169

‘If you are good, I get better’: the role of social hierarchy in perceptual decision-making

PubMed Central

Pannunzi, Mario; Ayneto, Alba; Deco, Gustavo; Sebastián-Gallés, Nuria

2014-01-01

So far, it was unclear if social hierarchy could influence sensory or perceptual cognitive processes. We evaluated the effects of social hierarchy on these processes using a basic visual perceptual decision task. We constructed a social hierarchy where participants performed the perceptual task separately with two covertly simulated players (superior, inferior). Participants were faster (better) when performing the discrimination task with the superior player. We studied the time course when social hierarchy was processed using event-related potentials and observed hierarchical effects even in early stages of sensory-perceptual processing, suggesting early top–down modulation by social hierarchy. Moreover, in a parallel analysis, we fitted a drift-diffusion model (DDM) to the results to evaluate the decision making process of this perceptual task in the context of a social hierarchy. Consistently, the DDM pointed to nondecision time (probably perceptual encoding) as the principal period influenced by social hierarchy. PMID:23946003

Can SNOMED CT be squeezed without losing its shape?

PubMed

López-García, Pablo; Schulz, Stefan

2016-09-21

In biomedical applications where the size and complexity of SNOMED CT become problematic, using a smaller subset that can act as a reasonable substitute is usually preferred. In a special class of use cases-like ontology-based quality assurance, or when performing scaling experiments for real-time performance-it is essential that modules show a similar shape than SNOMED CT in terms of concept distribution per sub-hierarchy. Exactly how to extract such balanced modules remains unclear, as most previous work on ontology modularization has focused on other problems. In this study, we investigate to what extent extracting balanced modules that preserve the original shape of SNOMED CT is possible, by presenting and evaluating an iterative algorithm. We used a graph-traversal modularization approach based on an input signature. To conform to our definition of a balanced module, we implemented an iterative algorithm that carefully bootstraped and dynamically adjusted the signature at each step. We measured the error for each sub-hierarchy and defined convergence as a residual sum of squares <1. Using 2000 concepts as an initial signature, our algorithm converged after seven iterations and extracted a module 4.7 % the size of SNOMED CT. Seven sub-hierarhies were either over or under-represented within a range of 1-8 %. Our study shows that balanced modules from large terminologies can be extracted using ontology graph-traversal modularization techniques under certain conditions: that the process is repeated a number of times, the input signature is dynamically adjusted in each iteration, and a moderate under/over-representation of some hierarchies is tolerated. In the case of SNOMED CT, our results conclusively show that it can be squeezed to less than 5 % of its size without any sub-hierarchy losing its shape more than 8 %, which is likely sufficient in most use cases.

Comparing the Hierarchy of Keywords in On-Line News Portals

PubMed Central

Tibély, Gergely; Sousa-Rodrigues, David; Pollner, Péter; Palla, Gergely

2016-01-01

Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorized low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals. PMID:27802319

Comparing the Hierarchy of Keywords in On-Line News Portals.

PubMed

Tibély, Gergely; Sousa-Rodrigues, David; Pollner, Péter; Palla, Gergely

2016-01-01

Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorized low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals.

The time course of auditory-visual processing of speech and body actions: evidence for the simultaneous activation of an extended neural network for semantic processing.

PubMed

Meyer, Georg F; Harrison, Neil R; Wuerger, Sophie M

2013-08-01

An extensive network of cortical areas is involved in multisensory object and action recognition. This network draws on inferior frontal, posterior temporal, and parietal areas; activity is modulated by familiarity and the semantic congruency of auditory and visual component signals even if semantic incongruences are created by combining visual and auditory signals representing very different signal categories, such as speech and whole body actions. Here we present results from a high-density ERP study designed to examine the time-course and source location of responses to semantically congruent and incongruent audiovisual speech and body actions to explore whether the network involved in action recognition consists of a hierarchy of sequentially activated processing modules or a network of simultaneously active processing sites. We report two main results:1) There are no significant early differences in the processing of congruent and incongruent audiovisual action sequences. The earliest difference between congruent and incongruent audiovisual stimuli occurs between 240 and 280 ms after stimulus onset in the left temporal region. Between 340 and 420 ms, semantic congruence modulates responses in central and right frontal areas. Late differences (after 460 ms) occur bilaterally in frontal areas.2) Source localisation (dipole modelling and LORETA) reveals that an extended network encompassing inferior frontal, temporal, parasaggital, and superior parietal sites are simultaneously active between 180 and 420 ms to process auditory–visual action sequences. Early activation (before 120 ms) can be explained by activity in mainly sensory cortices. . The simultaneous activation of an extended network between 180 and 420 ms is consistent with models that posit parallel processing of complex action sequences in frontal, temporal and parietal areas rather than models that postulate hierarchical processing in a sequence of brain regions. Copyright © 2013 Elsevier Ltd. All rights reserved.

SEPAC flight software detailed design specifications, volume 1

NASA Technical Reports Server (NTRS)

1982-01-01

The detailed design specifications (as built) for the SEPAC Flight Software are defined. The design includes a description of the total software system and of each individual module within the system. The design specifications describe the decomposition of the software system into its major components. The system structure is expressed in the following forms: the control-flow hierarchy of the system, the data-flow structure of the system, the task hierarchy, the memory structure, and the software to hardware configuration mapping. The component design description includes details on the following elements: register conventions, module (subroutines) invocaton, module functions, interrupt servicing, data definitions, and database structure.

Social hierarchy modulates responses of fish exposed to contaminants of emerging concern

EPA Science Inventory

Many organisms, including the fathead minnow (Pimephales promelas), a toxicological model organism, establish social hierarchies. The social rank of each male in a population is under the control of the hypothalamic-pituitary-gonadal (HPG) axis mainly through regulation of circul...

Self-organizing hierarchies in sensor and communication networks.

PubMed

Prokopenko, Mikhail; Wang, Peter; Valencia, Philip; Price, Don; Foreman, Mark; Farmer, Anthony

2005-01-01

We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separating chaotic dynamics from ordered and robust patterns.

Key Management Schemes for Peer-to-Peer Multimedia Streaming Overlay Networks

NASA Astrophysics Data System (ADS)

Naranjo, J. A. M.; López-Ramos, J. A.; Casado, L. G.

Key distribution for multimedia live streaming peer-to-peer overlay networks is a field still in its childhood stage. A scheme designed for networks of this kind must seek security and efficiency while keeping in mind the following restrictions: limited bandwidth, continuous playing, great audience size and clients churn. This paper introduces two novel schemes that allow a trade-off between security and efficiency by allowing to dynamically vary the number of levels used in the key hierarchy. These changes are motivated by great variations in audience size, and initiated by decision of the Key Server. Additionally, a comparative study of both is presented, focusing on security and audience size. Results show that larger key hierarchies can supply bigger audiences, but offer less security against statistical attacks. The opposite happens for shorter key hierarchies.

MFAHP: A novel method on the performance evaluation of the industrial wireless networked control system

NASA Astrophysics Data System (ADS)

Wu, Linqin; Xu, Sheng; Jiang, Dezhi

2015-12-01

Industrial wireless networked control system has been widely used, and how to evaluate the performance of the wireless network is of great significance. In this paper, considering the shortcoming of the existing performance evaluation methods, a comprehensive performance evaluation method of networks multi-indexes fuzzy analytic hierarchy process (MFAHP) combined with the fuzzy mathematics and the traditional analytic hierarchy process (AHP) is presented. The method can overcome that the performance evaluation is not comprehensive and subjective. Experiments show that the method can reflect the network performance of real condition. It has direct guiding role on protocol selection, network cabling, and node setting, and can meet the requirements of different occasions by modifying the underlying parameters.

Deriving an Abstraction Network to Support Quality Assurance in OCRe

PubMed Central

Ochs, Christopher; Agrawal, Ankur; Perl, Yehoshua; Halper, Michael; Tu, Samson W.; Carini, Simona; Sim, Ida; Noy, Natasha; Musen, Mark; Geller, James

2012-01-01

An abstraction network is an auxiliary network of nodes and links that provides a compact, high-level view of an ontology. Such a view lends support to ontology orientation, comprehension, and quality-assurance efforts. A methodology is presented for deriving a kind of abstraction network, called a partial-area taxonomy, for the Ontology of Clinical Research (OCRe). OCRe was selected as a representative of ontologies implemented using the Web Ontology Language (OWL) based on shared domains. The derivation of the partial-area taxonomy for the Entity hierarchy of OCRe is described. Utilizing the visualization of the content and structure of the hierarchy provided by the taxonomy, the Entity hierarchy is audited, and several errors and inconsistencies in OCRe’s modeling of its domain are exposed. After appropriate corrections are made to OCRe, a new partial-area taxonomy is derived. The generalizability of the paradigm of the derivation methodology to various families of biomedical ontologies is discussed. PMID:23304341

Social hierarchy modulates neural responses of empathy for pain.

PubMed

Feng, Chunliang; Li, Zhihao; Feng, Xue; Wang, Lili; Tian, Tengxiang; Luo, Yue-Jia

2016-03-01

Recent evidence indicates that empathic responses to others' pain are modulated by various situational and individual factors. However, few studies have examined how empathy and underlying brain functions are modulated by social hierarchies, which permeate human society with an enormous impact on social behavior and cognition. In this study, social hierarchies were established based on incidental skill in a perceptual task in which all participants were mediumly ranked. Afterwards, participants were scanned with functional magnetic resonance imaging while watching inferior-status or superior-status targets receiving painful or non-painful stimulation. The results revealed that painful stimulation applied to inferior-status targets induced higher activations in the anterior insula (AI) and anterior medial cingulate cortex (aMCC), whereas these empathic brain activations were significantly attenuated in response to superior-status targets' pain. Further, this neural empathic bias to inferior-status targets was accompanied by stronger functional couplings of AI with brain regions important in emotional processing (i.e. thalamus) and cognitive control (i.e. middle frontal gyrus). Our findings indicate that emotional sharing with others' pain is shaped by relative positions in a social hierarchy such that underlying empathic neural responses are biased toward inferior-status compared with superior-status individuals. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

'If you are good, I get better': the role of social hierarchy in perceptual decision-making.

PubMed

Santamaría-García, Hernando; Pannunzi, Mario; Ayneto, Alba; Deco, Gustavo; Sebastián-Gallés, Nuria

2014-10-01

So far, it was unclear if social hierarchy could influence sensory or perceptual cognitive processes. We evaluated the effects of social hierarchy on these processes using a basic visual perceptual decision task. We constructed a social hierarchy where participants performed the perceptual task separately with two covertly simulated players (superior, inferior). Participants were faster (better) when performing the discrimination task with the superior player. We studied the time course when social hierarchy was processed using event-related potentials and observed hierarchical effects even in early stages of sensory-perceptual processing, suggesting early top-down modulation by social hierarchy. Moreover, in a parallel analysis, we fitted a drift-diffusion model (DDM) to the results to evaluate the decision making process of this perceptual task in the context of a social hierarchy. Consistently, the DDM pointed to nondecision time (probably perceptual encoding) as the principal period influenced by social hierarchy. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Nodes, networks, and MUMs: Preserving diversity at all scales

NASA Astrophysics Data System (ADS)

Noss, Reed F.; Harris, Larry D.

1986-05-01

The present focus of practical conservation efforts is limited in scope. This narrowness results in an inability to evaluate and manage phenomena that operate at large spatiotemporal scales. Whereas real ecological phenomena function in a space-time mosaic across a full hierarchy of biological entities and processes, current conservation strategies address a limited spectrum of this complexity. Conservation typically is static (time-limited), concentrates on the habitat content rather than the landscape context of protected areas, evaluates relatively homogeneous communities instead of heterogeneous landscapes, and directs attention to particular species populations and/or the aggregate statistic of species diversity. Insufficient attention has been given to broad ecological patterns and processes and to the conservation of species in natural relative abundance patterns (native diversity). The authors present a conceptual scheme that evaluates not only habitat content within protected areas, but also the landscape context in which each preserve exists. Nodes of concentrated ecological value exist in each landscape at all levels in the biological hierarchy. Integration of these high-quality nodes into a functional network is possible through the establishment of a system of interconnected multiple-use modules (MUMs). The MUM network protects and buffers important ecological entities and phenomena, while encouraging movement of individuals, species, nutrients, energy, and even habitat patches across space and time. An example is presented for the southeastern USA (south Georgia-north Florida), that uses riparian and coastal corridors to interconnect existing protected areas. This scheme will facilitate reintroduction and preservation of wide-ranging species such as the Florida panther, and help reconcile species-level and ecosystem-level conservation approaches.

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

DTIC Science & Technology

2009-03-01

Normal hierarchy vs entangled hierarchy 2.5.7 Quantifying Entangledness . While self organization means that the swarm develops a consistent structure of...flexibility due to centralization of control and com- munication. Thus, self organized, entangled hierarchy multi-agent swarms are evolved in this study to...technique. The resulting design exhibits a self organized multi-agent swarm (SOMAS) with entangled hierarchical control and communication through the

Exploring the Complexities of Army Civilians and the Army Profession

DTIC Science & Technology

2013-03-01

61. 55 Gareth R. Jones, 2-4. 56 Thomas Diefenbach and Rune Todnem, “Bureaucracy and Hierarchy - What Else!? in Reinventing Hierarchy and...57 Thomas Diefenbach and Rune Todnem, “Introduction,” in Reinventing Hierarchy and Bureaucracy—From the Bureau to Network Organizations (Bingley...Handbook of Public Management (New York: Oxford University Press, 2005), 472; Gareth R. Jones, 313. 73 Thomas Diefenbach and Rune Todnem

Predicting multicellular function through multi-layer tissue networks

PubMed Central

Zitnik, Marinka; Leskovec, Jure

2017-01-01

Abstract Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu PMID:28881986

Organizing product innovation: hierarchy, market or triple-helix networks?

PubMed

Fitjar, Rune Dahl; Gjelsvik, Martin; Rodríguez-Pose, Andrés

This paper assesses the extent to which the organization of the innovation effort in firms, as well as the geographical scale at which this effort is pursued, affects the capacity to benefit from product innovations. Three alternative modes of organization are studied: hierarchy, market and triple-helix-type networks. Furthermore, we consider triple-helix networks at three geographical scales: local, national and international. These relationships are tested on a random sample of 763 firms located in five urban regions of Norway which reported having introduced new products or services during the preceding 3 years. The analysis shows that firms exploiting internal hierarchy or triple-helix networks with a wide range of partners managed to derive a significantly higher share of their income from new products, compared to those that mainly relied on outsourcing within the market. In addition, the analysis shows that the geographical scale of cooperation in networks, as well as the type of partner used, matters for the capacity of firms to benefit from product innovation. In particular, firms that collaborate in international triple-helix-type networks involving suppliers, customers and R&D institutions extract a higher share of their income from product innovations, regardless of whether they organize the processes internally or through the network.

The Reactome pathway Knowledgebase

PubMed Central

Fabregat, Antonio; Sidiropoulos, Konstantinos; Garapati, Phani; Gillespie, Marc; Hausmann, Kerstin; Haw, Robin; Jassal, Bijay; Jupe, Steven; Korninger, Florian; McKay, Sheldon; Matthews, Lisa; May, Bruce; Milacic, Marija; Rothfels, Karen; Shamovsky, Veronica; Webber, Marissa; Weiser, Joel; Williams, Mark; Wu, Guanming; Stein, Lincoln; Hermjakob, Henning; D'Eustachio, Peter

2016-01-01

The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations—an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently. PMID:26656494

Detecting and evaluating communities in complex human and biological networks

NASA Astrophysics Data System (ADS)

Morrison, Greg; Mahadevan, L.

2012-02-01

We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks

NASA Astrophysics Data System (ADS)

Smith, Eric; Krishnamurthy, Supriya

2017-12-01

Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical meaning of deficiency not only for first-moment conditions but for all orders in fluctuations.

Constituents and functional implications of the rat default mode network.

PubMed

Hsu, Li-Ming; Liang, Xia; Gu, Hong; Brynildsen, Julia K; Stark, Jennifer A; Ash, Jessica A; Lin, Ching-Po; Lu, Hanbing; Rapp, Peter R; Stein, Elliot A; Yang, Yihong

2016-08-02

The default mode network (DMN) has been suggested to support a variety of self-referential functions in humans and has been fractionated into subsystems based on distinct responses to cognitive tasks and functional connectivity architecture. Such subsystems are thought to reflect functional hierarchy and segregation within the network. Because preclinical models can inform translational studies of neuropsychiatric disorders, partitioning of the DMN in nonhuman species, which has previously not been reported, may inform both physiology and pathophysiology of the human DMN. In this study, we sought to identify constituents of the rat DMN using resting-state functional MRI (rs-fMRI) and diffusion tensor imaging. After identifying DMN using a group-level independent-component analysis on the rs-fMRI data, modularity analyses fractionated the DMN into an anterior and a posterior subsystem, which were further segregated into five modules. Diffusion tensor imaging tractography demonstrates a close relationship between fiber density and the functional connectivity between DMN regions, and provides anatomical evidence to support the detected DMN subsystems. Finally, distinct modulation was seen within and between these DMN subcomponents using a neurocognitive aging model. Taken together, these results suggest that, like the human DMN, the rat DMN can be partitioned into several subcomponents that may support distinct functions. These data encourage further investigation into the neurobiological mechanisms of DMN processing in preclinical models of both normal and disease states.

Vanishing point: Scale independence in geomorphological hierarchies

NASA Astrophysics Data System (ADS)

Phillips, Jonathan D.

2016-08-01

Scale linkage problems in geosciences are often associated with a hierarchy of components. Both dynamical systems perspectives and intuition suggest that processes or relationships operating at fundamentally different scales are independent with respect to influences on system dynamics. But how far apart is ;fundamentally different;-that is, what is the ;vanishing point; at which scales are no longer interdependent? And how do we reconcile that with the idea (again, supported by both theory and intuition) that we can work our way along scale hierarchies from microscale to planetary (and vice-versa)? Graph and network theory are employed here to address these questions. Analysis of two archetypal hierarchical networks shows low algebraic connectivity, indicating low levels of inferential synchronization. This explains the apparent paradox between scale independence and hierarchical linkages. Incorporating more hierarchical levels results in an increase in complexity or entropy of the network as a whole, but at a nonlinear rate. Complexity increases as a power α of the number of levels in the hierarchy, with α < 1 and usually ≤ 0.6. However, algebraic connectivity decreases at a more rapid rate. Thus, the ability to infer one part of the hierarchical network from other level decays rapidly as more levels are added. Relatedness among system components decreases with differences in scale or resolution, analogous to distance decay in the spatial domain. These findings suggest a strategy of identifying and focusing on the most important or interesting scale levels, rather than attempting to identify the smallest or largest scale levels and work top-down or bottom-up from there. Examples are given from soil geomorphology and karst flow networks.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence.

PubMed

Kerr, Robert R; Grayden, David B; Thomas, Doreen A; Gilson, Matthieu; Burkitt, Anthony N

2014-01-01

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence

PubMed Central

Kerr, Robert R.; Grayden, David B.; Thomas, Doreen A.; Gilson, Matthieu; Burkitt, Anthony N.

2014-01-01

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention. PMID:25152715

Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex.

PubMed

Yeo, B T Thomas; Krienen, Fenna M; Chee, Michael W L; Buckner, Randy L

2014-03-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. © 2013.

Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex

PubMed Central

Yeo, BT Thomas; Krienen, Fenna M; Chee, Michael WL; Buckner, Randy L

2014-01-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1,000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. PMID:24185018

CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

NASA Astrophysics Data System (ADS)

Franke, R.

2016-11-01

In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

Quality assurance of chemical ingredient classification for the National Drug File - Reference Terminology.

PubMed

Zheng, Ling; Yumak, Hasan; Chen, Ling; Ochs, Christopher; Geller, James; Kapusnik-Uner, Joan; Perl, Yehoshua

2017-09-01

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies. However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT's Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors. Copyright © 2017 Elsevier Inc. All rights reserved.

An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis.

PubMed

Walsh, Logan A; Alvarez, Mariano J; Sabio, Erich Y; Reyngold, Marsha; Makarov, Vladimir; Mukherjee, Suranjit; Lee, Ken-Wing; Desrichard, Alexis; Turcan, Şevin; Dalin, Martin G; Rajasekhar, Vinagolu K; Chen, Shuibing; Vahdat, Linda T; Califano, Andrea; Chan, Timothy A

2017-08-15

At the root of most fatal malignancies are aberrantly activated transcriptional networks that drive metastatic dissemination. Although individual metastasis-associated genes have been described, the complex regulatory networks presiding over the initiation and maintenance of metastatic tumors are still poorly understood. There is untapped value in identifying therapeutic targets that broadly govern coordinated transcriptional modules dictating metastatic progression. Here, we reverse engineered and interrogated a breast cancer-specific transcriptional interaction network (interactome) to define transcriptional control structures causally responsible for regulating genetic programs underlying breast cancer metastasis in individual patients. Our analyses confirmed established pro-metastatic transcription factors, and they uncovered TRIM25 as a key regulator of metastasis-related transcriptional programs. Further, in vivo analyses established TRIM25 as a potent regulator of metastatic disease and poor survival outcome. Our findings suggest that identifying and targeting keystone proteins, like TRIM25, can effectively collapse transcriptional hierarchies necessary for metastasis formation, thus representing an innovative cancer intervention strategy. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Face the hierarchy: ERP and oscillatory brain responses in social rank processing.

PubMed

Breton, Audrey; Jerbi, Karim; Henaff, Marie-Anne; Cheylus, Anne; Baudouin, Jean-Yves; Schmitz, Christina; Krolak-Salmon, Pierre; Van der Henst, Jean-Baptiste

2014-01-01

Recognition of social hierarchy is a key feature that helps us navigate through our complex social environment. Neuroimaging studies have identified brain structures involved in the processing of hierarchical stimuli but the precise temporal dynamics of brain activity associated with such processing remains largely unknown. Here, we used electroencephalography to examine the effect of social hierarchy on neural responses elicited by faces. In contrast to previous studies, the key manipulation was that a hierarchical context was constructed, not by varying facial expressions, but by presenting neutral-expression faces in a game setting. Once the performance-based hierarchy was established, participants were presented with high-rank, middle-rank and low-rank player faces and had to evaluate the rank of each face with respect to their own position. Both event-related potentials and task-related oscillatory activity were investigated. Three main findings emerge from the study. First, the experimental manipulation had no effect on the early N170 component, which may suggest that hierarchy did not modulate the structural encoding of neutral-expression faces. Second, hierarchy significantly modulated the amplitude of the late positive potential (LPP) within a 400-700 ms time-window, with more a prominent LPP occurring when the participants processed the face of the highest-rank player. Third, high-rank faces were associated with the highest reduction of alpha power. Taken together these findings provide novel electrophysiological evidence for enhanced allocation of attentional resource in the presence of high-rank faces. At a broader level, this study brings new insights into the neural processing underlying social categorization.

Comparing the hierarchy of author given tags and repository given tags in a large document archive

NASA Astrophysics Data System (ADS)

Tibély, Gergely; Pollner, Péter; Palla, Gergely

2016-10-01

Folksonomies - large databases arising from collaborative tagging of items by independent users - are becoming an increasingly important way of categorizing information. In these systems users can tag items with free words, resulting in a tripartite item-tag-user network. Although there are no prescribed relations between tags, the way users think about the different categories presumably has some built in hierarchy, in which more special concepts are descendants of some more general categories. Several applications would benefit from the knowledge of this hierarchy. Here we apply a recent method to check the differences and similarities of hierarchies resulting from tags given by independent individuals and from tags given by a centrally managed repository system. The results from our method showed substantial differences between the lower part of the hierarchies, and in contrast, a relatively high similarity at the top of the hierarchies.

Fighting for resources: Two leaders in the money addicted social hierarchies

NASA Astrophysics Data System (ADS)

Dybiec, Bartłomiej

Building of hierarchy is inevitably associated with the constant competition for resources and attention. Here, we show how presence of two favored (leading) nodes affects properties of the network connecting individuals. In particular, we study how nodes characteristics depend on relative asymmetry between two leading nodes. It is shown that without strong and rigorous avoidance mechanism, individuals can support both dominating nodes. Slow redistribution of resources enhances this effect. Moreover, slow redistribution of resources results in development of social networks with a very limited number of layers.

Time signal distribution in communication networks based on synchronous digital hierarchy

NASA Technical Reports Server (NTRS)

Imaoka, Atsushi; Kihara, Masami

1993-01-01

A new method that uses round-trip paths to accurately measure transmission delay for time synchronization is proposed. The performance of the method in Synchronous Digital Hierarchy networks is discussed. The feature of this method is that it separately measures the initial round trip path delay and the variations in round-trip path delay. The delay generated in SDH equipment is determined by measuring the initial round-trip path delay. In an experiment with actual SDH equipment, the error of initial delay measurement was suppressed to 30ns.

Modeling rises and falls in money addicted social hierarchies

NASA Astrophysics Data System (ADS)

Dybiec, Bartłomiej; Mitarai, Namiko; Sneppen, Kim

2014-08-01

The emergence of large communities is inherently associated with the creation of social structures. Connections between individuals are indispensable for cooperative action of agents building social groups. Moreover, social groups usually evolve and their structure changes over time. Consequently, an underlying network connecting individuals is not static, reflecting an ongoing adaptation to new conditions. The evolution of social connections is influenced by the relative position (hierarchy) of individuals building the system as well as by the availability of resources. We explore this aspect of human ambition by modeling the interplay of social networking and an uneven distribution of external resources. The model naturally generates social hierarchies. Remarkably, this social structure exhibits a rise-and-fall behavior. A well pronounced quasi-periodic dynamics, which is closely associated with the dissipation of resources that are needed to sustain the social links, is revealed.

Anatomy of the bacitracin resistance network in Bacillus subtilis.

PubMed

Radeck, Jara; Gebhard, Susanne; Orchard, Peter Shevlin; Kirchner, Marion; Bauer, Stephanie; Mascher, Thorsten; Fritz, Georg

2016-05-01

Protection against antimicrobial peptides (AMPs) often involves the parallel production of multiple, well-characterized resistance determinants. So far, little is known about how these resistance modules interact and how they jointly protect the cell. Here, we studied the interdependence between different layers of the envelope stress response of Bacillus subtilis when challenged with the lipid II cycle-inhibiting AMP bacitracin. The underlying regulatory network orchestrates the production of the ABC transporter BceAB, the UPP phosphatase BcrC and the phage-shock proteins LiaIH. Our systems-level analysis reveals a clear hierarchy, allowing us to discriminate between primary (BceAB) and secondary (BcrC and LiaIH) layers of bacitracin resistance. Deleting the primary layer provokes an enhanced induction of the secondary layer to partially compensate for this loss. This study reveals a direct role of LiaIH in bacitracin resistance, provides novel insights into the feedback regulation of the Lia system, and demonstrates a pivotal role of BcrC in maintaining cell wall homeostasis. The compensatory regulation within the bacitracin network can also explain how gene expression noise propagates between resistance layers. We suggest that this active redundancy in the bacitracin resistance network of B. subtilis is a general principle to be found in many bacterial antibiotic resistance networks. © 2016 John Wiley & Sons Ltd.

Phage-bacteria infection networks: From nestedness to modularity

NASA Astrophysics Data System (ADS)

Flores, Cesar O.; Valverde, Sergi; Weitz, Joshua S.

2013-03-01

Bacteriophages (viruses that infect bacteria) are the most abundant biological life-forms on Earth. However, very little is known regarding the structure of phage-bacteria infections. In a recent study we re-evaluated 38 prior studies and demonstrated that phage-bacteria infection networks tend to be statistically nested in small scale communities (Flores et al 2011). Nestedness is consistent with a hierarchy of infection and resistance within phages and bacteria, respectively. However, we predicted that at large scales, phage-bacteria infection networks should be typified by a modular structure. We evaluate and confirm this hypothesis using the most extensive study of phage-bacteria infections (Moebus and Nattkemper 1981). In this study, cross-infections were evaluated between 215 marine phages and 286 marine bacteria. We develop a novel multi-scale network analysis and find that the Moebus and Nattkemper (1981) study, is highly modular (at the whole network scale), yet also exhibits nestedness and modularity at the within-module scale. We examine the role of geography in driving these modular patterns and find evidence that phage-bacteria interactions can exhibit strong similarity despite large distances between sites. CFG acknowledges the support of CONACyT Foundation. JSW holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and acknowledges the support of the James S. McDonnell Foundation

Extending Stability Through Hierarchical Clusters in Echo State Networks

PubMed Central

Jarvis, Sarah; Rotter, Stefan; Egert, Ulrich

2009-01-01

Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analyzed the impact of reservoir substructures on stability in hierarchically clustered ESNs, as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius) as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius. PMID:20725523

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Changes of hierarchical network in local and world stock market

NASA Astrophysics Data System (ADS)

Patwary, Enayet Ullah; Lee, Jong Youl; Nobi, Ashadun; Kim, Doo Hwan; Lee, Jae Woo

2017-10-01

We consider the cross-correlation coefficients of the daily returns in the local and global stock markets. We generate the minimal spanning tree (MST) using the correlation matrix. We observe that the MSTs change their structure from chain-like networks to star-like networks during periods of market uncertainty. We quantify the measure of the hierarchical network utilizing the value of the hierarchy measured by the hierarchical path. The hierarchy and betweenness centrality characterize the state of the market regarding the impact of crises. During crises, the non-financial company is established as the central node of the MST. However, before the crisis and during stable periods, the financial company is occupying the central node of the MST in the Korean and the U.S. stock markets. The changes in the network structure and the central node are good indicators of an upcoming crisis.

Response Inhibition Is Facilitated by a Change to Red Over Green in the Stop Signal Paradigm

PubMed Central

Blizzard, Shawn; Fierro-Rojas, Adriela; Fallah, Mazyar

2017-01-01

Actions are informed by the complex interactions of response execution and inhibition networks. These networks integrate sensory information with internal states and behavioral goals to produce an appropriate action or to update an ongoing action. Recent investigations have shown that, behaviorally, attention is captured through a hierarchy of colors. These studies showed how the color hierarchy affected visual processing. To determine whether the color hierarchy can be extended to higher level executive functions such as response execution and inhibition, we conducted several experiments using the stop-signal task (SST). In the first experiment, we modified the classic paradigm so that the go signals could vary in task-irrelevant color, with an auditory stop signal. We found that the task-irrelevant color of the go signals did not differentially affect response times. In the second experiment we determined that making the color of the go signal relevant for response selection still did not affect reaction times(RTs) and, thus, execution. In the third experiment, we modified the paradigm so that the stop signal was a task relevant change in color of the go signal. The mean RT to the red stop signal was approximately 25 ms faster than to the green stop signal. In other words, red stop signals facilitated response inhibition more than green stop signals, however, there was no comparative facilitation of response execution. These findings suggest that response inhibition, but not execution, networks are sensitive to differences in color salience. They also suggest that the color hierarchy is based on attentional networks and not simply on early sensory processing. PMID:28101011

An Enriched Unified Medical Language System Semantic Network with a Multiple Subsumption Hierarchy

PubMed Central

Zhang, Li; Perl, Yehoshua; Halper, Michael; Geller, James; Cimino, James J.

2004-01-01

Objective: The Unified Medical Language System's (UMLS's) Semantic Network's (SN's) two-tree structure is restrictive because it does not allow a semantic type to be a specialization of several other semantic types. In this article, the SN is expanded into a multiple subsumption structure with a directed acyclic graph (DAG) IS-A hierarchy, allowing a semantic type to have multiple parents. New viable IS-A links are added as warranted. Design: Two methodologies are presented to identify and add new viable IS-A links. The first methodology is based on imposing the characteristic of connectivity on a previously presented partition of the SN. Four transformations are provided to find viable IS-A links in the process of converting the partition's disconnected groups into connected ones. The second methodology identifies new IS-A links through a string matching process involving names and definitions of various semantic types in the SN. A domain expert is needed to review all the results to determine the validity of the new IS-A links. Results: Nineteen new IS-A links are added to the SN, and four new semantic types are also created to support the multiple subsumption framework. The resulting network, called the Enriched Semantic Network (ESN), exhibits a DAG-structured hierarchy. A partition of the ESN containing 19 connected groups is also derived. Conclusion: The ESN is an expanded abstraction of the UMLS compared with the original SN. Its multiple subsumption hierarchy can accommodate semantic types with multiple parents. Its representation thus provides direct access to a broader range of subsumption knowledge. PMID:14764611

KEGG Bioinformatics Resource for Plant Genomics and Metabolomics.

PubMed

Kanehisa, Minoru

2016-01-01

In the era of high-throughput biology it is necessary to develop not only elaborate computational methods but also well-curated databases that can be used as reference for data interpretation. KEGG ( http://www.kegg.jp/ ) is such a reference knowledge base with two specific aims. One is to compile knowledge on high-level functions of the cell and the organism in terms of the molecular interaction and reaction networks, which is implemented in KEGG pathway maps, BRITE functional hierarchies, and KEGG modules. The other is to expand knowledge on genes and proteins involved in the molecular networks from experimentally observed organisms to other organisms using the concept of orthologs, which is implemented in the KEGG Orthology (KO) system. Thus, KEGG is a generic resource applicable to all organisms and enables interpretation of high-level functions from genomic and molecular data. Here we first present a brief overview of the entire KEGG resource, and then give an introduction of how to use KEGG in plant genomics and metabolomics research.

Hierarchical Address Event Routing for Reconfigurable Large-Scale Neuromorphic Systems.

PubMed

Park, Jongkil; Yu, Theodore; Joshi, Siddharth; Maier, Christoph; Cauwenberghs, Gert

2017-10-01

We present a hierarchical address-event routing (HiAER) architecture for scalable communication of neural and synaptic spike events between neuromorphic processors, implemented with five Xilinx Spartan-6 field-programmable gate arrays and four custom analog neuromophic integrated circuits serving 262k neurons and 262M synapses. The architecture extends the single-bus address-event representation protocol to a hierarchy of multiple nested buses, routing events across increasing scales of spatial distance. The HiAER protocol provides individually programmable axonal delay in addition to strength for each synapse, lending itself toward biologically plausible neural network architectures, and scales across a range of hierarchies suitable for multichip and multiboard systems in reconfigurable large-scale neuromorphic systems. We show approximately linear scaling of net global synaptic event throughput with number of routing nodes in the network, at 3.6×10 7 synaptic events per second per 16k-neuron node in the hierarchy.

Open Software Tools Applied to Jordan's National Multi-Agent Water Management Model

NASA Astrophysics Data System (ADS)

Knox, Stephen; Meier, Philipp; Harou, Julien; Yoon, Jim; Selby, Philip; Lachaut, Thibaut; Klassert, Christian; Avisse, Nicolas; Khadem, Majed; Tilmant, Amaury; Gorelick, Steven

2016-04-01

Jordan is the fourth most water scarce country in the world, where demand exceeds supply in a politically and demographically unstable context. The Jordan Water Project (JWP) aims to perform policy evaluation by modelling the hydrology, economics, and governance of Jordan's water resource system. The multidisciplinary nature of the project requires a modelling software system capable of integrating submodels from multiple disciplines into a single decision making process and communicating results to stakeholders. This requires a tool for building an integrated model and a system where diverse data sets can be managed and visualised. The integrated Jordan model is built using Pynsim, an open-source multi-agent simulation framework implemented in Python. Pynsim operates on network structures of nodes and links and supports institutional hierarchies, where an institution represents a grouping of nodes, links or other institutions. At each time step, code within each node, link and institution can executed independently, allowing for their fully autonomous behaviour. Additionally, engines (sub-models) perform actions over the entire network or on a subset of the network, such as taking a decision on a set of nodes. Pynsim is modular in design, allowing distinct modules to be modified easily without affecting others. Data management and visualisation is performed using Hydra (www.hydraplatform.org), an open software platform allowing users to manage network structure and data. The Hydra data manager connects to Pynsim, providing necessary input parameters for the integrated model. By providing a high-level portal to the model, Hydra removes a barrier between the users of the model (researchers, stakeholders, planners etc) and the model itself, allowing them to manage data, run the model and visualise results all through a single user interface. Pynsim's ability to represent institutional hierarchies, inter-network communication and the separation of node, link and institutional logic from higher level processes (engine) suit JWP's requirements. The use of Hydra Platform and Pynsim helps make complex customised models such as the JWP model easier to run and manage with international groups of researchers.

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

PubMed

Hao, Dapeng; Ren, Cong; Li, Chuanxing

2012-05-01

A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

PubMed

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-09-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. © 2015 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

PubMed Central

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-01-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. PMID:26073648

Modes of Interaction between Individuals Dominate the Topologies of Real World Networks

PubMed Central

Lee, Insuk; Kim, Eiru; Marcotte, Edward M.

2015-01-01

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Specifically, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to single- or broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types. PMID:25793969

Software architecture for a distributed real-time system in Ada, with application to telerobotics

NASA Technical Reports Server (NTRS)

Olsen, Douglas R.; Messiora, Steve; Leake, Stephen

1992-01-01

The architecture structure and software design methodology presented is described in the context of telerobotic application in Ada, specifically the Engineering Test Bed (ETB), which was developed to support the Flight Telerobotic Servicer (FTS) Program at GSFC. However, the nature of the architecture is such that it has applications to any multiprocessor distributed real-time system. The ETB architecture, which is a derivation of the NASA/NBS Standard Reference Model (NASREM), defines a hierarchy for representing a telerobot system. Within this hierarchy, a module is a logical entity consisting of the software associated with a set of related hardware components in the robot system. A module is comprised of submodules, which are cyclically executing processes that each perform a specific set of functions. The submodules in a module can run on separate processors. The submodules in the system communicate via command/status (C/S) interface channels, which are used to send commands down and relay status back up the system hierarchy. Submodules also communicate via setpoint data links, which are used to transfer control data from one submodule to another. A submodule invokes submodule algorithms (SMA's) to perform algorithmic operations. Data that describe or models a physical component of the system are stored as objects in the World Model (WM). The WM is a system-wide distributed database that is accessible to submodules in all modules of the system for creating, reading, and writing objects.

1986 Year End Report for Road Following at Carnegie-Mellon

DTIC Science & Technology

1987-05-01

how to make them work efficiently. We designed a hierarchical structure and a monitor module which manages all parts of the hierarchy (see figure 1...database, called the Local Map, is managed by a program known as the Local Map Builder (LMB). Each module stores and retrieves information in the...knowledge-intensive modules, and a database manager that synchronizes the modules-is characteristic of a traditional blackboard system. Such a system is

Organization and hierarchy of the human functional brain network lead to a chain-like core.

PubMed

Mastrandrea, Rossana; Gabrielli, Andrea; Piras, Fabrizio; Spalletta, Gianfranco; Caldarelli, Guido; Gili, Tommaso

2017-07-07

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming a highly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.

Complexity measures to track the evolution of a SNOMED hierarchy.

PubMed

Wei, Duo; Wang, Yue; Perl, Yehoshua; Xu, Junchuan; Halper, Michael; Spackman, Kent A; Spackman, Kent

2008-11-06

SNOMED CT is an extensive terminology with an attendant amount of complexity. Two measures are proposed for quantifying that complexity. Both are based on abstraction networks, called the area taxonomy and the partial-area taxonomy, that provide, for example, distributions of the relationships within a SNOMED hierarchy. The complexity measures are employed specifically to track the complexity of versions of the Specimen hierarchy of SNOMED before and after it is put through an auditing process. The pre-audit and post-audit versions are compared. The results show that the auditing process indeed leads to a simplification of the terminology's structure.

A Social Network Approach to Understanding an Insurgency

DTIC Science & Technology

2007-07-01

and a framework for testing theories regarding struc- tured social relationships.6 Equally relevant is the understanding of a social network approach...A Social Network Approach to Understanding an Insurgency BRIAN REED The study of networks, interactions, and relationships has a long history...characteristics of social network analysis is often counter-intuitive to traditional military thinking, rooted in the efficiency of a hierarchy that

Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace.

PubMed

Zhang, Lu; Du, Hongru; Zhao, Yannan; Wu, Rongwei; Zhang, Xiaolei

2017-01-01

"The Belt and Road" initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along "the Belt and Road" are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along "the Belt and Road". This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of "the Belt and Road", show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex.

PubMed

Mejias, Jorge F; Murray, John D; Kennedy, Henry; Wang, Xiao-Jing

2016-11-01

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

PubMed Central

Mejias, Jorge F.; Murray, John D.; Kennedy, Henry; Wang, Xiao-Jing

2016-01-01

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions. PMID:28138530

Multiple-region directed functional connectivity based on phase delays.

