Cooperation prevails when individuals adjust their social ties.

PubMed

Santos, Francisco C; Pacheco, Jorge M; Lenaerts, Tom

2006-10-20

Conventional evolutionary game theory predicts that natural selection favours the selfish and strong even though cooperative interactions thrive at all levels of organization in living systems. Recent investigations demonstrated that a limiting factor for the evolution of cooperative interactions is the way in which they are organized, cooperators becoming evolutionarily competitive whenever individuals are constrained to interact with few others along the edges of networks with low average connectivity. Despite this insight, the conundrum of cooperation remains since recent empirical data shows that real networks exhibit typically high average connectivity and associated single-to-broad-scale heterogeneity. Here, a computational model is constructed in which individuals are able to self-organize both their strategy and their social ties throughout evolution, based exclusively on their self-interest. We show that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of cooperation in social networks. For a given average connectivity of the population, there is a critical value for the ratio W between the time scales associated with the evolution of strategy and of structure above which cooperators wipe out defectors. Moreover, the emerging social networks exhibit an overall heterogeneity that accounts very well for the diversity of patterns recently found in acquired data on social networks. Finally, heterogeneity is found to become maximal when W reaches its critical value. These results show that simple topological dynamics reflecting the individual capacity for self-organization of social ties can produce realistic networks of high average connectivity with associated single-to-broad-scale heterogeneity. On the other hand, they show that cooperation cannot evolve as a result of "social viscosity" alone in heterogeneous networks with high average connectivity, requiring the additional mechanism of topological co-evolution to ensure the survival of cooperative behaviour.

Crossing the Technology Adoption Chasm in the Presence of Network Externalities: Implications for DoD

DTIC Science & Technology

2007-06-01

Innovations. New York: The Free Press. Rohlfs, J. (2001). Bandwagon Effects in High-Technology Industries. Massachusetts Institute of Technology...adoption. It focuses on cost and benefit uncertainty as well as network effects applied to end- users and their organizations. Specifically, it...as network effects applied to end- users and their organizations. Specifically, it explores Department of Defense (DoD) acquisition programs

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.

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

PubMed Central

Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

2016-01-01

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes. PMID:27148015

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain.

PubMed

Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

2016-01-01

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

Extracting microtubule networks from superresolution single-molecule localization microscopy data

PubMed Central

Zhang, Zhen; Nishimura, Yukako; Kanchanawong, Pakorn

2017-01-01

Microtubule filaments form ubiquitous networks that specify spatial organization in cells. However, quantitative analysis of microtubule networks is hampered by their complex architecture, limiting insights into the interplay between their organization and cellular functions. Although superresolution microscopy has greatly facilitated high-resolution imaging of microtubule filaments, extraction of complete filament networks from such data sets is challenging. Here we describe a computational tool for automated retrieval of microtubule filaments from single-molecule-localization–based superresolution microscopy images. We present a user-friendly, graphically interfaced implementation and a quantitative analysis of microtubule network architecture phenotypes in fibroblasts. PMID:27852898

The structure and resilience of financial market networks

NASA Astrophysics Data System (ADS)

Kauê Dal'Maso Peron, Thomas; da Fontoura Costa, Luciano; Rodrigues, Francisco A.

2012-03-01

Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

Temporary Network Development Capability in High Velocity Environments: A Dynamic Capability Study of Disaster Relief Organizations

ERIC Educational Resources Information Center

O'Brien, William Ross

2010-01-01

Organizations involved in crisis relief after a natural disaster face the multifaceted challenge of significantly changing needs of their various stakeholders, limited, ambiguous and even incorrect information, and highly compressed time limitations. Yet the performance of these organization in these high velocity environments is critical for the…

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

PubMed

Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

2017-10-01

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.

PubMed

Wen, Hongwei; Liu, Yue; Rekik, Islem; Wang, Shengpei; Zhang, Jishui; Zhang, Yue; Peng, Yun; He, Huiguang

2017-08-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

On the synthesis and structure of resorcinol-formaldehyde polymeric networks – Precursors to 3D-carbon macroassemblies

DOE PAGES

Lewicki, James P.; Fox, Christina A.; Worsley, Marcus A.

2015-05-15

With the new impetus towards the development of hierarchical graphene and CNT macro-assemblies for application in fields such as advanced energy storage, catalysis and electronics; there is much renewed interest in organic carbon-based sol–gel processes as a synthetically convenient and versatile means of forming three dimensional, covalently bonded organic/inorganic networks. Such matrices can act as highly effective precursors, scaffolds or molecular ‘glues’ for the assembly of a wide variety of functional carbon macro-assemblies. However, despite the utility and broad use of organic sol–gel processes – such as the ubiquitous resorcinol-formaldehyde (RF) reaction, there are details of the reaction chemistries ofmore » these important sol–gel processes that remain poorly understood at present. It is therefore both timely and necessary to examine these reactions in more detail using modern analytical techniques in order to gain a more rigorous understanding of the mechanisms by which these organic networks form. The goal of such studies is to obtain improved and rational control over the organic network structure, in order to better direct and tailor the architecture of the final inorganic carbon matrix. In this study we have investigated in detail, the mechanism of the organic sol–gel network forming reaction of resorcinol and formaldehyde from a structural and kinetic standpoint, by using a combination of real-time high field solution state nuclear magnetic resonance (NMR), low field NMR relaxometry and differential scanning calorimetry (DSC). These investigations have allowed us to track the network formation processes in real-time, gain both detailed structural information on the mechanisms of the RF sol–gel process and a quantitative assessment of the kinetics of the global network formation process. Here, it has been shown that the mechanism, by which the RF organic network forms, proceeds via an initial exothermic step correlated to the formation of a free aromatic aldehyde. The network growth reaction then proceeds in a statistical manner following a first order Arrhenius type kinetic relationship – characteristic of a typical thermoset network poly-condensation process. Finally, despite the relative complexity and ill-defined nature of the formaldehyde staring material, the final network structure is to a large extent, governed by the substitution pattern of the resorcinol molecule.« less

DEVELOPMENT OF THE “RICH CLUB” IN BRAIN CONNECTIVITY NETWORKS FROM 438 ADOLESCENTS & ADULTS AGED 12 TO 30

PubMed Central

Dennis, Emily L.; Jahanshad, Neda; Toga, Arthur W.; McMahon, Katie L.; de Zubicaray, Greig I.; Hickie, Ian; Wright, Margaret J.; Thompson, Paul M.

2014-01-01

The ‘rich club’ coefficient describes a phenomenon where a network's hubs (high-degree nodes) are on average more intensely interconnected than lower-degree nodes. Networks with rich clubs often have an efficient, higher-order organization, but we do not yet know how the rich club emerges in the living brain, or how it changes as our brain networks develop. Here we chart the developmental trajectory of the rich club in anatomical brain networks from 438 subjects aged 12-30. Cortical networks were constructed from 68×68 connectivity matrices of fiber density, using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). The adult and younger cohorts had rich clubs that included different nodes; the rich club effect intensified with age. Rich-club organization is a sign of a network's efficiency and robustness. These concepts and findings may be advantageous for studying brain maturation and abnormal brain development. PMID:24827471

Insights Into Collaborative Networks Of Nonprofit, Private, And Public Organizations That Address Complex Health Issues.

PubMed

Hogg, Rachel A; Varda, Danielle

2016-11-01

Community networks that include nonprofit, public, and private organizations have formed around many health issues, such as chronic disease management and healthy living and eating. Despite the increases in the numbers of and funding for cross-sector networks, and the growing literature about them, there are limited data and methods that can be used to assess their effectiveness and analyze their designs. We addressed this gap in knowledge by analyzing the characteristics of 260 cross-sector community health networks that collectively consisted of 7,816 organizations during the period 2008-15. We found that nonprofit organizations were more prevalent than private firms or government agencies in these networks. Traditional types of partners in community health networks such as hospitals, community health centers, and public health agencies were the most trusted and valued by other members of their networks. However, nontraditional partners, such as employer or business groups and colleges or universities, reported contributing relatively high numbers of resources to their networks. Further evidence is needed to inform collaborative management processes and policies as a mechanism for building what the Robert Wood Johnson Foundation describes as a culture of health. Project HOPE—The People-to-People Health Foundation, Inc.

A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks.

PubMed

Klados, Manousos A; Kanatsouli, Kassia; Antoniou, Ioannis; Babiloni, Fabio; Tsirka, Vassiliki; Bamidis, Panagiotis D; Micheloyannis, Sifis

2013-01-01

The two core systems of mathematical processing (subitizing and retrieval) as well as their functionality are already known and published. In this study we have used graph theory to compare the brain network organization of these two core systems in the cortical layer during difficult calculations. We have examined separately all the EEG frequency bands in healthy young individuals and we found that the network organization at rest, as well as during mathematical tasks has the characteristics of Small World Networks for all the bands, which is the optimum organization required for efficient information processing. The different mathematical stimuli provoked changes in the graph parameters of different frequency bands, especially the low frequency bands. More specific, in Delta band the induced network increases it's local and global efficiency during the transition from subitizing to retrieval system, while results suggest that difficult mathematics provoke networks with higher cliquish organization due to more specific demands. The network of the Theta band follows the same pattern as before, having high nodal and remote organization during difficult mathematics. Also the spatial distribution of the network's weights revealed more prominent connections in frontoparietal regions, revealing the working memory load due to the engagement of the retrieval system. The cortical networks of the alpha brainwaves were also more efficient, both locally and globally, during difficult mathematics, while the fact that alpha's network was more dense on the frontparietal regions as well, reveals the engagement of the retrieval system again. Concluding, this study gives more evidences regarding the interaction of the two core systems, exploiting the produced functional networks of the cerebral cortex, especially for the difficult mathematics.

Data Handling and Communication

NASA Astrophysics Data System (ADS)

Hemmer, FréDéRic Giorgio Innocenti, Pier

The following sections are included: * Introduction * Computing Clusters and Data Storage: The New Factory and Warehouse * Local Area Networks: Organizing Interconnection * High-Speed Worldwide Networking: Accelerating Protocols * Detector Simulation: Events Before the Event * Data Analysis and Programming Environment: Distilling Information * World Wide Web: Global Networking * References

Distributing stand inventory data and maps over a wide area network

Treesearch

Thomas E. Burk

2000-01-01

High-speed networks connecting multiple levels of management are becoming commonplace among forest resources organizations. Such networks can be used to deliver timely spatial and aspatial data relevant to the management of stands to field personnel. A network infrastructure allows maintenance of cost-effective, centralized databases with the potential for updating by...

Zone-Based Routing Protocol for Wireless Sensor Networks

PubMed Central

Venkateswarlu Kumaramangalam, Muni; Adiyapatham, Kandasamy; Kandasamy, Chandrasekaran

2014-01-01

Extensive research happening across the globe witnessed the importance of Wireless Sensor Network in the present day application world. In the recent past, various routing algorithms have been proposed to elevate WSN network lifetime. Clustering mechanism is highly successful in conserving energy resources for network activities and has become promising field for researches. However, the problem of unbalanced energy consumption is still open because the cluster head activities are tightly coupled with role and location of a particular node in the network. Several unequal clustering algorithms are proposed to solve this wireless sensor network multihop hot spot problem. Current unequal clustering mechanisms consider only intra- and intercluster communication cost. Proper organization of wireless sensor network into clusters enables efficient utilization of limited resources and enhances lifetime of deployed sensor nodes. This paper considers a novel network organization scheme, energy-efficient edge-based network partitioning scheme, to organize sensor nodes into clusters of equal size. Also, it proposes a cluster-based routing algorithm, called zone-based routing protocol (ZBRP), for elevating sensor network lifetime. Experimental results show that ZBRP out-performs interims of network lifetime and energy conservation with its uniform energy consumption among the cluster heads. PMID:27437455

Zone-Based Routing Protocol for Wireless Sensor Networks.

PubMed

Venkateswarlu Kumaramangalam, Muni; Adiyapatham, Kandasamy; Kandasamy, Chandrasekaran

2014-01-01

Extensive research happening across the globe witnessed the importance of Wireless Sensor Network in the present day application world. In the recent past, various routing algorithms have been proposed to elevate WSN network lifetime. Clustering mechanism is highly successful in conserving energy resources for network activities and has become promising field for researches. However, the problem of unbalanced energy consumption is still open because the cluster head activities are tightly coupled with role and location of a particular node in the network. Several unequal clustering algorithms are proposed to solve this wireless sensor network multihop hot spot problem. Current unequal clustering mechanisms consider only intra- and intercluster communication cost. Proper organization of wireless sensor network into clusters enables efficient utilization of limited resources and enhances lifetime of deployed sensor nodes. This paper considers a novel network organization scheme, energy-efficient edge-based network partitioning scheme, to organize sensor nodes into clusters of equal size. Also, it proposes a cluster-based routing algorithm, called zone-based routing protocol (ZBRP), for elevating sensor network lifetime. Experimental results show that ZBRP out-performs interims of network lifetime and energy conservation with its uniform energy consumption among the cluster heads.

Disease implications of animal social network structure: A synthesis across social systems.

PubMed

Sah, Pratha; Mann, Janet; Bansal, Shweta

2018-05-01

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

A Graph Theoretical Approach to Study the Organization of the Cortical Networks during Different Mathematical Tasks

PubMed Central

Klados, Manousos A.; Kanatsouli, Kassia; Antoniou, Ioannis; Babiloni, Fabio; Tsirka, Vassiliki; Bamidis, Panagiotis D.; Micheloyannis, Sifis

2013-01-01

The two core systems of mathematical processing (subitizing and retrieval) as well as their functionality are already known and published. In this study we have used graph theory to compare the brain network organization of these two core systems in the cortical layer during difficult calculations. We have examined separately all the EEG frequency bands in healthy young individuals and we found that the network organization at rest, as well as during mathematical tasks has the characteristics of Small World Networks for all the bands, which is the optimum organization required for efficient information processing. The different mathematical stimuli provoked changes in the graph parameters of different frequency bands, especially the low frequency bands. More specific, in Delta band the induced network increases it’s local and global efficiency during the transition from subitizing to retrieval system, while results suggest that difficult mathematics provoke networks with higher cliquish organization due to more specific demands. The network of the Theta band follows the same pattern as before, having high nodal and remote organization during difficult mathematics. Also the spatial distribution of the network’s weights revealed more prominent connections in frontoparietal regions, revealing the working memory load due to the engagement of the retrieval system. The cortical networks of the alpha brainwaves were also more efficient, both locally and globally, during difficult mathematics, while the fact that alpha’s network was more dense on the frontparietal regions as well, reveals the engagement of the retrieval system again. Concluding, this study gives more evidences regarding the interaction of the two core systems, exploiting the produced functional networks of the cerebral cortex, especially for the difficult mathematics. PMID:23990992

Isotropic actomyosin dynamics promote organization of the apical cell cortex in epithelial cells.

PubMed

Klingner, Christoph; Cherian, Anoop V; Fels, Johannes; Diesinger, Philipp M; Aufschnaiter, Roland; Maghelli, Nicola; Keil, Thomas; Beck, Gisela; Tolić-Nørrelykke, Iva M; Bathe, Mark; Wedlich-Soldner, Roland

2014-10-13

Although cortical actin plays an important role in cellular mechanics and morphogenesis, there is surprisingly little information on cortex organization at the apical surface of cells. In this paper, we characterize organization and dynamics of microvilli (MV) and a previously unappreciated actomyosin network at the apical surface of Madin-Darby canine kidney cells. In contrast to short and static MV in confluent cells, the apical surfaces of nonconfluent epithelial cells (ECs) form highly dynamic protrusions, which are often oriented along the plane of the membrane. These dynamic MV exhibit complex and spatially correlated reorganization, which is dependent on myosin II activity. Surprisingly, myosin II is organized into an extensive network of filaments spanning the entire apical membrane in nonconfluent ECs. Dynamic MV, myosin filaments, and their associated actin filaments form an interconnected, prestressed network. Interestingly, this network regulates lateral mobility of apical membrane probes such as integrins or epidermal growth factor receptors, suggesting that coordinated actomyosin dynamics contributes to apical cell membrane organization. © 2014 Klingner et al.

Enhancing gene regulatory network inference through data integration with markov random fields

DOE PAGES

Banf, Michael; Rhee, Seung Y.

2017-02-01

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less

Enhancing gene regulatory network inference through data integration with markov random fields

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

Banf, Michael; Rhee, Seung Y.

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

PubMed Central

Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living. PMID:28373567

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

USGS Publications Warehouse

Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks.