PubMed

Goelman, Gadi; Dan, Rotem

2017-03-01

Network analysis is increasingly advancing the field of neuroimaging. Neural networks are generally constructed from pairwise interactions with an assumption of linear relations between them. Here, a high-order statistical framework to calculate directed functional connectivity among multiple regions, using wavelet analysis and spectral coherence has been presented. The mathematical expression for 4 regions was derived and used to characterize a quartet of regions as a linear, combined (nonlinear), or disconnected network. Phase delays between regions were used to obtain network's temporal hierarchy and directionality. The validity of the mathematical derivation along with the effects of coupling strength and noise on its outcomes were studied by computer simulations of the Kuramoto model. The simulations demonstrated correct directionality for a large range of coupling strength and low sensitivity to Gaussian noise compared with pairwise coherences. The analysis was applied to resting-state fMRI data of 40 healthy young subjects to characterize the ventral visual system, motor system and default mode network (DMN). It was shown that the ventral visual system was predominantly composed of linear networks while the motor system and the DMN were composed of combined (nonlinear) networks. The ventral visual system exhibits its known temporal hierarchy, the motor system exhibits center ↔ out hierarchy and the DMN has dorsal ↔ ventral and anterior ↔ posterior organizations. The analysis can be applied in different disciplines such as seismology, or economy and in a variety of brain data including stimulus-driven fMRI, electrophysiology, EEG, and MEG, thus open new horizons in brain research. Hum Brain Mapp 38:1374-1386, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The CRYPTOCHROME photoreceptor gates PDF neuropeptide signaling to set circadian network hierarchy in Drosophila.

PubMed

Zhang, Luoying; Lear, Bridget C; Seluzicki, Adam; Allada, Ravi

2009-12-15

Circadian clocks in the brain are organized as coupled oscillators that integrate seasonal cues such as light and temperature to time daily behaviors. In Drosophila, the PIGMENT DISPERSING FACTOR (PDF) neuropeptide-expressing morning (M) and non-PDF evening (E) cells are coupled cell groups important for morning and evening behavior, respectively. Depending on day length, either M cells (short days) or E cells (long days) dictate both the morning and the evening phase, a phenomenon that we term network hierarchy. To examine the role of PDF in light-dark conditions, we examined flies lacking both the PDF receptor (PDFR) and the circadian photoreceptor CRYPTOCHROME (CRY). We found that subsets of E cells exhibit molecular oscillations antiphase to those of wild-type flies, single cry mutants, or single Pdfr mutants, demonstrating a potent role for PDF in light-mediated entrainment, specifically in the absence of CRY. Moreover, we find that the evening behavioral phase is more strongly reset by PDF(+) M cells in the absence of CRY. On the basis of our findings, we propose that CRY can gate PDF signaling to determine behavioral phase and network hierarchy.

Perspectives of the KM3NeT project

NASA Astrophysics Data System (ADS)

Margiotta, A.; KM3NeT Collaboration

2016-10-01

KM3NeT is a large distributed research infrastructure that comprises a network of deep-sea neutrino telescopes in the Mediterranean Sea with user ports for Earth and Sea sciences. The main objectives of KM3NeT are the discovery and subsequent observation of high-energy neutrino sources in the Universe (ARCA) and the determination of the mass hierarchy of neutrinos (ORCA). Technically, the network of telescopes will consist of building blocks of 115 vertical detection units anchored at the seabed and connected to shore via a deep sea electro-optical cable. Each detection unit carries 18 optical modules equipped with 31 3; photomultipliers. Two configurations for the building blocks are defined to optimally detect neutrinos in different ranges of energy. The modular technical design of the KM3NeT telescope allows for a progressive implementation and for data taking even with an incomplete detector. The first phase of implementation has started. The next phase foresees the installation of three building blocks: two building blocks, for a total instrumented volume of 1 km3 (ARCA), at the KM3NeT-It site, at a depth of 3500 m, about 100 km offshore Capo Passero, Sicily. The main scientific goals of the ARCA detector is the exploration of the neutrino sky with unprecedented resolution, searching for neutrinos coming from defined sources or sky regions, like the Galactic Plane. It will also look for diffuse high energy neutrino fluxes following the indication provided by the IceCube signal. The third building block, with a more compact distribution of the optical modules, will be deployed at the KM3NeT-Fr site, 40 km offshore Toulon at a depth of 2500 m (ORCA). The main objective of ORCA is studying the neutrino mass-hierarchy problem and exploring the low energy region of the spectrum. The status of the first phase of the KM3NeT implementation is described and a survey of the physics potentiality of the telescope is given in this contribution, with particular emphasis on the high energy studies.

Evaluating Implementations of Service Oriented Architecture for Sensor Network via Simulation

DTIC Science & Technology

2011-04-01

Subject: COMPUTER SCIENCE Approved: Boleslaw Szymanski , Thesis Adviser Rensselaer Polytechnic Institute Troy, New York April 2011 (For Graduation May 2011...simulation supports distributed and centralized composition with a type hierarchy and multiple -service statically-located nodes in a 2-dimensional space...distributed and centralized composition with a type hierarchy and multiple -service statically-located nodes in a 2-dimensional space. The second simulation

The Face-Processing Network Is Resilient to Focal Resection of Human Visual Cortex

PubMed Central

Jonas, Jacques; Gomez, Jesse; Maillard, Louis; Brissart, Hélène; Hossu, Gabriela; Jacques, Corentin; Loftus, David; Colnat-Coulbois, Sophie; Stigliani, Anthony; Barnett, Michael A.; Grill-Spector, Kalanit; Rossion, Bruno

2016-01-01

Human face perception requires a network of brain regions distributed throughout the occipital and temporal lobes with a right hemisphere advantage. Present theories consider this network as either a processing hierarchy beginning with the inferior occipital gyrus (occipital face area; IOG-faces/OFA) or a multiple-route network with nonhierarchical components. The former predicts that removing IOG-faces/OFA will detrimentally affect downstream stages, whereas the latter does not. We tested this prediction in a human patient (Patient S.P.) requiring removal of the right inferior occipital cortex, including IOG-faces/OFA. We acquired multiple fMRI measurements in Patient S.P. before and after a preplanned surgery and multiple measurements in typical controls, enabling both within-subject/across-session comparisons (Patient S.P. before resection vs Patient S.P. after resection) and between-subject/across-session comparisons (Patient S.P. vs controls). We found that the spatial topology and selectivity of downstream ipsilateral face-selective regions were stable 1 and 8 month(s) after surgery. Additionally, the reliability of distributed patterns of face selectivity in Patient S.P. before versus after resection was not different from across-session reliability in controls. Nevertheless, postoperatively, representations of visual space were typical in dorsal face-selective regions but atypical in ventral face-selective regions and V1 of the resected hemisphere. Diffusion weighted imaging in Patient S.P. and controls identifies white matter tracts connecting retinotopic areas to downstream face-selective regions, which may contribute to the stable and plastic features of the face network in Patient S.P. after surgery. Together, our results support a multiple-route network of face processing with nonhierarchical components and shed light on stable and plastic features of high-level visual cortex following focal brain damage. SIGNIFICANCE STATEMENT Brain networks consist of interconnected functional regions commonly organized in processing hierarchies. Prevailing theories predict that damage to the input of the hierarchy will detrimentally affect later stages. We tested this prediction with multiple brain measurements in a rare human patient requiring surgical removal of the putative input to a network processing faces. Surprisingly, the spatial topology and selectivity of downstream face-selective regions are stable after surgery. Nevertheless, representations of visual space were typical in dorsal face-selective regions but atypical in ventral face-selective regions and V1. White matter connections from outside the face network may support these stable and plastic features. As processing hierarchies are ubiquitous in biological and nonbiological systems, our results have pervasive implications for understanding the construction of resilient networks. PMID:27511014

DReAM: Demand Response Architecture for Multi-level District Heating and Cooling Networks

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

Bhattacharya, Saptarshi; Chandan, Vikas; Arya, Vijay

In this paper, we exploit the inherent hierarchy of heat exchangers in District Heating and Cooling (DHC) networks and propose DReAM, a novel Demand Response (DR) architecture for Multi-level DHC networks. DReAM serves to economize system operation while still respecting comfort requirements of individual consumers. Contrary to many present day DR schemes that work on a consumer level granularity, DReAM works at a level of hierarchy above buildings, i.e. substations that supply heat to a group of buildings. This improves the overall DR scalability and reduce the computational complexity. In the first step of the proposed approach, mathematical models ofmore » individual substations and their downstream networks are abstracted into appropriately constructed low-complexity structural forms. In the second step, this abstracted information is employed by the utility to perform DR optimization that determines the optimal heat inflow to individual substations rather than buildings, in order to achieve the targeted objectives across the network. We validate the proposed DReAM framework through experimental results under different scenarios on a test network.« less

Institutional networks and adaptive water governance in the Klamath River Basin, USA.

EPA Science Inventory

Polycentric networks of formal organizations and informal stakeholder groups, as opposed to centralized institutional hierarchies, can be critically important for strengthening the capacity of governance systems to adapt to unexpected social and biophysical change. Adaptive gover...

Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

NASA Astrophysics Data System (ADS)

Havemann, Frank; Heinz, Michael; Struck, Alexander; Gläser, Jochen

2011-01-01

We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This analytic procedure is not only more exact than its numerical alternatives such as LFM and GCE but also much faster. Critical resolution levels can be identified by searching for intervals in which large changes of the resolution do not lead to growth of communities. We tested our algorithm on benchmark graphs and on a network of 492 papers in information science. Combined with a specific post-processing, the algorithm gives much more precise results on LFR benchmarks with high overlap compared to other algorithms and performs very similarly to GCE.

Spatial and object-based attention modulates broadband high-frequency responses across the human visual cortical hierarchy.

PubMed

Davidesco, Ido; Harel, Michal; Ramot, Michal; Kramer, Uri; Kipervasser, Svetlana; Andelman, Fani; Neufeld, Miri Y; Goelman, Gadi; Fried, Itzhak; Malach, Rafael

2013-01-16

One of the puzzling aspects in the visual attention literature is the discrepancy between electrophysiological and fMRI findings: whereas fMRI studies reveal strong attentional modulation in the earliest visual areas, single-unit and local field potential studies yielded mixed results. In addition, it is not clear to what extent spatial attention effects extend from early to high-order visual areas. Here we addressed these issues using electrocorticography recordings in epileptic patients. The patients performed a task that allowed simultaneous manipulation of both spatial and object-based attention. They were presented with composite stimuli, consisting of a small object (face or house) superimposed on a large one, and in separate blocks, were instructed to attend one of the objects. We found a consistent increase in broadband high-frequency (30-90 Hz) power, but not in visual evoked potentials, associated with spatial attention starting with V1/V2 and continuing throughout the visual hierarchy. The magnitude of the attentional modulation was correlated with the spatial selectivity of each electrode and its distance from the occipital pole. Interestingly, the latency of the attentional modulation showed a significant decrease along the visual hierarchy. In addition, electrodes placed over high-order visual areas (e.g., fusiform gyrus) showed both effects of spatial and object-based attention. Overall, our results help to reconcile previous observations of discrepancy between fMRI and electrophysiology. They also imply that spatial attention effects can be found both in early and high-order visual cortical areas, in parallel with their stimulus tuning properties.

What Next for Networks and Netwars

DTIC Science & Technology

2001-01-01

Jacques Derrida , Michel Foucault, 30Standard sources on neorealism include a range of writings by Kenneth Waltz and John Mearshimer in particular. The...address banking networks, and Jacques (1990), which provides a classic defense of the importance of hierarchy in corporate structures. What Next for

A general model for memory interference in a multiprocessor system with memory hierarchy

NASA Technical Reports Server (NTRS)

Taha, Badie A.; Standley, Hilda M.

1989-01-01

The problem of memory interference in a multiprocessor system with a hierarchy of shared buses and memories is addressed. The behavior of the processors is represented by a sequence of memory requests with each followed by a determined amount of processing time. A statistical queuing network model for determining the extent of memory interference in multiprocessor systems with clusters of memory hierarchies is presented. The performance of the system is measured by the expected number of busy memory clusters. The results of the analytic model are compared with simulation results, and the correlation between them is found to be very high.

Network Policy Languages: A Survey and a New Approach

DTIC Science & Technology

2000-08-01

go, throwing a community into economic chaos. These events could result from discontinued funding from Silicon Valley investors who became aware of...prevented. C. POLICY HIERARCHIES FOR DISTRIBUTED SYSTEMS MANAGEMENT In [23], Moffett and Sloman form a policy hierarchy is by refining general high...management behavior of a system, without coding the behavior into the manager agents. Lupu and Sloman focus on techniques and tool support for off-line

Cellular network entropy as the energy potential in Waddington's differentiation landscape

PubMed Central

Banerji, Christopher R. S.; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X.; Teschendorff, Andrew E.

2013-01-01

Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape. PMID:24154593

Research on the architecture and key technologies of SIG

NASA Astrophysics Data System (ADS)

Fu, Zhongliang; Meng, Qingxiang; Huang, Yan; Liu, Shufan

2007-06-01

Along with the development of computer network, Grid has become one of the hottest issues of researches on sharing and cooperation of Internet resources throughout the world. This paper illustrates a new architecture of SIG-a five-hierarchy architecture (including Data Collecting Layer, Grid Layer, Service Layer, Application Layer and Client Layer) of SIG from the traditional three hierarchies (only including resource layer, service layer and client layer). In the paper, the author proposes a new mixed network mode of Spatial Information Grid which integrates CAG (Certificate Authority of Grid) and P2P (Peer to Peer) in the Grid Layer, besides, the author discusses some key technologies of SIG and analysis the functions of these key technologies.

Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace

PubMed Central

Du, Hongru; Zhao, Yannan; Wu, Rongwei; Zhang, Xiaolei

2017-01-01

“The Belt and Road” initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along “the Belt and Road” are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along “the Belt and Road”. This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of “the Belt and Road”, show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city. PMID:29200421

Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior.

PubMed

Pillai, Ajay S; Jirsa, Viktor K

2017-06-07

In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data. Copyright © 2017 Elsevier Inc. All rights reserved.

Complex networks as an emerging property of hierarchical preferential attachment.

PubMed

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

2015-12-01

Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

Complex networks as an emerging property of hierarchical preferential attachment

NASA Astrophysics Data System (ADS)

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

2015-12-01

Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

NASA Astrophysics Data System (ADS)

Yan, Fengxia; Udupa, Jayaram K.; Tong, Yubing; Xu, Guoping; Odhner, Dewey; Torigian, Drew A.

2018-03-01

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

10 CFR Appendix H to Subpart B of... - Uniform Test Method for Measuring the Power Consumption of Television Sets

Code of Federal Regulations, 2014 CFR

2014-01-01

... connections (e.g., Wi-Fi and Ethernet), the TV shall be configured and connected to a single network source in... Hierarchy Priority Network connection type 1 Wi-Fi (Institution of Electrical and Electronics Engineers—IEEE...

The Face-Processing Network Is Resilient to Focal Resection of Human Visual Cortex.

PubMed

Weiner, Kevin S; Jonas, Jacques; Gomez, Jesse; Maillard, Louis; Brissart, Hélène; Hossu, Gabriela; Jacques, Corentin; Loftus, David; Colnat-Coulbois, Sophie; Stigliani, Anthony; Barnett, Michael A; Grill-Spector, Kalanit; Rossion, Bruno

2016-08-10

Human face perception requires a network of brain regions distributed throughout the occipital and temporal lobes with a right hemisphere advantage. Present theories consider this network as either a processing hierarchy beginning with the inferior occipital gyrus (occipital face area; IOG-faces/OFA) or a multiple-route network with nonhierarchical components. The former predicts that removing IOG-faces/OFA will detrimentally affect downstream stages, whereas the latter does not. We tested this prediction in a human patient (Patient S.P.) requiring removal of the right inferior occipital cortex, including IOG-faces/OFA. We acquired multiple fMRI measurements in Patient S.P. before and after a preplanned surgery and multiple measurements in typical controls, enabling both within-subject/across-session comparisons (Patient S.P. before resection vs Patient S.P. after resection) and between-subject/across-session comparisons (Patient S.P. vs controls). We found that the spatial topology and selectivity of downstream ipsilateral face-selective regions were stable 1 and 8 month(s) after surgery. Additionally, the reliability of distributed patterns of face selectivity in Patient S.P. before versus after resection was not different from across-session reliability in controls. Nevertheless, postoperatively, representations of visual space were typical in dorsal face-selective regions but atypical in ventral face-selective regions and V1 of the resected hemisphere. Diffusion weighted imaging in Patient S.P. and controls identifies white matter tracts connecting retinotopic areas to downstream face-selective regions, which may contribute to the stable and plastic features of the face network in Patient S.P. after surgery. Together, our results support a multiple-route network of face processing with nonhierarchical components and shed light on stable and plastic features of high-level visual cortex following focal brain damage. Brain networks consist of interconnected functional regions commonly organized in processing hierarchies. Prevailing theories predict that damage to the input of the hierarchy will detrimentally affect later stages. We tested this prediction with multiple brain measurements in a rare human patient requiring surgical removal of the putative input to a network processing faces. Surprisingly, the spatial topology and selectivity of downstream face-selective regions are stable after surgery. Nevertheless, representations of visual space were typical in dorsal face-selective regions but atypical in ventral face-selective regions and V1. White matter connections from outside the face network may support these stable and plastic features. As processing hierarchies are ubiquitous in biological and nonbiological systems, our results have pervasive implications for understanding the construction of resilient networks. Copyright © 2016 the authors 0270-6474/16/368426-16$15.00/0.

Analysis of energy efficient routing protocols for implementation of a ubiquitous health system

NASA Astrophysics Data System (ADS)

Kwon, Jongwon; Park, Yongman; Koo, Sangjun; Ayurzana, Odgeral; Kim, Hiesik

2007-12-01

The innovative Ubiquitous-Health was born through convergence of medical service, with development of up to date information technologies and ubiquitous IT. The U-Health can be applied to a variety of special situations for managing functions of each medical center efficiently. This paper focuses on estimation of various routing protocols for implementation of U-health monitoring system. In order to facilitate wireless communication over the network, a routing protocol on the network layer is used to establish precise and efficient route between sensor nodes so that information acquired from sensors may be delivered in a timely manner. A route establishment should be considered to minimize overhead, data loss and power consumption because wireless networks for U-health are organized by a large number of sensor nodes which are small in size and have limited processing power, memory and battery life. In this paper a overview of wireless sensor network technologies commonly known is described as well as evaluation of three multi hop routing protocols which are flooding, gossiping and modified low energy adaptive clustering hierarchy(LEACH) for use with these networks using TOSSIM simulator. As a result of evaluation the integrated wireless sensor board was developed in particular. The board is embedded device based on AVR128 porting TinyOS. Also it employs bio sensor measures blood pressure, pulse frequency and ZigBee module for wireless communication. This paper accelerates the digital convergence age through continual research and development of technologies related the U-Health.

Accelerate!

PubMed

Kotter, John P

2012-11-01

The old ways of setting and implementing strategy are failing us, writes the author of Leading Change, in part because we can no longer keep up with the pace of change. Organizational leaders are torn between trying to stay ahead of increasingly fierce competition and needing to deliver this year's results. Although traditional hierarchies and managerial processes--the components of a company's "operating system"--can meet the daily demands of running an enterprise, they are rarely equipped to identify important hazards quickly, formulate creative strategic initiatives nimbly, and implement them speedily. The solution Kotter offers is a second system--an agile, networklike structure--that operates in concert with the first to create a dual operating system. In such a system the hierarchy can hand off the pursuit of big strategic initiatives to the strategy network, freeing itself to focus on incremental changes to improve efficiency. The network is populated by employees from all levels of the organization, giving it organizational knowledge, relationships, credibility, and influence. It can Liberate information from silos with ease. It has a dynamic structure free of bureaucratic layers, permitting a level of individualism, creativity, and innovation beyond the reach of any hierarchy. The network's core is a guiding coalition that represents each level and department in the hierarchy, with a broad range of skills. Its drivers are members of a "volunteer army" who are energized by and committed to the coalition's vividly formulated, high-stakes vision and strategy. Kotter has helped eight organizations, public and private, build dual operating systems over the past three years. He predicts that such systems will lead to long-term success in the 21st century--for shareholders, customers, employees, and companies themselves.

Acoustic-Emergent Phonology in the Amplitude Envelope of Child-Directed Speech

PubMed Central

Leong, Victoria; Goswami, Usha

2015-01-01

When acquiring language, young children may use acoustic spectro-temporal patterns in speech to derive phonological units in spoken language (e.g., prosodic stress patterns, syllables, phonemes). Children appear to learn acoustic-phonological mappings rapidly, without direct instruction, yet the underlying developmental mechanisms remain unclear. Across different languages, a relationship between amplitude envelope sensitivity and phonological development has been found, suggesting that children may make use of amplitude modulation (AM) patterns within the envelope to develop a phonological system. Here we present the Spectral Amplitude Modulation Phase Hierarchy (S-AMPH) model, a set of algorithms for deriving the dominant AM patterns in child-directed speech (CDS). Using Principal Components Analysis, we show that rhythmic CDS contains an AM hierarchy comprising 3 core modulation timescales. These timescales correspond to key phonological units: prosodic stress (Stress AM, ~2 Hz), syllables (Syllable AM, ~5 Hz) and onset-rime units (Phoneme AM, ~20 Hz). We argue that these AM patterns could in principle be used by naïve listeners to compute acoustic-phonological mappings without lexical knowledge. We then demonstrate that the modulation statistics within this AM hierarchy indeed parse the speech signal into a primitive hierarchically-organised phonological system comprising stress feet (proto-words), syllables and onset-rime units. We apply the S-AMPH model to two other CDS corpora, one spontaneous and one deliberately-timed. The model accurately identified 72–82% (freely-read CDS) and 90–98% (rhythmically-regular CDS) stress patterns, syllables and onset-rime units. This in-principle demonstration that primitive phonology can be extracted from speech AMs is termed Acoustic-Emergent Phonology (AEP) theory. AEP theory provides a set of methods for examining how early phonological development is shaped by the temporal modulation structure of speech across languages. The S-AMPH model reveals a crucial developmental role for stress feet (AMs ~2 Hz). Stress feet underpin different linguistic rhythm typologies, and speech rhythm underpins language acquisition by infants in all languages. PMID:26641472

Acoustic-Emergent Phonology in the Amplitude Envelope of Child-Directed Speech.

PubMed

Leong, Victoria; Goswami, Usha

2015-01-01

When acquiring language, young children may use acoustic spectro-temporal patterns in speech to derive phonological units in spoken language (e.g., prosodic stress patterns, syllables, phonemes). Children appear to learn acoustic-phonological mappings rapidly, without direct instruction, yet the underlying developmental mechanisms remain unclear. Across different languages, a relationship between amplitude envelope sensitivity and phonological development has been found, suggesting that children may make use of amplitude modulation (AM) patterns within the envelope to develop a phonological system. Here we present the Spectral Amplitude Modulation Phase Hierarchy (S-AMPH) model, a set of algorithms for deriving the dominant AM patterns in child-directed speech (CDS). Using Principal Components Analysis, we show that rhythmic CDS contains an AM hierarchy comprising 3 core modulation timescales. These timescales correspond to key phonological units: prosodic stress (Stress AM, ~2 Hz), syllables (Syllable AM, ~5 Hz) and onset-rime units (Phoneme AM, ~20 Hz). We argue that these AM patterns could in principle be used by naïve listeners to compute acoustic-phonological mappings without lexical knowledge. We then demonstrate that the modulation statistics within this AM hierarchy indeed parse the speech signal into a primitive hierarchically-organised phonological system comprising stress feet (proto-words), syllables and onset-rime units. We apply the S-AMPH model to two other CDS corpora, one spontaneous and one deliberately-timed. The model accurately identified 72-82% (freely-read CDS) and 90-98% (rhythmically-regular CDS) stress patterns, syllables and onset-rime units. This in-principle demonstration that primitive phonology can be extracted from speech AMs is termed Acoustic-Emergent Phonology (AEP) theory. AEP theory provides a set of methods for examining how early phonological development is shaped by the temporal modulation structure of speech across languages. The S-AMPH model reveals a crucial developmental role for stress feet (AMs ~2 Hz). Stress feet underpin different linguistic rhythm typologies, and speech rhythm underpins language acquisition by infants in all languages.

Structural Measures to Track the Evolution of SNOMED CT Hierarchies

PubMed Central

Wei, Duo; Gu, Huanying (Helen); Perl, Yehoshua; Halper, Michael; Ochs, Christopher; Elhanan, Gai; Chen, Yan

2015-01-01

The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is an extensive reference terminology with an attendant amount of complexity. It has been updated continuously and revisions have been released semi-annually to meet users’ needs and to reflect the results of quality assurance (QA) activities. Two measures based on structural features are proposed to track the effects of both natural terminology growth and QA activities based on aspects of the complexity of SNOMED CT. These two measures, called the structural density measure and accumulated structural measure, are derived based on two abstraction networks, the area taxonomy and the partial-area taxonomy. The measures derive from attribute relationship distributions and various concept groupings that are associated with the abstraction networks. They are used to track the trends in the complexity of structures as SNOMED CT changes over time. The measures were calculated for consecutive releases of five SNOMED CT hierarchies, including the Specimen hierarchy. The structural density measure shows that natural growth tends to move a hierarchy’s structure toward a more complex state, whereas the accumulated structural measure shows that QA processes tend to move a hierarchy’s structure toward a less complex state. It is also observed that both the structural density and accumulated structural measures are useful tools to track the evolution of an entire SNOMED CT hierarchy and reveal internal concept migration within it. PMID:26260003

The Young Psychiatrists’ Network: between past and future

PubMed Central

Los, T.

2016-01-01

The Young Psychiatrists’ Network is a movement of early-career psychiatrists that was established in 2009 with the principles of peer regulation, lack of hierarchy and transparency. From humble beginnings as a small group of professionals in Eastern Europe, the Network is expanding into a globally inclusive platform. Future challenges, such as retention of the Network’s core values, are now being debated. PMID:29093900

Neural representations of social status hierarchy in human inferior parietal cortex.

PubMed

Chiao, Joan Y; Harada, Tokiko; Oby, Emily R; Li, Zhang; Parrish, Todd; Bridge, Donna J

2009-01-01

Mental representations of social status hierarchy share properties with that of numbers. Previous neuroimaging studies have shown that the neural representation of numerical magnitude lies within a network of regions within inferior parietal cortex. However the neural basis of social status hierarchy remains unknown. Using fMRI, we studied subjects while they compared social status magnitude of people, objects and symbols, as well as numerical magnitude. Both social status and number comparisons recruited bilateral intraparietal sulci. We also observed a semantic distance effect whereby neural activity within bilateral intraparietal sulci increased for semantically close relative to far numerical and social status comparisons. These results demonstrate that social status and number comparisons recruit distinct and overlapping neuronal representations within human inferior parietal cortex.

Challenges to the Use of Artificial Neural Networks for Diagnostic Classifications with Student Test Data

ERIC Educational Resources Information Center

Briggs, Derek C.; Circi, Ruhan

2017-01-01

Artificial Neural Networks (ANNs) have been proposed as a promising approach for the classification of students into different levels of a psychological attribute hierarchy. Unfortunately, because such classifications typically rely upon internally produced item response patterns that have not been externally validated, the instability of ANN…

Differential activation of natriuretic peptide receptors modulates cardiomyocyte proliferation during development

PubMed Central

Becker, Jason R.; Chatterjee, Sneha; Robinson, Tamara Y.; Bennett, Jeffrey S.; Panáková, Daniela; Galindo, Cristi L.; Zhong, Lin; Shin, Jordan T.; Coy, Shannon M.; Kelly, Amy E.; Roden, Dan M.; Lim, Chee Chew; MacRae, Calum A.

2014-01-01

Organ development is a highly regulated process involving the coordinated proliferation and differentiation of diverse cellular populations. The pathways regulating cell proliferation and their effects on organ growth are complex and for many organs incompletely understood. In all vertebrate species, the cardiac natriuretic peptides (ANP and BNP) are produced by cardiomyocytes in the developing heart. However, their role during cardiogenesis is not defined. Using the embryonic zebrafish and neonatal mammalian cardiomyocytes we explored the natriuretic peptide signaling network during myocardial development. We observed that the cardiac natriuretic peptides ANP and BNP and the guanylate cyclase-linked natriuretic peptide receptors Npr1 and Npr2 are functionally redundant during early cardiovascular development. In addition, we demonstrate that low levels of the natriuretic peptides preferentially activate Npr3, a receptor with Gi activator sequences, and increase cardiomyocyte proliferation through inhibition of adenylate cyclase. Conversely, high concentrations of natriuretic peptides reduce cardiomyocyte proliferation through activation of the particulate guanylate cyclase-linked natriuretic peptide receptors Npr1 and Npr2, and activation of protein kinase G. These data link the cardiac natriuretic peptides in a complex hierarchy modulating cardiomyocyte numbers during development through opposing effects on cardiomyocyte proliferation mediated through distinct cyclic nucleotide signaling pathways. PMID:24353062

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Overlapping communities detection based on spectral analysis of line graphs

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Hu, Rongjing; Huang, Guoming; Wei, Jiaxuan

2018-05-01

Community in networks are often overlapping where one vertex belongs to several clusters. Meanwhile, many networks show hierarchical structure such that community is recursively grouped into hierarchical organization. In order to obtain overlapping communities from a global hierarchy of vertices, a new algorithm (named SAoLG) is proposed to build the hierarchical organization along with detecting the overlap of community structure. SAoLG applies the spectral analysis into line graphs to unify the overlap and hierarchical structure of the communities. In order to avoid the limitation of absolute distance such as Euclidean distance, SAoLG employs Angular distance to compute the similarity between vertices. Furthermore, we make a micro-improvement partition density to evaluate the quality of community structure and use it to obtain the more reasonable and sensible community numbers. The proposed SAoLG algorithm achieves a balance between overlap and hierarchy by applying spectral analysis to edge community detection. The experimental results on one standard network and six real-world networks show that the SAoLG algorithm achieves higher modularity and reasonable community number values than those generated by Ahn's algorithm, the classical CPM and GN ones.

40-Gbps optical backbone network deep packet inspection based on FPGA

NASA Astrophysics Data System (ADS)

Zuo, Yuan; Huang, Zhiping; Su, Shaojing

2014-11-01

In the era of information, the big data, which contains huge information, brings about some problems, such as high speed transmission, storage and real-time analysis and process. As the important media for data transmission, the Internet is the significant part for big data processing research. With the large-scale usage of the Internet, the data streaming of network is increasing rapidly. The speed level in the main fiber optic communication of the present has reached 40Gbps, even 100Gbps, therefore data on the optical backbone network shows some features of massive data. Generally, data services are provided via IP packets on the optical backbone network, which is constituted with SDH (Synchronous Digital Hierarchy). Hence this method that IP packets are directly mapped into SDH payload is named POS (Packet over SDH) technology. Aiming at the problems of real time process of high speed massive data, this paper designs a process system platform based on ATCA for 40Gbps POS signal data stream recognition and packet content capture, which employs the FPGA as the CPU. This platform offers pre-processing of clustering algorithms, service traffic identification and data mining for the following big data storage and analysis with high efficiency. Also, the operational procedure is proposed in this paper. Four channels of 10Gbps POS signal decomposed by the analysis module, which chooses FPGA as the kernel, are inputted to the flow classification module and the pattern matching component based on TCAM. Based on the properties of the length of payload and net flows, buffer management is added to the platform to keep the key flow information. According to data stream analysis, DPI (deep packet inspection) and flow balance distribute, the signal is transmitted to the backend machine through the giga Ethernet ports on back board. Practice shows that the proposed platform is superior to the traditional applications based on ASIC and NP.

Detecting Role Errors in the Gene Hierarchy of the NCI Thesaurus

PubMed Central

Min, Hua; Cohen, Barry; Halper, Michael; Oren, Marc; Perl, Yehoshua

2008-01-01

Gene terminologies are playing an increasingly important role in the ever-growing field of genomic research. While errors in large, complex terminologies are inevitable, gene terminologies are even more susceptible to them due to the rapid growth of genomic knowledge and the nature of its discovery. It is therefore very important to establish quality-assurance protocols for such genomic-knowledge repositories. Different kinds of terminologies oftentimes require auditing methodologies adapted to their particular structures. In light of this, an auditing methodology tailored to the characteristics of the NCI Thesaurus’s (NCIT’s) Gene hierarchy is presented. The Gene hierarchy is of particular interest to the NCIT’s designers due to the primary role of genomics in current cancer research. This multiphase methodology focuses on detecting role-errors, such as missing roles or roles with incorrect or incomplete target structures, occurring within that hierarchy. The methodology is based on two kinds of abstraction networks, called taxonomies, that highlight the role distribution among concepts within the IS-A (subsumption) hierarchy. These abstract views tend to highlight portions of the hierarchy having a higher concentration of errors. The errors found during an application of the methodology are reported. Hypotheses pertaining to the efficacy of our methodology are investigated. PMID:19221606

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

HEP - A semaphore-synchronized multiprocessor with central control. [Heterogeneous Element Processor

NASA Technical Reports Server (NTRS)

Gilliland, M. C.; Smith, B. J.; Calvert, W.

1976-01-01

The paper describes the design concept of the Heterogeneous Element Processor (HEP), a system tailored to the special needs of scientific simulation. In order to achieve high-speed computation required by simulation, HEP features a hierarchy of processes executing in parallel on a number of processors, with synchronization being largely accomplished by hardware. A full-empty-reserve scheme of synchronization is realized by zero-one-valued hardware semaphores. A typical system has, besides the control computer and the scheduler, an algebraic module, a memory module, a first-in first-out (FIFO) module, an integrator module, and an I/O module. The architecture of the scheduler and the algebraic module is examined in detail.

A linguistic geometry for 3D strategic planning

NASA Technical Reports Server (NTRS)

Stilman, Boris

1995-01-01

This paper is a new step in the development and application of the Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing Linguistic Geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in this paper on the new pilot example of the solution of the extremely complex 3D optimization problem of strategic planning for the space combat of autonomous vehicles. This example demonstrates deep and highly selective search in comparison with conventional search algorithms.

Reframing coalitions as systems interventions: a network study exploring the contribution of a youth violence prevention coalition to broader system capacity.

PubMed

Bess, Kimberly D

2015-06-01

This longitudinal research conceptualizes community coalitions as events in local intervention systems (Hawe et al. in Am J Commun Psychol 43(3-4):267-276, 2009). It explores the potential contribution coalitions make, through the collaborative activities of their members, to the broader intervention systems in which they are embedded. Using social network analysis, it examines patterns of structural change in a network of 99 organizations focused on youth violence prevention (YVP) over a 5-year period in which 30 of the 99 organizations were involved in a local YVP Coalition. Both longitudinal modeling and cross sectional analyses are used to examine change in system capacity-strong interorganizational networks-related to patterns of network density, centralization, and hierarchy. Somewhat surprisingly, the study found that capacity in the broader YVP Intervention System actually diminished during the 5-year period of the coalition's operation, though part of the system-the sub-network that made up the YVP Coalition-was marginally strengthened. In this case, therefore, the evidence suggests that power and relational resources in the broader YVP Intervention System were redistributed. The article explores how the definition of capacity related to density and hierarchy may be contextually dependent. Implications for the role of coalitions in building system capacity are discussed.

Voice over internet protocol with prepaid calling card solutions

NASA Astrophysics Data System (ADS)

Gunadi, Tri

2001-07-01

The VoIP technology is growing up rapidly, it has big network impact on PT Telkom Indonesia, the bigger telecommunication operator in Indonesia. Telkom has adopted VoIP and one other technology, Intelligent Network (IN). We develop those technologies together in one service product, called Internet Prepaid Calling Card (IPCC). IPCC is becoming new breakthrough for the Indonesia telecommunication services especially on VoIP and Prepaid Calling Card solutions. Network architecture of Indonesia telecommunication consists of three layer, Local, Tandem and Trunck Exchange layer. Network development researches for IPCC architecture are focus on network overlay hierarchy, Internet and PSTN. With this design hierarchy the goal of Interworking PSTN, VoIP and IN calling card, become reality. Overlay design for IPCC is not on Trunck Exchange, this is the new architecture, these overlay on Tandem and Local Exchange, to make the faster call processing. The nodes added: Gateway (GW) and Card Management Center (CMC) The GW do interfacing between PSTN and Internet Network used ISDN-PRA and Ethernet. The other functions are making bridge on circuit (PSTN) with packet (VoIP) based and real time billing process. The CMC used for data storage, pin validation, report activation, tariff system, directory number and all the administration transaction. With two nodes added the IPCC service offered to the market.

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

PubMed

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

Formal verification of a set of memory management units

NASA Technical Reports Server (NTRS)

Schubert, E. Thomas; Levitt, K.; Cohen, Gerald C.

1992-01-01

This document describes the verification of a set of memory management units (MMU). The verification effort demonstrates the use of hierarchical decomposition and abstract theories. The MMUs can be organized into a complexity hierarchy. Each new level in the hierarchy adds a few significant features or modifications to the lower level MMU. The units described include: (1) a page check translation look-aside module (TLM); (2) a page check TLM with supervisor line; (3) a base bounds MMU; (4) a virtual address translation MMU; and (5) a virtual address translation MMU with memory resident segment table.

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

PubMed Central

Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

2016-01-01

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

PubMed

Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

2017-07-19

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2014-01-01

Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

Ontologies and tag-statistics

NASA Astrophysics Data System (ADS)

Tibély, Gergely; Pollner, Péter; Vicsek, Tamás; Palla, Gergely

2012-05-01

Due to the increasing popularity of collaborative tagging systems, the research on tagged networks, hypergraphs, ontologies, folksonomies and other related concepts is becoming an important interdisciplinary area with great potential and relevance for practical applications. In most collaborative tagging systems the tagging by the users is completely ‘flat’, while in some cases they are allowed to define a shallow hierarchy for their own tags. However, usually no overall hierarchical organization of the tags is given, and one of the interesting challenges of this area is to provide an algorithm generating the ontology of the tags from the available data. In contrast, there are also other types of tagged networks available for research, where the tags are already organized into a directed acyclic graph (DAG), encapsulating the ‘is a sub-category of’ type of hierarchy between each other. In this paper, we study how this DAG affects the statistical distribution of tags on the nodes marked by the tags in various real networks. The motivation for this research was the fact that understanding the tagging based on a known hierarchy can help in revealing the hidden hierarchy of tags in collaborative tagging systems. We analyse the relation between the tag-frequency and the position of the tag in the DAG in two large sub-networks of the English Wikipedia and a protein-protein interaction network. We also study the tag co-occurrence statistics by introducing a two-dimensional (2D) tag-distance distribution preserving both the difference in the levels and the absolute distance in the DAG for the co-occurring pairs of tags. Our most interesting finding is that the local relevance of tags in the DAG (i.e. their rank or significance as characterized by, e.g., the length of the branches starting from them) is much more important than their global distance from the root. Furthermore, we also introduce a simple tagging model based on random walks on the DAG, capable of reproducing the main statistical features of tag co-occurrence. This model has high potential for further practical applications, e.g., it can provide the starting point for a benchmark system in ontology retrieval or it may help pinpoint unusual correlations in the co-occurrence of tags.

Language Networks in Anophthalmia: Maintained Hierarchy of Processing in "Visual" Cortex

ERIC Educational Resources Information Center

Watkins, Kate E.; Cowey, Alan; Alexander, Iona; Filippini, Nicola; Kennedy, James M.; Smith, Stephen M.; Ragge, Nicola; Bridge, Holly

2012-01-01

Imaging studies in blind subjects have consistently shown that sensory and cognitive tasks evoke activity in the occipital cortex, which is normally visual. The precise areas involved and degree of activation are dependent upon the cause and age of onset of blindness. Here, we investigated the cortical language network at rest and during an…

A Review of Verb Network Strengthening Treatment: Theory, Methods, Results, and Clinical Implications

ERIC Educational Resources Information Center

Edmonds, Lisa A.

2016-01-01

This article examines Verb Network Strengthening Treatment (VNeST), a relatively new treatment approach for anomia in people with aphasia. The VNeST protocol aims to promote generalization to increased lexical retrieval of untrained words across a hierarchy of linguistic tasks, including single-word naming of nouns and verbs, sentence production,…

Hierarchy of models: From qualitative to quantitative analysis of circadian rhythms in cyanobacteria

NASA Astrophysics Data System (ADS)

Chaves, M.; Preto, M.

2013-06-01

A hierarchy of models, ranging from high to lower levels of abstraction, is proposed to construct "minimal" but predictive and explanatory models of biological systems. Three hierarchical levels will be considered: Boolean networks, piecewise affine differential (PWA) equations, and a class of continuous, ordinary, differential equations' models derived from the PWA model. This hierarchy provides different levels of approximation of the biological system and, crucially, allows the use of theoretical tools to more exactly analyze and understand the mechanisms of the system. The Kai ABC oscillator, which is at the core of the cyanobacterial circadian rhythm, is analyzed as a case study, showing how several fundamental properties—order of oscillations, synchronization when mixing oscillating samples, structural robustness, and entrainment by external cues—can be obtained from basic mechanisms.