PubMed

Sah, Pratha; Leu, Stephan T; Cross, Paul C; Hudson, Peter J; Bansal, Shweta

2017-04-18

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Connectomics and neuroticism: an altered functional network organization.

PubMed

Servaas, Michelle N; Geerligs, Linda; Renken, Remco J; Marsman, Jan-Bernard C; Ormel, Johan; Riese, Harriëtte; Aleman, André

2015-01-01

The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the 'neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role.

Thermochemical Stability and Friction Properties of Soft Organosilica Networks for Solid Lubrication.

PubMed

Gonzalez Rodriguez, Pablo; Dral, A Petra; van den Nieuwenhuijzen, Karin J H; Lette, Walter; Schipper, Dik J; Ten Elshof, Johan E

2018-01-24

In view of their possible application as high temperature solid lubricants, the tribological and thermochemical properties of several organosilica networks were investigated over a range of temperatures between 25 and 580 °C. Organosilica networks, obtained from monomers with terminal and bridging organic groups, were synthesized by a sol-gel process. The influence of carbon content, crosslink density, rotational freedom of incorporated hydrocarbon groups, and network connectivity on the high temperature friction properties of the polymer was studied for condensed materials from silicon alkoxide precursors with terminating organic groups, i.e., methyltrimethoxysilane, propyltrimethoxysilane, diisopropyldimethoxysilane, cyclohexyltrimethoxysilane, phenyltrimethoxysilane and 4-biphenylyltriethoxysilane networks, as well as precursors with organic bridging groups between Si centers, i.e., 1,4-bis(triethoxysilyl)benzene and 4,4'-bis(triethoxysilyl)-1,1'-biphenyl. Pin-on-disc measurements were performed using all selected solid lubricants. It was found that materials obtained from phenyltrimethoxysilane and cyclohexyltrimethoxysilane precursors showed softening above 120 °C and performed best in terms of friction reduction, reaching friction coefficients as low as 0.01. This value is lower than that of graphite films (0.050 ± 0.005), a common bench mark for solid lubricants.

Thermochemical Stability and Friction Properties of Soft Organosilica Networks for Solid Lubrication

PubMed Central

Gonzalez Rodriguez, Pablo; van den Nieuwenhuijzen, Karin J. H.; Lette, Walter; Schipper, Dik J.

2018-01-01

In view of their possible application as high temperature solid lubricants, the tribological and thermochemical properties of several organosilica networks were investigated over a range of temperatures between 25 and 580 °C. Organosilica networks, obtained from monomers with terminal and bridging organic groups, were synthesized by a sol-gel process. The influence of carbon content, crosslink density, rotational freedom of incorporated hydrocarbon groups, and network connectivity on the high temperature friction properties of the polymer was studied for condensed materials from silicon alkoxide precursors with terminating organic groups, i.e., methyltrimethoxysilane, propyltrimethoxysilane, diisopropyldimethoxysilane, cyclohexyltrimethoxysilane, phenyltrimethoxysilane and 4-biphenylyltriethoxysilane networks, as well as precursors with organic bridging groups between Si centers, i.e., 1,4-bis(triethoxysilyl)benzene and 4,4′-bis(triethoxysilyl)-1,1′-biphenyl. Pin-on-disc measurements were performed using all selected solid lubricants. It was found that materials obtained from phenyltrimethoxysilane and cyclohexyltrimethoxysilane precursors showed softening above 120 °C and performed best in terms of friction reduction, reaching friction coefficients as low as 0.01. This value is lower than that of graphite films (0.050 ± 0.005), a common bench mark for solid lubricants. PMID:29364164

Social Networks as a Political Resource: Some Insights Drawn from the Community Organizational and Community Action Experiences.

ERIC Educational Resources Information Center

Rosenbaum, Allan

The development and functioning of urban social networks in highly politicized environments--particularly, the neighborhood based community organization, political coalition building of urban mayors, and community action programs--suggest implications for building locally based educational reform capacity through network development. Community…

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

PubMed

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

2016-11-30

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

George Washington Community High School: analysis of a partnership network.

PubMed

Bringle, Robert G; Officer, Starla D H; Grim, Jim; Hatcher, Julie A

2009-01-01

After five years with no public schools in their community, residents and neighborhood organizations of the Near Westside of Indianapolis advocated for the opening of George Washington Community High School (GWCHS). As a neighborhood in close proximity to the campus of Indiana University-Purdue University Indianapolis, the Near Westside and campus worked together to address this issue and improve the educational success of youth. In fall 2000, GWCHS opened as a community school and now thrives as a national model, due in part to its network of community relationships. This account analyzes the development of the school by focusing on the relationships among the university, the high school, community organizations, and the residents of the Near Westside and highlights the unique partnership between the campus and school by defining the relational qualities and describing the network created to make sustainable changes with the high school.

Identification of Modules in Protein-Protein Interaction Networks

NASA Astrophysics Data System (ADS)

Erten, Sinan; Koyutürk, Mehmet

In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

Signaling in large-scale neural networks.

PubMed

Berg, Rune W; Hounsgaard, Jørn

2009-02-01

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

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

PubMed Central

Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

2012-01-01

Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise. PMID:22276205

Benefits of Enterprise Social Networking Systems for High Energy Physics community

NASA Astrophysics Data System (ADS)

Silva de Sousa, B.; Wagner, A.; Ormancey, E.; Grzywaczewski, P.

2015-12-01

The emergence of social media platforms in the consumer space unlocked new ways of interaction between individuals on the Web. People develop now their social networks and relations based on common interests and activities with the choice to opt-in or opt-out on content of their interest. This kind of platforms have also an important place to fill inside large organizations and enterprises where communication and collaborators interaction are keys for development. Enterprise Social Networking Systems (ESN) add value to an organization by encouraging information sharing, capturing knowledge, enabling action and empowering people. CERN is currently rolling out an ESN which aims to unify and provide a single point of access to the multitude of information sources in the organization. It also implements social features that can be added on top of existing communication channels. While the deployment of this kind of platforms is not without risks we firmly believe that they are of the best interest for our community, opening the opportunity to evaluate a global social network for High Energy Physics (HEP).

Two-Dimensional Metal-Free Organic Multiferroic Material for Design of Multifunctional Integrated Circuits.

PubMed

Tu, Zhengyuan; Wu, Menghao; Zeng, Xiao Cheng

2017-05-04

Coexistence of ferromagnetism and ferroelectricity in a single 2D material is highly desirable for integration of multifunctional units in 2D material-based circuits. We report theoretical evidence of C 6 N 8 H organic network as being the first 2D organic multiferroic material with coexisting ferromagnetic and ferroelectric properties. The ferroelectricity stems from multimode proton-transfer within the 2D C 6 N 8 H network, in which a long-range proton-transfer mode is enabled by the facilitation of oxygen molecule when the network is exposed to the air. Such oxygen-assisted ferroelectricity also leads to a high Curie temperature and coupling between ferroelectricity and ferromagnetism. We also find that hydrogenation and carbon doping can transform the 2D g-C 3 N 4 network from an insulator to an n-type/p-type magnetic semiconductor with modest bandgap. Akin to the dopant induced n/p channels in silicon wafer, a variety of dopant created functional units can be integrated into the g-C 3 N 4 wafer by design for nanoelectronic applications.

Impulsivity and the Modular Organization of Resting-State Neural Networks

PubMed Central

Davis, F. Caroline; Knodt, Annchen R.; Sporns, Olaf; Lahey, Benjamin B.; Zald, David H.; Brigidi, Bart D.; Hariri, Ahmad R.

2013-01-01

Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation (“modularity”) of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors. PMID:22645253

Integration and segregation of large-scale brain networks during short-term task automatization

PubMed Central

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-01-01

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

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.

Flow rate of transport network controls uniform metabolite supply to tissue

PubMed Central

Meigel, Felix J.

2018-01-01

Life and functioning of higher organisms depends on the continuous supply of metabolites to tissues and organs. What are the requirements on the transport network pervading a tissue to provide a uniform supply of nutrients, minerals or hormones? To theoretically answer this question, we present an analytical scaling argument and numerical simulations on how flow dynamics and network architecture control active spread and uniform supply of metabolites by studying the example of xylem vessels in plants. We identify the fluid inflow rate as the key factor for uniform supply. While at low inflow rates metabolites are already exhausted close to flow inlets, too high inflow flushes metabolites through the network and deprives tissue close to inlets of supply. In between these two regimes, there exists an optimal inflow rate that yields a uniform supply of metabolites. We determine this optimal inflow analytically in quantitative agreement with numerical results. Optimizing network architecture by reducing the supply variance over all network tubes, we identify patterns of tube dilation or contraction that compensate sub-optimal supply for the case of too low or too high inflow rate. PMID:29720455

Characteristics of individuals with high information potential in government research and development organizations.

NASA Technical Reports Server (NTRS)

Holland, W. E.

1972-01-01

In order to study focal individuals within informal communications networks, a special variable was constructed: information potential (IP) was defined as the information-source value placed on an individual by his colleagues. Four hypotheses involving IP were tested in three R&D organizations using questionnaires and pencil-and-paper tests. Results indicated that the individual with high IP used more and different sources of technical information, was seen to be a credible information source and to have a strong ability to associate seemingly unrelated ideas, and was as approachable as the other members of his organization. Four tentative conclusions may be drawn from this study concerning the person with high IP. He is (1) an identifiable individual in several different kinds of organizations; (2) a distinctive information transceiver (transmitter and receiver); (3) both a producer and a catalyst in his own organization; and (4) an extender and an amplifier of information search. To affect the efficiency of informal information flow, the research manager's best hope for positively influencing informal networks lies in the identification and motivation of the special communicators in his organization.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Feature-based alert correlation in security systems using self organizing maps

NASA Astrophysics Data System (ADS)

Kumar, Munesh; Siddique, Shoaib; Noor, Humera

2009-04-01

The security of the networks has been an important concern for any organization. This is especially important for the defense sector as to get unauthorized access to the sensitive information of an organization has been the prime desire for cyber criminals. Many network security techniques like Firewall, VPN Concentrator etc. are deployed at the perimeter of network to deal with attack(s) that occur(s) from exterior of network. But any vulnerability that causes to penetrate the network's perimeter of defense, can exploit the entire network. To deal with such vulnerabilities a system has been evolved with the purpose of generating an alert for any malicious activity triggered against the network and its resources, termed as Intrusion Detection System (IDS). The traditional IDS have still some deficiencies like generating large number of alerts, containing both true and false one etc. By automatically classifying (correlating) various alerts, the high-level analysis of the security status of network can be identified and the job of network security administrator becomes much easier. In this paper we propose to utilize Self Organizing Maps (SOM); an Artificial Neural Network for correlating large amount of logged intrusion alerts based on generic features such as Source/Destination IP Addresses, Port No, Signature ID etc. The different ways in which alerts can be correlated by Artificial Intelligence techniques are also discussed. . We've shown that the strategy described in the paper improves the efficiency of IDS by better correlating the alerts, leading to reduced false positives and increased competence of network administrator.

Topology of Plant - Flower-Visitor Networks in a Tropical Mountain Forest: Insights on the Role of Altitudinal and Temporal Variation.

PubMed

Cuartas-Hernández, Sandra; Medel, Rodrigo

2015-01-01

Understanding the factors determining the spatial and temporal variation of ecological networks is fundamental to the knowledge of their dynamics and functioning. In this study, we evaluate the effect of elevation and time on the structure of plant-flower-visitor networks in a Colombian mountain forest. We examine the level of generalization of plant and animal species and the identity of interactions in 44 bipartite matrices obtained from eight altitudinal levels, from 2200 to 2900 m during eight consecutive months. The contribution of altitude and time to the overall variation in the number of plant (P) and pollinator (A) species, network size (M), number of interactions (I), connectance (C), and nestedness was evaluated. In general, networks were small, showed high connectance values and non-nested patterns of organization. Variation in P, M, I and C was better accounted by time than elevation, seemingly related to temporal variation in precipitation. Most plant and insect species were specialists and the identity of links showed a high turnover over months and at every 100 m elevation. The partition of the whole system into smaller network units allowed us to detect small-scale patterns of interaction that contrasted with patterns commonly described in cumulative networks. The specialized but erratic pattern of network organization observed in this tropical mountain suggests that high connectance coupled with opportunistic attachment may confer robustness to plant-flower-visitor networks occurring at small spatial and temporal units.

The enhancement of security in healthcare information systems.

PubMed

Liu, Chia-Hui; Chung, Yu-Fang; Chen, Tzer-Shyong; Wang, Sheng-De

2012-06-01

With the progress and the development of information technology, the internal data in medical organizations have become computerized and are further established the medical information system. Moreover, the use of the Internet enhances the information communication as well as affects the development of the medical information system that a lot of medical information is transmitted with the Internet. Since there is a network within another network, when all networks are connected together, they will form the "Internet". For this reason, the Internet is considered as a high-risk and public environment which is easily destroyed and invaded so that a relevant protection is acquired. Besides, the data in the medical network system are confidential that it is necessary to protect the personal privacy, such as electronic patient records, medical confidential information, and authorization-controlled data in the hospital. As a consequence, a medical network system is considered as a network requiring high security that excellent protections and managerial strategies are inevitable to prevent illegal events and external attacks from happening. This study proposes secure medical managerial strategies being applied to the network environment of the medical organization information system so as to avoid the external or internal information security events, allow the medical system to work smoothly and safely that not only benefits the patients, but also allows the doctors to use it more conveniently, and further promote the overall medical quality. The objectives could be achieved by preventing from illegal invasion or medical information being stolen, protecting the completeness and security of medical information, avoiding the managerial mistakes of the internal information system in medical organizations, and providing the highly-reliable medical information system.

Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks.

PubMed

Gorochowski, Thomas E; Grierson, Claire S; di Bernardo, Mario

2018-03-01

Network motifs are significantly overrepresented subgraphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue, we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organization. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli . Our findings have implications for understanding how networked systems are constructed from motif parts and elucidate constraints that guide their evolution.

Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks

PubMed Central

Grierson, Claire S.

2018-01-01

Network motifs are significantly overrepresented subgraphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue, we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organization. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli. Our findings have implications for understanding how networked systems are constructed from motif parts and elucidate constraints that guide their evolution. PMID:29670941

Structural and functional rich club organization of the brain in children and adults.

PubMed

Grayson, David S; Ray, Siddharth; Carpenter, Samuel; Iyer, Swathi; Dias, Taciana G Costa; Stevens, Corinne; Nigg, Joel T; Fair, Damien A

2014-01-01

Recent studies using Magnetic Resonance Imaging (MRI) have proposed that the brain's white matter is organized as a rich club, whereby the most highly connected regions of the brain are also highly connected to each other. Here we use both functional and diffusion-weighted MRI in the human brain to investigate whether the rich club phenomena is present with functional connectivity, and how this organization relates to the structural phenomena. We also examine whether rich club regions serve to integrate information between distinct brain systems, and conclude with a brief investigation of the developmental trajectory of rich-club phenomena. In agreement with prior work, both adults and children showed robust structural rich club organization, comprising regions of the superior medial frontal/dACC, medial parietal/PCC, insula, and inferior temporal cortex. We also show that these regions were highly integrated across the brain's major networks. Functional brain networks were found to have rich club phenomena in a similar spatial layout, but a high level of segregation between systems. While no significant differences between adults and children were found structurally, adults showed significantly greater functional rich club organization. This difference appeared to be driven by a specific set of connections between superior parietal, insula, and supramarginal cortex. In sum, this work highlights the existence of both a structural and functional rich club in adult and child populations with some functional changes over development. It also offers a potential target in examining atypical network organization in common developmental brain disorders, such as ADHD and Autism.

Quality control methodology for high-throughput protein-protein interaction screening.

PubMed

Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

2011-01-01

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

Browsing the Real World using Organic Electronics, Si-Chips, and a Human Touch.

PubMed

Berggren, Magnus; Simon, Daniel T; Nilsson, David; Dyreklev, Peter; Norberg, Petronella; Nordlinder, Staffan; Ersman, Peter Andersson; Gustafsson, Göran; Wikner, J Jacob; Hederén, Jan; Hentzell, Hans

2016-03-09

Organic electronics have been developed according to an orthodox doctrine advocating "all-printed'', "all-organic'' and "ultra-low-cost'' primarily targeting various e-paper applications. In order to harvest from the great opportunities afforded with organic electronics potentially operating as communication and sensor outposts within existing and future complex communication infrastructures, high-quality computing and communication protocols must be integrated with the organic electronics. Here, we debate and scrutinize the twinning of the signal-processing capability of traditional integrated silicon chips with organic electronics and sensors, and to use our body as a natural local network with our bare hand as the browser of the physical world. The resulting platform provides a body network, i.e., a personalized web, composed of e-label sensors, bioelectronics, and mobile devices that together make it possible to monitor and record both our ambience and health-status parameters, supported by the ubiquitous mobile network and the resources of the "cloud". © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

42 CFR 405.2112 - ESRD network organizations.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false ESRD network organizations. 405.2112 Section 405... End-Stage Renal Disease (ESRD) Services § 405.2112 ESRD network organizations. CMS will designate an administrative governing body (network organization) for each network. The functions of a network organization...

42 CFR 405.2112 - ESRD network organizations.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false ESRD network organizations. 405.2112 Section 405... End-Stage Renal Disease (ESRD) Services § 405.2112 ESRD network organizations. CMS will designate an administrative governing body (network organization) for each network. The functions of a network organization...

Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes.

PubMed

Hosseini, S M Hadi; Mazaika, Paul; Mauras, Nelly; Buckingham, Bruce; Weinzimer, Stuart A; Tsalikian, Eva; White, Neil H; Reiss, Allan L

2016-11-01

Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Community-based organizations in the health sector: A scoping review

PubMed Central

2012-01-01

Community-based organizations are important health system stakeholders as they provide numerous, often highly valued programs and services to the members of their community. However, community-based organizations are described using diverse terminology and concepts from across a range of disciplines. To better understand the literature related to community-based organizations in the health sector (i.e., those working in health systems or more broadly to address population or public health issues), we conducted a scoping review by using an iterative process to identify existing literature, conceptually map it, and identify gaps and areas for future inquiry. We searched 18 databases and conducted citation searches using 15 articles to identify relevant literature. All search results were reviewed in duplicate and were included if they addressed the key characteristics of community-based organizations or networks of community-based organizations. We then coded all included articles based on the country focus, type of literature, source of literature, academic discipline, disease sector, terminology used to describe organizations and topics discussed. We identified 186 articles addressing topics related to the key characteristics of community-based organizations and/or networks of community-based organizations. The literature is largely focused on high-income countries and on mental health and addictions, HIV/AIDS or general/unspecified populations. A large number of different terms have been used in the literature to describe community-based organizations and the literature addresses a range of topics about them (mandate, structure, revenue sources and type and skills or skill mix of staff), the involvement of community members in organizations, how organizations contribute to community organizing and development and how they function in networks with each other and with government (e.g., in policy networks). Given the range of terms used to describe community-based organizations, this scoping review can be used to further map their meanings/definitions to develop a more comprehensive typology and understanding of community-based organizations. This information can be used in further investigations about the ways in which community-based organizations can be engaged in health system decision-making and the mechanisms available for facilitating or supporting their engagement. PMID:23171160

Community-based organizations in the health sector: a scoping review.

PubMed

Wilson, Michael G; Lavis, John N; Guta, Adrian

2012-11-21

Community-based organizations are important health system stakeholders as they provide numerous, often highly valued programs and services to the members of their community. However, community-based organizations are described using diverse terminology and concepts from across a range of disciplines. To better understand the literature related to community-based organizations in the health sector (i.e., those working in health systems or more broadly to address population or public health issues), we conducted a scoping review by using an iterative process to identify existing literature, conceptually map it, and identify gaps and areas for future inquiry.We searched 18 databases and conducted citation searches using 15 articles to identify relevant literature. All search results were reviewed in duplicate and were included if they addressed the key characteristics of community-based organizations or networks of community-based organizations. We then coded all included articles based on the country focus, type of literature, source of literature, academic discipline, disease sector, terminology used to describe organizations and topics discussed. We identified 186 articles addressing topics related to the key characteristics of community-based organizations and/or networks of community-based organizations. The literature is largely focused on high-income countries and on mental health and addictions, HIV/AIDS or general/unspecified populations. A large number of different terms have been used in the literature to describe community-based organizations and the literature addresses a range of topics about them (mandate, structure, revenue sources and type and skills or skill mix of staff), the involvement of community members in organizations, how organizations contribute to community organizing and development and how they function in networks with each other and with government (e.g., in policy networks).Given the range of terms used to describe community-based organizations, this scoping review can be used to further map their meanings/definitions to develop a more comprehensive typology and understanding of community-based organizations. This information can be used in further investigations about the ways in which community-based organizations can be engaged in health system decision-making and the mechanisms available for facilitating or supporting their engagement.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

PubMed Central

Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M.; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

2017-01-01

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system. PMID:28129330

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

PubMed

Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M; Thiele, Sven; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

2017-01-01

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

Sorption of CO 2 in a hydrogen-bonded diamondoid network of sulfonylcalix[4]arene

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

Sinnwell, Michael A.; Atwood, Jerry L.; Thallapally, Praveen K.

An organic material, p-tert-butyltetrasulfonylcalix[4]arene, self-assembles via hydrogen bonding to form a diamondoid supramolecular network. Possessing discrete, zero-dimensional (0D) microcavities, the thiacalixarene derivative adsorbs CO2 at high pressures

Graph-based network analysis of resting-state functional MRI.

PubMed

Wang, Jinhui; Zuo, Xinian; He, Yong

2010-01-01

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.

Image and Video Compression with VLSI Neural Networks

NASA Technical Reports Server (NTRS)

Fang, W.; Sheu, B.

1993-01-01

An advanced motion-compensated predictive video compression system based on artificial neural networks has been developed to effectively eliminate the temporal and spatial redundancy of video image sequences and thus reduce the bandwidth and storage required for the transmission and recording of the video signal. The VLSI neuroprocessor for high-speed high-ratio image compression based upon a self-organization network and the conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results.

From microporous regular frameworks to mesoporous materials with ultrahigh surface area: dynamic reorganization of porous polymer networks.

PubMed

Kuhn, Pierre; Forget, Aurélien; Su, Dangsheng; Thomas, Arne; Antonietti, Markus

2008-10-08

High surface area organic materials featuring both micro- and mesopores were synthesized under ionothermal conditions via the formation of polyaryltriazine networks. While the polytrimerization of nitriles in zinc chloride at 400 degrees C produces microporous polymers, higher reaction temperatures induce the formation of additional spherical mesopores with a narrow dispersity. The nitrogen-rich carbonaceous polymer materials thus obtained present surface areas and porosities up to 3300 m(2) g(-1) and 2.4 cm(3) g(-1), respectively. The key point of this synthesis relies on the occurrence of several high temperature polymerization reactions, where irreversible carbonization reactions coupled with the reversible trimerization of nitriles allow the reorganization of the dynamic triazine network. The ZnCl2 molten salt fulfills the requirement of a high temperature solvent, but is also required as catalyst. Thus, this dynamic polymerization system provides not only highly micro- and mesoporous materials, but also allows controlling the pore structure in amorphous organic materials.

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.

Entropy and optimality in river deltas

NASA Astrophysics Data System (ADS)

Tejedor, Alejandro; Longjas, Anthony; Edmonds, Douglas A.; Zaliapin, Ilya; Georgiou, Tryphon T.; Rinaldo, Andrea; Foufoula-Georgiou, Efi

2017-10-01

The form and function of river deltas is intricately linked to the evolving structure of their channel networks, which controls how effectively deltas are nourished with sediments and nutrients. Understanding the coevolution of deltaic channels and their flux organization is crucial for guiding maintenance strategies of these highly stressed systems from a range of anthropogenic activities. To date, however, a unified theory explaining how deltas self-organize to distribute water and sediment up to the shoreline remains elusive. Here, we provide evidence for an optimality principle underlying the self-organized partition of fluxes in delta channel networks. By introducing a suitable nonlocal entropy rate (nER) and by analyzing field and simulated deltas, we suggest that delta networks achieve configurations that maximize the diversity of water and sediment flux delivery to the shoreline. We thus suggest that prograding deltas attain dynamically accessible optima of flux distributions on their channel network topologies, thus effectively decoupling evolutionary time scales of geomorphology and hydrology. When interpreted in terms of delta resilience, high nER configurations reflect an increased ability to withstand perturbations. However, the distributive mechanism responsible for both diversifying flux delivery to the shoreline and dampening possible perturbations might lead to catastrophic events when those perturbations exceed certain intensity thresholds.

Prediction based Greedy Perimeter Stateless Routing Protocol for Vehicular Self-organizing Network

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Fan, Quanrun; Chen, Xiaolin; Xu, Wanjin

2018-03-01

PGPSR (Prediction based Greedy Perimeter Stateless Routing) is based on and extended the GPSR protocol to adapt to the high speed mobility of the vehicle auto organization network (VANET) and the changes in the network topology. GPSR is used in the VANET network environment, the network loss rate and throughput are not ideal, even cannot work. Aiming at the problems of the GPSR, the proposed PGPSR routing protocol, it redefines the hello and query packet structure, in the structure of the new node speed and direction information, which received the next update before you can take advantage of its speed and direction to predict the position of node and new network topology, select the right the next hop routing and path. Secondly, the update of the outdated node information of the neighbor’s table is deleted in time. The simulation experiment shows the performance of PGPSR is better than that of GPSR.

Toward a systems-level view of dynamic phosphorylation networks

PubMed Central

Newman, Robert H.; Zhang, Jin; Zhu, Heng

2014-01-01

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks. PMID:25177341

Self-configuration and self-optimization process in heterogeneous wireless networks.

PubMed

Guardalben, Lucas; Villalba, Luis Javier García; Buiati, Fábio; Sobral, João Bosco Mangueira; Camponogara, Eduardo

2011-01-01

Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network's scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed.

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

The network organization of protein interactions in the spliceosome is reproduced by the simple rules of food-web models

PubMed Central

Pires, Mathias M.; Cantor, Maurício; Guimarães, Paulo R.; de Aguiar, Marcus A. M.; dos Reis, Sérgio F.; Coltri, Patricia P.

2015-01-01

The network structure of biological systems provides information on the underlying processes shaping their organization and dynamics. Here we examined the structure of the network depicting protein interactions within the spliceosome, the macromolecular complex responsible for splicing in eukaryotic cells. We show the interactions of less connected spliceosome proteins are nested subsets of the connections of the highly connected proteins. At the same time, the network has a modular structure with groups of proteins sharing similar interaction patterns. We then investigated the role of affinity and specificity in shaping the spliceosome network by adapting a probabilistic model originally designed to reproduce food webs. This food-web model was as successful in reproducing the structure of protein interactions as it is in reproducing interactions among species. The good performance of the model suggests affinity and specificity, partially determined by protein size and the timing of association to the complex, may be determining network structure. Moreover, because network models allow building ensembles of realistic networks while encompassing uncertainty they can be useful to examine the dynamics and vulnerability of intracelullar processes. Unraveling the mechanisms organizing the spliceosome interactions is important to characterize the role of individual proteins on splicing catalysis and regulation. PMID:26443080

An Integrated Architecture to Support Hastily Formed Network (HFN)

DTIC Science & Technology

2007-12-01

17 1. Creating Awareness of the Situation (intra-organization).............17 2. Sharing Awareness Among Organizations (inter...Convergence - Sharing a Common Goal to Achieve a Common Outcome...................................................................39 b. Interdependency and...Weaknesses, Opportunities and Threat UC Unclassified UCC Unified Command Center UHF Ultra High Frequency VHF Very High Frequency VoIP Voice over

Structural and functional connectivity of the human brain in autism spectrum disorders and attention-deficit/hyperactivity disorder: A rich club organization study

PubMed Central

Ray, Siddharth; Miller, Meghan; Karalunas, Sarah; Robertson, C.J.; Grayson, David; Cary, Paul; Hawkey, Elizabeth; Painter, Julia G.; Kriz, Daniel; Fombonne, Eric; Nigg, Joel T.; Fair, Damien A.

2015-01-01

Attention deficit hyperactive disorder (ADHD) and Autism spectrum disorders (ASD) are two of the most common and vexing neurodevelopmental disorders among children. Although the two disorders share many behavioral and neuropsychological characteristics, most MRI studies examine only one of the disorders at a time. Using graph theory combined with structural and functional connectivity, we examined the large-scale network organization among three groups of children: a group with ADHD (8-12 years, n = 20), a group with ASD (7-13 years, n = 16), and typically developing controls (TD) (8-12 years, n = 20). We apply the concept of the rich-club organization, whereby central, highly connected hub regions are also highly connected to themselves. We examine the brain into two different network domains: (1) inside a rich-club network phenomena, and (2) outside a rich-club network phenomena. ASD and ADHD populations had markedly different patterns of rich club and non rich-club connections in both functional and structural data. The ASD group exhibited higher connectivity in structural and functional networks but only inside the rich-club networks. These findings were replicated using the autism brain imaging data exchange (ABIDE) dataset with ASD (n = 85) and TD (n = 101). The ADHD group exhibited a lower generalized fractional anisotropy (GFA) and functional connectivity inside the rich-club networks, but a higher number of axonal fibers and correlation coefficient values outside the rich-club. Despite some shared biological features and frequent comorbity, these data suggest ADHD and ASD exhibit distinct large-scale connectivity patterns in middle childhood. PMID:25116862

Indications of marine bioinvasion from network theory. An analysis of the global cargo ship network

NASA Astrophysics Data System (ADS)

Kölzsch, A.; Blasius, B.

2011-12-01

The transport of huge amounts of small aquatic organisms in the ballast tanks and at the hull of large cargo ships leads to ever increasing rates of marine bioinvasion. In this study, we apply a network theoretic approach to examine the introduction of invasive species into new ports by global shipping. This is the first stage of the invasion process where it is still possible to intervene with regulating measures. We compile a selection of widely used and newly developed network properties and apply these to analyse the structure and spread characteristics of the directed and weighted global cargo ship network (GCSN). Our results reveal that the GCSN is highly efficient, shows small world characteristics and is positive assortative, indicating that quick spread of invasive organisms between ports is likely. The GCSN shows strong community structure and contains two large communities, the Atlantic and Pacific trading groups. Ports that appear as connector hubs and are of high centralities are the Suez and Panama Canal, Singapore and Shanghai. Furthermore, from robustness analyses and the network's percolation behaviour, we evaluate differences of onboard and in-port ballast water treatment, set them into context with previous studies and advise bioinvasion management strategies.

Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties.

PubMed

Ouma, Wilberforce Zachary; Pogacar, Katja; Grotewold, Erich

2018-04-01

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

NASA Astrophysics Data System (ADS)

Correa, R.; Chesta, M. A.; Morales, J. R.; Dinator, M. I.; Requena, I.; Vila, I.

2006-08-01

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

Heterogeneous fractionation profiles of meta-analytic coactivation networks.

PubMed

Laird, Angela R; Riedel, Michael C; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L; Eickhoff, Simon B; Smith, Stephen M; Fox, Peter T; Sutherland, Matthew T

2017-04-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. Copyright © 2017 Elsevier Inc. All rights reserved.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

PubMed Central

Laird, Angela R.; Riedel, Michael C.; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L.; Eickhoff, Simon B.; Smith, Stephen M.; Fox, Peter T.; Sutherland, Matthew T.

2017-01-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. PMID:28222386

Community Detection in Complex Networks via Clique Conductance.

PubMed

Lu, Zhenqi; Wahlström, Johan; Nehorai, Arye

2018-04-13

Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.

Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

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

Gearhart, Jared Lee; Kurtz, Nolan Scot

2014-09-01

The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance aremore » investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.« less

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain.

PubMed

Cao, Miao; He, Yong; Dai, Zhengjia; Liao, Xuhong; Jeon, Tina; Ouyang, Minhui; Chalak, Lina; Bi, Yanchao; Rollins, Nancy; Dong, Qi; Huang, Hao

2017-03-01

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

PubMed Central

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P.; Gerstein, Mark

2010-01-01

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems. PMID:20439753

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks.

PubMed

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P; Gerstein, Mark

2010-05-18

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers' continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

Internal structure analysis of particle-double network gels used in a gel organ replica

NASA Astrophysics Data System (ADS)

Abe, Mei; Arai, Masanori; Saito, Azusa; Sakai, Kazuyuki; Kawakami, Masaru; Furukawa, Hidemitsu

2016-04-01

In recent years, the fabrication of patient organ replicas using 3D printers has been attracting a great deal of attention in medical fields. However, the cost of these organ replicas is very high as it is necessary to employ very expensive 3D printers and printing materials. Here we present a new gel organ replica, of human kidney, fabricated with a conventional molding technique, using a particle-double network hydrogel (P-DN gel). The replica is transparent and has the feel of a real kidney. It is expected that gel organ replicas produced this way will be a useful tool for the education of trainee surgeons and clinical ultrasonography technologists. In addition to developing a gel organ replica, the internal structure of the P-DN gel used is also discussed. Because the P-DN gel has a complex structure comprised of two different types of network, it has not been possible to investigate them internally in detail. Gels have an inhomogeneous network structure. If it is able to get a more uniform structure, it is considered that this would lead to higher strength in the gel. In the present study we investigate the structure of P-DN gel, using the gel organ replica. We investigated the internal structure of P-DN gel using Scanning Microscopic Light Scattering (SMILS), a non-contacting and non-destructive.