Seismogeodesy for rapid earthquake and tsunami characterization

NASA Astrophysics Data System (ADS)

Bock, Y.

2016-12-01

Rapid estimation of earthquake magnitude and fault mechanism is critical for earthquake and tsunami warning systems. Traditionally, the monitoring of earthquakes and tsunamis has been based on seismic networks for estimating earthquake magnitude and slip, and tide gauges and deep-ocean buoys for direct measurement of tsunami waves. These methods are well developed for ocean basin-wide warnings but are not timely enough to protect vulnerable populations and infrastructure from the effects of local tsunamis, where waves may arrive within 15-30 minutes of earthquake onset time. Direct measurements of displacements by GPS networks at subduction zones allow for rapid magnitude and slip estimation in the near-source region, that are not affected by instrumental limitations and magnitude saturation experienced by local seismic networks. However, GPS displacements by themselves are too noisy for strict earthquake early warning (P-wave detection). Optimally combining high-rate GPS and seismic data (in particular, accelerometers that do not clip), referred to as seismogeodesy, provides a broadband instrument that does not clip in the near field, is impervious to magnitude saturation, and provides accurate real-time static and dynamic displacements and velocities in real time. Here we describe a NASA-funded effort to integrate GPS and seismogeodetic observations as part of NOAA's Tsunami Warning Centers in Alaska and Hawaii. It consists of a series of plug-in modules that allow for a hierarchy of rapid seismogeodetic products, including automatic P-wave picking, hypocenter estimation, S-wave prediction, magnitude scaling relationships based on P-wave amplitude (Pd) and peak ground displacement (PGD), finite-source CMT solutions and fault slip models as input for tsunami warnings and models. For the NOAA/NASA project, the modules are being integrated into an existing USGS Earthworm environment, currently limited to traditional seismic data. We are focused on a network of dozens of seismogeodetic stations available through the Pacific Northwest Seismic Network (University of Washington), the Plate Boundary Observatory (UNAVCO) and the Pacific Northwest Geodetic Array (Central Washington University) as the basis for local tsunami warnings for a large subduction zone earthquake in Cascadia.

Simulation verification techniques study. Subsystem simulation validation techniques

NASA Technical Reports Server (NTRS)

Duncan, L. M.; Reddell, J. P.; Schoonmaker, P. B.

1974-01-01

Techniques for validation of software modules which simulate spacecraft onboard systems are discussed. An overview of the simulation software hierarchy for a shuttle mission simulator is provided. A set of guidelines for the identification of subsystem/module performance parameters and critical performance parameters are presented. Various sources of reference data to serve as standards of performance for simulation validation are identified. Environment, crew station, vehicle configuration, and vehicle dynamics simulation software are briefly discussed from the point of view of their interfaces with subsystem simulation modules. A detailed presentation of results in the area of vehicle subsystems simulation modules is included. A list of references, conclusions and recommendations are also given.

The role of pulvinar in the transmission of information in the visual hierarchy.

PubMed

Cortes, Nelson; van Vreeswijk, Carl

2012-01-01

VISUAL RECEPTIVE FIELD (RF) ATTRIBUTES IN VISUAL CORTEX OF PRIMATES HAVE BEEN EXPLAINED MAINLY FROM CORTICAL CONNECTIONS: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down processes through the feedback pathway. Although the hierarchical organization explains the spatial properties of RFs, is unclear how a non-linear transmission of activity through the visual hierarchy can yield smooth contrast response functions in all level of the hierarchy. Depending on the gain, non-linear transfer functions create either a bimodal response to contrast, or no contrast dependence of the response in the highest level of the hierarchy. One possible mechanism to regulate this transmission of visual contrast information from low to high level involves an external component that shortcuts the flow of information through the hierarchy. A candidate for this shortcut is the Pulvinar nucleus of the thalamus. To investigate representation of stimulus contrast a hierarchical model network of ten cortical areas is examined. In each level of the network, the activity from the previous layer is integrated and then non-linearly transmitted to the next level. The arrangement of interactions creates a gradient from simple to complex RFs of increasing size as one moves from lower to higher cortical levels. The visual input is modeled as a Gaussian random input, whose width codes for the contrast. This input is applied to the first area. The output activity ratio among different contrast values is analyzed for the last level to observe sensitivity to a contrast and contrast invariant tuning. For a purely cortical system, the output of the last area can be approximately contrast invariant, but the sensitivity to contrast is poor. To account for an alternative visual processing pathway, non-reciprocal connections from and to a parallel pulvinar like structure of nine areas is coupled to the system. Compared to the pure feedforward model, cortico-pulvino-cortical output presents much more sensitivity to contrast and has a similar level of contrast invariance of the tuning.

The Role of Pulvinar in the Transmission of Information in the Visual Hierarchy

PubMed Central

Cortes, Nelson; van Vreeswijk, Carl

2012-01-01

Visual receptive field (RF) attributes in visual cortex of primates have been explained mainly from cortical connections: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down processes through the feedback pathway. Although the hierarchical organization explains the spatial properties of RFs, is unclear how a non-linear transmission of activity through the visual hierarchy can yield smooth contrast response functions in all level of the hierarchy. Depending on the gain, non-linear transfer functions create either a bimodal response to contrast, or no contrast dependence of the response in the highest level of the hierarchy. One possible mechanism to regulate this transmission of visual contrast information from low to high level involves an external component that shortcuts the flow of information through the hierarchy. A candidate for this shortcut is the Pulvinar nucleus of the thalamus. To investigate representation of stimulus contrast a hierarchical model network of ten cortical areas is examined. In each level of the network, the activity from the previous layer is integrated and then non-linearly transmitted to the next level. The arrangement of interactions creates a gradient from simple to complex RFs of increasing size as one moves from lower to higher cortical levels. The visual input is modeled as a Gaussian random input, whose width codes for the contrast. This input is applied to the first area. The output activity ratio among different contrast values is analyzed for the last level to observe sensitivity to a contrast and contrast invariant tuning. For a purely cortical system, the output of the last area can be approximately contrast invariant, but the sensitivity to contrast is poor. To account for an alternative visual processing pathway, non-reciprocal connections from and to a parallel pulvinar like structure of nine areas is coupled to the system. Compared to the pure feedforward model, cortico-pulvino-cortical output presents much more sensitivity to contrast and has a similar level of contrast invariance of the tuning. PMID:22654750

Neural correlates of consciousness: a definition of the dorsal and ventral streams and their relation to phenomenology.

PubMed

Vakalopoulos, Costa

2005-01-01

The paper presents a hypothesis for a neural correlate of consciousness. A proposal is made that both the dorsal and ventral streams must be concurrently active to generate conscious awareness and that V1 (striate cortex) provides a serial link between them. An argument is presented against a true extrastriate communication between the dorsal and ventral streams. Secondly, a detailed theory is developed for the structure of the visual hierarchy. Premotor theory states that each organism-object interaction can be described by the two quantitative measures of torque and change in joint position served by the basal ganglia and cerebellum, respectively. This leads to a component theory of motor efference copy providing a fundamental tool for categorizing dorsal and ventral stream networks. The rationale for this is that the dorsal stream specifies spatial coordinates of the external world, which can be coded by the reafference of changes in joint position. The ventral stream is concerned with object recognition and is coded for by forces exerted on the world during a developmental exploratory phase of the organism. The proposed pathways for a component motor efference copy from both the cerebellum and basal ganglia converge on the thalamus and modulate thalamocortical projections via the thalamic reticular nucleus. The origin of the corticopontine projections, which are a massive pathway for cortical information to reach the cerebellum, coincides with the area typically considered as part of the dorsal stream, whereas the entire cortex projects to the striatum. This adds empirical support for a new conceptualization of the visual streams. The model also presents a solution to the binding problem of a neural correlate of consciousness, that is, how a distributed neural network synchronizes its activity during a cognitive event. It represents a reinterpretation of the current status of the visual hierarchy.

The Impact of Hierarchy and Group Structure on Information Processing in Decision Making: Application of a Networks/Systems Approach.

ERIC Educational Resources Information Center

Ford, David L., Jr.

When one engages in organizational diagnosis, it has been suggested that greater understanding of the organization can come through: (1) an identification of all the channels conveying material and information, and (2) a description of the means by which this communication influences the behavior of the organization. A networks/system approach is…

Characterizing Communication Networks in a Web-Based Classroom: Cognitive Styles and Linguistic Behavior of Self-Organizing Groups in Online Discussions

ERIC Educational Resources Information Center

Vercellone-Smith, Pamela; Jablokow, Kathryn; Friedel, Curtis

2012-01-01

In this study, we explore the cognitive style profiles and linguistic patterns of self-organizing groups within a web-based graduate education course to determine how cognitive preferences and individual behaviors influence the patterns of information exchange and the formation of communication hierarchies in an online classroom. Network analysis…

A practical introduction to skeletons for the plant sciences1

PubMed Central

Bucksch, Alexander

2014-01-01

Before the availability of digital photography resulting from the invention of charged couple devices in 1969, the measurement of plant architecture was a manual process either on the plant itself or on traditional photographs. The introduction of cheap digital imaging devices for the consumer market enabled the wide use of digital images to capture the shape of plant networks such as roots, tree crowns, or leaf venation. Plant networks contain geometric traits that can establish links to genetic or physiological characteristics, support plant breeding efforts, drive evolutionary studies, or serve as input to plant growth simulations. Typically, traits are encoded in shape descriptors that are computed from imaging data. Skeletons are one class of shape descriptors that are used to describe the hierarchies and extent of branching and looping plant networks. While the mathematical understanding of skeletons is well developed, their application within the plant sciences remains challenging because the quality of the measurement depends partly on the interpretation of the skeleton. This article is meant to bridge the skeletonization literature in the plant sciences and related technical fields by discussing best practices for deriving diameters and approximating branching hierarchies in a plant network. PMID:25202645

Two candidate genes for two quantitative trait loci epistatically attenuate hypertension in a novel pathway.

PubMed

Chauvet, Cristina; Ménard, Annie; Deng, Alan Y

2015-09-01

Multiple quantitative trait loci (QTLs) for blood pressure (BP) have been detected in rat models of human polygenic hypertension. They influence BP physiologically via epistatic modules. Little is known about the causal genes and virtually nothing is known on modularized mechanisms governing their regulatory connections. Two genes responsible for two individual BP QTLs on rat Chromosome 18 have been identified that belong to the same epistatic module. Treacher Collins-Franceschetti syndrome 1 (Tcof1) gene is the only function candidate for C18QTL3. Haloacid dehalogenase like hydrolase domain containing 2 (Hdhd2), although a gene of previously unknown function, is C18QTL4, and encodes a newly identified phosphatase. The current work has provided the premier evidence that Hdhd2/C18QTL4 and Tcof1/C18QTL3 may be involved in polygenic hypertension. Hdhd2/C18QTL4 can regulate the function of Tcof1/C18QTL3 via de-phosphorylation, and, for the first time, furbishes a molecular mechanism in support of a genetically epistatic hierarchy between two BP QTLs, and thus authenticates the epistasis-common pathway paradigm. The pathway initiated by Hdhd2/C18QTL4 upstream of Tcof1/C18QTL3 reveals novel mechanistic insights into BP modulations. Their discovery might yield innovative therapeutic targets and diagnostic tools predicated on a novel BP cause and mechanism that is determined by a regulatory hierarchy. Optimizing the de-phosphorylation capability and its downstream target could be antihypertensive. The conceptual paradigm of an order and regulatory hierarchy may help unravel genetic and molecular relationships among certain human BP QTLs.

How to simulate global cosmic strings with large string tension

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

Klaer, Vincent B.; Moore, Guy D., E-mail: vklaer@theorie.ikp.physik.tu-darmstadt.de, E-mail: guy.moore@physik.tu-darmstadt.de

Global string networks may be relevant in axion production in the early Universe, as well as other cosmological scenarios. Such networks contain a large hierarchy of scales between the string core scale and the Hubble scale, ln( f {sub a} / H ) ∼ 70, which influences the network dynamics by giving the strings large tensions T ≅ π f {sub a} {sup 2} ln( f {sub a} / H ). We present a new numerical approach to simulate such global string networks, capturing the tension without an exponentially large lattice.

Modularization and epistatic hierarchy determine homeostatic actions of multiple blood pressure quantitative trait loci.

PubMed

Chauvet, Cristina; Crespo, Kimberley; Ménard, Annie; Roy, Julie; Deng, Alan Y

2013-11-15

Hypertension, the most frequently diagnosed clinical condition world-wide, predisposes individuals to morbidity and mortality, yet its underlying pathological etiologies are poorly understood. So far, a large number of quantitative trait loci (QTLs) have been identified in both humans and animal models, but how they function together in determining overall blood pressure (BP) in physiological settings is unknown. Here, we systematically and comprehensively performed pair-wise comparisons of individual QTLs to create a global picture of their functionality in an inbred rat model. Rather than each of numerous QTLs contributing to infinitesimal BP increments, a modularized pattern arises: two epistatic 'blocks' constitute basic functional 'units' for nearly all QTLs, designated as epistatic module 1 (EM1) and EM2. This modularization dictates the magnitude and scope of BP effects. Any EM1 member can contribute to BP additively to that of EM2, but not to those of the same module. Members of each EM display epistatic hierarchy, which seems to reflect a related functional pathway. Rat homologues of 11 human BP QTLs belong to either EM1 or EM2. Unique insights emerge into the novel genetic mechanism and hierarchy determining BP in the Dahl salt-sensitive SS/Jr (DSS) rat model that implicate a portion of human QTLs. Elucidating the pathways underlying EM1 and EM2 may reveal the genetic regulation of BP.

Deviance-Related Responses along the Auditory Hierarchy: Combined FFR, MLR and MMN Evidence.

PubMed

Shiga, Tetsuya; Althen, Heike; Cornella, Miriam; Zarnowiec, Katarzyna; Yabe, Hirooki; Escera, Carles

2015-01-01

The mismatch negativity (MMN) provides a correlate of automatic auditory discrimination in human auditory cortex that is elicited in response to violation of any acoustic regularity. Recently, deviance-related responses were found at much earlier cortical processing stages as reflected by the middle latency response (MLR) of the auditory evoked potential, and even at the level of the auditory brainstem as reflected by the frequency following response (FFR). However, no study has reported deviance-related responses in the FFR, MLR and long latency response (LLR) concurrently in a single recording protocol. Amplitude-modulated (AM) sounds were presented to healthy human participants in a frequency oddball paradigm to investigate deviance-related responses along the auditory hierarchy in the ranges of FFR, MLR and LLR. AM frequency deviants modulated the FFR, the Na and Nb components of the MLR, and the LLR eliciting the MMN. These findings demonstrate that it is possible to elicit deviance-related responses at three different levels (FFR, MLR and LLR) in one single recording protocol, highlight the involvement of the whole auditory hierarchy in deviance detection and have implications for cognitive and clinical auditory neuroscience. Moreover, the present protocol provides a new research tool into clinical neuroscience so that the functional integrity of the auditory novelty system can now be tested as a whole in a range of clinical populations where the MMN was previously shown to be defective.

Deviance-Related Responses along the Auditory Hierarchy: Combined FFR, MLR and MMN Evidence

PubMed Central

Shiga, Tetsuya; Althen, Heike; Cornella, Miriam; Zarnowiec, Katarzyna; Yabe, Hirooki; Escera, Carles

2015-01-01

The mismatch negativity (MMN) provides a correlate of automatic auditory discrimination in human auditory cortex that is elicited in response to violation of any acoustic regularity. Recently, deviance-related responses were found at much earlier cortical processing stages as reflected by the middle latency response (MLR) of the auditory evoked potential, and even at the level of the auditory brainstem as reflected by the frequency following response (FFR). However, no study has reported deviance-related responses in the FFR, MLR and long latency response (LLR) concurrently in a single recording protocol. Amplitude-modulated (AM) sounds were presented to healthy human participants in a frequency oddball paradigm to investigate deviance-related responses along the auditory hierarchy in the ranges of FFR, MLR and LLR. AM frequency deviants modulated the FFR, the Na and Nb components of the MLR, and the LLR eliciting the MMN. These findings demonstrate that it is possible to elicit deviance-related responses at three different levels (FFR, MLR and LLR) in one single recording protocol, highlight the involvement of the whole auditory hierarchy in deviance detection and have implications for cognitive and clinical auditory neuroscience. Moreover, the present protocol provides a new research tool into clinical neuroscience so that the functional integrity of the auditory novelty system can now be tested as a whole in a range of clinical populations where the MMN was previously shown to be defective. PMID:26348628

Does ℏ play a role in multidimensional spectroscopy? Reduced hierarchy equations of motion approach to molecular vibrations.

PubMed

Sakurai, Atsunori; Tanimura, Yoshitaka

2011-04-28

To investigate the role of quantum effects in vibrational spectroscopies, we have carried out numerically exact calculations of linear and nonlinear response functions for an anharmonic potential system nonlinearly coupled to a harmonic oscillator bath. Although one cannot carry out the quantum calculations of the response functions with full molecular dynamics (MD) simulations for a realistic system which consists of many molecules, it is possible to grasp the essence of the quantum effects on the vibrational spectra by employing a model Hamiltonian that describes an intra- or intermolecular vibrational motion in a condensed phase. The present model fully includes vibrational relaxation, while the stochastic model often used to simulate infrared spectra does not. We have employed the reduced quantum hierarchy equations of motion approach in the Wigner space representation to deal with nonperturbative, non-Markovian, and nonsecular system-bath interactions. Taking the classical limit of the hierarchy equations of motion, we have obtained the classical equations of motion that describe the classical dynamics under the same physical conditions as in the quantum case. By comparing the classical and quantum mechanically calculated linear and multidimensional spectra, we found that the profiles of spectra for a fast modulation case were similar, but different for a slow modulation case. In both the classical and quantum cases, we identified the resonant oscillation peak in the spectra, but the quantum peak shifted to the red compared with the classical one if the potential is anharmonic. The prominent quantum effect is the 1-2 transition peak, which appears only in the quantum mechanically calculated spectra as a result of anharmonicity in the potential or nonlinearity of the system-bath coupling. While the contribution of the 1-2 transition is negligible in the fast modulation case, it becomes important in the slow modulation case as long as the amplitude of the frequency fluctuation is small. Thus, we observed a distinct difference between the classical and quantum mechanically calculated multidimensional spectra in the slow modulation case where spectral diffusion plays a role. This fact indicates that one may not reproduce the experimentally obtained multidimensional spectrum for high-frequency vibrational modes based on classical molecular dynamics simulations if the modulation that arises from surrounding molecules is weak and slow. A practical way to overcome the difference between the classical and quantum simulations was discussed.

A STUDY ON THE HIERARCHY OF MANAGEMENT ELEMENTS

NASA Astrophysics Data System (ADS)

Suzuki, Nobuyuki; Watanabe, Tadashi

Compared to the late 20th century, the Japanese construction industry has drastically changed its business methodology, outlook and approach in response to global issues and the incredible advances in technology. Such influences, non-exhaustively include the; WTO Government procurement agreement, updating conditions of tendering and contracting, client demands for cost reduction and the rapid penetration of ICT (Information and Communication Technology) into modern society. These days, the significance of controlling Quality, Cost and Time (the so-called QCT) has been recognized as an eternal-triangle by almost all countries, Government organizations and the private sector. However, as the construction industry is exposed to , and influenced by, more and more internal and external dynamic factors, continued reliance on managing and controlling QCT elements on their own is no longer adequate in meeting the growing demands and expectations, and as such control of additional management elements is now essential to avoid problems, or minimize their potential impacts should they occur. This paper utilizes the results of a survey carried out amongst construction managers and consultants in Japan and overseas to develop a spatial network that defines the interaction of management factors as a weighted graphical model. The calculated closeness centrality index of the developed management network model is adopted to identify the initialelement hierarchy, which is then further analyzed using the minimum distance of independent relationships of management elements (Warshall-Floyd algorism methodology), to identify the optimum potential hierarchy for effective construction management. A key result of the analysis is the significance of "Human Resource" in the construction industry management element hierarchy alongside the traditional QCT elements.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks

PubMed Central

Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Li, Baoqing; Yuan, Xiaobing

2017-01-01

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms. PMID:28753962

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

PubMed

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

[Real-time detection and processing of medical signals under windows using Lcard analog interfaces].

PubMed

Kuz'min, A A; Belozerov, A E; Pronin, T V

2008-01-01

Multipurpose modular software for an analog interface based on Lcard 761 is considered. Algorithms for pipeline processing of medical signals under Windows with dynamic control of computational resources are suggested. The software consists of user-friendly completable modifiable modules. The module hierarchy is based on object-oriented heritage principles, which make it possible to construct various real-time systems for long-term detection, processing, and imaging of multichannel medical signals.

A Fuzzy-Based Decision Support Model for Selecting the Best Dialyser Flux in Haemodialysis.

PubMed

Oztürk, Necla; Tozan, Hakan

2015-01-01

Decision making is an important procedure for every organization. The procedure is particularly challenging for complicated multi-criteria problems. Selection of dialyser flux is one of the decisions routinely made for haemodialysis treatment provided for chronic kidney failure patients. This study provides a decision support model for selecting the best dialyser flux between high-flux and low-flux dialyser alternatives. The preferences of decision makers were collected via a questionnaire. A total of 45 questionnaires filled by dialysis physicians and nephrologists were assessed. A hybrid fuzzy-based decision support software that enables the use of Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Analytic Network Process (ANP), and Fuzzy Analytic Network Process (FANP) was used to evaluate the flux selection model. In conclusion, the results showed that a high-flux dialyser is the best. option for haemodialysis treatment.

The role of nonlinear viscoelasticity on the functionality of laminating shortenings

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

Macias-Rodriguez, Braulio A.; Peyronel, Fernanda; Marangoni, Alejandro G.

The rheology of fats is essential for the development of homogeneous and continuous layered structures of doughs. Here, we define laminating shortenings in terms of rheological behavior displayed during linear-to-nonlinear shear deformations, investigated by large amplitude oscillatory shear rheology. Likewise, we associate the rheological behavior of the shortenings with structural length scales elucidated by ultra-small angle x-ray scattering and cryo-electron microscopy. Shortenings exhibited solid-like viscoelastic and viscoelastoplastic behaviors in the linear and nonlinear regimes respectively. In the nonlinear region, laminating shortenings dissipated more viscous energy (larger normalized dynamic viscosities) than a cake bakery shortening. The fat solid-like network of laminatingmore » shortening displayed a three-hierarchy structure and layered crystal aggregates, in comparison to two-hierarchy structure and spherical-like crystal aggregates of a cake shortening. We argue that the observed rheology, correlated to the structural network, is crucial for optimal laminating performance of shortenings.« less

A neural network for intermale aggression to establish social hierarchy.

PubMed

Stagkourakis, Stefanos; Spigolon, Giada; Williams, Paul; Protzmann, Jil; Fisone, Gilberto; Broberger, Christian

2018-06-01

Intermale aggression is used to establish social rank. Several neuronal populations have been implicated in aggression, but the circuit mechanisms that shape this innate behavior and coordinate its different components (including attack execution and reward) remain elusive. We show that dopamine transporter-expressing neurons in the hypothalamic ventral premammillary nucleus (PMv DAT neurons) organize goal-oriented aggression in male mice. Activation of PMv DAT neurons triggers attack behavior; silencing these neurons interrupts attacks. Regenerative PMv DAT membrane conductances interacting with recurrent and reciprocal excitation explain how a brief trigger can elicit a long-lasting response (hysteresis). PMv DAT projections to the ventrolateral part of the ventromedial hypothalamic and the supramammillary nuclei control attack execution and aggression reward, respectively. Brief manipulation of PMv DAT activity switched the dominance relationship between males, an effect persisting for weeks. These results identify a network structure anchored in PMv DAT neurons that organizes aggressive behavior and, as a consequence, determines intermale hierarchy.

SemantGeo: Powering Ecological and Environment Data Discovery and Search with Standards-Based Geospatial Reasoning

NASA Astrophysics Data System (ADS)

Seyed, P.; Ashby, B.; Khan, I.; Patton, E. W.; McGuinness, D. L.

2013-12-01

Recent efforts to create and leverage standards for geospatial data specification and inference include the GeoSPARQL standard, Geospatial OWL ontologies (e.g., GAZ, Geonames), and RDF triple stores that support GeoSPARQL (e.g., AllegroGraph, Parliament) that use RDF instance data for geospatial features of interest. However, there remains a gap on how best to fuse software engineering best practices and GeoSPARQL within semantic web applications to enable flexible search driven by geospatial reasoning. In this abstract we introduce the SemantGeo module for the SemantEco framework that helps fill this gap, enabling scientists find data using geospatial semantics and reasoning. SemantGeo provides multiple types of geospatial reasoning for SemantEco modules. The server side implementation uses the Parliament SPARQL Endpoint accessed via a Tomcat servlet. SemantGeo uses the Google Maps API for user-specified polygon construction and JsTree for providing containment and categorical hierarchies for search. SemantGeo uses GeoSPARQL for spatial reasoning alone and in concert with RDFS/OWL reasoning capabilities to determine, e.g., what geofeatures are within, partially overlap with, or within a certain distance from, a given polygon. We also leverage qualitative relationships defined by the Gazetteer ontology that are composites of spatial relationships as well as administrative designations or geophysical phenomena. We provide multiple mechanisms for exploring data, such as polygon (map-based) and named-feature (hierarchy-based) selection, that enable flexible search constraints using boolean combination of selections. JsTree-based hierarchical search facets present named features and include a 'part of' hierarchy (e.g., measurement-site-01, Lake George, Adirondack Region, NY State) and type hierarchies (e.g., nodes in the hierarchy for WaterBody, Park, MeasurementSite), depending on the ';axis of choice' option selected. Using GeoSPARQL and aforementioned ontology, these hierarchies are constrained based on polygon selection, where the corresponding polygons of the contained features are visually rendered to assist exploration. Once measurement sites are plotted based on initial search, subsequent searches using JsTree selections can extend the previous based on nearby waterbodies in some semantic relationship of interest. For example, ';tributary of' captures water bodies that flow into the current one, and extending the original search to include tributaries of the observed water body is useful to environmental scientists for isolating the source of characteristic levels, including pollutants. Ultimately any SemantEco module can leverage SemantGeo's underlying APIs, leveraged in a deployment of SemantEco that combines EPA and USGS water quality data, and one customized for searching data available from the Darrin Freshwater Institute. Future work will address generating RDF geometry data from shape files, aligning RDF data sources to better leverage qualitative and spatial relationships, and validating newly generated RDF data adhering to the GeoSPARQL standard.

Growing trees in child brains: graph theoretical analysis of electroencephalography-derived minimum spanning tree in 5- and 7-year-old children reflects brain maturation.

PubMed

Boersma, Maria; Smit, Dirk J A; Boomsma, Dorret I; De Geus, Eco J C; Delemarre-van de Waal, Henriette A; Stam, Cornelis J

2013-01-01

The child brain is a small-world network, which is hypothesized to change toward more ordered configurations with development. In graph theoretical studies, comparing network topologies under different conditions remains a critical point. Constructing a minimum spanning tree (MST) might present a solution, since it does not require setting a threshold and uses a fixed number of nodes and edges. In this study, the MST method is introduced to examine developmental changes in functional brain network topology in young children. Resting-state electroencephalography was recorded from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) weighted matrices were calculated in three different frequency bands from which MSTs were constructed, which represent constructs of the most important routes for information flow in a network. From these trees, several parameters were calculated to characterize developmental change in network organization. The MST diameter and eccentricity significantly increased, while the leaf number and hierarchy significantly decreased in the alpha band with development. Boys showed significant higher leaf number, betweenness, degree and hierarchy and significant lower SL, diameter, and eccentricity than girls in the theta band. The developmental changes indicate a shift toward more decentralized line-like trees, which supports the previously hypothesized increase toward regularity of brain networks with development. Additionally, girls showed more line-like decentralized configurations, which is consistent with the view that girls are ahead of boys in brain development. MST provides an elegant method sensitive to capture subtle developmental changes in network organization without the bias of network comparison.

Emergence of structured communities through evolutionary dynamics.

PubMed

Shtilerman, Elad; Kessler, David A; Shnerb, Nadav M

2015-10-21

Species-rich communities, in which many competing species coexist in a single trophic level, are quite frequent in nature, but pose a formidable theoretical challenge. In particular, it is known that complex competitive systems become unstable and unfeasible when the number of species is large. Recently, many studies have attributed the stability of natural communities to the structure of the interspecific interaction network, yet the nature of such structures and the underlying mechanisms responsible for them remain open questions. Here we introduce an evolutionary model, based on the generic Lotka-Volterra competitive framework, from which a stable, structured, diverse community emerges spontaneously. The modular structure of the competition matrix reflects the phylogeny of the community, in agreement with the hierarchial taxonomic classification. Closely related species tend to have stronger niche overlap and weaker fitness differences, as opposed to pairs of species from different modules. The competitive-relatedness hypothesis and the idea of emergent neutrality are discussed in the context of this evolutionary model. Copyright © 2015 Elsevier Ltd. All rights reserved.

Feature-Selective Attentional Modulations in Human Frontoparietal Cortex.

PubMed

Ester, Edward F; Sutterer, David W; Serences, John T; Awh, Edward

2016-08-03

Control over visual selection has long been framed in terms of a dichotomy between "source" and "site," where top-down feedback signals originating in frontoparietal cortical areas modulate or bias sensory processing in posterior visual areas. This distinction is motivated in part by observations that frontoparietal cortical areas encode task-level variables (e.g., what stimulus is currently relevant or what motor outputs are appropriate), while posterior sensory areas encode continuous or analog feature representations. Here, we present evidence that challenges this distinction. We used fMRI, a roving searchlight analysis, and an inverted encoding model to examine representations of an elementary feature property (orientation) across the entire human cortical sheet while participants attended either the orientation or luminance of a peripheral grating. Orientation-selective representations were present in a multitude of visual, parietal, and prefrontal cortical areas, including portions of the medial occipital cortex, the lateral parietal cortex, and the superior precentral sulcus (thought to contain the human homolog of the macaque frontal eye fields). Additionally, representations in many-but not all-of these regions were stronger when participants were instructed to attend orientation relative to luminance. Collectively, these findings challenge models that posit a strict segregation between sources and sites of attentional control on the basis of representational properties by demonstrating that simple feature values are encoded by cortical regions throughout the visual processing hierarchy, and that representations in many of these areas are modulated by attention. Influential models of visual attention posit a distinction between top-down control and bottom-up sensory processing networks. These models are motivated in part by demonstrations showing that frontoparietal cortical areas associated with top-down control represent abstract or categorical stimulus information, while visual areas encode parametric feature information. Here, we show that multivariate activity in human visual, parietal, and frontal cortical areas encode representations of a simple feature property (orientation). Moreover, representations in several (though not all) of these areas were modulated by feature-based attention in a similar fashion. These results provide an important challenge to models that posit dissociable top-down control and sensory processing networks on the basis of representational properties. Copyright © 2016 the authors 0270-6474/16/368188-12$15.00/0.

Alcohol Use among Adolescent Youth: The Role of Friendship Networks and Family Factors in Multiple School Studies

PubMed Central

Wang, Cheng; Hipp, John R.; Butts, Carter T.; Jose, Rupa; Lakon, Cynthia M.

2015-01-01

To explore the co-evolution of friendship tie choice and alcohol use behavior among 1,284 adolescents from 12 small schools and 976 adolescents from one big school sampled in the National Longitudinal Study of Adolescent to Adult Health (AddHealth), we apply a Stochastic Actor-Based (SAB) approach implemented in the R-based Simulation Investigation for Empirical Network Analysis (RSiena) package. Our results indicate the salience of both peer selection and peer influence effects for friendship tie choice and adolescent drinking behavior. Concurrently, the main effect models indicate that parental monitoring and the parental home drinking environment affected adolescent alcohol use in the small school sample, and that parental home drinking environment affected adolescent drinking in the large school sample. In the small school sample, we detect an interaction between the parental home drinking environment and choosing friends that drink as they multiplicatively affect friendship tie choice. Our findings suggest that future research should investigate the synergistic effects of both peer and parental influences for adolescent friendship tie choices and drinking behavior. And given the tendency of adolescents to form ties with their friends' friends, and the evidence of local hierarchy in these networks, popular youth who do not drink may be uniquely positioned and uniquely salient as the highest rank of the hierarchy to cause anti-drinking peer influences to diffuse down the social hierarchy to less popular youth. As such, future interventions should harness prosocial peer influences simultaneously with strategies to increase parental support and monitoring among parents to promote affiliation with prosocial peers. PMID:25756364

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster.

PubMed

Fear, Justin M; Arbeitman, Michelle N; Salomon, Matthew P; Dalton, Justin E; Tower, John; Nuzhdin, Sergey V; McIntyre, Lauren M

2015-09-04

The Drosophila sex determination hierarchy is a classic example of a transcriptional regulatory hierarchy, with sex-specific isoforms regulating morphology and behavior. We use a structural equation modeling approach, leveraging natural genetic variation from two studies on Drosophila female head tissues--DSPR collection (596 F1-hybrids from crosses between DSPR sub-populations) and CEGS population (75 F1-hybrids from crosses between DGRP/Winters lines to a reference strain w1118)--to expand understanding of the sex hierarchy gene regulatory network (GRN). This approach is completely generalizable to any natural population, including humans. We expanded the sex hierarchy GRN adding novel links among genes, including a link from fruitless (fru) to Sex-lethal (Sxl) identified in both populations. This link is further supported by the presence of fru binding sites in the Sxl locus. 754 candidate genes were added to the pathway, including the splicing factors male-specific lethal 2 and Rm62 as downstream targets of Sxl which are well-supported links in males. Independent studies of doublesex and transformer mutants support many additions, including evidence for a link between the sex hierarchy and metabolism, via Insulin-like receptor. The genes added in the CEGS population were enriched for genes with sex-biased splicing and components of the spliceosome. A common goal of molecular biologists is to expand understanding about regulatory interactions among genes. Using natural alleles we can not only identify novel relationships, but using supervised approaches can order genes into a regulatory hierarchy. Combining these results with independent large effect mutation studies, allows clear candidates for detailed molecular follow-up to emerge.

Molecular systems biology of ErbB1 signaling: bridging the gap through multiscale modeling and high-performance computing.

PubMed

Shih, Andrew J; Purvis, Jeremy; Radhakrishnan, Ravi

2008-12-01

The complexity in intracellular signaling mechanisms relevant for the conquest of many diseases resides at different levels of organization with scales ranging from the subatomic realm relevant to catalytic functions of enzymes to the mesoscopic realm relevant to the cooperative association of molecular assemblies and membrane processes. Consequently, the challenge of representing and quantifying functional or dysfunctional modules within the networks remains due to the current limitations in our understanding of mesoscopic biology, i.e., how the components assemble into functional molecular ensembles. A multiscale approach is necessary to treat a hierarchy of interactions ranging from molecular (nm, ns) to signaling (microm, ms) length and time scales, which necessitates the development and application of specialized modeling tools. Complementary to multiscale experimentation (encompassing structural biology, mechanistic enzymology, cell biology, and single molecule studies) multiscale modeling offers a powerful and quantitative alternative for the study of functional intracellular signaling modules. Here, we describe the application of a multiscale approach to signaling mediated by the ErbB1 receptor which constitutes a network hub for the cell's proliferative, migratory, and survival programs. Through our multiscale model, we mechanistically describe how point-mutations in the ErbB1 receptor can profoundly alter signaling characteristics leading to the onset of oncogenic transformations. Specifically, we describe how the point mutations induce cascading fragility mechanisms at the molecular scale as well as at the scale of the signaling network to preferentially activate the survival factor Akt. We provide a quantitative explanation for how the hallmark of preferential Akt activation in cell-lines harboring the constitutively active mutant ErbB1 receptors causes these cell-lines to be addicted to ErbB1-mediated generation of survival signals. Consequently, inhibition of ErbB1 activity leads to a remarkable therapeutic response in the addicted cell lines.

Molecular mechanisms of OLIG2 transcription factor in brain cancer

PubMed Central

Lian, Nathan; Kesari, Santosh

2016-01-01

Oligodendrocyte lineage transcription factor 2 (OLIG2) plays a pivotal role in glioma development. Here we conducted a comprehensive study of the critical gene regulatory networks involving OLIG2. These include the networks responsible for OLIG2 expression, its translocation to nucleus, cell cycle, epigenetic regulation, and Rho-pathway interactions. We described positive feedback loops including OLIG2: loops of epigenetic regulation and loops involving receptor tyrosine kinases. These loops may be responsible for the prolonged oncogenic activity of OLIG2. The proposed schemes for epigenetic regulation of the gene networks involving OLIG2 are confirmed by patient survival (Kaplan–Meier) curves based on the cancer genome atlas (TCGA) datasets. Finally, we elucidate the Coherent-Gene Modules (CGMs) networks—framework of OLIG2 involvement in cancer. We showed that genes interacting with OLIG2 formed eight CGMs having a set of intermodular connections. We showed also that among the genes involved in these modules the most connected hub is EGFR, then, on lower level, HSP90 and CALM1, followed by three lower levels including epigenetic genes KDM1A and NCOR1. The genes on the six upper levels of the hierarchy are involved in interconnections of all eight CGMs and organize functionally defined gene-signaling subnetworks having specific functions. For example, CGM1 is involved in epigenetic control. CGM2 is significantly related to cell proliferation and differentiation. CGM3 includes a number of interconnected helix–loop–helix transcription factors (bHLH) including OLIG2. Many of these TFs are partially controlled by OLIG2. The CGM4 is involved in PDGF-related: angiogenesis, tumor cell proliferation and differentiation. These analyses provide testable hypotheses and approaches to inhibit OLIG2 pathway and relevant feed-forward and feedback loops to be interrogated. This broad approach can be applied to other TFs. PMID:27447975

Energy Systems Integration: Demonstrating Distributed Grid-Edge Control Hierarchy

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

2017-01-01

Overview fact sheet about the OMNETRIC Group Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project at the Energy Systems Integration Facility. INTEGRATE is part of the U.S. Department of Energy's Grid Modernization Initiative.

XCEDE: An Extensible Schema For Biomedical Data

PubMed Central

Gadde, Syam; Aucoin, Nicole; Grethe, Jeffrey S.; Keator, David B.; Marcus, Daniel S.; Pieper, Steve

2013-01-01

The XCEDE (XML-based Clinical and Experimental Data Exchange) XML schema, developed by members of the BIRN (Biomedical Informatics Research Network), provides an extensive metadata hierarchy for storing, describing and documenting the data generated by scientific studies. Currently at version 2.0, the XCEDE schema serves as a specification for the exchange of scientific data between databases, analysis tools, and web services. It provides a structured metadata hierarchy, storing information relevant to various aspects of an experiment (project, subject, protocol, etc.). Each hierarchy level also provides for the storage of data provenance information allowing for a traceable record of processing and/or changes to the underlying data. The schema is extensible to support the needs of various data modalities and to express types of data not originally envisioned by the developers. The latest version of the XCEDE schema and manual are available from http://www.xcede.org/ PMID:21479735

Scaling properties of cosmic (super)string networks

NASA Astrophysics Data System (ADS)

Martins, C. J. A. P.

2014-10-01

I use a combination of state-of-the-art numerical simulations and analytic modelling to discuss the scaling properties of cosmic defect networks, including superstrings. Particular attention is given to the role of extra degrees of freedom in the evolution of these networks. Compared to the 'plain vanilla' case of Goto-Nambu strings, three such extensions play important but distinct roles in the network dynamics: the presence of charges/currents on the string worldsheet, the existence of junctions, and the possibility of a hierarchy of string tensions. I also comment on insights gained from studying simpler defect networks, including Goto-Nambu strings themselves, domain walls and semilocal strings.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

Orion Passive Thermal Control Overview

NASA Technical Reports Server (NTRS)

Miller, Stephen W.

2007-01-01

An viewgraph presentation of Orion's passive thermal control system is shown. The topics include: 1) Orion in CxP Hierarchy; 2) General Orion Description/Orientation; 3) Module Descriptions and Images; 4) Orion PTCS Overview; 5) Requirements/Interfaces; 6) Design Reference Missions; 7) Natural Environments; 8) Thermal Models; 9) Challenges/Issues; and 10) Testing

Maintaining relationships is critical in network's success.

PubMed

Huerta, Timothy

2006-01-01

As the authors of the lead paper recognize, networks have become an increasingly popular form of organizing, both in the delivery of public services and within political arenas. A network is an arrangement of individuals and/or organizations that are linked through connections that range from informal relationships to formally agreed protocols. Networks have proved useful in addressing complex and intractable problems that require a holistic approach to identifying and implementing long-term solutions. They succeed in situations where hierarchies and "silo-based" systems have failed, and are particularly valuable in facilitating the transfer of resources and knowledge across sectoral and organizational boundaries.