Synchronization unveils the organization of ecological networks with positive and negative interactions

NASA Astrophysics Data System (ADS)

Girón, Andrea; Saiz, Hugo; Bacelar, Flora S.; Andrade, Roberto F. S.; Gómez-Gardeñes, Jesús

2016-06-01

Network science has helped to understand the organization principles of the interactions among the constituents of large complex systems. However, recently, the high resolution of the data sets collected has allowed to capture the different types of interactions coexisting within the same system. A particularly important example is that of systems with positive and negative interactions, a usual feature appearing in social, neural, and ecological systems. The interplay of links of opposite sign presents natural difficulties for generalizing typical concepts and tools applied to unsigned networks and, moreover, poses some questions intrinsic to the signed nature of the network, such as how are negative interactions balanced by positive ones so to allow the coexistence and survival of competitors/foes within the same system? Here, we show that synchronization phenomenon is an ideal benchmark for uncovering such balance and, as a byproduct, to assess which nodes play a critical role in the overall organization of the system. We illustrate our findings with the analysis of synthetic and real ecological networks in which facilitation and competitive interactions coexist.

The size, characteristics and partnership networks of the health-related non-profit sector in three regions of South Africa: implications of changing primary health care policy for community-based care.

PubMed

van Pletzen, Ermien; Zulliger, R; Moshabela, M; Schneider, H

2014-09-01

Health-related community-based care in South Africa is mostly provided through non-profit organizations (NPOs), but little is known about the sector. In the light of emerging government policy on greater formalization of community-based care in South Africa, this article assesses the size, characteristics and partnership networks of health-related NPOs in three South African communities and explores implications of changing primary health care policy for this sector. Data were collected (2009-11) from three sites: Khayelitsha (urban), Botshabelo (semi-rural) and Bushbuckridge (semi/deep rural). Separate data sources were used to identify all health-related NPOs in the sites. Key characteristics of identified NPOs were gathered using a standardized tool. A typology of NPOs was developed combining level of resources (well, moderate, poor) and orientation of activities ('Direct service', 'Developmental' and/or 'Activist'). Network analysis was performed to establish degree and density of partnerships among NPOs. The 138 NPOs (n = 56 in Khayelitsha, n = 47 in Bushbuckridge; n = 35 in Botshabelo) were mostly local community-based organizations (CBOs). The main NPO orientation was 'Direct service' (n = 120, 87%). Well- and moderately resourced NPOs were successful at combining orientations. Most organizations with an 'Activist' orientation were urban. No poorly resourced organizations had this orientation. Well-resourced organizations with an 'Activist' orientation were highly connected in Khayelitsha NPO networks, while poorly resourced CBOs were marginalized. A contrasting picture emerged in Botshabelo where CBOs were highly connected. Networks in Bushbuckridge were fragmented and linear. The NPO sector varies geographically in numbers, resources, orientation of activities and partnership networks. NPOs may perform important developmental roles and strong potential for social capital may reside in organizational networks operating in otherwise impoverished environments. A uniform approach to policy implementation may not accommodate variations in the NPO sector. Considerations for adaptation may be necessary in light of the observed differences between urban and rural settings. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2013; all rights reserved.

Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation

NASA Astrophysics Data System (ADS)

Tulbure, Mirela G.; Kininmonth, Stuart; Broich, Mark

2014-11-01

The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999-2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified ‘stepping stone’ water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as ‘rich get richer’ algorithm. The ‘stepping stone’ water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as ‘stepping stone’ over time may help prioritize surface water bodies that are essential for maintaining regional scale connectivity.

Three-Dimensional Networked Metal-Organic Frameworks with Conductive Polypyrrole Tubes for Flexible Supercapacitors.

PubMed

Xu, Xingtao; Tang, Jing; Qian, Huayu; Hou, Shujin; Bando, Yoshio; Hossain, Md Shahriar A; Pan, Likun; Yamauchi, Yusuke

2017-11-08

Metal-organic frameworks (MOFs) with high porosity and a regular porous structure have emerged as a promising electrode material for supercapacitors, but their poor electrical conductivity limits their utilization efficiency and capacitive performance. To increase the overall electrical conductivity as well as the efficiency of MOF particles, three-dimensional networked MOFs are developed via using preprepared conductive polypyrrole (PPy) tubes as the support for in situ growth of MOF particles. As a result, the highly conductive PPy tubes that run through the MOF particles not only increase the electron transfer between MOF particles and maintain the high effective porosity of the MOFs but also endow the MOFs with flexibility. Promoted by such elaborately designed MOF-PPy networks, the specific capacitance of MOF particles has been increased from 99.2 F g -1 for pristine zeolitic imidazolate framework (ZIF)-67 to 597.6 F g -1 for ZIF-PPy networks, indicating the importance of the design of the ZIF-PPy continuous microstructure. Furthermore, a flexible supercapacitor device based on ZIF-PPy networks shows an outstanding areal capacitance of 225.8 mF cm -2 , which is far above other MOFs-based supercapacitors reported up to date, confirming the significance of in situ synthetic chemistry as well as the importance of hybrid materials on the nanoscale.

The Sub-Regional Functional Organization of Neocortical Irritative Epileptic Networks in Pediatric Epilepsy

PubMed Central

Janca, Radek; Krsek, Pavel; Jezdik, Petr; Cmejla, Roman; Tomasek, Martin; Komarek, Vladimir; Marusic, Petr; Jiruska, Premysl

2018-01-01

Between seizures, irritative network generates frequent brief synchronous activity, which manifests on the EEG as interictal epileptiform discharges (IEDs). Recent insights into the mechanism of IEDs at the microscopic level have demonstrated a high variance in the recruitment of neuronal populations generating IEDs and a high variability in the trajectories through which IEDs propagate across the brain. These phenomena represent one of the major constraints for precise characterization of network organization and for the utilization of IEDs during presurgical evaluations. We have developed a new approach to dissect human neocortical irritative networks and quantify their properties. We have demonstrated that irritative network has modular nature and it is composed of multiple independent sub-regions, each with specific IED propagation trajectories and differing in the extent of IED activity generated. The global activity of the irritative network is determined by long-term and circadian fluctuations in sub-region spatiotemporal properties. Also, the most active sub-region co-localizes with the seizure onset zone in 12/14 cases. This study demonstrates that principles of recruitment variability and propagation are conserved at the macroscopic level and that they determine irritative network properties in humans. Functional stratification of the irritative network increases the diagnostic yield of intracranial investigations with the potential to improve the outcomes of surgical treatment of neocortical epilepsy. PMID:29628910

Hegemonic structure of basic, clinical and patented knowledge on Ebola research: a US army reductionist initiative.

PubMed

Fajardo-Ortiz, David; Ortega-Sánchez-de-Tagle, José; Castaño, Victor M

2015-04-19

Ebola hemorrhagic fever (Ebola) is still a highly lethal infectious disease long affecting mainly neglected populations in sub-Saharan Africa. Moreover, this disease is now considered a potential worldwide threat. In this paper, we present an approach to understand how the basic, clinical and patent knowledge on Ebola is organized and intercommunicated and what leading factor could be shaping the evolution of the knowledge translation process for this disease. A combination of citation network analysis; analysis of Medical heading Subject (MeSH) and Gene Ontology (GO) terms, and quantitative content analysis for patents and scientific literature, aimed to map the organization of Ebola research was carried out. We found six putative research fronts (i.e. clusters of high interconnected papers). Three research fronts are basic research on Ebola virus structural proteins: glycoprotein, VP40 and VP35, respectively. There is a fourth research front of basic research papers on pathogenesis, which is the organizing hub of Ebola research. A fifth research front is pre-clinical research focused on vaccines and glycoproteins. Finally, a clinical-epidemiology research front related to the disease outbreaks was identified. The network structure of patent families shows that the dominant design is the use of Ebola virus proteins as targets of vaccines and other immunological treatments. Therefore, patents network organization resembles the organization of the scientific literature. Specifically, the knowledge on Ebola would flow from higher (clinical-epidemiology) to intermediated (cellular-tissular pathogenesis) to lower (molecular interactions) levels of organization. Our results suggest a strong reductionist approach for Ebola research probably influenced by the lethality of the disease. On the other hand, the ownership profile of the patent families network and the main researches relationship with the United State Army suggest a strong involvement of this military institution in Ebola research.

Inborn errors of metabolism and the human interactome: a systems medicine approach.

PubMed

Woidy, Mathias; Muntau, Ania C; Gersting, Søren W

2018-02-05

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

A Network Perspective on Dropout Prevention in Two Cities

ERIC Educational Resources Information Center

Wells, Rebecca; Gifford, Elizabeth; Bai, Yu; Corra, Ashley

2015-01-01

Purpose: This exploratory case study examines how school systems and other local organizations have been working within two major U.S. cities to improve high school graduation rates. Systematically assessing active interorganizational dropout prevention networks may reveal characteristics affecting communities' capacity to support school…

The Bi-Modal Organization: Balancing Autopoiesis and Fluid Social Networks for Sustainability

ERIC Educational Resources Information Center

Smith, Peter A. C.; Sharicz, Carol Ann

2013-01-01

Purpose: The purpose of this paper is to assist an organization to restructure as a bi-modal organization in order to achieve sustainability in today's highly complex business world. Design/methodology/approach: The paper is conceptual and is based on relevant literature and the authors' research and practice. Findings: Although fluid…

Charter Management Organizations: An Emerging Approach to Scaling up What Works

ERIC Educational Resources Information Center

Farrell, Caitlin; Wohlstetter, Priscilla; Smith, Joanna

2012-01-01

Policymakers have shown increasing interest in replicating high-quality education models as a way to improve chronically underperforming schools. Charter management organizations (CMOs) have been touted as one organizational model poised to be such a vehicle for reform. CMOs are nonprofit organizations that operate a network of charter schools…

Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis.

PubMed

Frey, Olivier; Misun, Patrick M; Fluri, David A; Hengstler, Jan G; Hierlemann, Andreas

2014-06-30

Integration of multiple three-dimensional microtissues into microfluidic networks enables new insights in how different organs or tissues of an organism interact. Here, we present a platform that extends the hanging-drop technology, used for multi-cellular spheroid formation, to multifunctional complex microfluidic networks. Engineered as completely open, 'hanging' microfluidic system at the bottom of a substrate, the platform features high flexibility in microtissue arrangements and interconnections, while fabrication is simple and operation robust. Multiple spheroids of different cell types are formed in parallel on the same platform; the different tissues are then connected in physiological order for multi-tissue experiments through reconfiguration of the fluidic network. Liquid flow is precisely controlled through the hanging drops, which enable nutrient supply, substance dosage and inter-organ metabolic communication. The possibility to perform parallelized microtissue formation on the same chip that is subsequently used for complex multi-tissue experiments renders the developed platform a promising technology for 'body-on-a-chip'-related research.

Assembly, Structure, and Functionality of Metal-Organic Networks and Organic Semiconductor Layers at Surfaces

NASA Astrophysics Data System (ADS)

Tempas, Christopher D.

Self-assembled nanostructures at surfaces show promise for the development of next generation technologies including organic electronic devices and heterogeneous catalysis. In many cases, the functionality of these nanostructures is not well understood. This thesis presents strategies for the structural design of new on-surface metal-organic networks and probes their chemical reactivity. It is shown that creating uniform metal sites greatly increases selectivity when compared to ligand-free metal islands. When O2 reacts with single-site vanadium centers, in redox-active self-assembled coordination networks on the Au(100) surface, it forms one product. When O2 reacts with vanadium metal islands on the same surface, multiple products are formed. Other metal-organic networks described in this thesis include a mixed valence network containing Pt0 and PtII and a network where two Fe centers reside in close proximity. This structure is stable to temperatures >450 °C. These new on-surface assemblies may offer the ability to perform reactions of increasing complexity as future heterogeneous catalysts. The functionalization of organic semiconductor molecules is also shown. When a few molecular layers are grown on the surface, it is seen that the addition of functional groups changes both the film's structure and charge transport properties. This is due to changes in both first layer packing structure and the pi-electron distribution in the functionalized molecules compared to the original molecule. The systems described in this thesis were studied using high-resolution scanning tunneling microscopy, non-contact atomic force microscopy, and X-ray photoelectron spectroscopy. Overall, this work provides strategies for the creation of new, well-defined on-surface nanostructures and adds additional chemical insight into their properties.

Node property of weighted networks considering connectability to nodes within two degrees of separation.

PubMed

Amano, Sun-Ichi; Ogawa, Ken-Ichiro; Miyake, Yoshihiro

2018-05-31

Weighted networks have been extensively studied because they can represent various phenomena in which the diversity of edges is essential. To investigate the properties of weighted networks, various centrality measures have been proposed, such as strength, weighted clustering coefficients, and weighted betweenness centrality. In such measures, only direct connections or entire network connectivity from arbitrary nodes have been used to calculate the connectivity of each node. However, in weighted networks composed of autonomous elements such as humans, middle ranges from each node are also considered to be meaningful for characterizing each node's connectability. In this study, we define a new node property in weighted networks to consider connectability to nodes within a range of two degrees of separation, then apply this new centrality to face-to-face human communication networks in corporate organizations. Our results show that the proposed centrality distinguishes inherent communities corresponding to the job types in each organization with a high degree of accuracy. This indicates the possibility that connectability to nodes within two degrees of separation reveals potential trends of weighted networks that are not apparent from conventional measures.

Zinc oxide nanowire networks for macroelectronic devices

NASA Astrophysics Data System (ADS)

Unalan, Husnu Emrah; Zhang, Yan; Hiralal, Pritesh; Dalal, Sharvari; Chu, Daping; Eda, Goki; Teo, K. B. K.; Chhowalla, Manish; Milne, William I.; Amaratunga, Gehan A. J.

2009-04-01

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics.

Graph Theory-Based Analysis of the Lymph Node Fibroblastic Reticular Cell Network.

PubMed

Novkovic, Mario; Onder, Lucas; Bocharov, Gennady; Ludewig, Burkhard

2017-01-01

Secondary lymphoid organs have developed segregated niches that are able to initiate and maintain effective immune responses. Such global organization requires tight control of diverse cellular components, specifically those that regulate lymphocyte trafficking. Fibroblastic reticular cells (FRCs) form a densely interconnected network in lymph nodes and provide key factors necessary for T cell migration and retention, and foster subsequent interactions between T cells and dendritic cells. Development of integrative systems biology approaches has made it possible to elucidate this multilevel complexity of the immune system. Here, we present a graph theory-based analysis of the FRC network in murine lymph nodes, where generation of the network topology is performed using high-resolution confocal microscopy and 3D reconstruction. This approach facilitates the analysis of physical cell-to-cell connectivity, and estimation of topological robustness and global behavior of the network when it is subjected to perturbation in silico.

Self-Configuration and Self-Optimization Process in Heterogeneous Wireless Networks

PubMed Central

Guardalben, Lucas; Villalba, Luis Javier García; Buiati, Fábio; Sobral, João Bosco Mangueira; Camponogara, Eduardo

2011-01-01

Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network’s scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed. PMID:22346584

Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach.

PubMed

Alaerts, Kaat; Geerlings, Franca; Herremans, Lynn; Swinnen, Stephan P; Verhoeven, Judith; Sunaert, Stefan; Wenderoth, Nicole

2015-01-01

The ability to recognize, understand and interpret other's actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network. Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC). Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD. While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.

Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer’s disease

PubMed Central

Daianu, Madelaine; Jahanshad, Neda; Villalon-Reina, Julio E.; Mendez, Mario F.; Bartzokis, George; Jimenez, Elvira E.; Joshi, Aditi; Barsuglia, Joseph; Thompson, Paul M.

2015-01-01

Diffusion imaging and brain connectivity analyses can reveal the underlying organizational patterns of the human brain, described as complex networks of densely interlinked regions. Here, we analyzed 1.5-Tesla whole-brain diffusion-weighted images from 64 participants – 15 patients with behavioral variant frontotemporal (bvFTD) dementia, 19 with early-onset Alzheimer’s disease (EOAD), and 30 healthy elderly controls. Based on whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We examined how bvFTD and EOAD disrupt the weighted ‘rich club’ – a network property where high-degree network nodes are more interconnected than expected by chance. bvFTD disrupts both the nodal and global organization of the network in both low- and high-degree regions of the brain. EOAD targets the global connectivity of the brain, mainly affecting the fiber density of high-degree (highly connected) regions that form the rich club network. These rich club analyses suggest distinct patterns of disruptions among different forms of dementia. PMID:26161050

Family ties: the multilevel effects of households and kinship on the networks of individuals.

PubMed

Koster, Jeremy

2018-04-01

Among social mammals, humans uniquely organize themselves into communities of households that are centred around enduring, predominantly monogamous unions of men and women. As a consequence of this social organization, individuals maintain social relationships both within and across households, and potentially there is conflict among household members about which social ties to prioritize or de-emphasize. Extending the logic of structural balance theory, I predict that there will be considerable overlap in the social networks of individual household members, resulting in a pattern of group-level reciprocity. To test this prediction, I advance the Group-Structured Social Relations Model, a generalized linear mixed model that tests for group-level effects in the inter-household social networks of individuals. The empirical data stem from social support interviews conducted in a community of indigenous Nicaraguan horticulturalists, and model results show high group-level reciprocity among households. Although support networks are organized around kinship, covariates that test predictions of kin selection models do not receive strong support, potentially because most kin-directed altruism occurs within households, not between households. In addition, the models show that households with high genetic relatedness in part from children born to adulterous relationships are less likely to assist each other.

Phylogenetically informed logic relationships improve detection of biological network organization

PubMed Central

2011-01-01

Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058

Surveying hospital network structure in New York State: how are they structured?

PubMed

Nauenberg, E; Brewer, C S

2000-01-01

We determine the most common network structures in New York state. The taxonomy employed uses three structural dimensions: integration, complexity, and risk-sharing between organizations. Based on a survey conducted in 1996, the most common type of network (26.4 percent) had medium levels of integration, medium or high levels of complexity, and some risk-sharing. Also common were networks with low levels of integration, low levels of complexity, and no risk-sharing (22.1 percent).

A protocol for generating a high-quality genome-scale metabolic reconstruction.

PubMed

Thiele, Ines; Palsson, Bernhard Ø

2010-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

A protocol for generating a high-quality genome-scale metabolic reconstruction

PubMed Central

Thiele, Ines; Palsson, Bernhard Ø.

2011-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have developed over the past 10 years. These reconstructions represent structured knowledge-bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates myriad computational biological studies including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics, and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge-bases. Here, we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction as well as common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process. PMID:20057383

Social structure of Facebook networks

NASA Astrophysics Data System (ADS)

Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

2012-08-01

We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes-gender, class year, major, high school, and residence-at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

NASA Astrophysics Data System (ADS)

Hortos, William S.

2006-05-01

A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the algorithms from flat topologies to two-tier hierarchies of sensor nodes are presented. Results from a few simulations of the proposed algorithms are compared to the published results of other approaches to sensor network self-organization in common scenarios. The estimated network lifetime and extent under static resource allocations are computed.

Insights on the evolution of metabolic networks of unicellular translationally biased organisms from transcriptomic data and sequence analysis.

PubMed

Carbone, Alessandra; Madden, Richard

2005-10-01

Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.

A model for the emergence of cooperation, interdependence, and structure in evolving networks.

PubMed

Jain, S; Krishna, S

2001-01-16

Evolution produces complex and structured networks of interacting components in chemical, biological, and social systems. We describe a simple mathematical model for the evolution of an idealized chemical system to study how a network of cooperative molecular species arises and evolves to become more complex and structured. The network is modeled by a directed weighted graph whose positive and negative links represent "catalytic" and "inhibitory" interactions among the molecular species, and which evolves as the least populated species (typically those that go extinct) are replaced by new ones. A small autocatalytic set, appearing by chance, provides the seed for the spontaneous growth of connectivity and cooperation in the graph. A highly structured chemical organization arises inevitably as the autocatalytic set enlarges and percolates through the network in a short analytically determined timescale. This self organization does not require the presence of self-replicating species. The network also exhibits catastrophes over long timescales triggered by the chance elimination of "keystone" species, followed by recoveries.

A model for the emergence of cooperation, interdependence, and structure in evolving networks

NASA Astrophysics Data System (ADS)

Jain, Sanjay; Krishna, Sandeep

2001-01-01

Evolution produces complex and structured networks of interacting components in chemical, biological, and social systems. We describe a simple mathematical model for the evolution of an idealized chemical system to study how a network of cooperative molecular species arises and evolves to become more complex and structured. The network is modeled by a directed weighted graph whose positive and negative links represent "catalytic" and "inhibitory" interactions among the molecular species, and which evolves as the least populated species (typically those that go extinct) are replaced by new ones. A small autocatalytic set, appearing by chance, provides the seed for the spontaneous growth of connectivity and cooperation in the graph. A highly structured chemical organization arises inevitably as the autocatalytic set enlarges and percolates through the network in a short analytically determined timescale. This self organization does not require the presence of self-replicating species. The network also exhibits catastrophes over long timescales triggered by the chance elimination of "keystone" species, followed by recoveries.

High thermal stability and antiferromagnetic properties of a 3D Mn(II)-organic framework with metal carboxylate chains

NASA Astrophysics Data System (ADS)

Han, Lei; Zhou, Yan; Wang, Xiu-Teng; Li, Xing; Tong, Ming-Liang

2009-04-01

A novel three-dimensional metal-organic framework, [Mn 2(hfipbb) 2(bpy)] n ( 1) (H 2hfipbb = 4,4'-(hexafluoroisopropylidene)bis(benzoic acid), bpy = 4,4'-bipyridine), has been hydrothermally synthesized and structurally characterized. The complex consists of metal carboxylate chains, which are cross-linked to six adjacent chains through organic moieties forming extended three-dimensional networks. Complex 1 exhibits high thermal stability (450 °C) and antiferromagnetic properties.

Keys to successful organ procurement: An experience-based review of clinical practices at a high-performing health-care organization

PubMed Central

Wojda, Thomas R.; Stawicki, Stanislaw P.; Yandle, Kathy P.; Bleil, Maria; Axelband, Jennifer; Wilde-Onia, Rebecca; Thomas, Peter G.; Cipolla, James; Hoff, William S.; Shultz, Jill

2017-01-01

Organ procurement (OP) from donors after brain death and circulatory death represents the primary source of transplanted organs. Despite favorable laws and regulations, OP continues to face challenges for a number of reasons, including institutional, personal, and societal barriers. This focused review presents some of the key components of a successful OP program at a large, high-performing regional health network. This review focuses on effective team approaches, aggressive resuscitative strategies, optimal communication, family support, and community outreach efforts. PMID:28660162

The effects of working memory training on functional brain network efficiency.

PubMed

Langer, Nicolas; von Bastian, Claudia C; Wirz, Helen; Oberauer, Klaus; Jäncke, Lutz

2013-10-01

The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM. Copyright © 2013 Elsevier Ltd. All rights reserved.

English and Chinese languages as weighted complex networks

NASA Astrophysics Data System (ADS)

Sheng, Long; Li, Chunguang

2009-06-01

In this paper, we analyze statistical properties of English and Chinese written human language within the framework of weighted complex networks. The two language networks are based on an English novel and a Chinese biography, respectively, and both of the networks are constructed in the same way. By comparing the intensity and density of connections between the two networks, we find that high weight connections in Chinese language networks prevail more than those in English language networks. Furthermore, some of the topological and weighted quantities are compared. The results display some differences in the structural organizations between the two language networks. These observations indicate that the two languages may have different linguistic mechanisms and different combinatorial natures.

Real-time network traffic classification technique for wireless local area networks based on compressed sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza

2017-05-01

Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

Social-ecological network analysis of scale mismatches in estuary watershed restoration.

PubMed

Sayles, Jesse S; Baggio, Jacopo A

2017-03-07

Resource management boundaries seldom align with environmental systems, which can lead to social and ecological problems. Mapping and analyzing how resource management organizations in different areas collaborate can provide vital information to help overcome such misalignment. Few quantitative approaches exist, however, to analyze social collaborations alongside environmental patterns, especially among local and regional organizations (i.e., in multilevel governance settings). This paper develops and applies such an approach using social-ecological network analysis (SENA), which considers relationships among and between social and ecological units. The framework and methods are shown using an estuary restoration case from Puget Sound, United States. Collaboration patterns and quality are analyzed among local and regional organizations working in hydrologically connected areas. These patterns are correlated with restoration practitioners' assessments of the productivity of their collaborations to inform network theories for natural resource governance. The SENA is also combined with existing ecological data to jointly consider social and ecological restoration concerns. Results show potentially problematic areas in nearshore environments, where collaboration networks measured by density (percentage of possible network connections) and productivity are weakest. Many areas also have high centralization (a few nodes hold the network together), making network cohesion dependent on key organizations. Although centralization and productivity are inversely related, no clear relationship between density and productivity is observed. This research can help practitioners to identify where governance capacity needs strengthening and jointly consider social and ecological concerns. It advances SENA by developing a multilevel approach to assess social-ecological (or social-environmental) misalignments, also known as scale mismatches.

Social–ecological network analysis of scale mismatches in estuary watershed restoration

PubMed Central

Sayles, Jesse S.

2017-01-01

Resource management boundaries seldom align with environmental systems, which can lead to social and ecological problems. Mapping and analyzing how resource management organizations in different areas collaborate can provide vital information to help overcome such misalignment. Few quantitative approaches exist, however, to analyze social collaborations alongside environmental patterns, especially among local and regional organizations (i.e., in multilevel governance settings). This paper develops and applies such an approach using social–ecological network analysis (SENA), which considers relationships among and between social and ecological units. The framework and methods are shown using an estuary restoration case from Puget Sound, United States. Collaboration patterns and quality are analyzed among local and regional organizations working in hydrologically connected areas. These patterns are correlated with restoration practitioners’ assessments of the productivity of their collaborations to inform network theories for natural resource governance. The SENA is also combined with existing ecological data to jointly consider social and ecological restoration concerns. Results show potentially problematic areas in nearshore environments, where collaboration networks measured by density (percentage of possible network connections) and productivity are weakest. Many areas also have high centralization (a few nodes hold the network together), making network cohesion dependent on key organizations. Although centralization and productivity are inversely related, no clear relationship between density and productivity is observed. This research can help practitioners to identify where governance capacity needs strengthening and jointly consider social and ecological concerns. It advances SENA by developing a multilevel approach to assess social–ecological (or social–environmental) misalignments, also known as scale mismatches. PMID:28223529

Using In-Situ Optical Sensors to Understand the Biogeochemistry of Dissolved Organic Matter Across a Stream Network

NASA Astrophysics Data System (ADS)

Wymore, Adam S.; Potter, Jody; Rodríguez-Cardona, Bianca; McDowell, William H.

2018-04-01

The advent of high-frequency in situ optical sensors provides new opportunities to study the biogeochemistry of dissolved organic matter (DOM) in aquatic ecosystems. We used fDOM (fluorescent dissolved organic matter) to examine the spatial and temporal variability in dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) across a heterogeneous stream network that varies in NO3- concentration. Across the ten study streams fDOM explained twice the variability in the concentration of DOC (r2 = 0.82) compared to DON (r2 = 0.39), which suggests that the N-rich fraction of DOM is either more variable in its sources or more bioreactive than the more stable C-rich fraction. Among sites, DON molar fluorescence was approximately 3x more variable than DOC molar fluorescence and was correlated with changes in inorganic N, indicating that DON is both more variable in composition as well as highly responsive to changes in inorganic N. Laboratory results also indicate that the fDOM sensors we used perform as well as the excitation-emission wavelength pair generally referred to as the "tryptophan-like" peak when measured under laboratory conditions. However, since neither the field sensor not the laboratory measurements explained a large percentage of variation in DON concentrations, challenges still remain for monitoring the ambient pool of dissolved organic nitrogen. Sensor networks provide new insights into the potential reactivity of DOM and the variability in DOC and DON biogeochemistry across sites. These insights are needed to build spatially explicit models describing organic matter dynamics and water quality.

Topological distortion and reorganized modular structure of gut microbial co-occurrence networks in inflammatory bowel disease

NASA Astrophysics Data System (ADS)

Baldassano, Steven N.; Bassett, Danielle S.

2016-05-01

The gut microbiome plays a key role in human health, and alterations of the normal gut flora are associated with a variety of distinct disease states. Yet, the natural dependencies between microbes in healthy and diseased individuals remain far from understood. Here we use a network-based approach to characterize microbial co-occurrence in individuals with inflammatory bowel disease (IBD) and healthy (non-IBD control) individuals. We find that microbial networks in patients with IBD differ in both global structure and local connectivity patterns. While a “core” microbiome is preserved, network topology of other densely interconnected microbe modules is distorted, with potent inflammation-mediating organisms assuming roles as integrative and highly connected inter-modular hubs. We show that while both networks display a rich-club organization, in which a small set of microbes commonly co-occur, the healthy network is more easily disrupted by elimination of a small number of key species. Further investigation of network alterations in disease might offer mechanistic insights into the specific pathogens responsible for microbiome-mediated inflammation in IBD.

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

NASA Astrophysics Data System (ADS)

Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

2016-09-01

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses.

PubMed

Lin, Yu-Pu; Bennett, Christopher H; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

2016-09-07

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

PubMed Central

Ibarra-Arellano, Miguel A.; Campos-González, Adrián I.; Treviño-Quintanilla, Luis G.; Tauch, Andreas; Freyre-González, Julio A.

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them. Database URL: http://abasy.ccg.unam.mx PMID:27242034

Organic Wastewater Compounds, Pharmaceuticals, andColiphage in Ground Water Receiving Discharge from OnsiteWastewater Treatment Systems near La Pine, Oregon:Occurrence and Implications for Transport

USGS Publications Warehouse

Hinkle, Stephen J.; Weick, Rodney J.; Johnson, Jill M.; Cahill, Jeffery D.; Smith, Steven G.; Rich, Barbara J.

2005-01-01

The occurrence of organic wastewater compounds (components of 'personal care products' and other common household chemicals), pharmaceuticals (human prescription and nonprescription medical drugs), and coliphage (viruses that infect coliform bacteria, and found in high concentrations in municipal wastewater) in onsite wastewater (septic tank effluent) and in a shallow, unconfined, sandy aquifer that serves as the primary source of drinking water for most residents near La Pine, Oregon, was documented. Samples from two types of observation networks provided basic occurrence data for onsite wastewater and downgradient ground water. One observation network was a group of 28 traditional and innovative (advanced treatment) onsite wastewater treatment systems and associated downgradient drainfield monitoring wells, referred to as the 'innovative systems network'. The drainfield monitoring wells were located adjacent to or under onsite wastewater treatment system drainfield lines. Another observation network, termed the 'transect network', consisted of 31 wells distributed among three transects of temporary, stainless-steel-screened, direct-push monitoring wells installed along three plumes of onsite wastewater. The transect network, by virtue of its design, also provided a basis for increased understanding of the transport of analytes in natural systems. Coliphage were frequently detected in onsite wastewater. Coliphage concentrations in onsite wastewater were highly variable, ranging from less than 1 to 3,000,000 plaque forming units per 100 milliliters. Coliphage were occasionally detected (eight occurrences) at low concentrations in samples from wells located downgradient from onsite wastewater treatment system drainfield lines. However, coliphage concentrations were below method detection limits in replicate or repeat samples collected from the eight sites. The consistent absence of coliphage detections in the replicate or repeat samples is interpreted to indicate that the detections reported for ground-water samples represented low-level field or laboratory contamination, and it would appear that coliphage were effectively attenuated to less than 1 PFU/100 mL over distances of several feet of transport in the La Pine aquifer and (or) overlying unsaturated zone. Organic wastewater compounds were frequently detected in onsite wastewater. Of the 63 organic wastewater compounds in the analytical schedule, 45 were detected in the 21 samples of onsite wastewater. Concentrations of organic wastewater compounds reached a maximum of 1,300 ug/L (p-cresol). Caffeine was detected at concentrations as high as 320 ug/L. Fourteen of the 45 compounds were detected in more than 90 percent of onsite wastewater samples. Fewer (nine) organic wastewater compounds were detected in ground water, despite the presence of nitrate and chloride likely from onsite wastewater sources. The nine organic wastewater compounds that were detected in ground-water samples were acetyl-hexamethyl-tetrahydro-naphthalene (AHTN), caffeine, cholesterol, hexahydrohexamethyl-cyclopentabenzopyran, N,N-diethyl-meta-toluamide (DEET), tetrachloroethene, tris (2-chloroethyl) phosphate, tris (dichloroisopropyl) phosphate, and tributyl phosphate. Frequent detection of household-chemical type organic wastewater compounds in onsite wastewater provides evidence that some of these organic wastewater compounds may be useful indicators of human waste effluent dispersal in some hydrologic environments. The occurrence of organic wastewater compounds in ground water downgradient from onsite wastewater treatment systems demonstrates that a subgroup of organic wastewater compounds is transported in the La Pine aquifer. The consistently low concentrations (generally less than 1 ug/L) of organic wastewater compounds in water samples collected from wells located no more than 19 feet from drainfield lines indicates that the reactivity (sorption, degradation) of this suite of organic waste

Proceedings of the ASCUE Summer Conference (26th Annual, Myrtle Beach, South Carolina, June 20-24, 1993).