Risk Evaluation of Railway Coal Transportation Network Based on Multi Level Grey Evaluation Model

NASA Astrophysics Data System (ADS)

Niu, Wei; Wang, Xifu

2018-01-01

The railway transport mode is currently the most important way of coal transportation, and now China’s railway coal transportation network has become increasingly perfect, but there is still insufficient capacity, some lines close to saturation and other issues. In this paper, the theory and method of risk assessment, analytic hierarchy process and multi-level gray evaluation model are applied to the risk evaluation of coal railway transportation network in China. Based on the example analysis of Shanxi railway coal transportation network, to improve the internal structure and the competitiveness of the market.

Extraction of Multilayered Social Networks from Activity Data

PubMed Central

Bródka, Piotr; Kazienko, Przemysław; Gaworecki, Jarosław

2014-01-01

The data gathered in all kinds of web-based systems, which enable users to interact with each other, provides an opportunity to extract social networks that consist of people and relationships between them. The emerging structures are very complex due to the number and type of discovered connections. In web-based systems, the characteristic element of each interaction between users is that there is always an object that serves as a communication medium. This can be, for example, an e-mail sent from one user to another or post at the forum authored by one user and commented on by others. Based on these objects and activities that users perform towards them, different kinds of relationships can be identified and extracted. Additional challenge arises from the fact that hierarchies can exist between objects; for example, a forum consists of one or more groups of topics, and each of them contains topics that finally include posts. In this paper, we propose a new method for creation of multilayered social network based on the data about users activities towards different types of objects between which the hierarchy exists. Due to the flattening, preprocessing procedure of new layers and new relationships in the multilayered social network can be identified and analysed. PMID:25105159

Directed network modules

NASA Astrophysics Data System (ADS)

Palla, Gergely; Farkas, Illés J.; Pollner, Péter; Derényi, Imre; Vicsek, Tamás

2007-06-01

A search technique locating network modules, i.e. internally densely connected groups of nodes in directed networks is introduced by extending the clique percolation method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Rényi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own web-pages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and Google's web-pages, overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory network tend to overlap via out-hubs.

Implementing Network-Centric Operations in Joint Task Forces: Changes in Joint Doctrine

DTIC Science & Technology

2006-06-16

the hierarchy, explaining the relationship between vertically connected components (Gibson, Ivancevich , and Donnelly 1973, 289). The flow of...John M. Ivancevich , and James H. Donnelly. 1973. Organizations: Structure, Processes, Behavior. Dallas, TX: Business Publication, Inc. Gonzales

Social Network Analysis of Crowds

DTIC Science & Technology

2009-10-29

Response to non-lethal weapons fire depends on social relationships among crowd members – Pre-existing Personal Relationships – Ongoing Real Time...weapons and systems – Prior, existing social relationships – Real time social interactions – Formal/informal hierarchies 13-Feb-15 24UNCLASSIFIED

Quantifying Hydro-biogeochemical Model Sensitivity in Assessment of Climate Change Effect on Hyporheic Zone Processes

NASA Astrophysics Data System (ADS)

Song, X.; Chen, X.; Dai, H.; Hammond, G. E.; Song, H. S.; Stegen, J.

2016-12-01

The hyporheic zone is an active region for biogeochemical processes such as carbon and nitrogen cycling, where the groundwater and surface water mix and interact with each other with distinct biogeochemical and thermal properties. The biogeochemical dynamics within the hyporheic zone are driven by both river water and groundwater hydraulic dynamics, which are directly affected by climate change scenarios. Besides that, the hydraulic and thermal properties of local sediments and microbial and chemical processes also play important roles in biogeochemical dynamics. Thus for a comprehensive understanding of the biogeochemical processes in the hyporheic zone, a coupled thermo-hydro-biogeochemical model is needed. As multiple uncertainty sources are involved in the integrated model, it is important to identify its key modules/parameters through sensitivity analysis. In this study, we develop a 2D cross-section model in the hyporheic zone at the DOE Hanford site adjacent to Columbia River and use this model to quantify module and parametric sensitivity on assessment of climate change. To achieve this purpose, We 1) develop a facies-based groundwater flow and heat transfer model that incorporates facies geometry and heterogeneity characterized from a field data set, 2) derive multiple reaction networks/pathways from batch experiments with in-situ samples and integrate temperate dependent reactive transport modules to the flow model, 3) assign multiple climate change scenarios to the coupled model by analyzing historical river stage data, 4) apply a variance-based global sensitivity analysis to quantify scenario/module/parameter uncertainty in hierarchy level. The objectives of the research include: 1) identifing the key control factors of the coupled thermo-hydro-biogeochemical model in the assessment of climate change, and 2) quantify the carbon consumption in different climate change scenarios in the hyporheic zone.

A neural network model of causative actions.

PubMed

Lee-Hand, Jeremy; Knott, Alistair

2015-01-01

A common idea in models of action representation is that actions are represented in terms of their perceptual effects (see e.g., Prinz, 1997; Hommel et al., 2001; Sahin et al., 2007; Umiltà et al., 2008; Hommel, 2013). In this paper we extend existing models of effect-based action representations to account for a novel distinction. Some actions bring about effects that are independent events in their own right: for instance, if John smashes a cup, he brings about the event of the cup smashing. Other actions do not bring about such effects. For instance, if John grabs a cup, this action does not cause the cup to "do" anything: a grab action has well-defined perceptual effects, but these are not registered by the perceptual system that detects independent events involving external objects in the world. In our model, effect-based actions are implemented in several distinct neural circuits, which are organized into a hierarchy based on the complexity of their associated perceptual effects. The circuit at the top of this hierarchy is responsible for actions that bring about independently perceivable events. This circuit receives input from the perceptual module that recognizes arbitrary events taking place in the world, and learns movements that reliably cause such events. We assess our model against existing experimental observations about effect-based motor representations, and make some novel experimental predictions. We also consider the possibility that the "causative actions" circuit in our model can be identified with a motor pathway reported in other work, specializing in "functional" actions on manipulable tools (Bub et al., 2008; Binkofski and Buxbaum, 2013).

Compression of Flow Can Reveal Overlapping-Module Organization in Networks

NASA Astrophysics Data System (ADS)

Viamontes Esquivel, Alcides; Rosvall, Martin

2011-10-01

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

Interspecific aggression in hermatypic corals from Bermuda

NASA Astrophysics Data System (ADS)

Logan, A.

1984-11-01

Interspecific aggression between hermatypic corals on Bermudian reefs has been investigated by aquarium and field studies, the latter involving induced interactions, observations from 30 m-2 transects and random SCUBA traverses. Resultant hierarchies, constructed by ranking the abilities of species to damage competitors, show close similarities with each other and with the Jamaican hierarchy at the family level, notwithstanding some differences in the ranking of some species. Only 11% of natural-occurring interactions depart from the aquarium-derived results; in terms of species-pair combinations, 30% show partial or complete inversions from aquarium to field, with most changes involving species close together in the field hierarchy. Circular (intransitive) interactions occur mostly within a network of weakly-aggressive species in both aquarium- and fieldderived hierarchies. While number of potential interactions m-2 varies directly with density, frequency of aggression is positively correlated with coral diversity (species richness), while frequency of “no reactions” and conspecific fusion (combined) shows a correspondingly negative correlation with diversity. Frequency of aggression does not appear to be depth related. Comparison of aquarium and field hierarchies suggest that digestion by mesenterial filaments is the most important mechanism of aggression under natural conditions. Sweeper tentacle activity is the most likely cause of field reversals involving Madracis mirabilis and Montastrea cavernosa. Other factors, such as stress caused by seasonal environmental extremes, may be responsible for reversals or inconsistent behaviour in other species.

The middleware architecture supports heterogeneous network systems for module-based personal robot system

NASA Astrophysics Data System (ADS)

Choo, Seongho; Li, Vitaly; Choi, Dong Hee; Jung, Gi Deck; Park, Hong Seong; Ryuh, Youngsun

2005-12-01

On developing the personal robot system presently, the internal architecture is every module those occupy separated functions are connected through heterogeneous network system. This module-based architecture supports specialization and division of labor at not only designing but also implementation, as an effect of this architecture, it can reduce developing times and costs for modules. Furthermore, because every module is connected among other modules through network systems, we can get easy integrations and synergy effect to apply advanced mutual functions by co-working some modules. In this architecture, one of the most important technologies is the network middleware that takes charge communications among each modules connected through heterogeneous networks systems. The network middleware acts as the human nerve system inside of personal robot system; it relays, transmits, and translates information appropriately between modules that are similar to human organizations. The network middleware supports various hardware platform, heterogeneous network systems (Ethernet, Wireless LAN, USB, IEEE 1394, CAN, CDMA-SMS, RS-232C). This paper discussed some mechanisms about our network middleware to intercommunication and routing among modules, methods for real-time data communication and fault-tolerant network service. There have designed and implemented a layered network middleware scheme, distributed routing management, network monitoring/notification technology on heterogeneous networks for these goals. The main theme is how to make routing information in our network middleware. Additionally, with this routing information table, we appended some features. Now we are designing, making a new version network middleware (we call 'OO M/W') that can support object-oriented operation, also are updating program sources itself for object-oriented architecture. It is lighter, faster, and can support more operation systems and heterogeneous network systems, but other general purposed middlewares like CORBA, UPnP, etc. can support only one network protocol or operating system.

Is My Network Module Preserved and Reproducible?

PubMed Central

Langfelder, Peter; Luo, Rui; Oldham, Michael C.; Horvath, Steve

2011-01-01

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation. PMID:21283776

BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks.

PubMed

Maere, Steven; Heymans, Karel; Kuiper, Martin

2005-08-15

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.

Systems-level chromosomal parameters represent a suprachromosomal basis for the non-random chromosomal arrangement in human interphase nuclei

PubMed Central

Fatakia, Sarosh N.; Mehta, Ishita S.; Rao, Basuthkar J.

2016-01-01

Forty-six chromosome territories (CTs) are positioned uniquely in human interphase nuclei, wherein each of their positions can range from the centre of the nucleus to its periphery. A non-empirical basis for their non-random arrangement remains unreported. Here, we derive a suprachromosomal basis of that overall arrangement (which we refer to as a CT constellation), and report a hierarchical nature of the same. Using matrix algebra, we unify intrinsic chromosomal parameters (e.g., chromosomal length, gene density, the number of genes per chromosome), to derive an extrinsic effective gene density matrix, the hierarchy of which is dominated largely by extrinsic mathematical coupling of HSA19, followed by HSA17 (human chromosome 19 and 17, both preferentially interior CTs) with all CTs. We corroborate predicted constellations and effective gene density hierarchy with published reports from fluorescent in situ hybridization based microscopy and Hi-C techniques, and delineate analogous hierarchy in disparate vertebrates. Our theory accurately predicts CTs localised to the nuclear interior, which interestingly share conserved synteny with HSA19 and/or HSA17. Finally, the effective gene density hierarchy dictates how permutations among CT position represents the plasticity within its constellations, based on which we suggest that a differential mix of coding with noncoding genome modulates the same. PMID:27845379

Time Hierarchies and Model Reduction in Canonical Non-linear Models

PubMed Central

Löwe, Hannes; Kremling, Andreas; Marin-Sanguino, Alberto

2016-01-01

The time-scale hierarchies of a very general class of models in differential equations is analyzed. Classical methods for model reduction and time-scale analysis have been adapted to this formalism and a complementary method is proposed. A unified theoretical treatment shows how the structure of the system can be much better understood by inspection of two sets of singular values: one related to the stoichiometric structure of the system and another to its kinetics. The methods are exemplified first through a toy model, then a large synthetic network and finally with numeric simulations of three classical benchmark models of real biological systems. PMID:27708665

The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

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

Krishnan, J.; Mois, Kristina; Suwanmajo, Thapanar

2014-11-07

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

The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

NASA Astrophysics Data System (ADS)

Krishnan, J.; Mois, Kristina; Suwanmajo, Thapanar

2014-11-01

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

Revealing the Hidden Relationship by Sparse Modules in Complex Networks with a Large-Scale Analysis

PubMed Central

Jiao, Qing-Ju; Huang, Yan; Liu, Wei; Wang, Xiao-Fan; Chen, Xiao-Shuang; Shen, Hong-Bin

2013-01-01

One of the remarkable features of networks is module that can provide useful insights into not only network organizations but also functional behaviors between their components. Comprehensive efforts have been devoted to investigating cohesive modules in the past decade. However, it is still not clear whether there are important structural characteristics of the nodes that do not belong to any cohesive module. In order to answer this question, we performed a large-scale analysis on 25 complex networks with different types and scales using our recently developed BTS (bintree seeking) algorithm, which is able to detect both cohesive and sparse modules in the network. Our results reveal that the sparse modules composed by the cohesively isolated nodes widely co-exist with the cohesive modules. Detailed analysis shows that both types of modules provide better characterization for the division of a network into functional units than merely cohesive modules, because the sparse modules possibly re-organize the nodes in the so-called cohesive modules, which lack obvious modular significance, into meaningful groups. Compared with cohesive modules, the sizes of sparse ones are generally smaller. Sparse modules are also found to have preferences in social and biological networks than others. PMID:23762457

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

PubMed Central

Stetz, Gabrielle; Verkhivker, Gennady M.

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms. PMID:28095400

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

An iterative network partition algorithm for accurate identification of dense network modules

PubMed Central

Sun, Siqi; Dong, Xinran; Fu, Yao; Tian, Weidong

2012-01-01

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks. PMID:22121225

Stimulus Type, Level of Categorization, and Spatial-Frequencies Utilization: Implications for Perceptual Categorization Hierarchies

ERIC Educational Resources Information Center

Harel, Assaf; Bentin, Shlomo

2009-01-01

The type of visual information needed for categorizing faces and nonface objects was investigated by manipulating spatial frequency scales available in the image during a category verification task addressing basic and subordinate levels. Spatial filtering had opposite effects on faces and airplanes that were modulated by categorization level. The…

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

PubMed Central

Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

2012-01-01

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. PMID:23193391

A spiking neural network based cortex-like mechanism and application to facial expression recognition.

PubMed

Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

2012-01-01

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.

Architecture of the parallel hierarchical network for fast image recognition

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

2016-09-01

Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations

PubMed Central

2012-01-01

Visualization and analysis of molecular networks are both central to systems biology. However, there still exists a large technological gap between them, especially when assessing multiple network levels or hierarchies. Here we present RedeR, an R/Bioconductor package combined with a Java core engine for representing modular networks. The functionality of RedeR is demonstrated in two different scenarios: hierarchical and modular organization in gene co-expression networks and nested structures in time-course gene expression subnetworks. Our results demonstrate RedeR as a new framework to deal with the multiple network levels that are inherent to complex biological systems. RedeR is available from http://bioconductor.org/packages/release/bioc/html/RedeR.html. PMID:22531049

Topological Alterations and Symptom-Relevant Modules in the Whole-Brain Structural Network in Semantic Dementia.

PubMed

Ding, Junhua; Chen, Keliang; Zhang, Weibin; Li, Ming; Chen, Yan; Yang, Qing; Lv, Yingru; Guo, Qihao; Han, Zaizhu

2017-01-01

Semantic dementia (SD) is characterized by a selective decline in semantic processing. Although the neuropsychological pattern of this disease has been identified, its topological global alterations and symptom-relevant modules in the whole-brain anatomical network have not been fully elucidated. This study aims to explore the topological alteration of anatomical network in SD and reveal the modules associated with semantic deficits in this disease. We first constructed the whole-brain white-matter networks of 20 healthy controls and 19 patients with SD. Then, the network metrics of graph theory were compared between these two groups. Finally, we separated the network of SD patients into different modules and correlated the structural integrity of each module with the severity of the semantic deficits across patients. The network of the SD patients presented a significantly reduced global efficiency, indicating that the long-distance connections were damaged. The network was divided into the following four distinctive modules: the left temporal/occipital/parietal, frontal, right temporal/occipital, and frontal/parietal modules. The first two modules were associated with the semantic deficits of SD. These findings illustrate the skeleton of the neuroanatomical network of SD patients and highlight the key role of the left temporal/occipital/parietal module and the left frontal module in semantic processing.

Allocation of spectral and spatial modes in multidimensional metro-access optical networks

NASA Astrophysics Data System (ADS)

Gao, Wenbo; Cvijetic, Milorad

2018-04-01

Introduction of spatial division multiplexing (SDM) has added a new dimension in an effort to increase optical fiber channel capacity. At the same time, it can also be explored as an advanced optical networking tool. In this paper, we have investigated the resource allocation to end-users in multidimensional networking structure with plurality of spectral and spatial modes actively deployed in different networking segments. This presents a more comprehensive method as compared to the common practice where the segments of optical network are analyzed independently since the interaction between network hierarchies is included into consideration. We explored the possible transparency from the metro/core network to the optical access network, analyzed the potential bottlenecks from the network architecture perspective, and identified an optimized network structure. In our considerations, the viability of optical grooming through the entire hierarchical all-optical network is investigated by evaluating the effective utilization and spectral efficiency of the network architecture.

Modeling the Emergence of Modular Leadership Hierarchy During the Collective Motion of Herds Made of Harems

NASA Astrophysics Data System (ADS)

Ozogány, Katalin; Vicsek, Tamás

2015-02-01

Gregarious animals need to make collective decisions in order to keep their cohesiveness. Several species of them live in multilevel societies, and form herds composed of smaller communities. We present a model for the development of a leadership hierarchy in a herd consisting of loosely connected sub-groups (e.g. harems) by combining self organization and social dynamics. It starts from unfamiliar individuals without relationships and reproduces the emergence of a hierarchical and modular leadership network that promotes an effective spreading of the decisions from more capable individuals to the others, and thus gives rise to a beneficial collective decision. Our results stemming from the model are in a good agreement with our observations of a Przewalski horse herd (Hortobágy, Hungary). We find that the harem-leader to harem-member ratio observed in Przewalski horses corresponds to an optimal network in this approach regarding common success, and that the observed and modeled harem size distributions are close to a lognormal.

Epidemics and dimensionality in hierarchical networks

NASA Astrophysics Data System (ADS)

Zheng, Da-Fang; Hui, P. M.; Trimper, Steffen; Zheng, Bo

2005-07-01

Epidemiological processes are studied within a recently proposed hierarchical network model using the susceptible-infected-refractory dynamics of an epidemic. Within the network model, a population may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveal that for H>1, global spreading results regardless of the degree of homophily of the individuals forming a social circle. For H=1, a transition from global to local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large-scale outbreaks of infectious diseases (viruses).

Breather-to-soliton transformation rules in the hierarchy of nonlinear Schrödinger equations.

PubMed

Chowdury, Amdad; Krolikowski, Wieslaw

2017-06-01

We study the exact first-order soliton and breather solutions of the integrable nonlinear Schrödinger equations hierarchy up to fifth order. We reveal the underlying physical mechanism which transforms a breather into a soliton. Furthermore, we show how the dynamics of the Akhmediev breathers which exist on a constant background as a result of modulation instability, is connected with solitons on a zero background. We also demonstrate that, while a first-order rogue wave can be directly transformed into a soliton, higher-order rogue wave solutions become rational two-soliton solutions with complex collisional structure on a background. Our results will have practical implications in supercontinuum generation, turbulence, and similar other complex nonlinear scenarios.

Scalable Hierarchical Network Management System for Displaying Network Information in Three Dimensions

NASA Technical Reports Server (NTRS)

George, Jude (Inventor); Schlecht, Leslie (Inventor); McCabe, James D. (Inventor); LeKashman, John Jr. (Inventor)

1998-01-01

A network management system has SNMP agents distributed at one or more sites, an input output module at each site, and a server module located at a selected site for communicating with input output modules, each of which is configured for both SNMP and HNMP communications. The server module is configured exclusively for HNMP communications, and it communicates with each input output module according to the HNMP. Non-iconified, informationally complete views are provided of network elements to aid in network management.

The missing link: leadership, identity, and the social brain.

PubMed

van Vugt, Mark

2012-05-01

How the cohesion of a social network is being maintained in spite of having different layers of social interaction is an important question. I argue that the evolution of both (political) hierarchy and social identity play a crucial role in scaling up and bonding social networks. Together they are missing links in the social brain hypothesis, and further research is needed to understand the functions of leadership and social identity. ©2011 The British Psychological Society.

Supporting large scale applications on networks of workstations

NASA Technical Reports Server (NTRS)

Cooper, Robert; Birman, Kenneth P.

1989-01-01

Distributed applications on networks of workstations are an increasingly common way to satisfy computing needs. However, existing mechanisms for distributed programming exhibit poor performance and reliability as application size increases. Extension of the ISIS distributed programming system to support large scale distributed applications by providing hierarchical process groups is discussed. Incorporation of hierarchy in the program structure and exploitation of this to limit the communication and storage required in any one component of the distributed system is examined.

Neuroanatomical Markers of Social Hierarchy Recognition in Humans: A Combined ERP/MRI Study.

PubMed

Santamaría-García, Hernando; Burgaleta, Miguel; Sebastián-Gallés, Nuria

2015-07-29

Social hierarchy is an ubiquitous principle of social organization across animal species. Although some progress has been made in our understanding of how humans infer hierarchical identity, the neuroanatomical basis for perceiving key social dimensions of others remains unexplored. Here, we combined event-related potentials and structural MRI to reveal the neuroanatomical substrates of early status recognition. We designed a covertly simulated hierarchical setting in which participants performed a task either with a superior or with an inferior player. Participants showed higher amplitude in the N170 component when presented with a picture of a superior player compared with an inferior player. Crucially, the magnitude of this effect correlated with brain morphology of the posterior cingulate cortex, superior temporal gyrus, insula, fusiform gyrus, and caudate nucleus. We conclude that early recognition of social hierarchies relies on the structural properties of a network involved in the automatic recognition of social identity. Humans can perceive social hierarchies very rapidly, an ability that is key for social interactions. However, some individuals are more sensitive to hierarchical information than others. Currently, it is unknown how brain structure supports such fast-paced processes of social hierarchy perception and their individual differences. Here, we addressed this issue for the first time by combining the high temporal resolution of event-related potentials (ERPs) and the high spatial resolution of structural MRI. This methodological approach allowed us to unveil a novel association between ERP neuromarkers of social hierarchy perception and the morphology of several cortical and subcortical brain regions typically assumed to play a role in automatic processes of social cognition. Our results are a step forward in our understanding of the human social brain. Copyright © 2015 the authors 0270-6474/15/3510843-08$15.00/0.

Dynamic Bayesian Networks for Student Modeling

ERIC Educational Resources Information Center

Kaser, Tanja; Klingler, Severin; Schwing, Alexander G.; Gross, Markus

2017-01-01

Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and…

Adolescent Social Structure and the Spread of Linguistic Change.

ERIC Educational Resources Information Center

Eckert, Penelope

1988-01-01

Detailed study of Detroit-area adolescents provides explanations for the spread of sound change outward from urban areas and upward through the socioeconomic hierarchy. Social network structure, orientation to the urban area, and phonology are contrasted for the two adolescent social categories, "Jocks" (middle class) and…

Relations between Spatial Distribution, Social Affiliations and Dominance Hierarchy in a Semi-Free Mandrill Population

PubMed Central

Naud, Alexandre; Chailleux, Eloise; Kestens, Yan; Bret, Céline; Desjardins, Dominic; Petit, Odile; Ngoubangoye, Barthélémy; Sueur, Cédric

2016-01-01

Although there exist advantages to group-living in comparison to a solitary lifestyle, costs and gains of group-living may be unequally distributed among group members. Predation risk, vigilance levels and food intake may be unevenly distributed across group spatial geometry and certain within-group spatial positions may be more or less advantageous depending on the spatial distribution of these factors. In species characterized with dominance hierarchy, high-ranking individuals are commonly observed in advantageous spatial position. However, in complex social systems, individuals can develop affiliative relationships that may balance the effect of dominance relationships in individual's spatial distribution. The objective of the present study is to investigate how the group spatial distribution of a semi-free ranging colony of Mandrills relates to its social organization. Using spatial observations in an area surrounding the feeding zone, we tested the three following hypothesis: (1) does dominance hierarchy explain being observed in proximity or far from a food patch? (2) Do affiliative associations also explain being observed in proximity or far from a food patch? (3) Do the differences in rank in the group hierarchy explain being co-observed in proximity of a food patch? Our results showed that high-ranking individuals were more observed in proximity of the feeding zone while low-ranking individuals were more observed at the boundaries of the observation area. Furthermore, we observed that affiliative relationships were also associated with individual spatial distributions and explain more of the total variance of the spatial distribution in comparison with dominance hierarchy. Finally, we found that individuals observed at a same moment in proximity of the feeding zone were more likely to be distant in the hierarchy while controlling for maternal kinship, age and sex similarity. This study brings some elements about how affiliative networks and dominance hierarchy are related to spatial positions in primates. PMID:27199845

Relations between Spatial Distribution, Social Affiliations and Dominance Hierarchy in a Semi-Free Mandrill Population.

PubMed

Naud, Alexandre; Chailleux, Eloise; Kestens, Yan; Bret, Céline; Desjardins, Dominic; Petit, Odile; Ngoubangoye, Barthélémy; Sueur, Cédric

2016-01-01

Although there exist advantages to group-living in comparison to a solitary lifestyle, costs and gains of group-living may be unequally distributed among group members. Predation risk, vigilance levels and food intake may be unevenly distributed across group spatial geometry and certain within-group spatial positions may be more or less advantageous depending on the spatial distribution of these factors. In species characterized with dominance hierarchy, high-ranking individuals are commonly observed in advantageous spatial position. However, in complex social systems, individuals can develop affiliative relationships that may balance the effect of dominance relationships in individual's spatial distribution. The objective of the present study is to investigate how the group spatial distribution of a semi-free ranging colony of Mandrills relates to its social organization. Using spatial observations in an area surrounding the feeding zone, we tested the three following hypothesis: (1) does dominance hierarchy explain being observed in proximity or far from a food patch? (2) Do affiliative associations also explain being observed in proximity or far from a food patch? (3) Do the differences in rank in the group hierarchy explain being co-observed in proximity of a food patch? Our results showed that high-ranking individuals were more observed in proximity of the feeding zone while low-ranking individuals were more observed at the boundaries of the observation area. Furthermore, we observed that affiliative relationships were also associated with individual spatial distributions and explain more of the total variance of the spatial distribution in comparison with dominance hierarchy. Finally, we found that individuals observed at a same moment in proximity of the feeding zone were more likely to be distant in the hierarchy while controlling for maternal kinship, age and sex similarity. This study brings some elements about how affiliative networks and dominance hierarchy are related to spatial positions in primates.

Male urine signals social rank in the Mozambique tilapia (Oreochromis mossambicus)

PubMed Central

Barata, Eduardo N; Hubbard, Peter C; Almeida, Olinda G; Miranda, António; Canário, Adelino VM

2007-01-01

Background The urine of freshwater fish species investigated so far acts as a vehicle for reproductive pheromones affecting the behaviour and physiology of the opposite sex. However, the role of urinary pheromones in intra-sexual competition has received less attention. This is particularly relevant in lek-breeding species, such as the Mozambique tilapia (Oreochromis mossambicus), where males establish dominance hierarchies and there is the possibility for chemical communication in the modulation of aggression among males. To investigate whether males use urine during aggressive interactions, we measured urination frequency of dye-injected males during paired interactions between size-matched males. Furthermore, we assessed urinary volume stored in the bladder of males in a stable social hierarchy and the olfactory potency of their urine by recording of the electro-olfactogram. Results Males released urine in pulses of short duration (about one second) and markedly increased urination frequency during aggressive behaviour, but did not release urine whilst submissive. In the stable hierarchy, subordinate males stored less urine than males of higher social rank; the olfactory potency of the urine was positively correlated with the rank of the male donor. Conclusion Dominant males store urine and use it as a vehicle for odorants actively released during aggressive disputes. The olfactory potency of the urine is positively correlated with the social status of the male. We suggest that males actively advertise their dominant status through urinary odorants which may act as a 'dominance' pheromone to modulate aggression in rivals, thereby contributing to social stability within the lek. PMID:18076759

Topological properties of robust biological and computational networks

PubMed Central

Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

2014-01-01

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

Mind Operational Semantics and Brain Operational Architectonics: A Putative Correspondence

PubMed Central

Benedetti, Giulio; Marchetti, Giorgio; Fingelkurts, Alexander A; Fingelkurts, Andrew A

2010-01-01

Despite allowing for the unprecedented visualization of brain functional activity, modern neurobiological techniques have not yet been able to provide satisfactory answers to important questions about the relationship between brain and mind. The aim of this paper is to show how two different but complementary approaches, Mind Operational Semantics (OS) and Brain Operational Architectonics (OA), can help bridge the gap between a specific kind of mental activity—the higher-order reflective thought or linguistic thought—and brain. The fundamental notion that allows the two different approaches to be jointly used under a common framework is that of operation. According to OS, which is based on introspection and linguistic data, the meanings of words can be analyzed in terms of elemental mental operations (EOMC), amongst which those of attention play a key role. Linguistic thought is made possible by special kinds of elements, which OS calls “correlators”, which have the function of tying together the other elements of thought, which OS calls “correlata” (a "correlational network” that is, a sentence, is so formed). Therefore, OS conceives of linguistic thought as a hierarchy of operations of increasing complexity. Likewise, according to OA, which is based on the joint analysis of cognitive and electromagnetic data (EEG and MEG), every conscious phenomenon is brought to existence by the joint operations of many functional and transient neuronal assemblies in the brain. According to OA, the functioning of the brain is always operational (made up of operations), and its structure is characterized by a hierarchy of operations of increasing complexity: single neurons, single assemblies of neurons, synchronized neuronal assemblies or Operational Modules (OM), integrated or complex OMs. The authors put forward the hypothesis that the whole level of OS’s description (EOMC, correlators, and correlational networks) corresponds to the level of OMs (or set of them) of different complexity within OA’s theory: EOMC could correspond to simple OMs, correlators to complex OMs and the correlational network to a set of simple and complex OMs. Finally, a set of experiments is proposed to verify the putative correspondence between OS and OA and prove the existence of an integrated continuum between brain and mind. PMID:21113277

Finding Correlation between Protein Protein Interaction Modules Using Semantic Web Techniques

NASA Astrophysics Data System (ADS)

Kargar, Mehdi; Moaven, Shahrouz; Abolhassani, Hassan

Many complex networks such as social networks and computer show modular structures, where edges between nodes are much denser within modules than between modules. It is strongly believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. In this paper we used a human curated dataset. In this paper we consider each module in the PPI network as ontology. Using techniques in ontology alignment, we compare each pair of modules in the network. We want to see that is there a correlation between the structure of each module or they have totally different structures. Our results show that there is no correlation between proteins in a protein protein interaction network.

Modelling and analysis of gene regulatory network using feedback control theory

NASA Astrophysics Data System (ADS)

El-Samad, H.; Khammash, M.

2010-01-01

Molecular pathways are a part of a remarkable hierarchy of regulatory networks that operate at all levels of organisation. These regulatory networks are responsible for much of the biological complexity within the cell. The dynamic character of these pathways and the prevalence of feedback regulation strategies in their operation make them amenable to systematic mathematical analysis using the same tools that have been used with success in analysing and designing engineering control systems. In this article, we aim at establishing this strong connection through various examples where the behaviour exhibited by gene networks is explained in terms of their underlying control strategies. We complement our analysis by a survey of mathematical techniques commonly used to model gene regulatory networks and analyse their dynamic behaviour.

Hybrid routing technique for a fault-tolerant, integrated information network

NASA Technical Reports Server (NTRS)

Meredith, B. D.

1986-01-01

The evolutionary growth of the space station and the diverse activities onboard are expected to require a hierarchy of integrated, local area networks capable of supporting data, voice, and video communications. In addition, fault-tolerant network operation is necessary to protect communications between critical systems attached to the net and to relieve the valuable human resources onboard the space station of time-critical data system repair tasks. A key issue for the design of the fault-tolerant, integrated network is the development of a robust routing algorithm which dynamically selects the optimum communication paths through the net. A routing technique is described that adapts to topological changes in the network to support fault-tolerant operation and system evolvability.

World-System Mobility and Economic Growth, 1980-2000

ERIC Educational Resources Information Center

Clark, Rob

2010-01-01

World-system scholars have traditionally emphasized the stability of the core/periphery hierarchy. However, prior network studies employing both categorical and continuous measures of world-system position reveal substantial mobility across time, whereby a number of developing states have become more integrated in the world economy over the past…

A Methodological Framework to Analyze Stakeholder Preferences and Propose Strategic Pathways for a Sustainable University

ERIC Educational Resources Information Center

Turan, Fikret Korhan; Cetinkaya, Saadet; Ustun, Ceyda

2016-01-01

Building sustainable universities calls for participative management and collaboration among stakeholders. Combining analytic hierarchy and network processes (AHP/ANP) with statistical analysis, this research proposes a framework that can be used in higher education institutions for integrating stakeholder preferences into strategic decisions. The…

Occupation, Class, and Social Networks in Urban China

ERIC Educational Resources Information Center

Bian, Yanjie; Breiger, Ronald; Davis, Deborah; Galaskiewicz, Joseph

2005-01-01

China's class structure is changing dramatically in the wake of post-1978 market-oriented economic reforms. The creation of a mixed "market-socialist" economy has eroded the institutional bases of a cadre-dominated social hierarchy and created conditions for a new pattern of social stratification. Although conditions remain dynamic,…

Application of Hierarchy Theory to Cross-Scale Hydrologic Modeling of Nutrient Loads

EPA Science Inventory

We describe a model called Regional Hydrologic Modeling for Environmental Evaluation 16 (RHyME²) for quantifying annual nutrient loads in stream networks and watersheds. RHyME² is 17 a cross-scale statistical and process-based water-quality model. The model ...

It's the Little Things

ERIC Educational Resources Information Center

Waters, John K.

2007-01-01

Information technology (IT) departments are ruled by a kind of Maslow's Hierarchy of Needs: The big-tech stuff--the operating systems, the networks, the data centers--gets the priority, food-and-shelter attention, while upgrading the backup power supplies, evaluating new projector mounts, and taming that rat's nest of classroom cords fall…

The Homeland Security Ecosystem: An Analysis of Hierarchical and Ecosystem Models and Their Influence on Decision Makers

DTIC Science & Technology

2012-12-01

flows, diversity, emergence, networks, fusion, strategic planning, information sharing, ecosystem, hierarchy, NJ Regional Operations Intelligence ...Related Information...........................................................................79 viii 3. Production of Disaster Intelligence for... Intelligence for Field Personnel .................80 5. Focused Collection Efforts to Support FEMA and NJ OEM Operations

Dynamic Network Selection for Multicast Services in Wireless Cooperative Networks

NASA Astrophysics Data System (ADS)

Chen, Liang; Jin, Le; He, Feng; Cheng, Hanwen; Wu, Lenan

In next generation mobile multimedia communications, different wireless access networks are expected to cooperate. However, it is a challenging task to choose an optimal transmission path in this scenario. This paper focuses on the problem of selecting the optimal access network for multicast services in the cooperative mobile and broadcasting networks. An algorithm is proposed, which considers multiple decision factors and multiple optimization objectives. An analytic hierarchy process (AHP) method is applied to schedule the service queue and an artificial neural network (ANN) is used to improve the flexibility of the algorithm. Simulation results show that by applying the AHP method, a group of weight ratios can be obtained to improve the performance of multiple objectives. And ANN method is effective to adaptively adjust weight ratios when users' new waiting threshold is generated.

Evidence for a Functional Hierarchy of Association Networks.

PubMed

Choi, Eun Young; Drayna, Garrett K; Badre, David

2018-05-01

Patient lesion and neuroimaging studies have identified a rostral-to-caudal functional gradient in the lateral frontal cortex (LFC) corresponding to higher-order (complex or abstract) to lower-order (simple or concrete) cognitive control. At the same time, monkey anatomical and human functional connectivity studies show that frontal regions are reciprocally connected with parietal and temporal regions, forming parallel and distributed association networks. Here, we investigated the link between the functional gradient of LFC regions observed during control tasks and the parallel, distributed organization of association networks. Whole-brain fMRI task activity corresponding to four orders of hierarchical control [Badre, D., & D'Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19, 2082-2099, 2007] was compared with a resting-state functional connectivity MRI estimate of cortical networks [Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125-1165, 2011]. Critically, at each order of control, activity in the LFC and parietal cortex overlapped onto a common association network that differed between orders. These results are consistent with a functional organization based on separable association networks that are recruited during hierarchical control. Furthermore, corticostriatal functional connectivity MRI showed that, consistent with their participation in functional networks, rostral-to-caudal LFC and caudal-to-rostral parietal regions had similar, order-specific corticostriatal connectivity that agreed with a striatal gating model of hierarchical rule use. Our results indicate that hierarchical cognitive control is subserved by parallel and distributed association networks, together forming multiple localized functional gradients in different parts of association cortex. As such, association networks, while connectionally organized in parallel, may be functionally organized in a hierarchy via dynamic interaction with the striatum.

Lack of experience-based stratification in homing pigeon leadership hierarchies.

PubMed

Watts, Isobel; Pettit, Benjamin; Nagy, Máté; de Perera, Theresa Burt; Biro, Dora

2016-01-01

In societies that make collective decisions through leadership, a fundamental question concerns the individual attributes that allow certain group members to assume leadership roles over others. Homing pigeons form transitive leadership hierarchies during flock flights, where flock members are ranked according to the average time differences with which they lead or follow others' movement. Here, we test systematically whether leadership ranks in navigational hierarchies are correlated with prior experience of a homing task. We constructed experimental flocks of pigeons with mixed navigational experience: half of the birds within each flock had been familiarized with a specific release site through multiple previous releases, while the other half had never been released from the same site. We measured the birds' hierarchical leadership ranks, then switched the same birds' roles at a second site to test whether the relative hierarchical positions of the birds in the two subsets would reverse in response to the reversal in levels of experience. We found that while across all releases the top hierarchical positions were occupied by experienced birds significantly more often than by inexperienced ones, the remaining experienced birds were not consistently clustered in the top half-in other words, the network did not become stratified. We discuss our results in light of the adaptive value of structuring leadership hierarchies according to 'merit' (here, navigational experience).

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

PubMed Central

Leibo, Joel Z.; Liao, Qianli; Freiwald, Winrich A.; Anselmi, Fabio; Poggio, Tomaso

2017-01-01

SUMMARY The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations like depth-rotations [1, 2]. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3, 4, 5, 6]. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here we demonstrate that one specific biologically-plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli like faces at intermediate levels of the architecture and show why it does so. Thus the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. PMID:27916522

Abnormal frontoparietal synaptic gain mediating the P300 in patients with psychotic disorder and their unaffected relatives.

PubMed

Díez, Álvaro; Ranlund, Siri; Pinotsis, Dimitris; Calafato, Stella; Shaikh, Madiha; Hall, Mei-Hua; Walshe, Muriel; Nevado, Ángel; Friston, Karl J; Adams, Rick A; Bramon, Elvira

2017-06-01

The "dysconnection hypothesis" of psychosis suggests that a disruption of functional integration underlies cognitive deficits and clinical symptoms. Impairments in the P300 potential are well documented in psychosis. Intrinsic (self-)connectivity in a frontoparietal cortical hierarchy during a P300 experiment was investigated. Dynamic Causal Modeling was used to estimate how evoked activity results from the dynamics of coupled neural populations and how neural coupling changes with the experimental factors. Twenty-four patients with psychotic disorder, twenty-four unaffected relatives, and twenty-five controls underwent EEG recordings during an auditory oddball paradigm. Sixteen frontoparietal network models (including primary auditory, superior parietal, and superior frontal sources) were analyzed and an optimal model of neural coupling, explaining diagnosis and genetic risk effects, as well as their interactions with task condition were identified. The winning model included changes in connectivity at all three hierarchical levels. Patients showed decreased self-inhibition-that is, increased cortical excitability-in left superior frontal gyrus across task conditions, compared with unaffected participants. Relatives had similar increases in excitability in left superior frontal and right superior parietal sources, and a reversal of the normal synaptic gain changes in response to targets relative to standard tones. It was confirmed that both subjects with psychotic disorder and their relatives show a context-independent loss of synaptic gain control at the highest hierarchy levels. The relatives also showed abnormal gain modulation responses to task-relevant stimuli. These may be caused by NMDA-receptor and/or GABAergic pathologies that change the excitability of superficial pyramidal cells and may be a potential biological marker for psychosis. Hum Brain Mapp 38:3262-3276, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Extension of mixture-of-experts networks for binary classification of hierarchical data.

PubMed

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.

Bayesian module identification from multiple noisy networks.