ERIC Educational Resources Information Center

Association of Small Computer Users in Education, Greencastle, IN.

This proceedings report includes 37 papers presented at the 1993 presented on the following topics: information technology in college recruiting; introductory networks in the classroom; Total Quality Management in higher education and a computing services organization; a High Tech Student Workstation; network communication for students and…

Pharmacodynamic Assay Panel for Monitoring Phospho-Signaling Networks | Office of Cancer Clinical Proteomics Research

Cancer.gov

The DNA damage response (DDR) is a highly regulated signal transduction network that orchestrates the temporal and spatial organization of protein complexes required to repair (or tolerate) DNA damage (e.g., nucleotide excision repair, base excision repair, homologous recombination, non-homologous end joining, post-replication repair).

Mining and integration of pathway diagrams from imaging data.

PubMed

Kozhenkov, Sergey; Baitaluk, Michael

2012-03-01

Pathway diagrams from PubMed and World Wide Web (WWW) contain valuable highly curated information difficult to reach without tools specifically designed and customized for the biological semantics and high-content density of the images. There is currently no search engine or tool that can analyze pathway images, extract their pathway components (molecules, genes, proteins, organelles, cells, organs, etc.) and indicate their relationships. Here, we describe a resource of pathway diagrams retrieved from article and web-page images through optical character recognition, in conjunction with data mining and data integration methods. The recognized pathways are integrated into the BiologicalNetworks research environment linking them to a wealth of data available in the BiologicalNetworks' knowledgebase, which integrates data from >100 public data sources and the biomedical literature. Multiple search and analytical tools are available that allow the recognized cellular pathways, molecular networks and cell/tissue/organ diagrams to be studied in the context of integrated knowledge, experimental data and the literature. BiologicalNetworks software and the pathway repository are freely available at www.biologicalnetworks.org. Supplementary data are available at Bioinformatics online.

The Command and Control of the Grand Armee: Napoleon as Organizational Designer

DTIC Science & Technology

2009-06-01

AUTHOR(S) Norman L. Durham 5. FUNDING NUMBERS 7 . PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000...served as the framework for a highly effective command and control system. This command and control network allowed Napoleon to dominate a war with...within his organizational design was a vast information network that served as the framework for a highly effective command and control system. This

A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks

NASA Technical Reports Server (NTRS)

Shaw, Harry

2012-01-01

Users are pushing for greater physical mobility with their network and Internet access. Mobile ad hoc networks (MANET) can provide an efficient mobile network architecture, but security is a key concern. A figure summarizes differences in the state of network security for MANET and fixed networks. MANETs require the ability to distinguish trusted peers, and tolerate the ingress/egress of nodes on an unscheduled basis. Because the networks by their very nature are mobile and self-organizing, use of a Public Key Infra structure (PKI), X.509 certificates, RSA, and nonce ex changes becomes problematic if the ideal of MANET is to be achieved. Molecular biology models such as DNA evolution can provide a basis for a proprietary security architecture that achieves high degrees of diffusion and confusion, and resistance to cryptanalysis. A proprietary encryption mechanism was developed that uses the principles of DNA replication and steganography (hidden word cryptography) for confidentiality and authentication. The foundation of the approach includes organization of coded words and messages using base pairs organized into genes, an expandable genome consisting of DNA-based chromosome keys, and a DNA-based message encoding, replication, and evolution and fitness. In evolutionary computing, a fitness algorithm determines whether candidate solutions, in this case encrypted messages, are sufficiently encrypted to be transmitted. The technology provides a mechanism for confidential electronic traffic over a MANET without a PKI for authenticating users.

Unfolding similarity in interphysician networks: the impact of institutional and professional homophily.

PubMed

Mascia, Daniele; Di Vincenzo, Fausto; Iacopino, Valentina; Fantini, Maria Pia; Cicchetti, Americo

2015-03-10

Modern healthcare is characterized by high complexity due to the proliferation of specialties, professional roles, and priorities within organizations. To perform clinical interventions, knowledge distributed across units, directorates and individuals needs to be integrated. Formal and/or informal mechanisms may be used to coordinate knowledge and tasks within organizations. Although the literature has recently considered the role of physicians' professional networks in the diffusion of knowledge, several concerns remain about the mechanisms through which these networks emerge within healthcare organizations. The aim of the present paper is to explore the impact of institutional and professional homophilies on the formation of interphysician professional networks. We collected data on a community of around 300 physicians working at a local health authority within the Italian National Health Service. We employed multiple regression quadratic assignment procedures to explore the extent to which institutional and professional homophilies influence the formation of interphysician networks. We found that both institutional and professional homophilies matter in explaining interphysician networks. Physicians who had similar fields of interest or belonged to the same organizational structure were more likely to establish professional relationships. In addition, professional homophily was more relevant than institutional affiliation in explaining collaborative ties. Our findings have organizational implications and provide useful information for managers who are responsible for undertaking organizational restructuring. Healthcare executives and administrators may want to consider the structure of advice networks while adopting new organizational structures.

Mapping human brain networks with cortico-cortical evoked potentials

PubMed Central

Keller, Corey J.; Honey, Christopher J.; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D.

2014-01-01

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro.

PubMed

Gladkov, Arseniy; Grinchuk, Oleg; Pigareva, Yana; Mukhina, Irina; Kazantsev, Victor; Pimashkin, Alexey

2018-01-01

The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50-100 ms) small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz). The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two "functional" states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.

Topological patterns in street networks of self-organized urban settlements

NASA Astrophysics Data System (ADS)

Buhl, J.; Gautrais, J.; Reeves, N.; Solé, R. V.; Valverde, S.; Kuntz, P.; Theraulaz, G.

2006-02-01

Many urban settlements result from a spatially distributed, decentralized building process. Here we analyze the topological patterns of organization of a large collection of such settlements using the approach of complex networks. The global efficiency (based on the inverse of shortest-path lengths), robustness to disconnections and cost (in terms of length) of these graphs is studied and their possible origins analyzed. A wide range of patterns is found, from tree-like settlements (highly vulnerable to random failures) to meshed urban patterns. The latter are shown to be more robust and efficient.

The connectivity structure, giant strong component and centrality of metabolic networks.

PubMed

Ma, Hong-Wu; Zeng, An-Ping

2003-07-22

Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. http://genome.gbf.de/bioinformatics/

Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach

PubMed Central

Alaerts, Kaat; Geerlings, Franca; Herremans, Lynn; Swinnen, Stephan P.; Verhoeven, Judith; Sunaert, Stefan; Wenderoth, Nicole

2015-01-01

Background The ability to recognize, understand and interpret other’s actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network. Method Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC). Results Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD. Conclusion While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON. PMID:26317222

Research on Information Sharing Mechanism of Network Organization Based on Evolutionary Game

NASA Astrophysics Data System (ADS)

Wang, Lin; Liu, Gaozhi

2018-02-01

This article first elaborates the concept and effect of network organization, and the ability to share information is analyzed, secondly introduces the evolutionary game theory, network organization for information sharing all kinds of limitations, establishes the evolutionary game model, analyzes the dynamic evolution of network organization of information sharing, through reasoning and evolution. The network information sharing by the initial state and two sides of the game payoff matrix of excess profits and information is the information sharing of cost and risk sharing are the influence of network organization node information sharing decision.

How to Identify Success Among Networks That Promote Active Living.

PubMed

Litt, Jill; Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-11-01

We evaluated organization- and network-level factors that influence organizations' perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. A total of 53 of 59 "whole networks" met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%-100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Organizations' perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities.

Application of social network analysis in the assessment of organization infrastructure for service delivery: a three district case study from post-conflict northern Uganda.

PubMed

Ssengooba, Freddie; Kawooya, Vincent; Namakula, Justine; Fustukian, Suzanne

2017-10-01

In post-conflict settings, service coverage indices are unlikely to be sustained if health systems are built on weak and unstable inter-organization networks-here referred to as infrastructure. The objective of this study was to assess the inter-organization infrastructure that supports the provision of selected health services in the reconstruction phase after conflict in northern Uganda. Applied social network analysis was used to establish the structure, size and function among organizations supporting the provision of (1) HIV treatment, (2) maternal delivery services and (3) workforce strengthening. Overall, 87 organizations were identified from 48 respondent organizations in the three post-conflict districts in northern Uganda. A two-stage snowball approach was used starting with service provider organizations in each district. Data included a list of organizations and their key attributes related to the provision of each service for the year 2012-13. The findings show that inter-organization networks are mostly focused on HIV treatment and least for workforce strengthening. The networks for HIV treatment and maternal services were about 3-4 times denser relative to the network for workforce strengthening. The network for HIV treatment accounted for 69-81% of the aggregated network in Gulu and Kitgum districts. In contrast, the network for workforce strengthening contributed the least (6% and 10%) in these two districts. Likewise, the networks supporting a young district (Amuru) was under invested with few organizations and sparse connections. Overall, organizations exhibited a broad range of functional roles in supporting HIV treatment compared to other services in the study. Basic information about the inter-organization setup (infrastructure)-can contribute to knowledge for building organization networks in more equitable ways. More connected organizations can be leveraged for faster communication and resource flow to boost the delivery of health services. © The Author 2017. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.

Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration

PubMed Central

Cole, Michael W.

2016-01-01

The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability. SIGNIFICANCE STATEMENT The brain's network configuration varies based on current task demands. For example, functional brain connections are organized in one way when one is resting quietly but in another way if one is asked to make a decision. We found that the efficiency of these updates in brain network organization is positively related to general intelligence, the ability to perform a wide variety of cognitively challenging tasks well. Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence. PMID:27535904

Mechanically enhanced nested-network hydrogels as a coating material for biomedical devices.

PubMed

Wang, Zhengmu; Zhang, Hongbin; Chu, Axel J; Jackson, John; Lin, Karen; Lim, Chinten James; Lange, Dirk; Chiao, Mu

2018-04-01

Well-organized composite formations such as hierarchical nested-network (NN) structure in bone tissue and reticular connective tissue present remarkable mechanical strength and play a crucial role in achieving physical and biological functions for living organisms. Inspired by these delicate microstructures in nature, an analogous scaffold of double network hydrogel was fabricated by creating a poly(2-hydroxyethyl methacrylate) (pHEMA) network in the porous structure of alginate hydrogels. The resulting hydrogel possessed hierarchical NN structure and showed significantly improved mechanical strength but still maintained high elasticity comparable to soft tissues due to a mutual strengthening effect between the two networks. The tough hydrogel is also self-lubricated, exhibiting a surface friction coefficient comparable with polydimethylsiloxane (PDMS) substrates lubricated by a commercial aqueous lubricant (K-Y Jelly) and other low surface friction hydrogels. Additional properties of this hydrogel include high hydrophilicity, good biocompatibility, tunable cell adhesion and bacterial resistance after incorporation of silver nanoparticles. Firm bonding of the hydrogel on silicone substrates could be achieved through facile chemical modification, thus enabling the use of this hydrogel as a versatile coating material for biomedical applications. In this study, we developed a tough hydrogel by crosslinking HEMA monomers in alginate hydrogels and forming a well-organized structure of hierarchical nested network (NN). Different from most reported stretchable alginate-based hydrogels, the NN hydrogel shows higher compressive strength but retains comparable softness to alginate counterparts. This work further demonstrated the good integration of the tough hydrogel with silicone substrates through chemical modification and micropillar structures. Other properties including surface friction, biocompatibility and bacterial resistance were investigated and the hydrogel shows a great promise as a versatile coating material for biomedical applications. Copyright © 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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.

Multiscale pore networks and their effect on deformation and transport property alteration associated with hydraulic fracturing

NASA Astrophysics Data System (ADS)

Daigle, Hugh; Hayman, Nicholas; Jiang, Han; Tian, Xiao; Jiang, Chunbi

2017-04-01

Multiple lines of evidence indicate that, during a hydraulic fracture stimulation, the permeability of the unfractured matrix far from the main, induced tensile fracture increases by one to two orders of magnitude. This permeability enhancement is associated with pervasive shear failure in a large region surrounding the main induced fracture. We have performed low-pressure gas sorption, mercury intrusion, and nuclear magnetic resonance measurements along with high-resolution scanning electron microscope imaging on several preserved and unpreserved shale samples from North American basins before and after inducing failure in confined compressive strength tests. We have observed that the pore structure in intact samples exhibits multiscale behavior, with sub-micron-scale pores in organic matter connected in isolated, micron-scale clusters which themselves are connected to each other through a network of microcracks. The organic-hosted pore networks are poorly connected due to a significant number of dead-end pores within the organic matter. Following shear failure, we often observe an increase in pore volume in the sub-micron range, which appears to be related to the formation of microcracks that propagate along grain boundaries and other planes of mechanical strength contrast. This is consistent with other experimental and field evidence. In some cases these microcracks cross or terminate in organic matter, intersecting the organic-hosted pores. The induced microcrack networks typically have low connectivity and do not appreciably increase the connectivity of the overall pore network. However, in other cases the shear deformation results in an overall pore volume decrease; samples which exhibit this behavior tend to have more clay minerals. Our interpretation of these phenomena is as follows. As organic matter is converted to hydrocarbons, organic-hosted pores develop, and the hydrocarbons contained in these pores are overpressured. The disconnected nature of these clusters of organic-hosted pores prevents the overpressure from dissipating, resulting in localized overpressure at the micron scale. When the rock is subjected to a hydraulic fracture stimulation, the rock surrounding the main induced fracture experiences shear deformation. Those parts of the rock that contain overpressured fluids in the organic-hosted pores will be more likely to experience dilatancy in the form of brittle deformation; the portions of the rock lacking in organic-hosted pores will tend to experience compactive shear failure since the effective normal stresses are larger. The microcrack networks that propagate into the regions of organic-hosted porosity allow the hydrocarbons resident in those pores to migrate to the main induced tensile fractures. The disconnected nature of the microcrack networks causes only a slight increase in permeability, which is consistent with other observations. Our work illustrates how multiscale pore networks in shale interact with in situ stresses to affect the bulk shale rheology.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

Using Organizational Network Analysis to Plan Cancer Screening Programs for Vulnerable Populations

PubMed Central

Carothers, Bobbi J.; Lofters, Aisha K.

2014-01-01

Objectives. We examined relationships among organizations in a cancer screening network to inform the development of interventions to improve cancer screening for South Asians living in the Peel region of Ontario. Methods. From April to July 2012, we surveyed decision-makers, program managers, and program staff in 22 organizations in the South Asian cancer screening network in the Peel region. We used a network analytic approach to evaluate density (range = 0%–100%, number of ties among organizations in the network expressed as a percentage of all possible ties), centralization (range = 0–1, the extent of variability in centrality), and node characteristics for the communication, collaboration, and referral networks. Results. Density was similar across communication (15%), collaboration (17%), and referral (19%) networks. Centralization was greater in the collaboration network (0.30) than the communication network (0.24), and degree centralization was greater in the inbound (0.42) than the outbound (0.37) referral network. Diverse organizations were central to the networks. Conclusions. Certain organizations were unexpectedly important to the South Asian cancer screening network. Program planning was informed by identifying opportunities to strengthen linkages between key organizations and to leverage existing ties. PMID:24328613

Using organizational network analysis to plan cancer screening programs for vulnerable populations.