PubMed

Zamani Dadaneh, Siamak; Qian, Xiaoning

2016-12-01

Module identification has been studied extensively in order to gain deeper understanding of complex systems, such as social networks as well as biological networks. Modules are often defined as groups of vertices in these networks that are topologically cohesive with similar interaction patterns with the rest of the vertices. Most of the existing module identification algorithms assume that the given networks are faithfully measured without errors. However, in many real-world applications, for example, when analyzing protein-protein interaction networks from high-throughput profiling techniques, there is significant noise with both false positive and missing links between vertices. In this paper, we propose a new model for more robust module identification by taking advantage of multiple observed networks with significant noise so that signals in multiple networks can be strengthened and help improve the solution quality by combining information from various sources. We adopt a hierarchical Bayesian model to integrate multiple noisy snapshots that capture the underlying modular structure of the networks under study. By introducing a latent root assignment matrix and its relations to instantaneous module assignments in all the observed networks to capture the underlying modular structure and combine information across multiple networks, an efficient variational Bayes algorithm can be derived to accurately and robustly identify the underlying modules from multiple noisy networks. Experiments on synthetic and protein-protein interaction data sets show that our proposed model enhances both the accuracy and resolution in detecting cohesive modules, and it is less vulnerable to noise in the observed data. In addition, it shows higher power in predicting missing edges compared to individual-network methods.

Immunization of complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2002-03-01

Complex networks such as the sexual partnership web or the Internet often show a high degree of redundancy and heterogeneity in their connectivity properties. This peculiar connectivity provides an ideal environment for the spreading of infective agents. Here we show that the random uniform immunization of individuals does not lead to the eradication of infections in all complex networks. Namely, networks with scale-free properties do not acquire global immunity from major epidemic outbreaks even in the presence of unrealistically high densities of randomly immunized individuals. The absence of any critical immunization threshold is due to the unbounded connectivity fluctuations of scale-free networks. Successful immunization strategies can be developed only by taking into account the inhomogeneous connectivity properties of scale-free networks. In particular, targeted immunization schemes, based on the nodes' connectivity hierarchy, sharply lower the network's vulnerability to epidemic attacks.

Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules

NASA Astrophysics Data System (ADS)

Menon, Govind; Krishnan, J.

2016-07-01

While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules.

PubMed

Menon, Govind; Krishnan, J

2016-07-21

While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

Revisiting the hierarchy of urban areas in the Brazilian Amazon: a multilevel approach

PubMed Central

Costa, Sandra; Brondízio, Eduardo

2012-01-01

The Legal Brazilian Amazon, while the largest rainforest in the world, is also a region where most residents are urban. Despite close linkages between rural and urban processes in the region, rural areas have been the predominant focus of Amazon-based population-environment scholarship. Offering a focus on urban areas within the Brazilian Amazon, this paper examines the emergence of urban hierarchies within the region. Using a combination of nationally representative data and community based surveys, applied to a multivariate cluster methodology (Grade of Membership), we observe the emergence of sub-regional urban networks characterized by economic and political inter-dependency, population movement, and provision of services. These networks link rural areas, small towns, and medium and large cities. We also identify the emergence of medium-size cities as important nodes at a sub-regional level. In all, the work provides insight on the proposed model of ‘disarticulated urbanization’ within the Amazon by calling attention to the increasing role of regional and sub-regional urban networks in shaping the future expansion of land use and population distribution in the Amazon. We conclude with a discussion of implications for increasing intra-regional connectivity and fragmentation of conservation areas and ecosystems in the region. PMID:23129877

Computing Systemic Risk Using Multiple Behavioral and Keystone Networks: The Emergence of a Crisis in Primate Societies and Banks*

PubMed Central

Fushing, Hsieh; Jordà, Òscar; Beisner, Brianne; McCowan, Brenda

2015-01-01

What do the behavior of monkeys in captivity and the financial system have in common? The nodes in such social systems relate to each other through multiple and keystone networks, not just one network. Each network in the system has its own topology, and the interactions among the system’s networks change over time. In such systems, the lead into a crisis appears to be characterized by a decoupling of the networks from the keystone network. This decoupling can also be seen in the crumbling of the keystone’s power structure toward a more horizontal hierarchy. This paper develops nonparametric methods for describing the joint model of the latent architecture of interconnected networks in order to describe this process of decoupling, and hence provide an early warning system of an impending crisis. PMID:26056422

Strategies for synchronisation in an evolving telecommunications network

NASA Astrophysics Data System (ADS)

Avery, Rob

1992-06-01

The achievement of precise synchronization in the telecommunications environment is addressed. Transmitting the timing from node to node has been the inherent problem for all digital networks. Traditional network equipment used to transfer synchronization, such as digital switching ststems, adds impairments to the once traceable signal. As the synchronization signals are passed from node to node, they lose stability by passing through intervening clocks. Timing would be an integrated part of all new network and service deployments. New transmission methods, such as the Synchronous Digital Hierarchy (SDH), survivable network topologies and the issues that arise from them, necessitate a review of current network synchronization strategies. Challenges that face the network are itemized. A demonstration of why localized Primary Reference Clocks (PRC) in key nodes and the Synchronization Supply Unit (SSU) clock architecture of transit and local node clocks is a technically and economically viable solution to the issues facing network planners today is given.

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

PubMed

Astegiano, Julia; Altermatt, Florian; Massol, François

2017-11-13

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

Identification of common coexpression modules based on quantitative network comparison.

PubMed

Jo, Yousang; Kim, Sanghyeon; Lee, Doheon

2018-06-13

Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntington's disease and brain aging by the new method. We proposed two similarity measures for quantitative comparison of coexpression networks. Then, we performed experiments using known coexpression networks. We showed the validity of two measures and evaluated threshold values for similar coexpression network pairs from experiments. Using these similarity measures and thresholds, we quantitatively measured the similarity between disease-specific and aging-related coexpression modules and found similar Huntington's disease-aging coexpression module pairs. We identified similar Huntington's disease-aging coexpression module pairs and found that these modules are related to brain development, cell death, and immune response. It suggests that up-regulated cell signalling related cell death and immune/ inflammation response may be the common molecular mechanisms in the pathophysiology of HD and normal brain aging in the frontal cortex.

Large constraint length high speed viterbi decoder based on a modular hierarchial decomposition of the deBruijn graph

NASA Technical Reports Server (NTRS)

Collins, Oliver (Inventor); Dolinar, Jr., Samuel J. (Inventor); Hus, In-Shek (Inventor); Bozzola, Fabrizio P. (Inventor); Olson, Erlend M. (Inventor); Statman, Joseph I. (Inventor); Zimmerman, George A. (Inventor)

1991-01-01

A method of formulating and packaging decision-making elements into a long constraint length Viterbi decoder which involves formulating the decision-making processors as individual Viterbi butterfly processors that are interconnected in a deBruijn graph configuration. A fully distributed architecture, which achieves high decoding speeds, is made feasible by novel wiring and partitioning of the state diagram. This partitioning defines universal modules, which can be used to build any size decoder, such that a large number of wires is contained inside each module, and a small number of wires is needed to connect modules. The total system is modular and hierarchical, and it implements a large proportion of the required wiring internally within modules and may include some external wiring to fully complete the deBruijn graph. pg,14.

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Language networks in anophthalmia: maintained hierarchy of processing in 'visual' cortex.

PubMed

Watkins, Kate E; Cowey, Alan; Alexander, Iona; Filippini, Nicola; Kennedy, James M; Smith, Stephen M; Ragge, Nicola; Bridge, Holly

2012-05-01

Imaging studies in blind subjects have consistently shown that sensory and cognitive tasks evoke activity in the occipital cortex, which is normally visual. The precise areas involved and degree of activation are dependent upon the cause and age of onset of blindness. Here, we investigated the cortical language network at rest and during an auditory covert naming task in five bilaterally anophthalmic subjects, who have never received visual input. When listening to auditory definitions and covertly retrieving words, these subjects activated lateral occipital cortex bilaterally in addition to the language areas activated in sighted controls. This activity was significantly greater than that present in a control condition of listening to reversed speech. The lateral occipital cortex was also recruited into a left-lateralized resting-state network that usually comprises anterior and posterior language areas. Levels of activation to the auditory naming and reversed speech conditions did not differ in the calcarine (striate) cortex. This primary 'visual' cortex was not recruited to the left-lateralized resting-state network and showed high interhemispheric correlation of activity at rest, as is typically seen in unimodal cortical areas. In contrast, the interhemispheric correlation of resting activity in extrastriate areas was reduced in anophthalmia to the level of cortical areas that are heteromodal, such as the inferior frontal gyrus. Previous imaging studies in the congenitally blind show that primary visual cortex is activated in higher-order tasks, such as language and memory to a greater extent than during more basic sensory processing, resulting in a reversal of the normal hierarchy of functional organization across 'visual' areas. Our data do not support such a pattern of organization in anophthalmia. Instead, the patterns of activity during task and the functional connectivity at rest are consistent with the known hierarchy of processing in these areas normally seen for vision. The differences in cortical organization between bilateral anophthalmia and other forms of congenital blindness are considered to be due to the total absence of stimulation in 'visual' cortex by light or retinal activity in the former condition, and suggests development of subcortical auditory input to the geniculo-striate pathway.

Design of a 0.13-μm CMOS cascade expandable ΣΔ modulator for multi-standard RF telecom systems

NASA Astrophysics Data System (ADS)

Morgado, Alonso; del Río, Rocío; de la Rosa, José M.

2007-05-01

This paper reports a 130-nm CMOS programmable cascade ΣΔ modulator for multi-standard wireless terminals, capable of operating on three standards: GSM, Bluetooth and UMTS. The modulator is reconfigured at both architecture- and circuit- level in order to adapt its performance to the different standards specifications with optimized power consumption. The design of the building blocks is based upon a top-down CAD methodology that combines simulation and statistical optimization at different levels of the system hierarchy. Transistor-level simulations show correct operation for all standards, featuring 13-bit, 11.3-bit and 9-bit effective resolution within 200-kHz, 1-MHz and 4-MHz bandwidth, respectively.

A Developmental Systems Perspective on Epistasis: Computational Exploration of Mutational Interactions in Model Developmental Regulatory Networks

PubMed Central

Gutiérrez, Jayson

2009-01-01

The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns) depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks). Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/− feedback) and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs) epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected) networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1) the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2) the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of multiple perturbations are strongly conditioned by both the regulatory architecture (i.e. pattern of coupled feedback structures) and the dynamic nature of the spatio-temporal expression trajectories displayed by the simulated networks. PMID:19738908

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

PubMed

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences

PubMed Central

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns. PMID:26147887

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

PubMed

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

On Group Stability in Hierarchies and Networks.

ERIC Educational Resources Information Center

Demange, Gabrielle

2004-01-01

A hierarchical structure is a widespread organizational form in many areas. My aim in this paper is to provide a rationale for this fact based on two premises. First, a group organizes itself so as to achieve efficient coordination. Second, efficient coordination is achieved only if subgroups as well as individuals agree to cooperate. Even in…

Supporting Teachers' Equity-Oriented Learning and Identities: A Resource-Centered Perspective

ERIC Educational Resources Information Center

Louie, Nicole L.

2017-01-01

Background/Context: Despite calls for equity in education, the dominant mode of schooling reproduces hierarchies, positioning some students as bright, gifted, or fast learners and others as lazy, in need of remediation, or slow. A number of studies have shown that teachers' professional communities and networks can address this problem and enhance…

Complex Adaptive Systems Based Data Integration: Theory and Applications

ERIC Educational Resources Information Center

Rohn, Eliahu

2008-01-01

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an…

A template for constructing Bayesian networks in forensic biology cases when considering activity level propositions.

PubMed

Taylor, Duncan; Biedermann, Alex; Hicks, Tacha; Champod, Christophe

2018-03-01

The hierarchy of propositions has been accepted amongst the forensic science community for some time. It is also accepted that the higher up the hierarchy the propositions are, against which the scientist are competent to evaluate their results, the more directly useful the testimony will be to the court. Because each case represents a unique set of circumstances and findings, it is difficult to come up with a standard structure for evaluation. One common tool that assists in this task is Bayesian networks (BNs). There is much diversity in the way that BN can be constructed. In this work, we develop a template for BN construction that allows sufficient flexibility to address most cases, but enough commonality and structure that the flow of information in the BN is readily recognised at a glance. We provide seven steps that can be used to construct BNs within this structure and demonstrate how they can be applied, using a case example. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Behavioral networks as a model for intelligent agents

NASA Technical Reports Server (NTRS)

Sliwa, Nancy E.

1990-01-01

On-going work at NASA Langley Research Center in the development and demonstration of a paradigm called behavioral networks as an architecture for intelligent agents is described. This work focuses on the need to identify a methodology for smoothly integrating the characteristics of low-level robotic behavior, including actuation and sensing, with intelligent activities such as planning, scheduling, and learning. This work assumes that all these needs can be met within a single methodology, and attempts to formalize this methodology in a connectionist architecture called behavioral networks. Behavioral networks are networks of task processes arranged in a task decomposition hierarchy. These processes are connected by both command/feedback data flow, and by the forward and reverse propagation of weights which measure the dynamic utility of actions and beliefs.

The Gremlin Framework

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

2013-09-26

The Gremlin sofrware package is a performance analysis approach targeted to support the Co-Design process for future systems. It consists of a series of modules that can be used to alter a machine's behavior with the goal of emulating future machine properties. The modules can be divided into several classes; the most significant ones are detailed below. PowGre is a series of modules that help explore the power consumption properties of applications and to determine the impact of power constraints on applications. Most of them use low-level processor interfaces to directly control voltage and frequency settings as well as permore » nodes, socket, or memory power bounds. MemGre are memory Gremlins and implement a new performance analysis technique that captures the application's effective use of the storage capacity of different levels of the memory hierarchy as well as the bandwidth between adjacent levels. The approach models various memory components as resources and measures how much of each resource the application uses from the application's own perspective. To the application a given amount of a resource is "used" if not having this amount will degrade the application's performance. This is in contrast to the hardware-centric perspective that considers "use" as any hardware action that utilizes the resource, even if it has no effect on performance. ResGre are Gremlins that use fault injection techniques to emulate higher fault rates than currently present in today's systems. Faults can be injected through various means, including network interposition, static analysis, and code modification, or direct application notification. ResGre also includes patches to previously released LLNL codes that can counteract and react to injected failures.« less

SU-D-BRD-01: An Automated Physics Weekly Chart Checking System Supporting ARIA

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

Chang, X; Yang, D

Purpose: A software tool was developed in this study to perform automatic weekly physics chart check on the patient data in ARIA. The tool accesses the electronic patient data directly from ARIA server and checks the accuracy of treatment deliveries, and generates reports which summarize the delivery history and highlight the errors. Methods: The tool has four modules. 1) The database interface is designed to directly access treatment delivery data from the ARIA database before reorganizing the data into the patient chart tree (PCT). 2) PCT is a core data structure designed to store and organize the data in logicalmore » hierarchies, and to be passed among functions. 3) The treatment data check module analyzes the organized data in PCT and stores the checking results into PCT. 4) Report generation module generates reports containing the treatment delivery summary, chart checking results and plots of daily treatment setup parameters (couch table positions, shifts of image guidance). The errors that are found by the tool are highlighted with colors. Results: The weekly check tool has been implemented in MATLAB and clinically tested at two major cancer centers. Javascript, cascading style sheets (CSS) and dynamic HTML were employed to create the user-interactive reports. It takes 0.06 second to search the delivery records of one beam with PCT and compare the delivery records with beam plan. The reports, saved in the HTML files on shared network folder, can be accessed by web browser on computers and mobile devices. Conclusion: The presented weekly check tool is useful to check the electronic patient treatment data in Varian ARIA system. It could be more efficient and reliable than the manually check by physicists. The work was partially supported by a research grant from Varian Medical System.« less

Evidence of Rentian Scaling of Functional Modules in Diverse Biological Networks.

PubMed

How, Javier J; Navlakha, Saket

2018-06-12

Biological networks have long been known to be modular, containing sets of nodes that are highly connected internally. Less emphasis, however, has been placed on understanding how intermodule connections are distributed within a network. Here, we borrow ideas from engineered circuit design and study Rentian scaling, which states that the number of external connections between nodes in different modules is related to the number of nodes inside the modules by a power-law relationship. We tested this property in a broad class of molecular networks, including protein interaction networks for six species and gene regulatory networks for 41 human and 25 mouse cell types. Using evolutionarily defined modules corresponding to known biological processes in the cell, we found that all networks displayed Rentian scaling with a broad range of exponents. We also found evidence for Rentian scaling in functional modules in the Caenorhabditis elegans neural network, but, interestingly, not in three different social networks, suggesting that this property does not inevitably emerge. To understand how such scaling may have arisen evolutionarily, we derived a new graph model that can generate Rentian networks given a target Rent exponent and a module decomposition as inputs. Overall, our work uncovers a new principle shared by engineered circuits and biological networks.

Modeling the dynamics of evaluation: a multilevel neural network implementation of the iterative reprocessing model.

PubMed

Ehret, Phillip J; Monroe, Brian M; Read, Stephen J

2015-05-01

We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory. © 2014 by the Society for Personality and Social Psychology, Inc.

Neural network submodel as an abstraction tool: relating network performance to combat outcome

NASA Astrophysics Data System (ADS)

Jablunovsky, Greg; Dorman, Clark; Yaworsky, Paul S.

2000-06-01

Simulation of Command and Control (C2) networks has historically emphasized individual system performance with little architectural context or credible linkage to `bottom- line' measures of combat outcomes. Renewed interest in modeling C2 effects and relationships stems from emerging network intensive operational concepts. This demands improved methods to span the analytical hierarchy between C2 system performance models and theater-level models. Neural network technology offers a modeling approach that can abstract the essential behavior of higher resolution C2 models within a campaign simulation. The proposed methodology uses off-line learning of the relationships between network state and campaign-impacting performance of a complex C2 architecture and then approximation of that performance as a time-varying parameter in an aggregated simulation. Ultimately, this abstraction tool offers an increased fidelity of C2 system simulation that captures dynamic network dependencies within a campaign context.

Understanding network concepts in modules

PubMed Central

2007-01-01

Background Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory. Results Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks. Conclusion Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks PMID:17547772

Personality correlates (BAS-BIS), self-perception of social ranking, and cortical (alpha frequency band) modulation in peer-group comparison.

PubMed

Balconi, Michela; Pagani, Silvia

2014-06-22

The perception and interpretation of social hierarchies are a key part of our social life. In the present research we considered the activation of cortical areas, mainly the prefrontal cortex, related to social ranking perception in conjunction with some personality components (BAS - Behavioral Activation System - and BIS - Behavioral Inhibition System). In two experiments we manipulated the perceived superior/inferior status during a competitive cognitive task. Indeed, we created an explicit and strongly reinforced social hierarchy based on incidental rating in an attentional task. Specifically, a peer group comparison was undertaken and improved (Experiment 1) or decreased (Experiment 2) performance was artificially manipulated by the experimenter. For each experiment two groups were compared, based on a BAS and BIS dichotomy. Alpha band modulation in prefrontal cortex, behavioral measures (performance: error rate, ER; response times, RTs), and self-perceived ranking were considered. Repeated measures ANOVAs and regression analyses showed in Experiment 1 a significant improved cognitive performance (decreased ER and RTs) and higher self-perceived ranking in high-BAS participants. Moreover, their prefrontal activity was increased within the left side (alpha band decreasing). Conversely, in Experiment 2 a significant decreased cognitive performance (increased ER and RTs) and lower self-perceived ranking was observed in higher-BIS participants. Their prefrontal right activity was increased in comparison with higher BAS. The regression analyses confirmed the significant predictive role of alpha band modulation with respect of subjects' performance and self-perception of social ranking, differently for BAS/BIS components. The present results suggest that social status perception is directly modulated by cortical activity and personality correlates. Copyright © 2014 Elsevier Inc. All rights reserved.

Fault tolerant hypercube computer system architecture

NASA Technical Reports Server (NTRS)

Madan, Herb S. (Inventor); Chow, Edward (Inventor)

1989-01-01

A fault-tolerant multiprocessor computer system of the hypercube type comprising a hierarchy of computers of like kind which can be functionally substituted for one another as necessary is disclosed. Communication between the working nodes is via one communications network while communications between the working nodes and watch dog nodes and load balancing nodes higher in the structure is via another communications network separate from the first. A typical branch of the hierarchy reporting to a master node or host computer comprises, a plurality of first computing nodes; a first network of message conducting paths for interconnecting the first computing nodes as a hypercube. The first network provides a path for message transfer between the first computing nodes; a first watch dog node; and a second network of message connecting paths for connecting the first computing nodes to the first watch dog node independent from the first network, the second network provides an independent path for test message and reconfiguration affecting transfers between the first computing nodes and the first switch watch dog node. There is additionally, a plurality of second computing nodes; a third network of message conducting paths for interconnecting the second computing nodes as a hypercube. The third network provides a path for message transfer between the second computing nodes; a fourth network of message conducting paths for connecting the second computing nodes to the first watch dog node independent from the third network. The fourth network provides an independent path for test message and reconfiguration affecting transfers between the second computing nodes and the first watch dog node; and a first multiplexer disposed between the first watch dog node and the second and fourth networks for allowing the first watch dog node to selectively communicate with individual ones of the computing nodes through the second and fourth networks; as well as, a second watch dog node operably connected to the first multiplexer whereby the second watch dog node can selectively communicate with individual ones of the computing nodes through the second and fourth networks. The branch is completed by a first load balancing node; and a second multiplexer connected between the first load balancing node and the first and second watch dog nodes, allowing the first load balancing node to selectively communicate with the first and second watch dog nodes.

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

Melián, Carlos J; Matthews, Blake; de Andreazzi, Cecilia S; Rodríguez, Jorge P; Harmon, Luke J; Fortuna, Miguel A

2018-05-24

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems. Copyright © 2018 Elsevier Ltd. All rights reserved.

A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data

NASA Astrophysics Data System (ADS)

Feng, Shou; Fu, Ping; Zheng, Wenbin

2018-03-01

Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is usually needed. This paper proposed a novel preliminary results processing method called the nodes interaction method. The nodes interaction method revises the preliminary results and guarantees that the predictions are consistent with the hierarchy constraint. This method exploits the label dependency and considers the hierarchical interaction between nodes when making decisions based on the Bayesian network in its first phase. In the second phase, this method further adjusts the results according to the hierarchy constraint. Implementing the nodes interaction method in the HMC framework also enhances the HMC performance for solving the gene function prediction problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph that is more difficult to tackle. The experimental results validate the promising performance of the proposed method compared to state-of-the-art methods on eight benchmark yeast data sets annotated by the GO.

Knowledge-Based Motion Control of AN Intelligent Mobile Autonomous System

NASA Astrophysics Data System (ADS)

Isik, Can

An Intelligent Mobile Autonomous System (IMAS), which is equipped with vision and low level sensors to cope with unknown obstacles, is modeled as a hierarchy of path planning and motion control. This dissertation concentrates on the lower level of this hierarchy (Pilot) with a knowledge-based controller. The basis of a theory of knowledge-based controllers is established, using the example of the Pilot level motion control of IMAS. In this context, the knowledge-based controller with a linguistic world concept is shown to be adequate for the minimum time control of an autonomous mobile robot motion. The Pilot level motion control of IMAS is approached in the framework of production systems. The three major components of the knowledge-based control that are included here are the hierarchies of the database, the rule base and the rule evaluator. The database, which is the representation of the state of the world, is organized as a semantic network, using a concept of minimal admissible vocabulary. The hierarchy of rule base is derived from the analytical formulation of minimum-time control of IMAS motion. The procedure introduced for rule derivation, which is called analytical model verbalization, utilizes the concept of causalities to describe the system behavior. A realistic analytical system model is developed and the minimum-time motion control in an obstacle strewn environment is decomposed to a hierarchy of motion planning and control. The conditions for the validity of the hierarchical problem decomposition are established, and the consistency of operation is maintained by detecting the long term conflicting decisions of the levels of the hierarchy. The imprecision in the world description is modeled using the theory of fuzzy sets. The method developed for the choice of the rule that prescribes the minimum-time motion control among the redundant set of applicable rules is explained and the usage of fuzzy set operators is justified. Also included in the dissertation are the description of the computer simulation of Pilot within the hierarchy of IMAS control and the simulated experiments that demonstrate the theoretical work.

Effects of Radiation on Proteasome Function in Prostate Cancer Cells

DTIC Science & Technology

2012-02-01

California Breast Cancer Research Program Innovative Development and Exploratory Award (IDEA) Competitive Renewal Modulation of...Weissman IL. Establishment of a normal hemato- poietic and leukemia stem cell hierarchy. Cold Spring Harb Symp Quant Biol 2008;73:439–449. 9 Trott KR...existence of CSCs in solid cancer has been advocated by radiobiologists for decades [Withers et al., 1988; Trott , 1994]. However, until recently this

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.

PubMed

Leibo, Joel Z; Liao, Qianli; Anselmi, Fabio; Freiwald, Winrich A; Poggio, Tomaso

2017-01-09

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cyclotomic Gaudin Models: Construction and Bethe Ansatz

NASA Astrophysics Data System (ADS)

Vicedo, Benoît; Young, Charles

2016-05-01

To any finite-dimensional simple Lie algebra g and automorphism {σ: gto g we associate a cyclotomic Gaudin algebra. This is a large commutative subalgebra of {U(g)^{⊗ N}} generated by a hierarchy of cyclotomic Gaudin Hamiltonians. It reduces to the Gaudin algebra in the special case {σ =id}. We go on to construct joint eigenvectors and their eigenvalues for this hierarchy of cyclotomic Gaudin Hamiltonians, in the case of a spin chain consisting of a tensor product of Verma modules. To do so we generalize an approach to the Bethe ansatz due to Feigin, Frenkel and Reshetikhin involving vertex algebras and the Wakimoto construction. As part of this construction, we make use of a theorem concerning cyclotomic coinvariants, which we prove in a companion paper. As a byproduct, we obtain a cyclotomic generalization of the Schechtman-Varchenko formula for the weight function.

Hierarchical image feature extraction by an irregular pyramid of polygonal partitions

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

Skurikhin, Alexei N

2008-01-01

We present an algorithmic framework for hierarchical image segmentation and feature extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions of the original image. This multiscale hierarchy forms the basis for object-oriented image analysis. The framework incorporates the Gestalt principles of visual perception, such as proximity and closure, and exploits spectral and textural similarities of polygonal partitions, while iteratively grouping them until dissimilarity criteria are exceeded. Seed polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on themore » top of detected spectral discontinuities (such as edges), which form a network of constraints for the Delaunay triangulation. The image is then represented as a spatial network in the form of a graph with vertices corresponding to the polygonal partitions and edges reflecting their relations. The iterative agglomeration of partitions into object-oriented segments is formulated as Minimum Spanning Tree (MST) construction. An important characteristic of the approach is that the agglomeration of polygonal partitions is constrained by the detected edges; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects. The constructed partitions and their spatial relations are characterized using spectral, textural and structural features based on proximity graphs. The framework allows searching for object-oriented features of interest across multiple levels of details of the built hierarchy and can be generalized to the multi-criteria MST to account for multiple criteria important for an application.« less

Tools and Models for Integrating Multiple Cellular Networks

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

Gerstein, Mark

2015-11-06

In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novelmore » algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed CRIT for correlation analysis in systems biology [5]. For Aim 3, we have further investigated the scaling relationship that the number of Transcription Factors (TFs) in a genome is proportional to the square of the total number of genes. We have extended the analysis from transcription factors to various classes of functional categories, and from individual categories to joint distribution [6]. By introducing a new analytical framework, we have generalized the original toolbox model to take into account of metabolic network with arbitrary network topology [7].« less

Individual preferences modulate incentive values: Evidence from functional MRI

PubMed Central

Koeneke, Susan; Pedroni, Andreas F; Dieckmann, Anja; Bosch, Volker; Jäncke, Lutz

2008-01-01

Background In most studies on human reward processing, reward intensity has been manipulated on an objective scale (e.g., varying monetary value). Everyday experience, however, teaches us that objectively equivalent rewards may differ substantially in their subjective incentive values. One factor influencing incentive value in humans is branding. The current study explores the hypothesis that individual brand preferences modulate activity in reward areas similarly to objectively measurable differences in reward intensity. Methods A wheel-of-fortune game comprising an anticipation phase and a subsequent outcome evaluation phase was implemented. Inside a 3 Tesla MRI scanner, 19 participants played for chocolate bars of three different brands that differed in subjective attractiveness. Results Parametrical analysis of the obtained fMRI data demonstrated that the level of activity in anatomically distinct neural networks was linearly associated with the subjective preference hierarchy of the brands played for. During the anticipation phases, preference-dependent neural activity has been registered in premotor areas, insular cortex, orbitofrontal cortex, and in the midbrain. During the outcome phases, neural activity in the caudate nucleus, precuneus, lingual gyrus, cerebellum, and in the pallidum was influenced by individual preference. Conclusion Our results suggest a graded effect of differently preferred brands onto the incentive value of objectively equivalent rewards. Regarding the anticipation phase, the results reflect an intensified state of wanting that facilitates action preparation when the participants play for their favorite brand. This mechanism may underlie approach behavior in real-life choice situations. PMID:19032746

Google in a Quantum Network

PubMed Central

Paparo, G. D.; Martin-Delgado, M. A.

2012-01-01

We introduce the characterization of a class of quantum PageRank algorithms in a scenario in which some kind of quantum network is realizable out of the current classical internet web, but no quantum computer is yet available. This class represents a quantization of the PageRank protocol currently employed to list web pages according to their importance. We have found an instance of this class of quantum protocols that outperforms its classical counterpart and may break the classical hierarchy of web pages depending on the topology of the web. PMID:22685626

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.

DiME: A Scalable Disease Module Identification Algorithm with Application to Glioma Progression

PubMed Central

Liu, Yunpeng; Tennant, Daniel A.; Zhu, Zexuan; Heath, John K.; Yao, Xin; He, Shan

2014-01-01

Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II) - and high (GBM) - grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME. PMID:24523864

The Trigger and Data Acquisition System for the KM3NeT neutrino telescope

NASA Astrophysics Data System (ADS)

Pellegrino, Carmelo; Chiarusi, Tommaso

2016-04-01

KM3NeT is a large research infrastructure in the Mediterranean Sea that includes a network of deep-sea neutrino telescopes. The telescopes consist of vertical detection units carrying optical modules, whose separation is optimised according to the different ranges of neutrino energy that shall be explored. Two building blocks, each one made of 115 detection units, will be deployed at the KM3NeT-IT site, about 80 km from Capo Passero, Italy, to search for high-energy neutrino sources (ARCA); another building block will be installed at the KM3NeT-Fr site, about 40 km from Toulon, France, to study the hierarchy of neutrino masses (ORCA). The modular design of the KM3NeT allows for a progressive implementation and data taking even with an incomplete detector. The same scalable design is used for the Trigger and Data Acquisition Systems (TriDAS). In order to reduce the complexity of the hardware inside the optical modules, the "all data to shore" concept is adopted. This implies that the throughput is dominated by the optical background due to the decay of 40K dissolved in the sea water and to the bursts of bioluminescence, about 3 orders of magnitude larger than the physics signal, ranging from 20 Gbps to several hundreds Gbps, according to the number of detection units. In addition, information from the acoustic positioning system of the detection units must be transmitted. As a consequence of the detector construction, the on-shore DAQ infrastructure must be expanded to handle an increasing data-rate and implement an efficient fast data filtering for both the optical and acoustic channels. In this contribution, the Trigger and Data Acquisition System designed for the Phase 1 of KM3NeT and its future expansion are presented. The network infrastructure, the shore computing resources and the developed applications for handling, filtering and monitoring the optical and acoustic data-streams are described.

Virtual Sensor Test Instrumentation

NASA Technical Reports Server (NTRS)

Wang, Roy

2011-01-01

Virtual Sensor Test Instrumentation is based on the concept of smart sensor technology for testing with intelligence needed to perform sell-diagnosis of health, and to participate in a hierarchy of health determination at sensor, process, and system levels. A virtual sensor test instrumentation consists of five elements: (1) a common sensor interface, (2) microprocessor, (3) wireless interface, (4) signal conditioning and ADC/DAC (analog-to-digital conversion/ digital-to-analog conversion), and (5) onboard EEPROM (electrically erasable programmable read-only memory) for metadata storage and executable software to create powerful, scalable, reconfigurable, and reliable embedded and distributed test instruments. In order to maximize the efficient data conversion through the smart sensor node, plug-and-play functionality is required to interface with traditional sensors to enhance their identity and capabilities for data processing and communications. Virtual sensor test instrumentation can be accessible wirelessly via a Network Capable Application Processor (NCAP) or a Smart Transducer Interlace Module (STIM) that may be managed under real-time rule engines for mission-critical applications. The transducer senses the physical quantity being measured and converts it into an electrical signal. The signal is fed to an A/D converter, and is ready for use by the processor to execute functional transformation based on the sensor characteristics stored in a Transducer Electronic Data Sheet (TEDS). Virtual sensor test instrumentation is built upon an open-system architecture with standardized protocol modules/stacks to interface with industry standards and commonly used software. One major benefit for deploying the virtual sensor test instrumentation is the ability, through a plug-and-play common interface, to convert raw sensor data in either analog or digital form, to an IEEE 1451 standard-based smart sensor, which has instructions to program sensors for a wide variety of functions. The sensor data is processed in a distributed fashion across the network, providing a large pool of resources in real time to meet stringent latency requirements.

An AHP-Based Weighted Analysis of Network Knowledge Management Platforms for Elementary School Students

ERIC Educational Resources Information Center

Lee, Chung-Ping; Lou, Shi-Jer; Shih, Ru-Chu; Tseng, Kuo-Hung

2011-01-01

This study uses the analytical hierarchy process (AHP) to quantify important knowledge management behaviors and to analyze the weight scores of elementary school students' behaviors in knowledge transfer, sharing, and creation. Based on the analysis of Expert Choice and tests for validity and reliability, this study identified the weight scores of…

Can the Analytical Hierarchy Process Model Be Effectively Applied in the Prioritization of Information Assurance Defense In-Depth Measures? --A Quantitative Study

ERIC Educational Resources Information Center

Alexander, Rodney T.

2017-01-01

Organizational computing devices are increasingly becoming targets of cyber-attacks, and organizations have become dependent on the safety and security of their computer networks and their organizational computing devices. Business and government often use defense in-depth information assurance measures such as firewalls, intrusion detection…

Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs

PubMed Central

Yan, Koon-Kiu; Wang, Daifeng; Sethi, Anurag; Muir, Paul; Kitchen, Robert; Cheng, Chao; Gerstein, Mark

2016-01-01

Biological systems are complex. In particular, the interactions between molecular components often form dense networks that, more often than not, are criticized for being inscrutable ‘hairballs’. We argue that one way of untangling these hairballs is through cross-disciplinary network comparison—leveraging advances in other disciplines to obtain new biological insights. In some cases, such comparisons enable the direct transfer of mathematical formalism between disciplines, precisely describing the abstract associations between entities and allowing us to apply a variety of sophisticated formalisms to biology. In cases where the detailed structure of the network does not permit the transfer of complete formalisms between disciplines, comparison of mechanistic interactions in systems for which we have significant day-to-day experience can provide analogies for interpreting relatively more abstruse biological networks. Here, we illustrate how these comparisons benefit the field with a few specific examples related to network growth, organizational hierarchies, and the evolution of adaptive systems. PMID:27047991

A Social Network System Based on an Ontology in the Korea Institute of Oriental Medicine

NASA Astrophysics Data System (ADS)

Kim, Sang-Kyun; Han, Jeong-Min; Song, Mi-Young

We in this paper propose a social network based on ontology in Korea Institute of Oriental Medicine (KIOM). By using the social network, researchers can find collaborators and share research results with others so that studies in Korean Medicine fields can be activated. For this purpose, first, personal profiles, scholarships, careers, licenses, academic activities, research results, and personal connections for all of researchers in KIOM are collected. After relationship and hierarchy among ontology classes and attributes of classes are defined through analyzing the collected information, a social network ontology are constructed using FOAF and OWL. This ontology can be easily interconnected with other social network by FOAF and provide the reasoning based on OWL ontology. In future, we construct the search and reasoning system using the ontology. Moreover, if the social network is activated, we will open it to whole Korean Medicine fields.

An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks

PubMed Central

Nasim, Mehwish; Qaisar, Saad; Lee, Sungyoung

2012-01-01

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes. PMID:22368459

Network Ecology and Adolescent Social Structure

PubMed Central

McFarland, Daniel A.; Moody, James; Diehl, David; Smith, Jeffrey A.; Thomas, Reuben J.

2014-01-01

Adolescent societies—whether arising from weak, short-term classroom friendships or from close, long-term friendships—exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time. PMID:25535409

Interplay between geo-population factors and hierarchy of cities in multilayer urban networks.

PubMed

Makarov, Vladimir V; Hramov, Alexander E; Kirsanov, Daniil V; Maksimenko, Vladimir A; Goremyko, Mikhail V; Ivanov, Alexey V; Yashkov, Ivan A; Boccaletti, Stefano

2017-12-08

Only taking into consideration the interplay between processes occurring at different levels of a country can provide the complete social and geopolitical plot of its urban system. We study the interaction of the administrative structure and the geographical connectivity between cities with the help of a multiplex network approach. We found that a spatially-distributed geo-network imposes its own ranking to the hierarchical administrative network, while the latter redistributes the shortest paths between nodes in the geographical layer. Using both real demographic data of population censuses of the Republic of Kazakhstan and theoretical models, we show that in a country-scale urban network and for each specific city, the geographical neighbouring with highly populated areas is more important than its political setting. Furthermore, the structure of political subordination is instead crucial for the wealth of transportation network and communication between populated regions of the country.

Network Ecology and Adolescent Social Structure.

PubMed

McFarland, Daniel A; Moody, James; Diehl, David; Smith, Jeffrey A; Thomas, Reuben J

2014-12-01

Adolescent societies-whether arising from weak, short-term classroom friendships or from close, long-term friendships-exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

Comprehensive evaluation of impacts of distributed generation integration in distribution network

NASA Astrophysics Data System (ADS)

Peng, Sujiang; Zhou, Erbiao; Ji, Fengkun; Cao, Xinhui; Liu, Lingshuang; Liu, Zifa; Wang, Xuyang; Cai, Xiaoyu

2018-04-01

All Distributed generation (DG) as the supplement to renewable energy centralized utilization, is becoming the focus of development direction of renewable energy utilization. With the increasing proportion of DG in distribution network, the network power structure, power flow distribution, operation plans and protection are affected to some extent. According to the main impacts of DG, a comprehensive evaluation model of distributed network with DG is proposed in this paper. A comprehensive evaluation index system including 7 aspects, along with their corresponding index calculation method is established for quantitative analysis. The indices under different access capacity of DG in distribution network are calculated based on the IEEE RBTS-Bus 6 system and the evaluation result is calculated by analytic hierarchy process (AHP). The proposed model and method are verified effective and validity through case study.

Gene networks and the evolution of plant morphology.

PubMed

Das Gupta, Mainak; Tsiantis, Miltos

2018-06-06

Elaboration of morphology depends on the precise orchestration of gene expression by key regulatory genes. The hierarchy and relationship among the participating genes is commonly known as gene regulatory network (GRN). Therefore, the evolution of morphology ultimately occurs by the rewiring of gene network structures or by the co-option of gene networks to novel domains. The availability of high-resolution expression data combined with powerful statistical tools have opened up new avenues to formulate and test hypotheses on how diverse gene networks influence trait development and diversity. Here we summarize recent studies based on both big-data and genetics approaches to understand the evolution of plant form and physiology. We also discuss recent genome-wide investigations on how studying open-chromatin regions may help study the evolution of gene expression patterns. Copyright © 2018. Published by Elsevier Ltd.

A Notch-dependent transcriptional hierarchy promotes mesenchymal transdifferentiation in the cardiac cushion.

PubMed

Chang, Alex C Y; Garside, Victoria C; Fournier, Michele; Smrz, Justin; Vrljicak, Pavle; Umlandt, Patricia; Fuller, Megan; Robertson, Gordon; Zhao, Yongjun; Tam, Angela; Jones, Steven J M; Marra, Marco A; Hoodless, Pamela A; Karsan, Aly

2014-07-01

Valvuloseptal defects are the most common congenital heart defects. Notch signaling-induced endothelial-to-mesenchymal transition (EMT) in the atrioventricular canal (AVC) cushions at murine embryonic day (E)9.5 is a required step during early valve development. Insights to the transcriptional network that is activated in endocardial cells (EC) during EMT and how these pathways direct valve maturation are lacking. We show that at E11.5, AVC-EC retain the ability to undergo Notch-dependent EMT when explanted on collagen. EC-Notch inhibition at E10.5 blocks expression of known mesenchymal genes in E11.5 AVC-EC. To understand the genetic network and AVC development downstream of Notch signaling beyond E9.5, we constructed Tag-Seq libraries corresponding to different cell types of the E11.5 AVC and atrium in wild-type mice and in EC-Notch inhibited mice. We identified 1,400 potential Notch targets in the AVC-EC, of which 124 are transcription factors (TF). From the 124 TFs, we constructed a transcriptional hierarchy and identify 10 upstream TFs within the network. We validated 4 of the upstream TFs as Notch targets that are enriched in AVC-EC. Functionally, we show these 4 TFs regulate EMT in AVC explant assays. These novel signaling pathways downstream of Notch are potentially relevant to valve development. © 2014 Wiley Periodicals, Inc.