PubMed

Lobb, Rebecca; Carothers, Bobbi J; Lofters, Aisha K

2014-02-01

We examined relationships among organizations in a cancer screening network to inform the development of interventions to improve cancer screening for South Asians living in the Peel region of Ontario. From April to July 2012, we surveyed decision-makers, program managers, and program staff in 22 organizations in the South Asian cancer screening network in the Peel region. We used a network analytic approach to evaluate density (range = 0%-100%, number of ties among organizations in the network expressed as a percentage of all possible ties), centralization (range = 0-1, the extent of variability in centrality), and node characteristics for the communication, collaboration, and referral networks. Density was similar across communication (15%), collaboration (17%), and referral (19%) networks. Centralization was greater in the collaboration network (0.30) than the communication network (0.24), and degree centralization was greater in the inbound (0.42) than the outbound (0.37) referral network. Diverse organizations were central to the networks. Certain organizations were unexpectedly important to the South Asian cancer screening network. Program planning was informed by identifying opportunities to strengthen linkages between key organizations and to leverage existing ties.

The influence of partnership centrality on organizational perceptions of support: a case study of the AHLN structure.

PubMed

Moore, Spencer; Smith, Cynthia; Simpson, Tammy; Minke, Sharlene Wolbeck

2006-10-31

Knowledge of the structure and character of inter-organizational relationships found among health promotion organizations is a prerequisite for the development of evidence-based network-level intervention activities. The Alberta Healthy Living Network (AHLN) mapped the inter-organizational structure of its members to examine the effects of the network environment on organizational-level perceptions. This exploratory analysis examines whether network structure, specifically partnership ties among AHLN members, influences organizational perceptions of support after controlling for organizational-level attributes. Organizational surveys were conducted with representatives from AHLN organizations as of February 2004 (n = 54). Organizational attribute and inter-organizational data on various network dimensions were collected. Organizations were classified into traditional and non-traditional categories. We examined the partnership network dimension. In- and out-degree centrality scores on partnership ties were calculated for each organization and tested against organizational perceptions of available financial support. Non-traditional organizations are more likely to view financial support as more readily available for their HEALTR programs and activities than traditional organizations (1.57, 95% CI: .34, 2.79). After controlling for organizational characteristics, organizations that have been frequently identified by other organizations as valuable partners in the AHLN network were found significantly more likely to perceive a higher sense of funding availability (In-degree partnership value) (.03, 95% CI: .01, .05). Organizational perceptions of a supportive environment are framed not only by organizational characteristics but also by an organization's position in an inter-organizational network. Network contexts can influence the way that organizations perceive their environment and potentially the actions that organizations may take in light of such perceptions. By developing evidence-based understandings on the influence of network contexts, the AHLN can better target the particularities of its specific health promotion network.

The influence of partnership centrality on organizational perceptions of support: a case study of the AHLN structure

PubMed Central

Moore, Spencer; Smith, Cynthia; Simpson, Tammy; Minke, Sharlene Wolbeck

2006-01-01

Background Knowledge of the structure and character of inter-organizational relationships found among health promotion organizations is a prerequisite for the development of evidence-based network-level intervention activities. The Alberta Healthy Living Network (AHLN) mapped the inter-organizational structure of its members to examine the effects of the network environment on organizational-level perceptions. This exploratory analysis examines whether network structure, specifically partnership ties among AHLN members, influences organizational perceptions of support after controlling for organizational-level attributes. Methods Organizational surveys were conducted with representatives from AHLN organizations as of February 2004 (n = 54). Organizational attribute and inter-organizational data on various network dimensions were collected. Organizations were classified into traditional and non-traditional categories. We examined the partnership network dimension. In- and out-degree centrality scores on partnership ties were calculated for each organization and tested against organizational perceptions of available financial support. Results Non-traditional organizations are more likely to view financial support as more readily available for their HEALTR programs and activities than traditional organizations (1.57, 95% CI: .34, 2.79). After controlling for organizational characteristics, organizations that have been frequently identified by other organizations as valuable partners in the AHLN network were found significantly more likely to perceive a higher sense of funding availability (In-degree partnership value) (.03, 95% CI: .01, .05). Conclusion Organizational perceptions of a supportive environment are framed not only by organizational characteristics but also by an organization's position in an inter-organizational network. Network contexts can influence the way that organizations perceive their environment and potentially the actions that organizations may take in light of such perceptions. By developing evidence-based understandings on the influence of network contexts, the AHLN can better target the particularities of its specific health promotion network. PMID:17076906

Network organization is globally atypical in autism: A graph theory study of intrinsic functional connectivity.

PubMed

Keown, Christopher L; Datko, Michael C; Chen, Colleen P; Maximo, José Omar; Jahedi, Afrooz; Müller, Ralph-Axel

2017-01-01

Despite abundant evidence of brain network anomalies in autism spectrum disorder (ASD), findings have varied from broad functional underconnectivity to broad overconnectivity. Rather than pursuing overly simplifying general hypotheses ('under' vs. 'over'), we tested the hypothesis of atypical network distribution in ASD (i.e., participation of unusual loci in distributed functional networks). We used a selective high-quality data subset from the ABIDE datashare (including 111 ASD and 174 typically developing [TD] participants) and several graph theory metrics. Resting state functional MRI data were preprocessed and analyzed for detection of low-frequency intrinsic signal correlations. Groups were tightly matched for available demographics and head motion. As hypothesized, the Rand Index (reflecting how similar network organization was to a normative set of networks) was significantly lower in ASD than TD participants. This was accounted for by globally reduced cohesion and density, but increased dispersion of networks. While differences in hub architecture did not survive correction, rich club connectivity (among the hubs) was increased in the ASD group. Our findings support the model of reduced network integration (connectivity with networks) and differentiation (or segregation; based on connectivity outside network boundaries) in ASD. While the findings applied at the global level, they were not equally robust across all networks and in one case (greater cohesion within ventral attention network in ASD) even reversed.

A taxonomy of health networks and systems: bringing order out of chaos.

PubMed Central

Bazzoli, G J; Shortell, S M; Dubbs, N; Chan, C; Kralovec, P

1999-01-01

OBJECTIVE: To use existing theory and data for empirical development of a taxonomy that identifies clusters of organizations sharing common strategic/structural features. DATA SOURCES: Data from the 1994 and 1995 American Hospital Association Annual Surveys, which provide extensive data on hospital involvement in hospital-led health networks and systems. STUDY DESIGN: Theories of organization behavior and industrial organization economics were used to identify three strategic/structural dimensions: differentiation, which refers to the number of different products/services along a healthcare continuum; integration, which refers to mechanisms used to achieve unity of effort across organizational components; and centralization, which relates to the extent to which activities take place at centralized versus dispersed locations. These dimensions were applied to three components of the health service/product continuum: hospital services, physician arrangements, and provider-based insurance activities. DATA EXTRACTION METHODS: We identified 295 health systems and 274 health networks across the United States in 1994, and 297 health systems and 306 health networks in 1995 using AHA data. Empirical measures aggregated individual hospital data to the health network and system level. PRINCIPAL FINDINGS: We identified a reliable, internally valid, and stable four-cluster solution for health networks and a five-cluster solution for health systems. We found that differentiation and centralization were particularly important in distinguishing unique clusters of organizations. High differentiation typically occurred with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration was also important, but we found that health networks and systems typically engaged in both ownership-based and contractual-based integration or they were not integrated at all. CONCLUSIONS: Overall, we were able to classify approximately 70 percent of hospital-led health networks and 90 percent of hospital-led health systems into well-defined organizational clusters. Given the widespread perception that organizational change in healthcare has been chaotic, our research suggests that important and meaningful similarities exist across many evolving organizations. The resulting taxonomy provides a new lexicon for researchers, policymakers, and healthcare executives for characterizing key strategic and structural features of evolving organizations. The taxonomy also provides a framework for future inquiry about the relationships between organizational strategy, structure, and performance, and for assessing policy issues, such as Medicare Provider Sponsored Organizations, antitrust, and insurance regulation. Images Figure 2A Figure 2A Figure 2B Figure 2B PMID:10029504

GIANT API: an application programming interface for functional genomics

PubMed Central

Roberts, Andrew M.; Wong, Aaron K.; Fisk, Ian; Troyanskaya, Olga G.

2016-01-01

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu. PMID:27098035

Optimality and self-organization in river deltas

NASA Astrophysics Data System (ADS)

Tejedor, A.; Longjas, A.; Edmonds, D. A.; Zaliapin, I. V.; Georgiou, T. T.; Rinaldo, A.; Foufoula-Georgiou, E.

2017-12-01

Deltas are nourished by channel networks, whose connectivity constrains, if not drives, the evolution, functionality and resilience of these systems. Understanding the coevolution of deltaic channels and their flux organization is crucial for guiding maintenance strategies of these highly stressed systems from a range of anthropogenic activities. However, in contrast to tributary channel networks, to date, no theory has been proposed to explain how deltas self-organize to distribute water and sediment to the delta top and the shoreline. Here, we hypothesize the existence of an optimality principle underlying the self-organized partition of fluxes in delta channel networks. Specifically, we hypothesize that deltas distribute water and sediment fluxes on a given delta topology such as to maximize the diversity of flux delivery to the shoreline. By introducing the concept of nonlocal Entropy Rate (nER) and analyzing ten field deltas in diverse environments, we present evidence that supports our hypothesis, suggesting that delta networks achieve dynamically accessible maxima of their nER. Furthermore, by analyzing six simulated deltas using the Delf3D model and following their topologic and flux re-organization before and after major avulsions, we further study the evolution of nER and confirm our hypothesis. We discuss how optimal flux distributions in terms of nER, when interpreted in terms of resilience, are configurations that reflect an increased ability to withstand perturbations.

Unions Set Sights on High-Profile Charter-Network Schools

ERIC Educational Resources Information Center

Sawchuk, Stephen

2009-01-01

What started as a ripple in the charter community shows signs of becoming a wave as major charter school networks scramble to respond to an unfamiliar phenomenon: moves by their teachers to organize unions. In the first half of this year, teachers formed collective bargaining units in schools run by several of the best-known and highest-profile…

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.

Assembly kinetics determine the architecture of α-actinin crosslinked F-actin networks.

PubMed

Falzone, Tobias T; Lenz, Martin; Kovar, David R; Gardel, Margaret L

2012-05-29

The actin cytoskeleton is organized into diverse meshworks and bundles that support many aspects of cell physiology. Understanding the self-assembly of these actin-based structures is essential for developing predictive models of cytoskeletal organization. Here we show that the competing kinetics of bundle formation with the onset of dynamic arrest arising from filament entanglements and crosslinking determine the architecture of reconstituted actin networks formed with α-actinin crosslinks. Crosslink-mediated bundle formation only occurs in dilute solutions of highly mobile actin filaments. As actin polymerization proceeds, filament mobility and bundle formation are arrested concomitantly. By controlling the onset of dynamic arrest, perturbations to actin assembly kinetics dramatically alter the architecture of biochemically identical samples. Thus, the morphology of reconstituted F-actin networks is a kinetically determined structure similar to those formed by physical gels and glasses. These results establish mechanisms controlling the structure and mechanics in diverse semiflexible biopolymer networks.

Supramolecular Organization of the α121-α565 Collagen IV Network*

PubMed Central

Robertson, Wesley E.; Rose, Kristie L.; Hudson, Billy G.; Vanacore, Roberto M.

2014-01-01

Collagen IV is a family of 6 chains (α1-α6), that form triple-helical protomers that assemble into supramolecular networks. Two distinct networks with chain compositions of α121 and α345 have been established. These oligomerize into separate α121 and α345 networks by a homotypic interaction through their trimeric noncollagenous (NC1) domains, forming α121 and α345 NC1 hexamers, respectively. These are stabilized by novel sulfilimine (SN) cross-links, a covalent cross-link that forms between Met93 and Hyl211 at the trimer-trimer interface. A third network with a composition of α1256 has been proposed, but its supramolecular organization has not been established. In this study we investigated the supramolecular organization of this network by determining the chain identity of sulfilimine-cross-linked NC1 domains derived from the α1256 NC1 hexamer. High resolution mass spectrometry analyses of peptides revealed that sulfilimine bonds specifically cross-link α1 to α5 and α2 to α6 NC1 domains, thus providing the spatial orientation between interacting α121 and α565 trimers. Using this information, we constructed a three-dimensional homology model in which the α565 trimer shows a good chemical and structural complementarity to the α121 trimer. Our studies provide the first chemical evidence for an α565 protomer and its heterotypic interaction with the α121 protomer. Moreover, our findings, in conjunction with our previous studies, establish that the six collagen IV chains are organized into three canonical protomers α121, α345, and α565 forming three distinct networks: α121, α345, and α121-α565, each of which is stabilized by sulfilimine bonds between their C-terminal NC1 domains. PMID:25006246

How can the functioning and effectiveness of networks in the settings approach of health promotion be understood, achieved and researched?

PubMed

Dietscher, Christina

2017-02-01

Networks in health promotion (HP) have, after the launch of WHO's Ottawa Charter [(World Health Organization (WHO) (eds). (1986) Ottawa Charter on Health Promotion. Towards A New Public Health. World Health Organization, Geneva], become a widespread tool to disseminate HP especially in conjunction with the settings approach. Despite their allegedly high importance for HP practice and more than two decades of experiences with networking so far, a sound theoretical basis to support effective planning, formation, coordination and strategy development for networks in the settings approach of HP (HPSN) is still widely missing. Brößkamp-Stone's multi-facetted interorganizational network assessment framework (2004) provides a starting point but falls short of specifying the outcomes that can be reasonably expected from the specific network type of HPSN, and the specific processes/strategies and structures that are needed to achieve them. Based on outcome models in HP, on social, managerial and health science theories of networks, settings and organizations, a sociological systems theory approach and the capacity approach in HP, this article points out why existing approaches to studying networks are insufficient for HPSN, what can be understood by their functioning and effectiveness, what preconditions there are for HPSN effectiveness and how an HPSN functioning and effectiveness framework proposed on these grounds can be used for researching networks in practice, drawing on experiences from the ‘Project on an Internationally Comparative Evaluation Study of the International Network of Health Promoting Hospitals and Health Services’ (PRICES-HPH), which was coordinated by the WHO Collaborating Centre for Health Promotion in Hospitals and Health Services (Vienna WHO-CC) from 2008 to 2012.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

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

Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the networkmore » reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.« less

Structure, function, and control of the human musculoskeletal network

PubMed Central

Murphy, Andrew C.; Muldoon, Sarah F.; Baker, David; Lastowka, Adam; Bennett, Brittany; Yang, Muzhi

2018-01-01

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments. PMID:29346370

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

The Dynamical Balance of the Brain at Rest

PubMed Central

Deco, Gustavo; Corbetta, Maurizio

2014-01-01

We review evidence that spontaneous, i.e. not stimulus- or task-driven, activity in the brain is not noise, but orderly organized at the level of large scale systems in a series of functional networks that maintain at all times a high level of coherence. These networks of spontaneous activity correlation or resting state networks (RSN) are closely related to the underlying anatomical connectivity, but their topography is also gated by the history of prior task activation. Network coherence does not depend on covert cognitive activity, but its strength and integrity relates to behavioral performance. Some RSN are functionally organized as dynamically competing systems both at rest and during tasks. Computational studies show that one of such dynamics, the anti-correlation between networks, depends on noise driven transitions between different multi-stable cluster synchronization states. These multi-stable states emerge because of transmission delays between regions that are modeled as coupled oscillators systems. Large-scale systems dynamics are useful for keeping different functional sub-networks in a state of heightened competition, which can be stabilized and fired by even small modulations of either sensory or internal signals. PMID:21196530

Applications considerations in the system design of highly concurrent multiprocessors

NASA Technical Reports Server (NTRS)

Lundstrom, Stephen F.

1987-01-01

A flow model processor approach to parallel processing is described, using very-high-performance individual processors, high-speed circuit switched interconnection networks, and a high-speed synchronization capability to minimize the effect of the inherently serial portions of applications on performance. Design studies related to the determination of the number of processors, the memory organization, and the structure of the networks used to interconnect the processor and memory resources are discussed. Simulations indicate that applications centered on the large shared data memory should be able to sustain over 500 million floating point operations per second.