A novel framework for command and control of networked sensor systems

NASA Astrophysics Data System (ADS)

Chen, Genshe; Tian, Zhi; Shen, Dan; Blasch, Erik; Pham, Khanh

2007-04-01

In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigation for future military operations by graphical game theory and advanced knowledge information fusion infrastructures. The problem is approached by representing distributed sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information superiority-shared, accurate, and timely situational awareness upon which advanced automated management aids for IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach.

Motivation but not valence modulates neuroticism-dependent cingulate cortex and insula activity.

PubMed

Deng, Yaling; Li, Shijia; Zhou, Renlai; Walter, Martin

2018-04-01

Neuroticism has been found to specifically modulate amygdala activations during differential processing of valence and motivation while other brain networks yet are unexplored for associated effects. The main purpose of this study was to investigate whether neural mechanisms processing valence or motivation are prone to neuroticism in the salience network (SN), a network that is anchored in the anterior cingulate cortex (ACC) and the anterior insula. This study used functional magnetic resonance imaging (fMRI) and an approach/avoid emotional pictures task to investigate brain activations modulated by pictures' valence or motivational status between high and low neurotic individuals. We found that neuroticism-dependent SN and the parahippocampal-fusiform area activations were modulated by motivation but not valence. Valence in contrast interacted with neuroticism in the lateral orbitofrontal cortex. We suggested that neuroticism modulated valence and motivation processing, however, under the influence of the two distinct networks. Neuroticism modulated the motivation through the SN while it modulated the valence through the orbitofrontal networks. © 2018 Wiley Periodicals, Inc.

Dense module enumeration in biological networks

NASA Astrophysics Data System (ADS)

Tsuda, Koji; Georgii, Elisabeth

2009-12-01

Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

A Novel Modulation Classification Approach Using Gabor Filter Network

PubMed Central

Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

2014-01-01

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

Information technology, Part 3. The technology hierarchy.

PubMed

Ruffin, M

1996-09-01

The era of the networked society--and medical care depending on networked intelligence--is dawning. Physicians need to plan for office practice information systems in common, with an eye to conveying data electronically between all the locations of care and all the providers involved in caring for defined populations of people. The shared database will become the most important asset of the collection of providers who make up the delivery system that creates it. This will be accomplished by layering technology on local and wide-area networks of group practices, hospitals, health plans, and payers and developing standards that make data accessible in the same format to all users, no matter where they are.

Social networks as embedded complex adaptive systems.

PubMed

Benham-Hutchins, Marge; Clancy, Thomas R

2010-09-01

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 15th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, the authors discuss healthcare social networks as a hierarchy of embedded complex adaptive systems. The authors further examine the use of social network analysis tools as a means to understand complex communication patterns and reduce medical errors.

Construct mine environment monitoring system based on wireless mesh network

NASA Astrophysics Data System (ADS)

Chen, Xin; Ge, Gengyu; Liu, Yinmei; Cheng, Aimin; Wu, Jun; Fu, Jun

2018-04-01

The system uses wireless Mesh network as a network transmission medium, and strive to establish an effective and reliable underground environment monitoring system. The system combines wireless network technology and embedded technology to monitor the internal data collected in the mine and send it to the processing center for analysis and environmental assessment. The system can be divided into two parts: the main control network module and the data acquisition terminal, and the SPI bus technology is used for mutual communication between them. Multi-channel acquisition and control interface design Data acquisition and control terminal in the analog signal acquisition module, digital signal acquisition module, and digital signal output module. The main control network module running Linux operating system, in which the transplant SPI driver, USB card driver and AODV routing protocol. As a result, the internal data collection and reporting of the mine are realized.

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

PubMed Central

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

2013-01-01

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

How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks

PubMed Central

Chen, Heng; Chen, Xinying

2018-01-01

Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system. PMID:29489837

How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks.

PubMed

Chen, Heng; Chen, Xinying; Liu, Haitao

2018-01-01

Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system.

Environmental versatility promotes modularity in genome-scale metabolic networks.

PubMed

Samal, Areejit; Wagner, Andreas; Martin, Olivier C

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-03-12

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

PubMed Central

Schrum, Jacob; Miikkulainen, Risto

2015-01-01

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games. PMID:27030803

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

PubMed Central

Park, Juyong; Yook, Soon-Hyung

2014-01-01

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest – essential in determining reward and penalty – is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the “Natural Ranking,” an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks. PMID:25163528

Probabilistic Analysis of Hierarchical Cluster Protocols for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Kaj, Ingemar

Wireless sensor networks are designed to extract data from the deployment environment and combine sensing, data processing and wireless communication to provide useful information for the network users. Hundreds or thousands of small embedded units, which operate under low-energy supply and with limited access to central network control, rely on interconnecting protocols to coordinate data aggregation and transmission. Energy efficiency is crucial and it has been proposed that cluster based and distributed architectures such as LEACH are particularly suitable. We analyse the random cluster hierarchy in this protocol and provide a solution for low-energy and limited-loss optimization. Moreover, we extend these results to a multi-level version of LEACH, where clusters of nodes again self-organize to form clusters of clusters, and so on.

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

NASA Astrophysics Data System (ADS)

Park, Juyong; Yook, Soon-Hyung

2014-08-01

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest - essential in determining reward and penalty - is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the ``Natural Ranking,'' an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks.

Apparatus for fixing latency

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

Hall, David R; Bartholomew, David B; Moon, Justin

2009-09-08

An apparatus for fixing computational latency within a deterministic region on a network comprises a network interface modem, a high priority module and at least one deterministic peripheral device. The network interface modem is in communication with the network. The high priority module is in communication with the network interface modem. The at least one deterministic peripheral device is connected to the high priority module. The high priority module comprises a packet assembler/disassembler, and hardware for performing at least one operation. Also disclosed is an apparatus for executing at least one instruction on a downhole device within a deterministic region,more » the apparatus comprising a control device, a downhole network, and a downhole device. The control device is near the surface of a downhole tool string. The downhole network is integrated into the tool string. The downhole device is in communication with the downhole network.« less

Silicon photonic Mach Zehnder modulators for next-generation short-reach optical communication networks

NASA Astrophysics Data System (ADS)

Lacava, C.; Liu, Z.; Thomson, D.; Ke, Li; Fedeli, J. M.; Richardson, D. J.; Reed, G. T.; Petropoulos, P.

2016-02-01

Communication traffic grows relentlessly in today's networks, and with ever more machines connected to the network, this trend is set to continue for the foreseeable future. It is widely accepted that increasingly faster communications are required at the point of the end users, and consequently optical transmission plays a progressively greater role even in short- and medium-reach networks. Silicon photonic technologies are becoming increasingly attractive for such networks, due to their potential for low cost, energetically efficient, high-speed optical components. A representative example is the silicon-based optical modulator, which has been actively studied. Researchers have demonstrated silicon modulators in different types of structures, such as ring resonators or slow light based devices. These approaches have shown remarkably good performance in terms of modulation efficiency, however their operation could be severely affected by temperature drifts or fabrication errors. Mach-Zehnder modulators (MZM), on the other hand, show good performance and resilience to different environmental conditions. In this paper we present a CMOS-compatible compact silicon MZM. We study the application of the modulator to short-reach interconnects by realizing data modulation using some relevant advanced modulation formats, such as 4-level Pulse Amplitude Modulation (PAM-4) and Discrete Multi-Tone (DMT) modulation and compare the performance of the different systems in transmission.

Top-down preparation modulates visual categorization but not subjective awareness of objects presented in natural backgrounds.

PubMed

Koivisto, Mika; Kahila, Ella

2017-04-01

Top-down processes are widely assumed to be essential in visual awareness, subjective experience of seeing. However, previous studies have not tried to separate directly the roles of different types of top-down influences in visual awareness. We studied the effects of top-down preparation and object substitution masking (OSM) on visual awareness during categorization of objects presented in natural scene backgrounds. The results showed that preparation facilitated categorization but did not influence visual awareness. OSM reduced visual awareness and impaired categorization. The dissociations between the effects of preparation and OSM on visual awareness and on categorization imply that they influence at different stages of cognitive processing. We propose that preparation influences at the top of the visual hierarchy, whereas OSM interferes with processes occurring at lower levels of the hierarchy. These lower level processes play an essential role in visual awareness. Copyright © 2017 Elsevier Ltd. All rights reserved.

The challenge of a data storage hierarchy

NASA Technical Reports Server (NTRS)

Ruderman, Michael

1992-01-01

A discussion of Mesa Archival Systems' data archiving system is presented. This data archiving system is strictly a software system that is implemented on a mainframe and manages the data into permanent file storage. Emphasis is placed on the fact that any kind of client system on the network can be connected through the Unix interface of the data archiving system.

Classifiers in ASL: A Manual for Instructors. American Sign Language Community College Network.

ERIC Educational Resources Information Center

Peralta Community Coll. System, Berkeley, CA. Vista Coll.

Following a discussion of the role of classifiers (i.e., verbs of motion and location) in American Sign Language, this manual presents a six-unit program designed to teach students to produce sentences with classifiers. First, an overview is provided of the hierarchy of verbs of motion and location produced when the resources of the body are…

Social Network Analysis of Crowds

DTIC Science & Technology

2009-08-06

crowd responses to non-lethal weapons d tan sys ems – Prior, existing social relationships – Real time social interactions – Formal/informal...Crowd Behavior Testbed Layout Video Cameras on Trusses Importance of Social Factors • Response to non-lethal weapons fire depends on social ... relationships among crowd members – Pre-existing Personal Relationships – Ongoing Real Time Social Interactions – Formal/Informal Hierarchies • Therefore

A Study towards Building An Optimal Graph Theory Based Model For The Design of Tourism Website

NASA Astrophysics Data System (ADS)

Panigrahi, Goutam; Das, Anirban; Basu, Kajla

2010-10-01

Effective tourism website is a key to attract tourists from different parts of the world. Here we identify the factors of improving the effectiveness of website by considering it as a graph, where web pages including homepage are the nodes and hyperlinks are the edges between the nodes. In this model, the design constraints for building a tourism website are taken into consideration. Our objectives are to build a framework of an effective tourism website providing adequate level of information, service and also to enable the users to reach to the desired page by spending minimal loading time. In this paper an information hierarchy specifying the upper limit of outgoing link of a page has also been proposed. Following the hierarchy, the web developer can prepare an effective tourism website. Here loading time depends on page size and network traffic. We have assumed network traffic as uniform and the loading time is directly proportional with page size. This approach is done by quantifying the link structure of a tourism website. In this approach we also propose a page size distribution pattern of a tourism website.

Devices development and techniques research for space life sciences

NASA Astrophysics Data System (ADS)

Zhang, A.; Liu, B.; Zheng, C.

The development process and the status quo of the devices and techniques for space life science in China and the main research results in this field achieved by Shanghai Institute of Technical Physics SITP CAS are reviewed concisely in this paper On the base of analyzing the requirements of devices and techniques for supporting space life science experiments and researches one designment idea of developing different intelligent modules with professional function standard interface and easy to be integrated into system is put forward and the realization method of the experiment system with intelligent distributed control based on the field bus are discussed in three hierarchies Typical sensing or control function cells with certain self-determination control data management and communication abilities are designed and developed which are called Intelligent Agents Digital hardware network system which are consisted of the distributed Agents as the intelligent node is constructed with the normative opening field bus technology The multitask and real-time control application softwares are developed in the embedded RTOS circumstance which is implanted into the system hardware and space life science experiment system platform with characteristic of multitasks multi-courses professional and instant integration will be constructed

Fault-tolerant battery system employing intra-battery network architecture

DOEpatents

Hagen, Ronald A.; Chen, Kenneth W.; Comte, Christophe; Knudson, Orlin B.; Rouillard, Jean

2000-01-01

A distributed energy storing system employing a communications network is disclosed. A distributed battery system includes a number of energy storing modules, each of which includes a processor and communications interface. In a network mode of operation, a battery computer communicates with each of the module processors over an intra-battery network and cooperates with individual module processors to coordinate module monitoring and control operations. The battery computer monitors a number of battery and module conditions, including the potential and current state of the battery and individual modules, and the conditions of the battery's thermal management system. An over-discharge protection system, equalization adjustment system, and communications system are also controlled by the battery computer. The battery computer logs and reports various status data on battery level conditions which may be reported to a separate system platform computer. A module transitions to a stand-alone mode of operation if the module detects an absence of communication connectivity with the battery computer. A module which operates in a stand-alone mode performs various monitoring and control functions locally within the module to ensure safe and continued operation.

Enhancing biological relevance of a weighted gene co-expression network for functional module identification.

PubMed

Prom-On, Santitham; Chanthaphan, Atthawut; Chan, Jonathan Hoyin; Meechai, Asawin

2011-02-01

Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.

Microarray and network-based identification of functional modules and pathways of active tuberculosis.

PubMed

Bian, Zhong-Rui; Yin, Juan; Sun, Wen; Lin, Dian-Jie

2017-04-01

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB. Copyright © 2017 Elsevier Ltd. All rights reserved.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Hierarchical surface code for network quantum computing with modules of arbitrary size

NASA Astrophysics Data System (ADS)

Li, Ying; Benjamin, Simon C.

2016-10-01

The network paradigm for quantum computing involves interconnecting many modules to form a scalable machine. Typically it is assumed that the links between modules are prone to noise while operations within modules have a significantly higher fidelity. To optimize fault tolerance in such architectures we introduce a hierarchical generalization of the surface code: a small "patch" of the code exists within each module and constitutes a single effective qubit of the logic-level surface code. Errors primarily occur in a two-dimensional subspace, i.e., patch perimeters extruded over time, and the resulting noise threshold for intermodule links can exceed ˜10 % even in the absence of purification. Increasing the number of qubits within each module decreases the number of qubits necessary for encoding a logical qubit. But this advantage is relatively modest, and broadly speaking, a "fine-grained" network of small modules containing only about eight qubits is competitive in total qubit count versus a "course" network with modules containing many hundreds of qubits.

Network Analysis Implicates Alpha-Synuclein (Snca) in the Regulation of Ovariectomy-Induced Bone Loss

PubMed Central

Calabrese, Gina; Mesner, Larry D.; Foley, Patricia L.; Rosen, Clifford J.; Farber, Charles R.

2016-01-01

The postmenopausal period in women is associated with decreased circulating estrogen levels, which accelerate bone loss and increase the risk of fracture. Here, we gained novel insight into the molecular mechanisms mediating bone loss in ovariectomized (OVX) mice, a model of human menopause, using co-expression network analysis. Specifically, we generated a co-expression network consisting of 53 gene modules using expression profiles from intact and OVX mice from a panel of inbred strains. The expression of four modules was altered by OVX, including module 23 whose expression was decreased by OVX across all strains. Module 23 was enriched for genes involved in the response to oxidative stress, a process known to be involved in OVX-induced bone loss. Additionally, module 23 homologs were co-expressed in human bone marrow. Alpha synuclein (Snca) was one of the most highly connected “hub” genes in module 23. We characterized mice deficient in Snca and observed a 40% reduction in OVX-induced bone loss. Furthermore, protection was associated with the altered expression of specific network modules, including module 23. In summary, the results of this study suggest that Snca regulates bone network homeostasis and ovariectomy-induced bone loss. PMID:27378017

Conceptual Hierarchies in a Flat Attractor Network

PubMed Central

O’Connor, Christopher M.; Cree, George S.; McRae, Ken

2009-01-01

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. PMID:19543434

Damage Response in Fluid Flow Networks

NASA Astrophysics Data System (ADS)

Gavrilchenko, Tatyana; Katifori, Eleni

The networks found in biological fluid flow systems such as leaf venation and animal vasculature are characterized by hierarchically nested loops. This structure allows the system to be resilient against fluctuations in the flow of fluid and to be robust against damage. We analytically and computationally investigate how this loopy hierarchy determines the extent of disruption in fluid flow in the vicinity of a damage site. Perturbing the network with the removal of a single edge results in the differential flow as a function of distance from the perturbation decaying as a power law. The power law exponent is generally around -2 in 2D, but we find that it varies due to edge effects, initial edge conductivity, and local topology. We expect that these network flow findings, directly applicable to plant and animal veins, will have analogues in electrical grids, traffic flow and other transport networks.

Utilizing semantic networks to database and retrieve generalized stochastic colored Petri nets

NASA Technical Reports Server (NTRS)

Farah, Jeffrey J.; Kelley, Robert B.

1992-01-01

Previous work has introduced the Planning Coordinator (PCOORD), a coordinator functioning within the hierarchy of the Intelligent Machine Mode. Within the structure of the Planning Coordinator resides the Primitive Structure Database (PSDB) functioning to provide the primitive structures utilized by the Planning Coordinator in the establishing of error recovery or on-line path plans. This report further explores the Primitive Structure Database and establishes the potential of utilizing semantic networks as a means of efficiently storing and retrieving the Generalized Stochastic Colored Petri Nets from which the error recovery plans are derived.

The varieties of immunological experience: of pathogens, stress, and dendritic cells.

PubMed

Pulendran, Bali

2015-01-01

In the 40 years since their discovery, dendritic cells (DCs) have been recognized as central players in immune regulation. DCs sense microbial stimuli through pathogen-recognition receptors (PRRs) and decode, integrate, and present information derived from such stimuli to T cells, thus stimulating immune responses. DCs can also regulate the quality of immune responses. Several functionally specialized subsets of DCs exist, but DCs also display functional plasticity in response to diverse stimuli. In addition to sensing pathogens via PRRs, emerging evidence suggests that DCs can also sense stress signals, such as amino acid starvation, through ancient stress and nutrient sensing pathways, to stimulate adaptive immunity. Here, I discuss these exciting advances in the context of a historic perspective on the discovery of DCs and their role in immune regulation. I conclude with a discussion of emerging areas in DC biology in the systems immunology era and suggest that the impact of DCs on immunity can be usefully contextualized in a hierarchy-of-organization model in which DCs, their receptors and signaling networks, cell-cell interactions, tissue microenvironment, and the host macroenvironment represent different levels of the hierarchy. Immunity or tolerance can then be represented as a complex function of each of these hierarchies.

Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

PubMed Central

2014-01-01

Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

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

PubMed

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

2008-08-15

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

A study of complications affecting surgery performance: an ISM-based roadmap to patient flow.

PubMed

Dev, Navin K; Shankar, Ravi; Arvind, Kamal

2013-01-01

The aim of this study is to highlight the value of the success rate performance of a surgery while planning patient flow within a supply chain of a health care organization/hospital. The paper has considered one of the common surgeries, cataract, and the complications that subsequently result from this surgery. The study employs interpretive structural modeling (ISM) approach to draw a roadmap to study various complications causing cataract that subsequently help in planning and coordination of patient flow. The study finds that there is a hierarchy of causes and certain complications, the persistence of which gives a higher success rate performance in cataract surgery as compared to others. The paper provides leverage to the decision maker while organizing the patient flow depending upon the information of hierarchy of complication of a disease, and accordingly ensures the availability of resources to the patient. The study is of value in identifying the degree of complications from cataract surgery. Given the degree of complication, the patient logistics can be planned myopically in a health care organization which largely depends upon the degree of success rate. The paper attempts to suggest that the hierarchy obtained through ISM can be implemented in the modules of an enterprise resource planning (ERP) set up.

Functional modules by relating protein interaction networks and gene expression.

PubMed

Tornow, Sabine; Mewes, H W

2003-11-01

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

Functional modules by relating protein interaction networks and gene expression

PubMed Central

Tornow, Sabine; Mewes, H. W.

2003-01-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships. PMID:14576317

A Cobalt Supramolecular Triple-Stranded Helicate-based Discrete Molecular Cage

PubMed Central

Mai, Hien Duy; Kang, Philjae; Kim, Jin Kyung; Yoo, Hyojong

2017-01-01

We report a strategy to achieve a discrete cage molecule featuring a high level of structural hierarchy through a multiple-assembly process. A cobalt (Co) supramolecular triple-stranded helicate (Co-TSH)-based discrete molecular cage (1) is successfully synthesized and fully characterized. The solid-state structure of 1 shows that it is composed of six triple-stranded helicates interconnected by four linking cobalt species. This is an unusual example of a highly symmetric cage architecture resulting from the coordination-driven assembly of metallosupramolecular modules. The molecular cage 1 shows much higher CO2 uptake properties and selectivity compared with the separate supramolecular modules (Co-TSH, complex 2) and other molecular platforms. PMID:28262690

Ecological modules and roles of species in heathland plant-insect flower visitor networks.

PubMed

Dupont, Yoko L; Olesen, Jens M

2009-03-01

1. Co-existing plants and flower-visiting animals often form complex interaction networks. A long-standing question in ecology and evolutionary biology is how to detect nonrandom subsets (compartments, blocks, modules) of strongly interacting species within such networks. Here we use a network analytical approach to (i) detect modularity in pollination networks, (ii) investigate species composition of modules, and (iii) assess the stability of modules across sites. 2. Interactions between entomophilous plants and their flower-visitors were recorded throughout the flowering season at three heathland sites in Denmark, separated by >or= 10 km. Among sites, plant communities were similar, but composition of flower-visiting insect faunas differed. Visitation frequencies of visitor species were recorded as a measure of insect abundance. 3. Qualitative (presence-absence) interaction networks were tested for modularity. Modules were identified, and species classified into topological roles (peripherals, connectors, or hubs) using 'functional cartography by simulated annealing', a method recently developed by Guimerà & Amaral (2005a). 4. All networks were significantly modular. Each module consisted of 1-6 plant species and 18-54 insect species. Interactions aggregated around one or two hub plant species, which were largely identical at the three study sites. 5. Insect species were categorized in taxonomic groups, mostly at the level of orders. When weighted by visitation frequency, each module was dominated by one or few insect groups. This pattern was consistent across sites. 6. Our study adds support to the conclusion that certain plant species and flower-visitor groups are nonrandomly and repeatedly associated. Within a network, these strongly interacting subgroups of species may exert reciprocal selection pressures on each other. Thus, modules may be candidates for the long-sought key units of co-evolution.

Automatically identifying health- and clinical-related content in wikipedia.

PubMed

Liu, Feifan; Moosavinasab, Soheil; Agarwal, Shashank; Bennett, Andrew S; Yu, Hong

2013-01-01

Physicians are increasingly using the Internet for finding medical information related to patient care. Wikipedia is a valuable online medical resource to be integrated into existing clinical question answering (QA) systems. On the other hand, Wikipedia contains a full spectrum of world's knowledge and therefore comprises a large partition of non-health-related content, which makes disambiguation more challenging and consequently leads to large overhead for existing systems to effectively filter irrelevant information. To overcome this, we have developed both unsupervised and supervised approaches to identify health-related articles as well as clinically relevant articles. Furthermore, we explored novel features by extracting health related hierarchy from the Wikipedia category network, from which a variety of features were derived and evaluated. Our experiments show promising results and also demonstrate that employing the category hierarchy can effectively improve the system performance.

Auditing Complex Concepts in Overlapping Subsets of SNOMED

PubMed Central

Wang, Yue; Wei, Duo; Xu, Junchuan; Elhanan, Gai; Perl, Yehoshua; Halper, Michael; Chen, Yan; Spackman, Kent A.; Hripcsak, George

2008-01-01

Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED’s Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries. PMID:18998838

Auditing complex concepts in overlapping subsets of SNOMED.

PubMed

Wang, Yue; Wei, Duo; Xu, Junchuan; Elhanan, Gai; Perl, Yehoshua; Halper, Michael; Chen, Yan; Spackman, Kent A; Hripcsak, George

2008-11-06

Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries.

MINE: Module Identification in Networks

PubMed Central

2011-01-01

Background Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks. Results MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties. Conclusions MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans. PMID:21605434

Detecting phenotype-driven transitions in regulatory network structure.

PubMed

Padi, Megha; Quackenbush, John

2018-01-01

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.

Social networks and spreading of epidemics

NASA Astrophysics Data System (ADS)

Trimper, Steffen; Zheng, Dafang; Brandau, Marian

2004-05-01

Epidemiological processes are studied within a recently proposed social network model using the susceptible-infected-refractory dynamics (SIR) of an epidemic. Within the network model, a population of individuals may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveals that for H > 1, the global spreading results regardless of the degree of homophily α of the individuals forming a social circle. For H = 1, a transition from a global to a local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large scale outbreaks of infectious diseases (viruses). The SIR-model can be extended by the inclusion of waiting times resulting in modified distribution function of the recovered.

Graph coarse-graining reveals differences in the module-level structure of functional brain networks.

PubMed

Kujala, Rainer; Glerean, Enrico; Pan, Raj Kumar; Jääskeläinen, Iiro P; Sams, Mikko; Saramäki, Jari

2016-11-01

Networks have become a standard tool for analyzing functional magnetic resonance imaging (fMRI) data. In this approach, brain areas and their functional connections are mapped to the nodes and links of a network. Even though this mapping reduces the complexity of the underlying data, it remains challenging to understand the structure of the resulting networks due to the large number of nodes and links. One solution is to partition networks into modules and then investigate the modules' composition and relationship with brain functioning. While this approach works well for single networks, understanding differences between two networks by comparing their partitions is difficult and alternative approaches are thus necessary. To this end, we present a coarse-graining framework that uses a single set of data-driven modules as a frame of reference, enabling one to zoom out from the node- and link-level details. As a result, differences in the module-level connectivity can be understood in a transparent, statistically verifiable manner. We demonstrate the feasibility of the method by applying it to networks constructed from fMRI data recorded from 13 healthy subjects during rest and movie viewing. While independently partitioning the rest and movie networks is shown to yield little insight, the coarse-graining framework enables one to pinpoint differences in the module-level structure, such as the increased number of intra-module links within the visual cortex during movie viewing. In addition to quantifying differences due to external stimuli, the approach could also be applied in clinical settings, such as comparing patients with healthy controls. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

A mean field neural network for hierarchical module placement

NASA Technical Reports Server (NTRS)

Unaltuna, M. Kemal; Pitchumani, Vijay

1992-01-01

This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules.

Mapping, Awareness, And Virtualization Network Administrator Training Tool Virtualization Module

DTIC Science & Technology

2016-03-01

AND VIRTUALIZATION NETWORK ADMINISTRATOR TRAINING TOOL VIRTUALIZATION MODULE by Erik W. Berndt March 2016 Thesis Advisor: John Gibson...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE MAPPING, AWARENESS, AND VIRTUALIZATION NETWORK ADMINISTRATOR TRAINING TOOL... VIRTUALIZATION MODULE 5. FUNDING NUMBERS 6. AUTHOR(S) Erik W. Berndt 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School

The Impact of The Fractal Paradigm on Geography

NASA Astrophysics Data System (ADS)

De Cola, L.

2001-12-01

Being itself somewhat fractal, Benoit Mandelbrot's magnum opus THE FRACTAL GEOMETRY OF NATURE may be deconstructed in many ways, including geometrically, systematically, and epistemologically. Viewed as a work of geography it may be used to organize the major topics of interest to scientists preoccupied with the understanding of real-world space in astronomy, geology, meteorology, hydrology, and biology. We shall use it to highlight such recent geographic accomplishments as automated feature detection, understanding urban growth, and modeling the spread of disease in space and time. However, several key challenges remain unsolved, among them: 1. It is still not possible to move continuously from one map scale to another so that objects change their dimension smoothly. I.e. as a viewer zooms in on a map the zero-dimensional location of a city should gradually become a 2-dimensional polygon, then a network of 1-dimensional streets, then 3-dimensional buildings, etc. 2. Spatial autocorrelation continues to be regarded more as an econometric challenge than as a problem of scaling. Similarities of values among closely-spaced observation is not so much a problem to be overcome as a source of information about spatial structure. 3. Although the fractal paradigm is a powerful model for data analysis, its ideas and techniques need to be brought to bear on the problems of understanding such hierarchies as ecosystems (the flow networks of energy and matter), taxonomies (biological classification), and knowledge (hierarchies of bureaucratic information, networks of linked data, etc).

Quality assurance of the gene ontology using abstraction networks.

PubMed

Ochs, Christopher; Perl, Yehoshua; Halper, Michael; Geller, James; Lomax, Jane

2016-06-01

The gene ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks (AbNs) are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology. They have been shown to successfully support QA work in the context of various ontologies. Two kinds of AbNs, called the area taxonomy and the partial-area taxonomy, are developed for GO hierarchies and derived specifically for the biological process (BP) hierarchy. Within this framework, several QA heuristics, based on the identification of groups of anomalous terms which exhibit certain taxonomy-defined characteristics, are introduced. Such groups are expected to have higher error rates when compared to other terms. Thus, by focusing QA efforts on anomalous terms one would expect to find relatively more erroneous content. By automatically identifying these potential problem areas within an ontology, time and effort will be saved during manual reviews of GO's content. BP is used as a testbed, with samples of three kinds of anomalous BP terms chosen for a taxonomy-based QA review. Additional heuristics for QA are demonstrated. From the results of this QA effort, it is observed that different kinds of inconsistencies in the modeling of GO can be exposed with the use of the proposed heuristics. For comparison, the results of QA work on a sample of terms chosen from GO's general population are presented.

A new multi-scale method to reveal hierarchical modular structures in biological networks.

PubMed

Jiao, Qing-Ju; Huang, Yan; Shen, Hong-Bin

2016-11-15

Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

Network Control Center User Planning System (NCC UPS)

NASA Astrophysics Data System (ADS)

Dealy, Brian

1991-09-01

NCC UPS is presented in the form of the viewgraphs. The following subject areas are covered: UPS overview; NCC UPS role; major NCC UPS functional requirements; interactive user access levels; UPS interfaces; interactive user subsystem; interface navigation; scheduling screen hierarchy; interactive scheduling input panels; autogenerated schedule request panel; schedule data tabular display panel; schedule data graphic display panel; graphic scheduling aid design; and schedule data graphic display.

Network Control Center User Planning System (NCC UPS)

NASA Technical Reports Server (NTRS)

Dealy, Brian

1991-01-01

NCC UPS is presented in the form of the viewgraphs. The following subject areas are covered: UPS overview; NCC UPS role; major NCC UPS functional requirements; interactive user access levels; UPS interfaces; interactive user subsystem; interface navigation; scheduling screen hierarchy; interactive scheduling input panels; autogenerated schedule request panel; schedule data tabular display panel; schedule data graphic display panel; graphic scheduling aid design; and schedule data graphic display.

Evaluation of Organisational Interoperabiity in a Network Centric Warfare Environment

DTIC Science & Technology

2004-09-01

understanding developed. Command and Coordination examines issues related to command structure, command and leadership styles . Ethos covers socio...harmonisation of command arrangements and the accommodation of differences in command and leadership styles . 3.2.4 Ethos Future warfare will... leadership styles . • changes to give less emphasis to hierarchy and command and more to coordination. Any reference to a single chain of command has been

State-dependent, bidirectional modulation of neural network activity by endocannabinoids.

PubMed

Piet, Richard; Garenne, André; Farrugia, Fanny; Le Masson, Gwendal; Marsicano, Giovanni; Chavis, Pascale; Manzoni, Olivier J

2011-11-16

The endocannabinoid (eCB) system and the cannabinoid CB1 receptor (CB1R) play key roles in the modulation of brain functions. Although actions of eCBs and CB1Rs are well described at the synaptic level, little is known of their modulation of neural activity at the network level. Using microelectrode arrays, we have examined the role of CB1R activation in the modulation of the electrical activity of rat and mice cortical neural networks in vitro. We find that exogenous activation of CB1Rs expressed on glutamatergic neurons decreases the spontaneous activity of cortical neural networks. Moreover, we observe that the net effect of the CB1R antagonist AM251 inversely correlates with the initial level of activity in the network: blocking CB1Rs increases network activity when basal network activity is low, whereas it depresses spontaneous activity when its initial level is high. Our results reveal a complex role of CB1Rs in shaping spontaneous network activity, and suggest that the outcome of endogenous neuromodulation on network function might be state dependent.

RM-SORN: a reward-modulated self-organizing recurrent neural network.

PubMed

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Prokaryotic regulatory systems biology: Common principles governing the functional architectures of Bacillus subtilis and Escherichia coli unveiled by the natural decomposition approach.

PubMed

Freyre-González, Julio A; Treviño-Quintanilla, Luis G; Valtierra-Gutiérrez, Ilse A; Gutiérrez-Ríos, Rosa María; Alonso-Pavón, José A

2012-10-31

Escherichia coli and Bacillus subtilis are two of the best-studied prokaryotic model organisms. Previous analyses of their transcriptional regulatory networks have shown that they exhibit high plasticity during evolution and suggested that both converge to scale-free-like structures. Nevertheless, beyond this suggestion, no analyses have been carried out to identify the common systems-level components and principles governing these organisms. Here we show that these two phylogenetically distant organisms follow a set of common novel biologically consistent systems principles revealed by the mathematically and biologically founded natural decomposition approach. The discovered common functional architecture is a diamond-shaped, matryoshka-like, three-layer (coordination, processing, and integration) hierarchy exhibiting feedback, which is shaped by four systems-level components: global transcription factors (global TFs), locally autonomous modules, basal machinery and intermodular genes. The first mathematical criterion to identify global TFs, the κ-value, was reassessed on B. subtilis and confirmed its high predictive power by identifying all the previously reported, plus three potential, master regulators and eight sigma factors. The functionally conserved cores of modules, basal cell machinery, and a set of non-orthologous common physiological global responses were identified via both orthologous genes and non-orthologous conserved functions. This study reveals novel common systems principles maintained between two phylogenetically distant organisms and provides a comparison of their lifestyle adaptations. Our results shed new light on the systems-level principles and the fundamental functions required by bacteria to sustain life. Copyright © 2012 Elsevier B.V. All rights reserved.

Letter of intent for KM3NeT 2.0

NASA Astrophysics Data System (ADS)

Adrián-Martínez, S.; Ageron, M.; Aharonian, F.; Aiello, S.; Albert, A.; Ameli, F.; Anassontzis, E.; Andre, M.; Androulakis, G.; Anghinolfi, M.; Anton, G.; Ardid, M.; Avgitas, T.; Barbarino, G.; Barbarito, E.; Baret, B.; Barrios-Martí, J.; Belhorma, B.; Belias, A.; Berbee, E.; van den Berg, A.; Bertin, V.; Beurthey, S.; van Beveren, V.; Beverini, N.; Biagi, S.; Biagioni, A.; Billault, M.; Bondì, M.; Bormuth, R.; Bouhadef, B.; Bourlis, G.; Bourret, S.; Boutonnet, C.; Bouwhuis, M.; Bozza, C.; Bruijn, R.; Brunner, J.; Buis, E.; Busto, J.; Cacopardo, G.; Caillat, L.; Calamai, M.; Calvo, D.; Capone, A.; Caramete, L.; Cecchini, S.; Celli, S.; Champion, C.; Cherkaoui El Moursli, R.; Cherubini, S.; Chiarusi, T.; Circella, M.; Classen, L.; Cocimano, R.; Coelho, J. A. B.; Coleiro, A.; Colonges, S.; Coniglione, R.; Cordelli, M.; Cosquer, A.; Coyle, P.; Creusot, A.; Cuttone, G.; D'Amico, A.; De Bonis, G.; De Rosa, G.; De Sio, C.; Di Capua, F.; Di Palma, I.; Díaz García, A. F.; Distefano, C.; Donzaud, C.; Dornic, D.; Dorosti-Hasankiadeh, Q.; Drakopoulou, E.; Drouhin, D.; Drury, L.; Durocher, M.; Eberl, T.; Eichie, S.; van Eijk, D.; El Bojaddaini, I.; El Khayati, N.; Elsaesser, D.; Enzenhöfer, A.; Fassi, F.; Favali, P.; Fermani, P.; Ferrara, G.; Filippidis, C.; Frascadore, G.; Fusco, L. A.; Gal, T.; Galatà, S.; Garufi, F.; Gay, P.; Gebyehu, M.; Giordano, V.; Gizani, N.; Gracia, R.; Graf, K.; Grégoire, T.; Grella, G.; Habel, R.; Hallmann, S.; van Haren, H.; Harissopulos, S.; Heid, T.; Heijboer, A.; Heine, E.; Henry, S.; Hernández-Rey, J. J.; Hevinga, M.; Hofestädt, J.; Hugon, C. M. F.; Illuminati, G.; James, C. W.; Jansweijer, P.; Jongen, M.; de Jong, M.; Kadler, M.; Kalekin, O.; Kappes, A.; Katz, U. F.; Keller, P.; Kieft, G.; Kießling, D.; Koffeman, E. N.; Kooijman, P.; Kouchner, A.; Kulikovskiy, V.; Lahmann, R.; Lamare, P.; Leisos, A.; Leonora, E.; Clark, M. Lindsey; Liolios, A.; Llorens Alvarez, C. D.; Lo Presti, D.; Löhner, H.; Lonardo, A.; Lotze, M.; Loucatos, S.; Maccioni, E.; Mannheim, K.; Margiotta, A.; Marinelli, A.; Mariş, O.; Markou, C.; Martínez-Mora, J. A.; Martini, A.; Mele, R.; Melis, K. W.; Michael, T.; Migliozzi, P.; Migneco, E.; Mijakowski, P.; Miraglia, A.; Mollo, C. M.; Mongelli, M.; Morganti, M.; Moussa, A.; Musico, P.; Musumeci, M.; Navas, S.; Nicolau, C. A.; Olcina, I.; Olivetto, C.; Orlando, A.; Papaikonomou, A.; Papaleo, R.; Păvălaş, G. E.; Peek, H.; Pellegrino, C.; Perrina, C.; Pfutzner, M.; Piattelli, P.; Pikounis, K.; Poma, G. E.; Popa, V.; Pradier, T.; Pratolongo, F.; Pühlhofer, G.; Pulvirenti, S.; Quinn, L.; Racca, C.; Raffaelli, F.; Randazzo, N.; Rapidis, P.; Razis, P.; Real, D.; Resvanis, L.; Reubelt, J.; Riccobene, G.; Rossi, C.; Rovelli, A.; Saldaña, M.; Salvadori, I.; Samtleben, D. F. E.; Sánchez García, A.; Sánchez Losa, A.; Sanguineti, M.; Santangelo, A.; Santonocito, D.; Sapienza, P.; Schimmel, F.; Schmelling, J.; Sciacca, V.; Sedita, M.; Seitz, T.; Sgura, I.; Simeone, F.; Siotis, I.; Sipala, V.; Spisso, B.; Spurio, M.; Stavropoulos, G.; Steijger, J.; Stellacci, S. M.; Stransky, D.; Taiuti, M.; Tayalati, Y.; Tézier, D.; Theraube, S.; Thompson, L.; Timmer, P.; Tönnis, C.; Trasatti, L.; Trovato, A.; Tsirigotis, A.; Tzamarias, S.; Tzamariudaki, E.; Vallage, B.; Van Elewyck, V.; Vermeulen, J.; Vicini, P.; Viola, S.; Vivolo, D.; Volkert, M.; Voulgaris, G.; Wiggers, L.; Wilms, J.; de Wolf, E.; Zachariadou, K.; Zornoza, J. D.; Zúñiga, J.

2016-08-01

The main objectives of the KM3NeT Collaboration are (i) the discovery and subsequent observation of high-energy neutrino sources in the Universe and (ii) the determination of the mass hierarchy of neutrinos. These objectives are strongly motivated by two recent important discoveries, namely: (1) the high-energy astrophysical neutrino signal reported by IceCube and (2) the sizable contribution of electron neutrinos to the third neutrino mass eigenstate as reported by Daya Bay, Reno and others. To meet these objectives, the KM3NeT Collaboration plans to build a new Research Infrastructure consisting of a network of deep-sea neutrino telescopes in the Mediterranean Sea. A phased and distributed implementation is pursued which maximises the access to regional funds, the availability of human resources and the synergistic opportunities for the Earth and sea sciences community. Three suitable deep-sea sites are selected, namely off-shore Toulon (France), Capo Passero (Sicily, Italy) and Pylos (Peloponnese, Greece). The infrastructure will consist of three so-called building blocks. A building block comprises 115 strings, each string comprises 18 optical modules and each optical module comprises 31 photo-multiplier tubes. Each building block thus constitutes a three-dimensional array of photo sensors that can be used to detect the Cherenkov light produced by relativistic particles emerging from neutrino interactions. Two building blocks will be sparsely configured to fully explore the IceCube signal with similar instrumented volume, different methodology, improved resolution and complementary field of view, including the galactic plane. One building block will be densely configured to precisely measure atmospheric neutrino oscillations.

Object-oriented approach for gas turbine engine simulation

NASA Technical Reports Server (NTRS)

Curlett, Brian P.; Felder, James L.

1995-01-01

An object-oriented gas turbine engine simulation program was developed. This program is a prototype for a more complete, commercial grade engine performance program now being proposed as part of the Numerical Propulsion System Simulator (NPSS). This report discusses architectural issues of this complex software system and the lessons learned from developing the prototype code. The prototype code is a fully functional, general purpose engine simulation program, however, only the component models necessary to model a transient compressor test rig have been written. The production system will be capable of steady state and transient modeling of almost any turbine engine configuration. Chief among the architectural considerations for this code was the framework in which the various software modules will interact. These modules include the equation solver, simulation code, data model, event handler, and user interface. Also documented in this report is the component based design of the simulation module and the inter-component communication paradigm. Object class hierarchies for some of the code modules are given.

Dynamic Neural Networks Supporting Memory Retrieval

PubMed Central

St. Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

2011-01-01

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) Medial Prefrontal Cortex (PFC) Network, associated with self-referential processes, 2) Medial Temporal Lobe (MTL) Network, associated with memory, 3) Frontoparietal Network, associated with strategic search, and 4) Cingulooperculum Network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior. PMID:21550407

Uni10: an open-source library for tensor network algorithms

NASA Astrophysics Data System (ADS)

Kao, Ying-Jer; Hsieh, Yun-Da; Chen, Pochung

2015-09-01

We present an object-oriented open-source library for developing tensor network algorithms written in C++ called Uni10. With Uni10, users can build a symmetric tensor from a collection of bonds, while the bonds are constructed from a list of quantum numbers associated with different quantum states. It is easy to label and permute the indices of the tensors and access a block associated with a particular quantum number. Furthermore a network class is used to describe arbitrary tensor network structure and to perform network contractions efficiently. We give an overview of the basic structure of the library and the hierarchy of the classes. We present examples of the construction of a spin-1 Heisenberg Hamiltonian and the implementation of the tensor renormalization group algorithm to illustrate the basic usage of the library. The library described here is particularly well suited to explore and fast prototype novel tensor network algorithms and to implement highly efficient codes for existing algorithms.

Targeted Victimization: Exploring Linear and Curvilinear Associations Between Social Network Prestige and Victimization.

PubMed

Andrews, Naomi C Z; Hanish, Laura D; Updegraff, Kimberly A; Martin, Carol Lynn; Santos, Carlos E

2016-09-01

Are early adolescent victims of peer-directed aggression youth who hold prominent positions in the social hierarchy or those who are socially marginalized? The present study tackles this question by testing for linear and curvilinear relationships between social network prestige and physical and relational forms of peer victimization for boys and girls. Participants were 952 middle schoolers (age range = 10-14 years; 49.9 % girls; 44 % Latino). Participants nominated victims and friends; friendship nominations were used to calculate social network prestige. Both hypotheses received support, with variation by gender. Girls high in social network prestige were highly victimized. For boys, those both high and low in social network prestige were highly victimized, whereas those at mid-levels of social network prestige were low in victimization. The findings are discussed in relation to a social dominance model of peer-directed aggression, and the practical implications are discussed in relation to protecting youth who are frequent targets of peer victimization.

Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis

PubMed Central

Chauhan, Rinki; Ravi, Janani; Datta, Pratik; Chen, Tianlong; Schnappinger, Dirk; Bassler, Kevin E.; Balázsi, Gábor; Gennaro, Maria Laura

2016-01-01

Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics. PMID:27029515

A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network

PubMed Central

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. PMID:25927435

Human connectome module pattern detection using a new multi-graph MinMax cut model.

PubMed

De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng

2014-01-01

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.

Dispatching packets on a global combining network of a parallel computer

DOEpatents

Almasi, Gheorghe [Ardsley, NY; Archer, Charles J [Rochester, MN

2011-07-19

Methods, apparatus, and products are disclosed for dispatching packets on a global combining network of a parallel computer comprising a plurality of nodes connected for data communications using the network capable of performing collective operations and point to point operations that include: receiving, by an origin system messaging module on an origin node from an origin application messaging module on the origin node, a storage identifier and an operation identifier, the storage identifier specifying storage containing an application message for transmission to a target node, and the operation identifier specifying a message passing operation; packetizing, by the origin system messaging module, the application message into network packets for transmission to the target node, each network packet specifying the operation identifier and an operation type for the message passing operation specified by the operation identifier; and transmitting, by the origin system messaging module, the network packets to the target node.

A biological approach to assembling tissue modules through endothelial capillary network formation.

PubMed

Riesberg, Jeremiah J; Shen, Wei

2015-09-01

To create functional tissues having complex structures, bottom-up approaches to assembling small tissue modules into larger constructs have been emerging. Most of these approaches are based on chemical reactions or physical interactions at the interface between tissue modules. Here we report a biological assembly approach to integrate small tissue modules through endothelial capillary network formation. When adjacent tissue modules contain appropriate extracellular matrix materials and cell types that support robust endothelial capillary network formation, capillary tubules form and grow across the interface, resulting in assembly of the modules into a single, larger construct. It was shown that capillary networks formed in modules of dense fibrin gels seeded with human umbilical vein endothelial cells (HUVECs) and mesenchymal stem cells (MSCs); adjacent modules were firmly assembled into an integrated construct having a strain to failure of 117 ± 26%, a tensile strength of 2208 ± 83 Pa and a Young's modulus of 2548 ± 574 Pa. Under the same culture conditions, capillary networks were absent in modules of dense fibrin gels seeded with either HUVECs or MSCs alone; adjacent modules disconnected even when handled gently. This biological assembly approach eliminates the need for chemical reactions or physical interactions and their associated limitations. In addition, the integrated constructs are prevascularized, and therefore this bottom-up assembly approach may also help address the issue of vascularization, another key challenge in tissue engineering. Copyright © 2015 John Wiley & Sons, Ltd.

Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults

PubMed Central

Schacter, Daniel L.

2012-01-01

We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands. PMID:22128194

Application of neural networks to software quality modeling of a very large telecommunications system.

PubMed

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides

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

Pena-Castillo, Lourdes; Mercer, Ryan; Gurinovich, Anastasia

2014-08-28

The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigatedmore » preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data. Results: The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles. Conclusions: Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional annotation. We identified R. capsulatus modules enriched with genes for ribosomal proteins, porphyrin and bacteriochlorophyll anabolism, and biosynthesis of secondary metabolites to be preserved in R. sphaeroides whereas modules related to RcGTA production and signalling showed lack of preservation in R. sphaeroides. In addition, we demonstrated that network statistics may also be applied within-species to identify congruence between mRNA expression and protein abundance data for which simple correlation measurements have previously had mixed results.« less

Semantic integration to identify overlapping functional modules in protein interaction networks

PubMed Central

Cho, Young-Rae; Hwang, Woochang; Ramanathan, Murali; Zhang, Aidong

2007-01-01

Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification. PMID:17650343

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2016-04-01

Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

Oxytocin receptors modulate a social salience neural network in male prairie voles.

PubMed

Johnson, Zachary V; Walum, Hasse; Xiao, Yao; Riefkohl, Paula C; Young, Larry J

2017-01-01

Social behavior is regulated by conserved neural networks across vertebrates. Variation in the organization of neuropeptide systems across these networks is thought to contribute to individual and species diversity in network function during social contexts. For example, oxytocin (OT) is an ancient neuropeptide that binds to OT receptors (OTRs) in the brain and modulates social and reproductive behavior across vertebrate species, including humans. Central OTRs exhibit extraordinarily diverse expression patterns that are associated with individual and species differences in social behavior. In voles, OTR density in the nucleus accumbens (NAc)-a region important for social and reward learning-is associated with individual and species variation in social attachment behavior. Here we test whether OTRs in the NAc modulate a social salience network (SSN)-a network of interconnected brain nuclei thought to encode valence and incentive salience of sociosensory cues-during a social context in the socially monogamous male prairie vole. Using a selective OTR antagonist, we test whether activation of OTRs in the NAc during sociosexual interaction and mating modulates expression of the immediate early gene product Fos across nuclei of the SSN. We show that blockade of endogenous OTR signaling in the NAc during sociosexual interaction and mating does not strongly modulate levels of Fos expression in individual nodes of the network, but strongly modulates patterns of correlated Fos expression between the NAc and other SSN nuclei. Published by Elsevier Inc.

Multi-equilibrium property of metabolic networks: SSI module.

PubMed

Lei, Hong-Bo; Zhang, Ji-Feng; Chen, Luonan

2011-06-20

Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

Multi-equilibrium property of metabolic networks: SSI module

PubMed Central

2011-01-01

Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module. PMID:21689474

Late electrophysiological modulations of feature-based attention to object shapes.

PubMed

Stojanoski, Bobby Boge; Niemeier, Matthias

2014-03-01

Feature-based attention has been shown to aid object perception. Our previous ERP effects revealed temporally late feature-based modulation in response to objects relative to motion. The aim of the current study was to confirm the timing of feature-based influences on object perception while cueing within the feature dimension of shape. Participants were told to expect either "pillow" or "flower" objects embedded among random white and black lines. Participants more accurately reported the object's main color for valid compared to invalid shapes. ERPs revealed modulation from 252-502 ms, from occipital to frontal electrodes. Our results are consistent with previous findings examining the time course for processing similar stimuli (illusory contours). Our results provide novel insights into how attending to features of higher complexity aids object perception presumably via feed-forward and feedback mechanisms along the visual hierarchy. Copyright © 2014 Society for Psychophysiological Research.

Social hierarchy modulates responses of fish exposed to contaminants of emerging concern.

PubMed

Ivanova, Jelena; Zhang, Shiju; Wang, Rong-Lin; Schoenfuss, Heiko L

2017-01-01

Many organisms, including the fathead minnow (Pimephales promelas), a toxicological model organism, establish social hierarchies. The social rank of each male in a population is under the control of the hypothalamic-pituitary-gonadal (HPG) axis mainly through regulation of circulating androgen concentrations, which in turn drive the expression of secondary sex characteristics (SSCs). As dominant and subordinate males in an exposure study are initially under different physiological conditions (i.e., differing plasma androgen concentrations), we proposed that they belong to different subpopulations in the context of exposure to compounds that may interact with the HPG axis. Using a meta-analysis of our data from several previously published studies, we corroborated the hypothesis that social status, as indicated by SSCs, results in distinct clusters (eigenvalues >0.8 explaining >80% of variability) with differential expression of plasma vitellogenin, a commonly used biomarker of exposure to contaminants of emerging concern (CEC). Furthermore, we confirmed our predictions that exposure to estrogenic CECs would homogenize plasma vitellogenin response (E1: cluster mean SSC values decreased to 4.33 and 4.86 relative to those of control; E2: decreased to 4.8 and 5.37) across the social hierarchy. In contrast, serotonin-specific reuptake inhibitors expand this response range (cluster mean SSC increased to 5.21 and 6.5 relative to those of control). Our results demonstrated that social hierarchies in male fathead minnows result in heterogeneous responses to chemical exposure. These results represent a cautionary note for the experimental design of single-sex exposure studies. We anticipate our study to be a starting point for the re-evaluation of toxicological data analyses in single sex exposure experiments.

Exploring novel key regulators in breast cancer network.

PubMed

Ali, Shahnawaz; Malik, Md Zubbair; Singh, Soibam Shyamchand; Chirom, Keilash; Ishrat, Romana; Singh, R K Brojen

2018-01-01

The breast cancer network constructed from 70 experimentally verified genes is found to follow hierarchical scale free nature with heterogeneous modular organization and diverge leading hubs. The topological parameters (degree distributions, clustering co-efficient, connectivity and centralities) of this network obey fractal rules indicating absence of centrality lethality rule, and efficient communication among the components. From the network theoretical approach, we identified few key regulators out of large number of leading hubs, which are deeply rooted from top to down of the network, serve as backbone of the network, and possible target genes. However, p53, which is one of these key regulators, is found to be in low rank and keep itself at low profile but directly cross-talks with important genes BRCA2 and BRCA3. The popularity of these hubs gets changed in unpredictable way at various levels of organization thus showing disassortive nature. The local community paradigm approach in this network shows strong correlation of nodes in majority of modules/sub-modules (fast communication among nodes) and weak correlation of nodes only in few modules/sub-modules (slow communication among nodes) at various levels of network organization.

Optical interconnect for large-scale systems

NASA Astrophysics Data System (ADS)

Dress, William

2013-02-01

This paper presents a switchless, optical interconnect module that serves as a node in a network of identical distribution modules for large-scale systems. Thousands to millions of hosts or endpoints may be interconnected by a network of such modules, avoiding the need for multi-level switches. Several common network topologies are reviewed and their scaling properties assessed. The concept of message-flow routing is discussed in conjunction with the unique properties enabled by the optical distribution module where it is shown how top-down software control (global routing tables, spanning-tree algorithms) may be avoided.

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues.

PubMed

Gepperth, Alexander R T; Rebhan, Sven; Hasler, Stephan; Fritsch, Jannik

2011-03-01

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.

Re-modulated technology of WDM-PON employing different DQPSK downstream signals

NASA Astrophysics Data System (ADS)

Gao, Chao; Xin, Xiang-jun; Yu, Chong-xiu

2012-11-01

This paper proposes a kind of modulation architecture for wavelength-division-multiplexing passive optical network (WDMPON) employing optical differential quadrature phase shift keying (DQPSK) downstream signals and two different modulation formats of re-modulated upstream signals. At the optical line terminal (OLT), 10 Gbit/s signal is modulated with DQPSK. At the optical network unit (ONU), part of the downstream signal is re-modulated with on-off keying (OOK) or inverse-return-to-zero (IRZ). Simulation results show the impact on the system employing NRZ, RZ and carrier-suppressed return-to-zero (CSRZ). The analyses also reflect that the architecture can restrain chromatic dispersion and channel crosstalk, which makes it the best architecture of access network in the future.

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

NASA Technical Reports Server (NTRS)

Malin, Jane T. (Inventor); Basham, Bryan D. (Inventor); Harris, Richard A. (Inventor)

1990-01-01

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons.

INfORM: Inference of NetwOrk Response Modules.

PubMed

Marwah, Veer Singh; Kinaret, Pia Anneli Sofia; Serra, Angela; Scala, Giovanni; Lauerma, Antti; Fortino, Vittorio; Greco, Dario

2018-06-15

Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary data are available at Bioinformatics online.

A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression.

PubMed

So, Nina; Franks, Becca; Lim, Sean; Curley, James P

2015-01-01

Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David's Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg's Hub Centrality and Bonacich's Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive behavioral characterization of group-living animals with the utilization of novel statistical methods to further our understanding of the neurobiological basis of social behavior at the individual, relationship and group levels.

A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression

PubMed Central

So, Nina; Franks, Becca; Lim, Sean; Curley, James P.

2015-01-01

Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David’s Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg’s Hub Centrality and Bonacich’s Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive behavioral characterization of group-living animals with the utilization of novel statistical methods to further our understanding of the neurobiological basis of social behavior at the individual, relationship and group levels. PMID:26226265

Noncoherent Physical-Layer Network Coding with FSK Modulation: Relay Receiver Design Issues

DTIC Science & Technology

2011-03-01

222 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 59, NO. 9, SEPTEMBER 2011 2595 Noncoherent Physical-Layer Network Coding with FSK Modulation: Relay... noncoherent reception, channel estima- tion. I. INTRODUCTION IN the two-way relay channel (TWRC), a pair of sourceterminals exchange information...2011 4. TITLE AND SUBTITLE Noncoherent Physical-Layer Network Coding with FSK Modulation:Relay Receiver Design Issues 5a. CONTRACT NUMBER 5b

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Slotline fed microstrip antenna array modules

NASA Technical Reports Server (NTRS)

Lo, Y. T.; Oberhart, M. L.; Brenneman, J. S.; Aoyagi, P.; Moore, J.; Lee, R. Q. H.

1988-01-01

A feed network comprised of a combination of coplanar waveguide and slot transmission line is described for use in an array module of four microstrip elements. Examples of the module incorporating such networks are presented as well as experimentally obtained impedance and radiation characteristics.

Dynamic storage in resource-scarce browsing multimedia applications

NASA Astrophysics Data System (ADS)

Elenbaas, Herman; Dimitrova, Nevenka

1998-10-01

In the convergence of information and entertainment there is a conflict between the consumer's expectation of fast access to high quality multimedia content through narrow bandwidth channels versus the size of this content. During the retrieval and information presentation of a multimedia application there are two problems that have to be solved: the limited bandwidth during transmission of the retrieved multimedia content and the limited memory for temporary caching. In this paper we propose an approach for latency optimization in information browsing applications. We proposed a method for flattening hierarchically linked documents in a manner convenient for network transport over slow channels to minimize browsing latency. Flattening of the hierarchy involves linearization, compression and bundling of the document nodes. After the transfer, the compressed hierarchy is stored on a local device where it can be partly unbundled to fit the caching limits at the local site while giving the user availability to the content.

Dendritic cell control of tolerogenic responses

PubMed Central

Manicassamy, Santhakumar; Pulendran, Bali

2011-01-01

Summary One of the most fundamental problems in immunology is the seemingly schizophrenic ability of the immune system to launch robust immunity against pathogens, while acquiring and maintaining a state of tolerance to the body’s own tissues and the trillions of commensal microorganisms and food antigens that confront it every day. A fundamental role for the innate immune system, particularly dendritic cells (DCs), in orchestrating immunological tolerance has been appreciated, but emerging studies have highlighted the nature of the innate receptors and the signaling pathways that program DCs to a tolerogenic state. Furthermore, several studies have emphasized the major role played by cellular interactions, and the microenvironment in programming tolerogenic DCs. Here we review these studies and suggest that the innate control of tolerogenic responses can be viewed as different hierarchies of organization, in which DCs, their innate receptors and signaling networks, and their interactions with other cells and local microenvironments represent different levels of the hierarchy. PMID:21488899

Altered brain network modules induce helplessness in major depressive disorder.

PubMed

Peng, Daihui; Shi, Feng; Shen, Ting; Peng, Ziwen; Zhang, Chen; Liu, Xiaohua; Qiu, Meihui; Liu, Jun; Jiang, Kaida; Fang, Yiru; Shen, Dinggang

2014-10-01

The abnormal brain functional connectivity (FC) has been assumed to be a pathophysiological aspect of major depressive disorder (MDD). However, it is poorly understood, regarding the underlying patterns of global FC network and their relationships with the clinical characteristics of MDD. Resting-state functional magnetic resonance imaging data were acquired from 16 first episode, medication-naïve MDD patients and 16 healthy control subjects. The global FC network was constructed using 90 brain regions. The global topological patterns, e.g., small-worldness and modularity, and their relationships with depressive characteristics were investigated. Furthermore, the participant coefficient and module degree of MDD patients were measured to reflect the regional roles in module network, and the impairment of FC was examined by network based statistic. Small-world property was not altered in MDD. However, MDD patients exhibited 5 atypically reorganized modules compared to the controls. A positive relationship was also found among MDD patients between the intra-module I and helplessness factor evaluated via the Hamilton Depression Scale. Specifically, eight regions exhibited the abnormal participant coefficient or module degree, e.g., left superior orbital frontal cortex and right amygdala. The decreased FC was identified among the sub-network of 24 brain regions, e.g., frontal cortex, supplementary motor area, amygdala, thalamus, and hippocampus. The limited size of MDD samples precluded meaningful study of distinct clinical characteristics in relation to aberrant FC. The results revealed altered patterns of brain module network at the global level in MDD patients, which might contribute to the feelings of helplessness. Copyright © 2014 Elsevier B.V. All rights reserved.

Altered brain network modules induce helplessness in major depressive disorder

PubMed Central

Peng, Daihui; Shi, Feng; Shen, Ting; Peng, Ziwen; Zhang, Chen; Liu, Xiaohua; Qiu, Meihui; Liu, Jun; Jiang, Kaida; Shen, Dinggang

2017-01-01

Objective The abnormal brain functional connectivity (FC) has been assumed to be a pathophysiological aspect of major depressive disorder (MDD). However, it is poorly understood, regarding the underlying patterns of global FC network and their relationships with the clinical characteristics of MDD. Methods Resting-state functional magnetic resonance imaging data were acquired from 16 first episode, medication-naïve MDD patients and 16 healthy control subjects. The global FC network was constructed using 90 brain regions. The global topological patterns, e.g., small-worldness and modularity, and their relationships with depressive characteristics were investigated. Furthermore, the participant coefficient and module degree of MDD patients were measured to reflect the regional roles in module network, and the impairment of FC was examined by network based statistic. Results Small-world property was not altered in MDD. However, MDD patients exhibited 5 atypically reorganized modules compared to the controls. A positive relationship was also found among MDD patients between the intra-module I and helplessness factor evaluated via the Hamilton Depression Scale. Specifically, eight regions exhibited the abnormal participant coefficient or module degree, e.g., left superior orbital frontal cortex and right amygdala. The decreased FC was identified among the sub-network of 24 brain regions, e.g., frontal cortex, supplementary motor area, amygdala, thalamus, and hippocampus. Limitation The limited size of MDD samples precluded meaningful study of distinct clinical characteristics in relation to aberrant FC. Conclusions The results revealed altered patterns of brain module network at the global level in MDD patients, which might contribute to the feelings of helplessness. PMID:25033474

Silicon Modulators, Switches and Sub-systems for Optical Interconnect

NASA Astrophysics Data System (ADS)

Li, Qi

Silicon photonics is emerging as a promising platform for manufacturing and integrating photonic devices for light generation, modulation, switching and detection. The compatibility with existing CMOS microelectronic foundries and high index contrast in silicon could enable low cost and high performance photonic systems, which find many applications in optical communication, data center networking and photonic network-on-chip. This thesis first develops and demonstrates several experimental work on high speed silicon modulators and switches with record performance and novel functionality. A 8x40 Gb/s transmitter based on silicon microrings is first presented. Then an end-to-end link using microrings for Binary Phase Shift Keying (BPSK) modulation and demodulation is shown, and its performance with conventional BPSK modulation/ demodulation techniques is compared. Next, a silicon traveling-wave Mach- Zehnder modulator is demonstrated at data rate up to 56 Gb/s for OOK modulation and 48 Gb/s for BPSK modulation, showing its capability at high speed communication systems. Then a single silicon microring is shown with 2x2 full crossbar switching functionality, enabling optical interconnects with ultra small footprint. Then several other experiments in the silicon platform are presented, including a fully integrated in-band Optical Signal to Noise Ratio (OSNR) monitor, characterization of optical power upper bound in a silicon microring modulator, and wavelength conversion in a dispersion-engineered waveguide. The last part of this thesis is on network-level application of photonics, specically a broadcast-and-select network based on star coupler is introduced, and its scalability performance is studied. Finally a novel switch architecture for data center networks is discussed, and its benefits as a disaggregated network are presented.

The Social Influence Qualities of Social Network Sites: A Qualitative and Experimental Investigation

DTIC Science & Technology

2009-03-01

Designing and Conducting Mixed Methods Research. Thousand Oaks, CA: Sage Publications, 2007. Daft , Richard L. and Robert H. Lengel...Organizational Information Requirements, Media Richness and Structural Design ,” Management Science, 32 (5): 554-571 (May 1986). Daft , Richard L., Robert H...equivocality. Figure 2 ranks types of media according to the media richness theory . 22 Figure 2. Hierarchy of Media Richness ( Daft et al

Computer architecture evaluation for structural dynamics computations: Project summary

NASA Technical Reports Server (NTRS)

Standley, Hilda M.

1989-01-01

The intent of the proposed effort is the examination of the impact of the elements of parallel architectures on the performance realized in a parallel computation. To this end, three major projects are developed: a language for the expression of high level parallelism, a statistical technique for the synthesis of multicomputer interconnection networks based upon performance prediction, and a queueing model for the analysis of shared memory hierarchies.

Measuring the hierarchy of feedforward networks

NASA Astrophysics Data System (ADS)

Corominas-Murtra, Bernat; Rodríguez-Caso, Carlos; Goñi, Joaquín; Solé, Ricard

2011-03-01

In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion. We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.

AES based secure low energy adaptive clustering hierarchy for WSNs

NASA Astrophysics Data System (ADS)

Kishore, K. R.; Sarma, N. V. S. N.

2013-01-01

Wireless sensor networks (WSNs) provide a low cost solution in diversified application areas. The wireless sensor nodes are inexpensive tiny devices with limited storage, computational capability and power. They are being deployed in large scale in both military and civilian applications. Security of the data is one of the key concerns where large numbers of nodes are deployed. Here, an energy-efficient secure routing protocol, secure-LEACH (Low Energy Adaptive Clustering Hierarchy) for WSNs based on the Advanced Encryption Standard (AES) is being proposed. This crypto system is a session based one and a new session key is assigned for each new session. The network (WSN) is divided into number of groups or clusters and a cluster head (CH) is selected among the member nodes of each cluster. The measured data from the nodes is aggregated by the respective CH's and then each CH relays this data to another CH towards the gateway node in the WSN which in turn sends the same to the Base station (BS). In order to maintain confidentiality of data while being transmitted, it is necessary to encrypt the data before sending at every hop, from a node to the CH and from the CH to another CH or to the gateway node.

Managing search complexity in linguistic geometry.

PubMed

Stilman, B

1997-01-01

This paper is a new step in the development of linguistic geometry. This formal theory is intended to discover and generalize the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper, we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing linguistic geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in the paper on two pilot examples of the solution of complex optimization problems. The first example is a problem of strategic planning for the air combat, in which concurrent actions of four vehicles are simulated as serial interleaving moves. The second example is a problem of strategic planning for the space comb of eight autonomous vehicles (with interleaving moves) that requires generation of the search tree of the depth 25 with the branching factor 30. This is beyond the capabilities of modern and conceivable future computers (employing conventional approaches). In both examples the linguistic geometry tools showed deep and highly selective searches in comparison with conventional search algorithms. For the first example a sketch of the proof of optimality of the solution is considered.

Emerging hierarchies in dynamically adapting webs

NASA Astrophysics Data System (ADS)

Katifori, Eleni; Graewer, Johannes; Magnasco, Marcelo; Modes, Carl

Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. We quantify the hierarchical organization of the networks by developing an algorithm that decomposes the architecture to multiple scales and analyzes how the organization in each scale relates to that of the scale above and below it. The methodologies developed in this work are applicable to a wide range of systems including the slime mold physarum polycephalum, human microvasculature, and force chains in granular media.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Heterarchies: Reconciling Networks and Hierarchies.

PubMed

Cumming, Graeme S

2016-08-01

Social-ecological systems research suffers from a disconnect between hierarchical (top-down or bottom-up) and network (peer-to-peer) analyses. The concept of the heterarchy unifies these perspectives in a single framework. Here, I review the history and application of 'heterarchy' in neuroscience, ecology, archaeology, multiagent control systems, business and organisational studies, and politics. Recognising complex system architecture as a continuum along vertical and lateral axes ('flat versus hierarchical' and 'individual versus networked') suggests four basic types of heterarchy: reticulated, polycentric, pyramidal, and individualistic. Each has different implications for system functioning and resilience. Systems can also shift predictably and abruptly between architectures. Heterarchies suggest new ways of contextualising and generalising from case studies and new methods for analysing complex structure-function relations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mitigating wildland fire hazard using complex network centrality measures

NASA Astrophysics Data System (ADS)

Russo, Lucia; Russo, Paola; Siettos, Constantinos I.

2016-12-01

We show how to distribute firebreaks in heterogeneous forest landscapes in the presence of strong wind using complex network centrality measures. The proposed framework is essentially a two-tire one: at the inner part a state-of- the-art Cellular Automata model is used to compute the weights of the underlying lattice network while at the outer part the allocation of the fire breaks is scheduled in terms of a hierarchy of centralities which influence the most the spread of fire. For illustration purposes we applied the proposed framework to a real-case wildfire that broke up in Spetses Island, Greece in 1990. We evaluate the scheme against the benchmark of random allocation of firebreaks under the weather conditions of the real incident i.e. in the presence of relatively strong winds.

Structural methodologies for auditing SNOMED.

PubMed

Wang, Yue; Halper, Michael; Min, Hua; Perl, Yehoshua; Chen, Yan; Spackman, Kent A

2007-10-01

SNOMED is one of the leading health care terminologies being used worldwide. As such, quality assurance is an important part of its maintenance cycle. Methodologies for auditing SNOMED based on structural aspects of its organization are presented. In particular, automated techniques for partitioning SNOMED into smaller groups of concepts based primarily on relationships patterns are defined. Two abstraction networks, the area taxonomy and p-area taxonomy, are derived from the partitions. The high-level views afforded by these abstraction networks form the basis for systematic auditing. The networks tend to highlight errors that manifest themselves as irregularities at the abstract level. They also support group-based auditing, where sets of purportedly similar concepts are focused on for review. The auditing methodologies are demonstrated on one of SNOMED's top-level hierarchies. Errors discovered during the auditing process are reported.

Features of the Correlation Structure of Price Indices

PubMed Central

Gao, Xiangyun; An, Haizhong; Zhong, Weiqiong

2013-01-01

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network's structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies. PMID:23593399

Relationships between cortical myeloarchitecture and electrophysiological networks

PubMed Central

Hunt, Benjamin A. E.; Tewarie, Prejaas K.; Mougin, Olivier E.; Geades, Nicolas; Singh, Krish D.; Morris, Peter G.; Gowland, Penny A.; Brookes, Matthew J.

2016-01-01

The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology. PMID:27830650

Abstraction of complex concepts with a refined partial-area taxonomy of SNOMED

PubMed Central

Wang, Yue; Halper, Michael; Wei, Duo; Perl, Yehoshua; Geller, James

2012-01-01

An algorithmically-derived abstraction network, called the partial-area taxonomy, for a SNOMED hierarchy has led to the identification of concepts considered complex. The designation “complex” is arrived at automatically on the basis of structural analyses of overlap among the constituent concept groups of the partial-area taxonomy. Such complex concepts, called overlapping concepts, constitute a tangled portion of a hierarchy and can be obstacles to users trying to gain an understanding of the hierarchy’s content. A new methodology for partitioning the entire collection of overlapping concepts into singly-rooted groups, that are more manageable to work with and comprehend, is presented. Different kinds of overlapping concepts with varying degrees of complexity are identified. This leads to an abstract model of the overlapping concepts called the disjoint partial-area taxonomy, which serves as a vehicle for enhanced, high-level display. The methodology is demonstrated with an application to SNOMED’s Specimen hierarchy. Overall, the resulting disjoint partial-area taxonomy offers a refined view of the hierarchy’s structural organization and conceptual content that can aid users, such as maintenance personnel, working with SNOMED. The utility of the disjoint partial-area taxonomy as the basis for a SNOMED auditing regimen is presented in a companion paper. PMID:21878396

Immediate early gene activation throughout the brain is associated with dynamic changes in social context.

PubMed

Williamson, Cait M; Klein, Inbal S; Lee, Won; Curley, James P

2018-05-31

Social competence is dependent on successful processing of social context information. The social opportunity paradigm is a methodology in which dynamic shifts in social context are induced through removal of the alpha male in a dominance hierarchy, leading to rapid ascent in the hierarchy of the beta male and of other subordinate males in the social group. In the current study, we use the social opportunity paradigm to determine what brain regions respond to this dynamic change in social context, allowing an individual to recognize the absence of the alpha male and subsequently perform status-appropriate social behaviors. Replicating our previous work, we show that following removal of the alpha male, beta males rapidly ascend the social hierarchy and attain dominant status by increasing aggression towards more subordinate individuals. Analysis of patterns of Fos immunoreactivity throughout the brain indicates that in individuals undergoing social ascent, there is increased activity in regions of the social behavior network, as well as the infralimbic and prelimbic regions of the prefrontal cortex and areas of the hippocampus. Our findings demonstrate that male mice are able to respond to changes in social context and provide insight into the how the brain processes these complex behavioral changes.

The mathematical relationship between Zipf’s law and the hierarchical scaling law

NASA Astrophysics Data System (ADS)

Chen, Yanguang

2012-06-01

The empirical studies of city-size distribution show that Zipf's law and the hierarchical scaling law are linked in many ways. The rank-size scaling and hierarchical scaling seem to be two different sides of the same coin, but their relationship has never been revealed by strict mathematical proof. In this paper, the Zipf's distribution of cities is abstracted as a q-sequence. Based on this sequence, a self-similar hierarchy consisting of many levels is defined and the numbers of cities in different levels form a geometric sequence. An exponential distribution of the average size of cities is derived from the hierarchy. Thus we have two exponential functions, from which follows a hierarchical scaling equation. The results can be statistically verified by simple mathematical experiments and observational data of cities. A theoretical foundation is then laid for the conversion from Zipf's law to the hierarchical scaling law, and the latter can show more information about city development than the former. Moreover, the self-similar hierarchy provides a new perspective for studying networks of cities as complex systems. A series of mathematical rules applied to cities such as the allometric growth law, the 2n principle and Pareto's law can be associated with one another by the hierarchical organization.

Understanding Social Hierarchies: The Neural and Psychological Foundations of Status Perception

PubMed Central

Koski, Jessica; Xie, Hongling; Olson, Ingrid R.

2017-01-01

Social groups across species rapidly self-organize into hierarchies, where members vary in their level of power, influence, skill, or dominance. In this review we explore the nature of social hierarchies and the traits associated with status in both humans and nonhuman primates, and how status varies across development in humans. Our review finds that we can rapidly identify social status based on a wide range of cues. Like monkeys, we tend to use certain cues, like physical strength, to make status judgments, although layered on top of these more primitive perceptual cues are socio-cultural status cues like job titles and educational attainment. One's relative status has profound effects on attention, memory, and social interactions, as well as health and wellness. These effects can be particularly pernicious in children and adolescents. Developmental research on peer groups and social exclusion suggests teenagers may be particularly sensitive to social status information, but research focused specifically on status processing and associated brain areas is very limited. Recent evidence from neuroscience suggests there may be an underlying neural network, including regions involved in executive, emotional, and reward processing, that is sensitive to status information. We conclude with questions for future research as well as stressing the need to expand social neuroscience research on status processing to adolescents. PMID:25697184

Group social rank is associated with performance on a spatial learning task.

PubMed

Langley, Ellis J G; van Horik, Jayden O; Whiteside, Mark A; Madden, Joah R

2018-02-01

Dominant individuals differ from subordinates in their performances on cognitive tasks across a suite of taxa. Previous studies often only consider dyadic relationships, rather than the more ecologically relevant social hierarchies or networks, hence failing to account for how dyadic relationships may be adjusted within larger social groups. We used a novel statistical method: randomized Elo-ratings, to infer the social hierarchy of 18 male pheasants, Phasianus colchicus , while in a captive, mixed-sex group with a linear hierarchy. We assayed individual learning performance of these males on a binary spatial discrimination task to investigate whether inter-individual variation in performance is associated with group social rank. Task performance improved with increasing trial number and was positively related to social rank, with higher ranking males showing greater levels of success. Motivation to participate in the task was not related to social rank or task performance, thus indicating that these rank-related differences are not a consequence of differences in motivation to complete the task. Our results provide important information about how variation in cognitive performance relates to an individual's social rank within a group. Whether the social environment causes differences in learning performance or instead, inherent differences in learning ability predetermine rank remains to be tested.

Dual regulation of gene expression mediated by extended MAPK activation and salicylic acid contributes to robust innate immunity in Arabidopsis thaliana.

PubMed

Tsuda, Kenichi; Mine, Akira; Bethke, Gerit; Igarashi, Daisuke; Botanga, Christopher J; Tsuda, Yayoi; Glazebrook, Jane; Sato, Masanao; Katagiri, Fumiaki

2013-01-01

Network robustness is a crucial property of the plant immune signaling network because pathogens are under a strong selection pressure to perturb plant network components to dampen plant immune responses. Nevertheless, modulation of network robustness is an area of network biology that has rarely been explored. While two modes of plant immunity, Effector-Triggered Immunity (ETI) and Pattern-Triggered Immunity (PTI), extensively share signaling machinery, the network output is much more robust against perturbations during ETI than PTI, suggesting modulation of network robustness. Here, we report a molecular mechanism underlying the modulation of the network robustness in Arabidopsis thaliana. The salicylic acid (SA) signaling sector regulates a major portion of the plant immune response and is important in immunity against biotrophic and hemibiotrophic pathogens. In Arabidopsis, SA signaling was required for the proper regulation of the vast majority of SA-responsive genes during PTI. However, during ETI, regulation of most SA-responsive genes, including the canonical SA marker gene PR1, could be controlled by SA-independent mechanisms as well as by SA. The activation of the two immune-related MAPKs, MPK3 and MPK6, persisted for several hours during ETI but less than one hour during PTI. Sustained MAPK activation was sufficient to confer SA-independent regulation of most SA-responsive genes. Furthermore, the MPK3 and SA signaling sectors were compensatory to each other for inhibition of bacterial growth as well as for PR1 expression during ETI. These results indicate that the duration of the MAPK activation is a critical determinant for modulation of robustness of the immune signaling network. Our findings with the plant immune signaling network imply that the robustness level of a biological network can be modulated by the activities of network components.

WGCNA: an R package for weighted correlation network analysis

PubMed Central

Langfelder, Peter; Horvath, Steve

2008-01-01

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme.

PubMed

Li, Dong; Pan, Zhisong; Hu, Guyu; Zhu, Zexuan; He, Shan

2017-03-14

Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. The effectiveness of proposed algorithm is validated on both small and large protein interaction networks.

Modular sensor network node

DOEpatents

Davis, Jesse Harper Zehring [Berkeley, CA; Stark, Jr., Douglas Paul; Kershaw, Christopher Patrick [Hayward, CA; Kyker, Ronald Dean [Livermore, CA

2008-06-10

A distributed wireless sensor network node is disclosed. The wireless sensor network node includes a plurality of sensor modules coupled to a system bus and configured to sense a parameter. The parameter may be an object, an event or any other parameter. The node collects data representative of the parameter. The node also includes a communication module coupled to the system bus and configured to allow the node to communicate with other nodes. The node also includes a processing module coupled to the system bus and adapted to receive the data from the sensor module and operable to analyze the data. The node also includes a power module connected to the system bus and operable to generate a regulated voltage.

Multiple supervised residual network for osteosarcoma segmentation in CT images.

PubMed

Zhang, Rui; Huang, Lin; Xia, Wei; Zhang, Bo; Qiu, Bensheng; Gao, Xin

2018-01-01

Automatic and accurate segmentation of osteosarcoma region in CT images can help doctor make a reasonable treatment plan, thus improving cure rate. In this paper, a multiple supervised residual network (MSRN) was proposed for osteosarcoma image segmentation. Three supervised side output modules were added to the residual network. The shallow side output module could extract image shape features, such as edge features and texture features. The deep side output module could extract semantic features. The side output module could compute the loss value between output probability map and ground truth and back-propagate the loss information. Then, the parameters of residual network could be modified by gradient descent method. This could guide the multi-scale feature learning of the network. The final segmentation results were obtained by fusing the results output by the three side output modules. A total of 1900 CT images from 15 osteosarcoma patients were used to train the network and a total of 405 CT images from another 8 osteosarcoma patients were used to test the network. Results indicated that MSRN enabled a dice similarity coefficient (DSC) of 89.22%, a sensitivity of 88.74% and a F1-measure of 0.9305, which were larger than those obtained by fully convolutional network (FCN) and U-net. Thus, MSRN for osteosarcoma segmentation could give more accurate results than FCN and U-Net. Copyright © 2018 Elsevier Ltd. All rights reserved.

Interface Control Document for the EMPACT Module that Estimates Electric Power Transmission System Response to EMP-Caused Damage

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

Werley, Kenneth Alan; Mccown, Andrew William

The EPREP code is designed to evaluate the effects of an Electro-Magnetic Pulse (EMP) on the electric power transmission system. The EPREP code embodies an umbrella framework that allows a user to set up analysis conditions and to examine analysis results. The code links to three major physics/engineering modules. The first module describes the EM wave in space and time. The second module evaluates the damage caused by the wave on specific electric power (EP) transmission system components. The third module evaluates the consequence of the damaged network on its (reduced) ability to provide electric power to meet demand. Thismore » third module is the focus of the present paper. The EMPACT code serves as the third module. The EMPACT name denotes EMP effects on Alternating Current Transmission systems. The EMPACT algorithms compute electric power transmission network flow solutions under severely damaged network conditions. Initial solutions are often characterized by unacceptible network conditions including line overloads and bad voltages. The EMPACT code contains algorithms to adjust optimally network parameters to eliminate network problems while minimizing outages. System adjustments include automatically adjusting control equipment (generator V control, variable transformers, and variable shunts), as well as non-automatic control of generator power settings and minimal load shedding. The goal is to evaluate the minimal loss of customer load under equilibrium (steady-state) conditions during peak demand.« less

Improving the neutrino mass hierarchy identification with inelasticity measurement in PINGU and ORCA

NASA Astrophysics Data System (ADS)

Ribordy, Mathieu; Smirnov, A. Yu.