Engineering highly organized and aligned single walled carbon nanotube networks for electronic device applications: Interconnects, chemical sensor, and optoelectronics

NASA Astrophysics Data System (ADS)

Kim, Young Lae

For 20 years, single walled carbon nanotubes (SWNTs) have been studied actively due to their unique one-dimensional nanostructure and superior electrical, thermal, and mechanical properties. For these reasons, they offer the potential to serve as building blocks for future electronic devices such as field effect transistors (FETs), electromechanical devices, and various sensors. In order to realize these applications, it is crucial to develop a simple, scalable, and reliable nanomanufacturing process that controllably places aligned SWNTs in desired locations, orientations, and dimensions. Also electronic properties (semiconducting/metallic) of SWNTs and their organized networks must be controlled for the desired performance of devices and systems. These fundamental challenges are significantly limiting the use of SWNTs for future electronic device applications. Here, we demonstrate a strategy to fabricate highly controlled micro/nanoscale SWNT network structures and present the related assembly mechanism to engineer the SWNT network topology and its electrical transport properties. A method designed to evaluate the electrical reliability of such nano- and microscale SWNT networks is also presented. Moreover, we develop and investigate a robust SWNT based multifunctional selective chemical sensor and a range of multifunctional optoelectronic switches, photo-transistors, optoelectronic logic gates and complex optoelectronic digital circuits.

Network portal: a database for storage, analysis and visualization of biological networks

PubMed Central

Turkarslan, Serdar; Wurtmann, Elisabeth J.; Wu, Wei-Ju; Jiang, Ning; Bare, J. Christopher; Foley, Karen; Reiss, David J.; Novichkov, Pavel; Baliga, Nitin S.

2014-01-01

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal. PMID:24271392

Brain Anatomical Network and Intelligence

PubMed Central

Li, Jun; Qin, Wen; Li, Kuncheng; Yu, Chunshui; Jiang, Tianzi

2009-01-01

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence. PMID:19492086

Multi-scale modularity and motif distributional effect in metabolic networks.

PubMed

Gao, Shang; Chen, Alan; Rahmani, Ali; Zeng, Jia; Tan, Mehmet; Alhajj, Reda; Rokne, Jon; Demetrick, Douglas; Wei, Xiaohui

2016-01-01

Metabolism is a set of fundamental processes that play important roles in a plethora of biological and medical contexts. It is understood that the topological information of reconstructed metabolic networks, such as modular organization, has crucial implications on biological functions. Recent interpretations of modularity in network settings provide a view of multiple network partitions induced by different resolution parameters. Here we ask the question: How do multiple network partitions affect the organization of metabolic networks? Since network motifs are often interpreted as the super families of evolved units, we further investigate their impact under multiple network partitions and investigate how the distribution of network motifs influences the organization of metabolic networks. We studied Homo sapiens, Saccharomyces cerevisiae and Escherichia coli metabolic networks; we analyzed the relationship between different community structures and motif distribution patterns. Further, we quantified the degree to which motifs participate in the modular organization of metabolic networks.

Bioengineering and Coordination of Regulatory Networks and Intracellular Complexes to Maximize Hydrogen Production by Phototrophic Microorganisms

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

Tabita, F. Robert

2013-07-30

In this study, the Principal Investigator, F.R. Tabita has teamed up with J. C. Liao from UCLA. This project's main goal is to manipulate regulatory networks in phototrophic bacteria to affect and maximize the production of large amounts of hydrogen gas under conditions where wild-type organisms are constrained by inherent regulatory mechanisms from allowing this to occur. Unrestrained production of hydrogen has been achieved and this will allow for the potential utilization of waste materials as a feed stock to support hydrogen production. By further understanding the means by which regulatory networks interact, this study will seek to maximize themore » ability of currently available “unrestrained” organisms to produce hydrogen. The organisms to be utilized in this study, phototrophic microorganisms, in particular nonsulfur purple (NSP) bacteria, catalyze many significant processes including the assimilation of carbon dioxide into organic carbon, nitrogen fixation, sulfur oxidation, aromatic acid degradation, and hydrogen oxidation/evolution. Moreover, due to their great metabolic versatility, such organisms highly regulate these processes in the cell and since virtually all such capabilities are dispensable, excellent experimental systems to study aspects of molecular control and biochemistry/physiology are available.« less

Transformation of metal-organic framework to polymer gel by cross-linking the organic ligands preorganized in metal-organic framework.

PubMed

Ishiwata, Takumi; Furukawa, Yuki; Sugikawa, Kouta; Kokado, Kenta; Sada, Kazuki

2013-04-10

Until now, seamless fusion of metal-organic frameworks (MOFs) and covalently cross-linked polymer gels (PG) at molecular level has been extremely rare, since these two matters have been regarded as opposite, that is, hard versus soft. In this report, we demonstrate transformation of cubic MOF crystals to PG via inner cross-linking of the organic linkers in the void space of MOF, followed by decomposition of the metal coordination. The obtained PG behaved as a polyelectrolyte gel, indicating the high content of ionic groups inside. Metal ions were well adsorbed in the PG due to its densely packed carboxylate groups. A chimera-type hybrid material consisting of MOF and PG was obtained by partial hydrolysis of resulting cross-linked MOF. The shape of resulting PG network well reflected the crystal structure of MOF employed as a template. Our results will connect the two different network materials that have been ever studied in the two different fields to provide new soft and hard hybrid materials, and the unique copolymerization in the large void space of the MOF will open a new horizon toward "ideal network polymers" never prepared before now.

Application of social network analysis in the assessment of organization infrastructure for service delivery: a three district case study from post-conflict northern Uganda

PubMed Central

Ssengooba, Freddie; Kawooya, Vincent; Namakula, Justine; Fustukian, Suzanne

2017-01-01

Abstract In post-conflict settings, service coverage indices are unlikely to be sustained if health systems are built on weak and unstable inter-organization networks—here referred to as infrastructure. The objective of this study was to assess the inter-organization infrastructure that supports the provision of selected health services in the reconstruction phase after conflict in northern Uganda. Applied social network analysis was used to establish the structure, size and function among organizations supporting the provision of (1) HIV treatment, (2) maternal delivery services and (3) workforce strengthening. Overall, 87 organizations were identified from 48 respondent organizations in the three post-conflict districts in northern Uganda. A two-stage snowball approach was used starting with service provider organizations in each district. Data included a list of organizations and their key attributes related to the provision of each service for the year 2012–13. The findings show that inter-organization networks are mostly focused on HIV treatment and least for workforce strengthening. The networks for HIV treatment and maternal services were about 3–4 times denser relative to the network for workforce strengthening. The network for HIV treatment accounted for 69–81% of the aggregated network in Gulu and Kitgum districts. In contrast, the network for workforce strengthening contributed the least (6% and 10%) in these two districts. Likewise, the networks supporting a young district (Amuru) was under invested with few organizations and sparse connections. Overall, organizations exhibited a broad range of functional roles in supporting HIV treatment compared to other services in the study. Basic information about the inter-organization setup (infrastructure)—can contribute to knowledge for building organization networks in more equitable ways. More connected organizations can be leveraged for faster communication and resource flow to boost the delivery of health services. PMID:28637228

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks.

PubMed

Gallos, Lazaros K; Makse, Hernán A; Sigman, Mariano

2012-02-21

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks

PubMed Central

Gallos, Lazaros K.; Makse, Hernán A.; Sigman, Mariano

2012-01-01

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are “large-world” self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the “strength of weak ties” crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain. PMID:22308319

Functional Specialization and Flexibility in Human Association Cortex

PubMed Central

Yeo, B. T. Thomas; Krienen, Fenna M.; Eickhoff, Simon B.; Yaakub, Siti N.; Fox, Peter T.; Buckner, Randy L.; Asplund, Christopher L.; Chee, Michael W.L.

2015-01-01

The association cortex supports cognitive functions enabling flexible behavior. Here, we explored the organization of human association cortex by mathematically formalizing the notion that a behavioral task engages multiple cognitive components, which are in turn supported by multiple overlapping brain regions. Application of the model to a large data set of neuroimaging experiments (N = 10 449) identified complex zones of frontal and parietal regions that ranged from being highly specialized to highly flexible. The network organization of the specialized and flexible regions was explored with an independent resting-state fMRI data set (N = 1000). Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Functionally flexible regions participated in multiple components to different degrees. This heterogeneous selectivity was predicted by the connectivity between flexible and specialized regions. Functionally flexible regions might support binding or integrating specialized brain networks that, in turn, contribute to the ability to execute multiple and varied tasks. PMID:25249407

Disrupting Cocaine Trafficking Networks: Interdicting a Combined Social-Functional Network Model

DTIC Science & Technology

2016-03-01

organizations,  (6.3.C) target transnational money laundering networks to deny drug trafficking organizations illicit financing and money laundering ...6.3.C) target transnational money laundering networks to deny drug trafficking organizations illicit financing and money laundering capabilities...tonne or metric ton (1000 kg) MCO major combat operations MLO money laundering organization MM million NDIC National Drug Intelligence Center

Growing Carbon Nanotubes

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

None

In situ transmission electron microscope (TEM) video (accelerated 10 times) of nucleation and self-organization of a high-density carbon nanotube network from catalytic iron nanoparticles, forming a vertically aligned forest.

Small worlds in space: Synchronization, spatial and relational modularity

NASA Astrophysics Data System (ADS)

Brede, M.

2010-06-01

In this letter we investigate networks that have been optimized to realize a trade-off between enhanced synchronization and cost of wire to connect the nodes in space. Analyzing the evolved arrangement of nodes in space and their corresponding network topology, a class of small-world networks characterized by spatial and network modularity is found. More precisely, for low cost of wire optimal configurations are characterized by a division of nodes into two spatial groups with maximum distance from each other, whereas network modularity is low. For high cost of wire, the nodes organize into several distinct groups in space that correspond to network modules connected on a ring. In between, spatially and relationally modular small-world networks are found.

European network for health technology assessment, EUnetHTA: planning, development, and implementation of a sustainable European network for health technology assessment.

PubMed

Kristensen, Finn Børlum; Mäkelä, Marjukka; Neikter, Susanna Allgurin; Rehnqvist, Nina; Håheim, Lise Lund; Mørland, Berit; Milne, Ruairidh; Nielsen, Camilla Palmhøj; Busse, Reinhard; Lee-Robin, Sun Hae; Wild, Claudia; Espallargues, Mireia; Chamova, Julia

2009-12-01

The European network on Health Technology Assessment (EUnetHTA) aimed to produce tangible and practical results to be used in the various phases of health technology assessment and to establish a framework and processes to support this. This article presents the background, objectives, and organization of EUnetHTA, which involved a total of sixty-four partner organizations. Establishing an effective and sustainable structure for a transnational network involved many managerial, policy, and methodological tools, according to the objective of each task or Work Package. Transparency in organization, financial transactions, and decision making was a key principle in the management of the Project as was the commitment to appropriately involve stakeholders. EUnetHTA activities resulted in a clear management and governance structure, efficient partnership, and transnational cooperation. The Project developed a model for sustainable continuation of the EUnetHTA Collaboration. The EUnetHTA Project achieved its goals by producing a suite of practical tools, a strong network, and plans for continuing the work in a sustainable EUnetHTA Collaboration that facilitates and promotes the use of HTA at national and regional levels. Responsiveness to political developments in Europe should be balanced with maintaining a high level of ambition to promote independent, evidence-based information and well-tested tools for best practice based on a strong network of HTA institutions.

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

PubMed

Durán, Claudio; Daminelli, Simone; Thomas, Josephine M; Haupt, V Joachim; Schroeder, Michael; Cannistraci, Carlo Vittorio

2017-04-26

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering. © The Author 2017. Published by Oxford University Press.

Mapping human brain networks with cortico-cortical evoked potentials.

PubMed

Keller, Corey J; Honey, Christopher J; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D

2014-10-05

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

PubMed

Gratton, Caterina; Laumann, Timothy O; Nielsen, Ashley N; Greene, Deanna J; Gordon, Evan M; Gilmore, Adrian W; Nelson, Steven M; Coalson, Rebecca S; Snyder, Abraham Z; Schlaggar, Bradley L; Dosenbach, Nico U F; Petersen, Steven E

2018-04-18

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. Copyright © 2018 Elsevier Inc. All rights reserved.

Communication Policies in Knowledge Networks

NASA Astrophysics Data System (ADS)

Ioannidis, Evangelos; Varsakelis, Nikos; Antoniou, Ioannis

2018-02-01

Faster knowledge attainment within organizations leads to improved innovation, and therefore competitive advantage. Interventions on the organizational network may be risky or costly or time-demanding. We investigate several communication policies in knowledge networks, which reduce the knowledge attainment time without interventions. We examine the resulting knowledge dynamics for real organizational networks, as well as for artificial networks. More specifically, we investigate the dependence of knowledge dynamics on: (1) the Selection Rule of agents for knowledge acquisition, and (2) the Order of implementation of "Selection" and "Filtering". Significant decrease of the knowledge attainment time (up to -74%) can be achieved by: (1) selecting agents of both high knowledge level and high knowledge transfer efficiency, and (2) implementing "Selection" after "Filtering" in contrast to the converse, implicitly assumed, conventional prioritization. The Non-Commutativity of "Selection" and "Filtering", reveals a Non-Boolean Logic of the Network Operations. The results demonstrate that significant improvement of knowledge dynamics can be achieved by implementing "fruitful" communication policies, by raising the awareness of agents, without any intervention on the network structure.

Highly sensitive antenna using inkjet overprinting with particle-free conductive inks.

PubMed

Komoda, Natsuki; Nogi, Masaya; Suganuma, Katsuaki; Otsuka, Kanji

2012-11-01

Printed antennas with low signal losses and fast response in high-frequency bands have been required. Here we reported on highly sensitive antennas using additive patterning of particle-free metallo-organic decomposition silver inks. Inkjet overprinting of metallo-organic decomposition inks onto copper foil and silver nanowire line produced antenna with mirror surfaces. As a result, the overprinted antennas decreased their return losses at 0.5-4.0 GHz and increased the speed of data communication in WiFi network.

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.

Self-organized topology of recurrence-based complex networks

NASA Astrophysics Data System (ADS)

Yang, Hui; Liu, Gang

2013-12-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

Self-organized topology of recurrence-based complex networks.

PubMed

Yang, Hui; Liu, Gang

2013-12-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

Self-organized topology of recurrence-based complex networks

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

Yang, Hui, E-mail: huiyang@usf.edu; Liu, Gang

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article ismore » to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.« less

Establishing the Rule of Law through Networking and Leadership

DTIC Science & Technology

2016-06-24

proposed network will include governmental organizations, intergovernmental organizations, nongovernmental organizations, and multinational corporations ...of projects which were quickly and poorly executed. They would also likely argue for the inclusion of corporate entities in the network, and for the...third suggestion is that this network be mobilized early; plans should be in place, and the nodes of the network, including finance , personnel, and

The Rich Club of the C. elegans Neuronal Connectome

PubMed Central

Vértes, Petra E.; Ahnert, Sebastian E.; Schafer, William R.; Bullmore, Edward T.

2013-01-01

There is increasing interest in topological analysis of brain networks as complex systems, with researchers often using neuroimaging to represent the large-scale organization of nervous systems without precise cellular resolution. Here we used graph theory to investigate the neuronal connectome of the nematode worm Caenorhabditis elegans, which is defined anatomically at a cellular scale as 2287 synaptic connections between 279 neurons. We identified a small number of highly connected neurons as a rich club (N = 11) interconnected with high efficiency and high connection distance. Rich club neurons comprise almost exclusively the interneurons of the locomotor circuits, with known functional importance for coordinated movement. The rich club neurons are connector hubs, with high betweenness centrality, and many intermodular connections to nodes in different modules. On identifying the shortest topological paths (motifs) between pairs of peripheral neurons, the motifs that are found most frequently traverse the rich club. The rich club neurons are born early in development, before visible movement of the animal and before the main phase of developmental elongation of its body. We conclude that the high wiring cost of the globally integrative rich club of neurons in the C. elegans connectome is justified by the adaptive value of coordinated movement of the animal. The economical trade-off between physical cost and behavioral value of rich club organization in a cellular connectome confirms theoretical expectations and recapitulates comparable results from human neuroimaging on much larger scale networks, suggesting that this may be a general and scale-invariant principle of brain network organization. PMID:23575836

High accuracy operon prediction method based on STRING database scores.

PubMed

Taboada, Blanca; Verde, Cristina; Merino, Enrique

2010-07-01

We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/.

Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

PubMed

Trullo, Roger; Petitjean, Caroline; Nie, Dong; Shen, Dinggang; Ruan, Su

2017-09-01

Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

Being Close: The Quality of Social Relationships in a Local Organic Cereal and Bread Network in Lower Austria

ERIC Educational Resources Information Center

Milestad, Rebecka; Bartel-Kratochvil, Ruth; Leitner, Heidrun; Axmann, Paul

2010-01-01

Experience of the drawbacks of a globalised and