2013-06-01

Multimegaton scale in under-ice and underwater detectors of atmospheric neutrinos with a few GeV energy threshold (PINGU, ORCA) open up new possibilities in the determination of neutrino properties, and in particular the neutrino mass hierarchy. With a dense array of optical modules it will be possible to determine the inelasticity, y, of the charged current νμ events in addition to the neutrino energy Eν and the muon zenith angle θμ. The discovery potential of the detectors will substantially increase with the measurement of y. It will enable (i) a partial separation of the neutrino and antineutrino signals, (ii) a better reconstruction of the neutrino direction, (iii) the reduction of the neutrino parameters degeneracy, (iv) a better control of systematic uncertainties, and (v) a better identification of the νμ events. It will improve the sensitivity to the CP-violation phase. The three-dimensional (Eν,θμ,y), νμ oscillograms with the kinematical as well as the experimental smearing are computed. We present the asymmetry distributions in the Eν-θμ plane for different intervals of y and study their properties. We show that the inelasticity information reduces the effect of degeneracy of parameters by 30%. With the inelasticity, the total significance of establishing mass hierarchy may increase by (20-50)%, thus effectively increasing the volume of the detector by a factor of 1.5-2.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome.

PubMed

Menche, Jörg; Sharma, Amitabh; Kitsak, Maksim; Ghiassian, Susan Dina; Vidal, Marc; Loscalzo, Joseph; Barabási, Albert-László

2015-02-20

According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes. Copyright © 2015, American Association for the Advancement of Science.

Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks.

PubMed

Kim, Inhae; Lee, Heetak; Han, Seong Kyu; Kim, Sanguk

2014-10-01

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

Network module detection: Affinity search technique with the multi-node topological overlap measure

PubMed Central

Li, Ai; Horvath, Steve

2009-01-01

Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: PMID:19619323

Network module detection: Affinity search technique with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2009-07-20

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks

PubMed Central

Shen, Hong-Bin

2011-01-01

Modern science of networks has brought significant advances to our understanding of complex systems biology. As a representative model of systems biology, Protein Interaction Networks (PINs) are characterized by a remarkable modular structures, reflecting functional associations between their components. Many methods were proposed to capture cohesive modules so that there is a higher density of edges within modules than those across them. Recent studies reveal that cohesively interacting modules of proteins is not a universal organizing principle in PINs, which has opened up new avenues for revisiting functional modules in PINs. In this paper, functional clusters in PINs are found to be able to form unorthodox structures defined as bi-sparse module. In contrast to the traditional cohesive module, the nodes in the bi-sparse module are sparsely connected internally and densely connected with other bi-sparse or cohesive modules. We present a novel protocol called the BinTree Seeking (BTS) for mining both bi-sparse and cohesive modules in PINs based on Edge Density of Module (EDM) and matrix theory. BTS detects modules by depicting links and nodes rather than nodes alone and its derivation procedure is totally performed on adjacency matrix of networks. The number of modules in a PIN can be automatically determined in the proposed BTS approach. BTS is tested on three real PINs and the results demonstrate that functional modules in PINs are not dominantly cohesive but can be sparse. BTS software and the supporting information are available at: www.csbio.sjtu.edu.cn/bioinf/BTS/. PMID:22140454

Identification of Unstable Network Modules Reveals Disease Modules Associated with the Progression of Alzheimer’s Disease

PubMed Central

Kikuchi, Masataka; Ogishima, Soichi; Miyamoto, Tadashi; Miyashita, Akinori; Kuwano, Ryozo; Nakaya, Jun; Tanaka, Hiroshi

2013-01-01

Alzheimer’s disease (AD), the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs), we identified the PINs expressed in three brain regions: the entorhinal cortex (EC), hippocampus (HIP) and superior frontal gyrus (SFG). Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system. PMID:24348898

The biometric-based module of smart grid system

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Ermoshkina, A.

2015-10-01

Within Smart Grid concept the flexible biometric-based module base on Principal Component Analysis (PCA) and selective Neural Network is developed. The formation of the selective Neural Network the biometric-based module uses the method which includes three main stages: preliminary processing of the image, face localization and face recognition. Experiments on the Yale face database show that (i) selective Neural Network exhibits promising classification capability for face detection, recognition problems; and (ii) the proposed biometric-based module achieves near real-time face detection, recognition speed and the competitive performance, as compared to some existing subspaces-based methods.

To cut or not to cut? Assessing the modular structure of brain networks.

PubMed

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M

2014-05-01

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Multispectral embedding-based deep neural network for three-dimensional human pose recovery

NASA Astrophysics Data System (ADS)

Yu, Jialin; Sun, Jifeng

2018-01-01

Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

A mechanism of institutional isomorphism in referral networks among hospitals in Seoul, South Korea.

PubMed

Jung, Minsoo; Choi, Mankyu

2010-01-01

Hospitals engage in medical referral system relations voluntarily, by virtue of their own service capacities. These capacities include medical technology, equipment supply, and patient management, which are assessed individually by medical institutions in efforts to control costs and maintain efficiency in tertiary hospitals. This study assessed referral networks according to the institutional isomorphism theory of new economic sociology. As a result, the referral networks were shown to exhibit emergent structural hierarchy via cumulative clustering by established year and were not affected by attributive variables such as region, bed number, and year of establishment. In particular, the networks evidenced institutional isomorphism with certain central hospitals. As a consequence, personal indices were shown to decrease in accordance with its period, and only the structural index increased. Normative pressures cause organizations to become hierarchically homogenized, in accordance with the principle of organizational learning in specialized fields. Therefore, normative isomorphism on the basis of public domains should be considered an inherent factor in the development of referral networks.

Distinct sets of locomotor modules control the speed and modes of human locomotion

PubMed Central

Yokoyama, Hikaru; Ogawa, Tetsuya; Kawashima, Noritaka; Shinya, Masahiro; Nakazawa, Kimitaka

2016-01-01

Although recent vertebrate studies have revealed that different spinal networks are recruited in locomotor mode- and speed-dependent manners, it is unknown whether humans share similar neural mechanisms. Here, we tested whether speed- and mode-dependence in the recruitment of human locomotor networks exists or not by statistically extracting locomotor networks. From electromyographic activity during walking and running over a wide speed range, locomotor modules generating basic patterns of muscle activities were extracted using non-negative matrix factorization. The results showed that the number of modules changed depending on the modes and speeds. Different combinations of modules were extracted during walking and running, and at different speeds even during the same locomotor mode. These results strongly suggest that, in humans, different spinal locomotor networks are recruited while walking and running, and even in the same locomotor mode different networks are probably recruited at different speeds. PMID:27805015

Figure-ground segregation modulates apparent motion.

PubMed

Ramachandran, V S; Anstis, S

1986-01-01

We explored the relationship between figure-ground segmentation and apparent motion. Results suggest that: static elements in the surround can eliminate apparent motion of a cluster of dots in the centre, but only if the cluster and surround have similar "grain" or texture; outlines that define occluding surfaces are taken into account by the motion mechanism; the brain uses a hierarchy of precedence rules in attributing motion to different segments of the visual scene. Being designated as "figure" confers a high rank in this scheme of priorities.

Protein complexes and functional modules in molecular networks

NASA Astrophysics Data System (ADS)

Spirin, Victor; Mirny, Leonid A.

2003-10-01

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

Network-dependent modulation of brain activity during sleep.

PubMed

Watanabe, Takamitsu; Kan, Shigeyuki; Koike, Takahiko; Misaki, Masaya; Konishi, Seiki; Miyauchi, Satoru; Miyahsita, Yasushi; Masuda, Naoki

2014-09-01

Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Preliminary Evidence for the Emergence of a Health Care Online Community of Practice: Using a Netnographic Framework for Twitter Hashtag Analytics.

PubMed

Roland, Damian; Spurr, Jesse; Cabrera, Daniel

2017-07-14

Online communities of practice (oCoPs) may emerge from interactions on social media. These communities offer an open digital space and flat role hierarchy for information sharing and provide a strong group identity, rapid flow of information, content curation, and knowledge translation. To date, there is only a small body of evidence in medicine or health care to verify the existence of an oCoP. We aimed to examine the emergence of an oCoP through the study of social media interactions of the free open access medical education (FOAM) movement. We examined social media activity in Twitter by analyzing the network centrality metrics of tweets with the #FOAMed hashtag and compared them with previously validated criteria of a community of practice (CoP). The centrality analytics of the FOAM community showed concordance with aspects of a general CoP (in terms of community, domain, and practice), as well as some specific traits of a health care community, including social control, common purpose, flat hierarchy, and network-based and concrete achievement. This study demonstrated preliminary evidence of an oCoP focused on education and based on social media interactions. Further examination of the topology of the network is needed to definitely prove the existence of an oCoP. Given that these communities result in significant knowledge translation and practice change, further research in this area appears warranted. ©Damian Roland, Jesse Spurr, Daniel Cabrera. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.07.2017.

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Reactive Goal Decomposition Hierarchies for On-Board Autonomy

NASA Astrophysics Data System (ADS)

Hartmann, L.

2002-01-01

As our experience grows, space missions and systems are expected to address ever more complex and demanding requirements with fewer resources (e.g., mass, power, budget). One approach to accommodating these higher expectations is to increase the level of autonomy to improve the capabilities and robustness of on- board systems and to simplify operations. The goal decomposition hierarchies described here provide a simple but powerful form of goal-directed behavior that is relatively easy to implement for space systems. A goal corresponds to a state or condition that an operator of the space system would like to bring about. In the system described here goals are decomposed into simpler subgoals until the subgoals are simple enough to execute directly. For each goal there is an activation condition and a set of decompositions. The decompositions correspond to different ways of achieving the higher level goal. Each decomposition contains a gating condition and a set of subgoals to be "executed" sequentially or in parallel. The gating conditions are evaluated in order and for the first one that is true, the corresponding decomposition is executed in order to achieve the higher level goal. The activation condition specifies global conditions (i.e., for all decompositions of the goal) that need to hold in order for the goal to be achieved. In real-time, parameters and state information are passed between goals and subgoals in the decomposition; a termination indication (success, failure, degree) is passed up when a decomposition finishes executing. The lowest level decompositions include servo control loops and finite state machines for generating control signals and sequencing i/o. Semaphores and shared memory are used to synchronize and coordinate decompositions that execute in parallel. The goal decomposition hierarchy is reactive in that the generated behavior is sensitive to the real-time state of the system and the environment. That is, the system is able to react to state and environment and in general can terminate the execution of a decomposition and attempt a new decomposition at any level in the hierarchy. This goal decomposition system is suitable for workstation, microprocessor and fpga implementation and thus is able to support the full range of prototyping activities, from mission design in the laboratory to development of the fpga firmware for the flight system. This approach is based on previous artificial intelligence work including (1) Brooks' subsumption architecture for robot control, (2) Firby's Reactive Action Package System (RAPS) for mediating between high level automated planning and low level execution and (3) hierarchical task networks for automated planning. Reactive goal decomposition hierarchies can be used for a wide variety of on-board autonomy applications including automating low level operation sequences (such as scheduling prerequisite operations, e.g., heaters, warm-up periods, monitoring power constraints), coordinating multiple spacecraft as in formation flying and constellations, robot manipulator operations, rendez-vous, docking, servicing, assembly, on-orbit maintenance, planetary rover operations, solar system and interstellar probes, intelligent science data gathering and disaster early warning. Goal decomposition hierarchies can support high level fault tolerance. Given models of on-board resources and goals to accomplish, the decomposition hierarchy could allocate resources to goals taking into account existing faults and in real-time reallocating resources as new faults arise. Resources to be modeled include memory (e.g., ROM, FPGA configuration memory, processor memory, payload instrument memory), processors, on-board and interspacecraft network nodes and links, sensors, actuators (e.g., attitude determination and control, guidance and navigation) and payload instruments. A goal decomposition hierarchy could be defined to map mission goals and tasks to available on-board resources. As faults occur and are detected the resource allocation is modified to avoid using the faulty resource. Goal decomposition hierarchies can implement variable autonomy (in which the operator chooses to command the system at a high or low level, mixed initiative planning (in which the system is able to interact with the operator, e.g, to request operator intervention when a working envelope is exceeded) and distributed control (in which, for example, multiple spacecraft cooperate to accomplish a task without a fixed master). The full paper will describe in greater detail how goal decompositions work, how they can be implemented, techniques for implementing a candidate application and the current state of the fpga implementation.

Ad hoc Laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Ma, Zongfeng; Shen, Jingshi

2016-03-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

Ad hoc laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Shen, Jingshi

2017-10-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

Intrinsic functional network architecture of human semantic processing: Modules and hubs.

PubMed

Xu, Yangwen; Lin, Qixiang; Han, Zaizhu; He, Yong; Bi, Yanchao

2016-05-15

Semantic processing entails the activation of widely distributed brain areas across the temporal, parietal, and frontal lobes. To understand the functional structure of this semantic system, we examined its intrinsic functional connectivity pattern using a database of 146 participants. Focusing on areas consistently activated during semantic processing generated from a meta-analysis of 120 neuroimaging studies (Binder et al., 2009), we found that these regions were organized into three stable modules corresponding to the default mode network (Module DMN), the left perisylvian network (Module PSN), and the left frontoparietal network (Module FPN). These three dissociable modules were integrated by multiple connector hubs-the left angular gyrus (AG) and the left superior/middle frontal gyrus linking all three modules, the left anterior temporal lobe linking Modules DMN and PSN, the left posterior portion of dorsal intraparietal sulcus (IPS) linking Modules DMN and FPN, and the left posterior middle temporal gyrus (MTG) linking Modules PSN and FPN. Provincial hubs, which converge local information within each system, were also identified: the bilateral posterior cingulate cortices/precuneus, the bilateral border area of the posterior AG and the superior lateral occipital gyrus for Module DMN; the left supramarginal gyrus, the middle part of the left MTG and the left orbital inferior frontal gyrus (IFG) for Module FPN; and the left triangular IFG and the left IPS for Module FPN. A neuro-functional model for semantic processing was derived based on these findings, incorporating the interactions of memory, language, and control. Copyright © 2016 Elsevier Inc. All rights reserved.

From sensation to cognition.

PubMed

Mesulam, M M

1998-06-01

Sensory information undergoes extensive associative elaboration and attentional modulation as it becomes incorporated into the texture of cognition. This process occurs along a core synaptic hierarchy which includes the primary sensory, upstream unimodal, downstream unimodal, heteromodal, paralimbic and limbic zones of the cerebral cortex. Connections from one zone to another are reciprocal and allow higher synaptic levels to exert a feedback (top-down) influence upon earlier levels of processing. Each cortical area provides a nexus for the convergence of afferents and divergence of efferents. The resultant synaptic organization supports parallel as well as serial processing, and allows each sensory event to initiate multiple cognitive and behavioural outcomes. Upstream sectors of unimodal association areas encode basic features of sensation such as colour, motion, form and pitch. More complex contents of sensory experience such as objects, faces, word-forms, spatial locations and sound sequences become encoded within downstream sectors of unimodal areas by groups of coarsely tuned neurons. The highest synaptic levels of sensory-fugal processing are occupied by heteromodal, paralimbic and limbic cortices, collectively known as transmodal areas. The unique role of these areas is to bind multiple unimodal and other transmodal areas into distributed but integrated multimodal representations. Transmodal areas in the midtemporal cortex, Wernicke's area, the hippocampal-entorhinal complex and the posterior parietal cortex provide critical gateways for transforming perception into recognition, word-forms into meaning, scenes and events into experiences, and spatial locations into targets for exploration. All cognitive processes arise from analogous associative transformations of similar sets of sensory inputs. The differences in the resultant cognitive operation are determined by the anatomical and physiological properties of the transmodal node that acts as the critical gateway for the dominant transformation. Interconnected sets of transmodal nodes provide anatomical and computational epicentres for large-scale neurocognitive networks. In keeping with the principles of selectively distributed processing, each epicentre of a large-scale network displays a relative specialization for a specific behavioural component of its principal neurospychological domain. The destruction of transmodal epicentres causes global impairments such as multimodal anomia, neglect and amnesia, whereas their selective disconnection from relevant unimodal areas elicits modality-specific impairments such as prosopagnosia, pure word blindness and category-specific anomias. The human brain contains at least five anatomically distinct networks. The network for spatial awareness is based on transmodal epicentres in the posterior parietal cortex and the frontal eye fields; the language network on epicentres in Wernicke's and Broca's areas; the explicit memory/emotion network on epicentres in the hippocampal-entorhinal complex and the amygdala; the face-object recognition network on epicentres in the midtemporal and temporopolar cortices; and the working memory-executive function network on epicentres in the lateral prefrontal cortex and perhaps the posterior parietal cortex. Individual sensory modalities give rise to streams of processing directed to transmodal nodes belonging to each of these networks. The fidelity of sensory channels is actively protected through approximately four synaptic levels of sensory-fugal processing. The modality-specific cortices at these four synaptic levels encode the most veridical representations of experience. Attentional, motivational and emotional modulations, including those related to working memory, novelty-seeking and mental imagery, become increasingly more pronounced within downstream components of unimodal areas, where they help to create a highly edited subjective version of the world. (ABSTRACT TRUNCATED)

Silent Expectations: Dynamic Causal Modeling of Cortical Prediction and Attention to Sounds That Weren't.

PubMed

Chennu, Srivas; Noreika, Valdas; Gueorguiev, David; Shtyrov, Yury; Bekinschtein, Tristan A; Henson, Richard

2016-08-10

There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called "mismatch response"). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an "omission" response). This situation arguably provides a more direct measure of "top-down" predictions in the absence of confounding "bottom-up" input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of "bottom-up" stimuli with the presence versus absence of "top-down" attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward "prediction" connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction. Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known "mismatch response." But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain's electromagnetic activity, we show that it also generates an "omission response" that is contingent on the presence of attention. We model these responses computationally, revealing that mismatch and omission responses only differ in the location of inputs into the same underlying neuronal network. In both cases, we show that attention selectively strengthens the brain's prediction of the future. Copyright © 2016 Chennu et al.

Attention to Color Sharpens Neural Population Tuning via Feedback Processing in the Human Visual Cortex Hierarchy.

PubMed

Bartsch, Mandy V; Loewe, Kristian; Merkel, Christian; Heinze, Hans-Jochen; Schoenfeld, Mircea A; Tsotsos, John K; Hopf, Jens-Max

2017-10-25

Attention can facilitate the selection of elementary object features such as color, orientation, or motion. This is referred to as feature-based attention and it is commonly attributed to a modulation of the gain and tuning of feature-selective units in visual cortex. Although gain mechanisms are well characterized, little is known about the cortical processes underlying the sharpening of feature selectivity. Here, we show with high-resolution magnetoencephalography in human observers (men and women) that sharpened selectivity for a particular color arises from feedback processing in the human visual cortex hierarchy. To assess color selectivity, we analyze the response to a color probe that varies in color distance from an attended color target. We find that attention causes an initial gain enhancement in anterior ventral extrastriate cortex that is coarsely selective for the target color and transitions within ∼100 ms into a sharper tuned profile in more posterior ventral occipital cortex. We conclude that attention sharpens selectivity over time by attenuating the response at lower levels of the cortical hierarchy to color values neighboring the target in color space. These observations support computational models proposing that attention tunes feature selectivity in visual cortex through backward-propagating attenuation of units less tuned to the target. SIGNIFICANCE STATEMENT Whether searching for your car, a particular item of clothing, or just obeying traffic lights, in everyday life, we must select items based on color. But how does attention allow us to select a specific color? Here, we use high spatiotemporal resolution neuromagnetic recordings to examine how color selectivity emerges in the human brain. We find that color selectivity evolves as a coarse to fine process from higher to lower levels within the visual cortex hierarchy. Our observations support computational models proposing that feature selectivity increases over time by attenuating the responses of less-selective cells in lower-level brain areas. These data emphasize that color perception involves multiple areas across a hierarchy of regions, interacting with each other in a complex, recursive manner. Copyright © 2017 the authors 0270-6474/17/3710346-12$15.00/0.

A computerized tomography system for transcranial ultrasound imaging.

PubMed

Tang, Sai Chun; Clement, Gregory T

Hardware for tomographic imaging presents both challenge and opportunity for simplification when compared with traditional pulse-echo imaging systems. Specifically, point diffraction tomography does not require simultaneous powering of elements, in theory allowing just a single transmit channel and a single receive channel to be coupled with a switching or multiplexing network. In our ongoing work on transcranial imaging, we have developed a 512-channel system designed to transmit and/or receive a high voltage signal from/to arbitrary elements of an imaging array. The overall design follows a hierarchy of modules including a software interface, microcontroller, pulse generator, pulse amplifier, high-voltage power converter, switching mother board, switching daughter board, receiver amplifier, analog-to-digital converter, peak detector, memory, and USB communication. Two pulse amplifiers are included, each capable of producing up to 400Vpp via power MOSFETS. Switching is based around mechanical relays that allow passage of 200V, while still achieving switching times of under 2ms, with an operating frequency ranging from below 100kHz to 10MHz. The system is demonstrated through ex vivo human skulls using 1MHz transducers. The overall system design is applicable to planned human studies in transcranial image acquisition, and may have additional tomographic applications for other materials necessitating a high signal output.

Software Issues in High-Performance Computing and a Framework for the Development of HPC Applications

DTIC Science & Technology

1995-01-01

possible to determine communication points. For this version, a C program spawning Posix threads and using semaphores to synchronize would have to...performance such as the time required for network communication and synchronization as well as issues of asynchrony and memory hierarchy. For example...enhances reusability. Process (or task) parallel computations can also be succinctly expressed with a small set of process creation and synchronization

Conversion of Mass Storage Hierarchy in an IBM Computer Network

DTIC Science & Technology

1989-03-01

storage devices GUIDE IBM users’ group for DOS operating systems IBM International Business Machines IBM 370/145 CPU introduced in 1970 IBM 370/168 CPU...February 12, 1985, Information Systems Group, International Business Machines Corporation. "IBM 3090 Processor Complex" and 񓼪 Mass Storage System...34 Mainframe Journal, pp. 15-26, 64-65, Dallas, Texas, September-October 1987. 3. International Business Machines Corporation, Introduction to IBM 3S80 Storage

Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

PubMed

He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

2012-06-25

Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Ten steps to plan, design, and implement an endocrinology and endocrine surgery module for the Faculty of Medicine, Al-Baha University.

PubMed

Elfakey, Walyeldin Em; Al-Ghamdi, Ahmed H

2016-01-01

The Faculty of Medicine, Al-Baha University (FMBU), is a newly established medical school that implements a community-oriented and integrated system-based curriculum which is suitable for both medical students and serving the needs of the local community. The aim of this study is to describe the steps that were followed to plan, design, and implement an endocrinology and endocrine surgery module (EESM) for the fourth-year medical students, as an example of how system-based modules are designed at FMBU. Ten questions based on Harden's methodolgy were asked in order to design, plan, and implement an endocrinology and endocrine surgery module. The module committee determined the needs of the module and accordingly stated the aims and objectives of the module. The module planners selected the relevant contents, teaching methods, and assessment strategies and organized them. After addressing each of the ten questions, the results indicated the need, aim, objectives, and contents for the endocrinology and endocrine surgery module at FMBU. The implementation strategies were chosen according to the SPICES model. The teaching methods and the assessment strategies were selected and arranged. The module is well communicated at all levels, and the module committee used every effort to create a productive teaching environment. The module is well managed and follows the hierarchy of FMBU. Implementing Harden's ten steps methodology resulted in an integrated module of endocrinology and endocrine surgery where related disciplines and systems were merged and medical and surgical endocrine topics were included.

Modulation aware cluster size optimisation in wireless sensor networks

NASA Astrophysics Data System (ADS)

Sriram Naik, M.; Kumar, Vinay

2017-07-01

Wireless sensor networks (WSNs) play a great role because of their numerous advantages to the mankind. The main challenge with WSNs is the energy efficiency. In this paper, we have focused on the energy minimisation with the help of cluster size optimisation along with consideration of modulation effect when the nodes are not able to communicate using baseband communication technique. Cluster size optimisations is important technique to improve the performance of WSNs. It provides improvement in energy efficiency, network scalability, network lifetime and latency. We have proposed analytical expression for cluster size optimisation using traditional sensing model of nodes for square sensing field with consideration of modulation effects. Energy minimisation can be achieved by changing the modulation schemes such as BPSK, 16-QAM, QPSK, 64-QAM, etc., so we are considering the effect of different modulation techniques in the cluster formation. The nodes in the sensing fields are random and uniformly deployed. It is also observed that placement of base station at centre of scenario enables very less number of modulation schemes to work in energy efficient manner but when base station placed at the corner of the sensing field, it enable large number of modulation schemes to work in energy efficient manner.

Array processor architecture connection network

NASA Technical Reports Server (NTRS)

Barnes, George H. (Inventor); Lundstrom, Stephen F. (Inventor); Shafer, Philip E. (Inventor)

1982-01-01

A connection network is disclosed for use between a parallel array of processors and a parallel array of memory modules for establishing non-conflicting data communications paths between requested memory modules and requesting processors. The connection network includes a plurality of switching elements interposed between the processor array and the memory modules array in an Omega networking architecture. Each switching element includes a first and a second processor side port, a first and a second memory module side port, and control logic circuitry for providing data connections between the first and second processor ports and the first and second memory module ports. The control logic circuitry includes strobe logic for examining data arriving at the first and the second processor ports to indicate when the data arriving is requesting data from a requesting processor to a requested memory module. Further, connection circuitry is associated with the strobe logic for examining requesting data arriving at the first and the second processor ports for providing a data connection therefrom to the first and the second memory module ports in response thereto when the data connection so provided does not conflict with a pre-established data connection currently in use.

A novel network module for medical devices.

PubMed

Chen, Ping-Yu

2008-01-01

In order to allow medical devices to upload the vital signs to a server on a network without manually configuring for end-users, a new network module is proposed. The proposed network module, called Medical Hub (MH), functions as a bridge to fetch the data from all connecting medical devices, and then upload these data to the server. When powering on, the MH can immediately establish network configuration automatically. Network Address Translation (NAT) traversal is also supported by the MH with the UPnP Internet Gateway Device (IGD) methodology. Besides the network configuration, other configuration in the MH is automatically established by using the remote management protocol TR-069. On the other hand, a mechanism for updating software automatically according to the variant connected medical device is proposed. With this mechanism, newcome medical devices can be detected and supported by the MH without manual operation.

The improved degree of urban road traffic network: A case study of Xiamen, China

NASA Astrophysics Data System (ADS)

Wang, Shiguang; Zheng, Lili; Yu, Dexin

2017-03-01

The complex network theory is applied to the study of urban road traffic network topology, and we constructed a new measure to characterize an urban road network. It is inspiring to quantify the interaction more appropriately between nodes in complex networks, especially in the field of traffic. The measure takes into account properties of lanes (e.g. number of lanes, width, traffic direction). As much, it is a more comprehensive measure in comparison to previous network measures. It can be used to grasp the features of urban street network more clearly. We applied this measure to the road network in Xiamen, China. Based on a standard method from statistical physics, we examined in more detail the distribution of this new measure and found that (1) due to the limitation of space geographic attributes, traditional research conclusions acquired by using the original definition of degree to study the primal approach modeled urban street network are not very persuasive; (2) both of the direction of the network connection and the degree's odd or even classifications need to be analyzed specifically; (3) the improved degree distribution presents obvious hierarchy, and hierarchical values conform to the power-law distribution, and correlation of our new measure shows some significant segmentation of the urban road network.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

PubMed

Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

2016-01-01

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. © 2015 Wiley Periodicals, Inc.

Preservation affinity in consensus modules among stages of HIV-1 progression.

PubMed

Mosaddek Hossain, Sk Md; Ray, Sumanta; Mukhopadhyay, Anirban

2017-03-20

Analysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding. HIV-1 disease provides a well-documented progression pattern through three stages of infection: acute, chronic and non-progressor. In this article, we have developed a novel framework to describe the relationship among the consensus (or shared) co-expression modules for each pair of HIV-1 infection stages. The consensus modules are identified to assess the preservation of network properties. We have investigated the preservation patterns of co-expression networks during HIV-1 disease progression through an eigengene-based approach. We discovered that the expression patterns of consensus modules have a strong preservation during the transitions of three infection stages. In particular, it is noticed that between acute and non-progressor stages the preservation is slightly more than the other pair of stages. Moreover, we have constructed eigengene networks for the identified consensus modules and observed the preservation structure among them. Some consensus modules are marked as preserved in two pairs of stages and are analyzed further to form a higher order meta-network consisting of a group of preserved modules. Additionally, we observed that module membership (MM) values of genes within a module are consistent with the preservation characteristics. The MM values of genes within a pair of preserved modules show strong correlation patterns across two infection stages. We have performed an extensive analysis to discover preservation pattern of co-expression network constructed from microarray gene expression data of three different HIV-1 progression stages. The preservation pattern is investigated through identification of consensus modules in each pair of infection stages. It is observed that the preservation of the expression pattern of consensus modules remains more prominent during the transition of infection from acute stage to non-progressor stage. Additionally, we observed that the module membership values of genes are coherent with preserved modules across the HIV-1 progression stages.

Quality Assurance of UMLS Semantic Type Assignments Using SNOMED CT Hierarchies.

PubMed

Gu, H; Chen, Y; He, Z; Halper, M; Chen, L

2016-01-01

The Unified Medical Language System (UMLS) is one of the largest biomedical terminological systems, with over 2.5 million concepts in its Metathesaurus repository. The UMLS's Semantic Network (SN) with its collection of 133 high-level semantic types serves as an abstraction layer on top of the Metathesaurus. In particular, the SN elaborates an aspect of the Metathesaurus's concepts via the assignment of one or more types to each concept. Due to the scope and complexity of the Metathesaurus, errors are all but inevitable in this semantic-type assignment process. To develop a semi-automated methodology to help assure the quality of semantic-type assignments within the UMLS. The methodology uses a cross-validation strategy involving SNOMED CT's hierarchies in combination with UMLS semantic types. Semantically uniform, disjoint concept groups are generated programmatically by partitioning the collection of all concepts in the same SNOMED CT hierarchy according to their respective semantic-type assignments in the UMLS. Domain experts are then called upon to review the concepts in any group having a small number of concepts. It is our hypothesis that a semantic-type assignment combination applicable only to a very small number of concepts in a SNOMED CT hierarchy is an indicator of potential problems. The methodology was applied to the UMLS 2013AA release along with the SNOMED CT from January 2013. An overall error rate of 33% was found for concepts proposed by the quality-assurance methodology. Supporting our hypothesis, that number was four times higher than the error rate found in control samples. The results show that the quality-assurance methodology can aid in effective and efficient identification of UMLS semantic-type assignment errors.

Infectious disease control using contact tracing in random and scale-free networks

PubMed Central

Kiss, Istvan Z; Green, Darren M; Kao, Rowland R

2005-01-01

Contact tracing aims to identify and isolate individuals that have been in contact with infectious individuals. The efficacy of contact tracing and the hierarchy of traced nodes—nodes with higher degree traced first—is investigated and compared on random and scale-free (SF) networks with the same number of nodes N and average connection K. For values of the transmission rate larger than a threshold, the final epidemic size on SF networks is smaller than that on corresponding random networks. While in random networks new infectious and traced nodes from all classes have similar average degrees, in SF networks the average degree of nodes that are in more advanced stages of the disease is higher at any given time. On SF networks tracing removes possible sources of infection with high average degree. However a higher tracing effort is required to control the epidemic than on corresponding random networks due to the high initial velocity of spread towards the highly connected nodes. An increased latency period fails to significantly improve contact tracing efficacy. Contact tracing has a limited effect if the removal rate of susceptible nodes is relatively high, due to the fast local depletion of susceptible nodes. PMID:16849217

Hierarchical organization of brain functional networks during visual tasks.

PubMed

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

Identification of functional modules using network topology and high-throughput data.

PubMed

Ulitsky, Igor; Shamir, Ron

2007-01-26

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data. We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity. We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Correlation between Academic and Skills-Based Tests in Computer Networks

ERIC Educational Resources Information Center

Buchanan, William

2006-01-01

Computing-related programmes and modules have many problems, especially related to large class sizes, large-scale plagiarism, module franchising, and an increased requirement from students for increased amounts of hands-on, practical work. This paper presents a practical computer networks module which uses a mixture of online examinations and a…

Generation of oscillating gene regulatory network motifs

NASA Astrophysics Data System (ADS)

van Dorp, M.; Lannoo, B.; Carlon, E.

2013-07-01

Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations.

PubMed

Shannon, Casey P; Chen, Virginia; Takhar, Mandeep; Hollander, Zsuzsanna; Balshaw, Robert; McManus, Bruce M; Tebbutt, Scott J; Sin, Don D; Ng, Raymond T

2016-11-14

Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues.

Excavation of attractor modules for nasopharyngeal carcinoma via integrating systemic module inference with attract method.

PubMed

Jiang, T; Jiang, C-Y; Shu, J-H; Xu, Y-J

2017-07-10

The molecular mechanism of nasopharyngeal carcinoma (NPC) is poorly understood and effective therapeutic approaches are needed. This research aimed to excavate the attractor modules involved in the progression of NPC and provide further understanding of the underlying mechanism of NPC. Based on the gene expression data of NPC, two specific protein-protein interaction networks for NPC and control conditions were re-weighted using Pearson correlation coefficient. Then, a systematic tracking of candidate modules was conducted on the re-weighted networks via cliques algorithm, and a total of 19 and 38 modules were separately identified from NPC and control networks, respectively. Among them, 8 pairs of modules with similar gene composition were selected, and 2 attractor modules were identified via the attract method. Functional analysis indicated that these two attractor modules participate in one common bioprocess of cell division. Based on the strategy of integrating systemic module inference with the attract method, we successfully identified 2 attractor modules. These attractor modules might play important roles in the molecular pathogenesis of NPC via affecting the bioprocess of cell division in a conjunct way. Further research is needed to explore the correlations between cell division and NPC.

Decision-Oriented Health Technology Assessment: One Step Forward in Supporting the Decision-Making Process in Hospitals.

PubMed

Ritrovato, Matteo; Faggiano, Francesco C; Tedesco, Giorgia; Derrico, Pietro

2015-06-01

This article outlines the Decision-Oriented Health Technology Assessment: a new implementation of the European network for Health Technology Assessment Core Model, integrating the multicriteria decision-making analysis by using the analytic hierarchy process to introduce a standardized methodological approach as a valued and shared tool to support health care decision making within a hospital. Following the Core Model as guidance (European network for Health Technology Assessment. HTA core model for medical and surgical interventions. Available from: http://www.eunethta.eu/outputs/hta-core-model-medical-and-surgical-interventions-10r. [Accessed May 27, 2014]), it is possible to apply the analytic hierarchy process to break down a problem into its constituent parts and identify priorities (i.e., assigning a weight to each part) in a hierarchical structure. Thus, it quantitatively compares the importance of multiple criteria in assessing health technologies and how the alternative technologies perform in satisfying these criteria. The verbal ratings are translated into a quantitative form by using the Saaty scale (Saaty TL. Decision making with the analytic hierarchy process. Int J Serv Sci 2008;1:83-98). An eigenvectors analysis is used for deriving the weights' systems (i.e., local and global weights' system) that reflect the importance assigned to the criteria and the priorities related to the performance of the alternative technologies. Compared with the Core Model, this methodological approach supplies a more timely as well as contextualized evidence for a specific technology, making it possible to obtain data that are more relevant and easier to interpret, and therefore more useful for decision makers to make investment choices with greater awareness. We reached the conclusion that although there may be scope for improvement, this implementation is a step forward toward the goal of building a "solid bridge" between the scientific evidence and the final decision maker's choice. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data.

PubMed

Farber, Charles R

2010-11-01

Bone mineral density (BMD) is influenced by a complex network of gene interactions; therefore, elucidating the relationships between genes and how those genes, in turn, influence BMD is critical for developing a comprehensive understanding of osteoporosis. To investigate the role of transcriptional networks in the regulation of BMD, we performed a weighted gene coexpression network analysis (WGCNA) using microarray expression data on monocytes from young individuals with low or high BMD. WGCNA groups genes into modules based on patterns of gene coexpression. and our analysis identified 11 gene modules. We observed that the overall expression of one module (referred to as module 9) was significantly higher in the low-BMD group (p = .03). Module 9 was highly enriched for genes belonging to the immune system-related gene ontology (GO) category "response to virus" (p = 7.6 × 10(-11)). Using publically available genome-wide association study data, we independently validated the importance of module 9 by demonstrating that highly connected module 9 hubs were more likely, relative to less highly connected genes, to be genetically associated with BMD. This study highlights the advantages of systems-level analyses to uncover coexpression modules associated with bone mass and suggests that particular monocyte expression patterns may mediate differences in BMD. © 2010 American Society for Bone and Mineral Research.

Planning in subsumption architectures

NASA Technical Reports Server (NTRS)

Chalfant, Eugene C.

1994-01-01

A subsumption planner using a parallel distributed computational paradigm based on the subsumption architecture for control of real-world capable robots is described. Virtual sensor state space is used as a planning tool to visualize the robot's anticipated effect on its environment. Decision sequences are generated based on the environmental situation expected at the time the robot must commit to a decision. Between decision points, the robot performs in a preprogrammed manner. A rudimentary, domain-specific partial world model contains enough information to extrapolate the end results of the rote behavior between decision points. A collective network of predictors operates in parallel with the reactive network forming a recurrrent network which generates plans as a hierarchy. Details of a plan segment are generated only when its execution is imminent. The use of the subsumption planner is demonstrated by a simple maze navigation problem.

The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements

NASA Astrophysics Data System (ADS)

Kazadzis, Stelios; Kouremeti, Natalia; Nyeki, Stephan; Gröbner, Julian; Wehrli, Christoph

2018-02-01

The World Optical Depth Research Calibration Center (WORCC) is a section within the World Radiation Center at Physikalisches-Meteorologisches Observatorium (PMOD/WRC), Davos, Switzerland, established after the recommendations of the World Meteorological Organization for calibration of aerosol optical depth (AOD)-related Sun photometers. WORCC is mandated to develop new methods for instrument calibration, to initiate homogenization activities among different AOD networks and to run a network (GAW-PFR) of Sun photometers. In this work we describe the calibration hierarchy and methods used under WORCC and the basic procedures, tests and processing techniques in order to ensure the quality assurance and quality control of the AOD-retrieved data.

ModuleRole: a tool for modulization, role determination and visualization in protein-protein interaction networks.

PubMed

Li, Guipeng; Li, Ming; Zhang, Yiwei; Wang, Dong; Li, Rong; Guimerà, Roger; Gao, Juntao Tony; Zhang, Michael Q

2014-01-01

Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by "User Guide" in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user's own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID. ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.

Hubless satellite communications networks

NASA Technical Reports Server (NTRS)

Robinson, Peter Alan

1994-01-01

Frequency Comb Multiple Access (FCMA) is a new combined modulation and multiple access method which will allow cheap hubless Very Small Aperture Terminal (VSAT) networks to be constructed. Theoretical results show bandwidth efficiency and power efficiency improvements over other modulation and multiple access methods. Costs of the VSAT network are reduced dramatically since a hub station is not required.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

PubMed

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network

PubMed Central

Qin, Wei; Tian, Jie; Bai, Lijun; Pan, Xiaohong; Yang, Lin; Chen, Peng; Dai, Jianping; Ai, Lin; Zhao, Baixiao; Gong, Qiyong; Wang, Wei; von Deneen, Karen M; Liu, Yijun

2008-01-01

Background Recently, increasing evidence has indicated that the primary acupuncture effects are mediated by the central nervous system. However, specific brain networks underpinning these effects remain unclear. Results In the present study using fMRI, we employed a within-condition interregional covariance analysis method to investigate functional connectivity of brain networks involved in acupuncture. The fMRI experiment was performed before, during and after acupuncture manipulations on healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway for pain modulation. We first identified significant fMRI signal changes during acupuncture stimulation in the left amygdala, which was subsequently selected as a functional reference for connectivity analyses. Our results have demonstrated that there is a brain network associated with the amygdala during a resting condition. This network encompasses the brain structures that are implicated in both pain sensation and pain modulation. We also found that such a pain-related network could be modulated by both verum acupuncture and sham acupuncture. Furthermore, compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations among the amygdala-associated network. Conclusion Our findings indicate that acupuncture may change this amygdala-specific brain network into a functional state that underlies pain perception and pain modulation. PMID:19014532

The SLH framework for modeling quantum input-output networks

DOE PAGES

Combes, Joshua; Kerckhoff, Joseph; Sarovar, Mohan

2017-09-04

Here, many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, e.g. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields bymore » an operator triple ( S,L,H). Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.« less

The SLH framework for modeling quantum input-output networks

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

Combes, Joshua; Kerckhoff, Joseph; Sarovar, Mohan

Here, many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, e.g. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields bymore » an operator triple ( S,L,H). Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.« less

Identifying module biomarkers from gastric cancer by differential correlation network

PubMed Central

Liu, Xiaoping; Chang, Xiao

2016-01-01

Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer. PMID:27703371

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-02-01

Call for Papers: Convergence Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to: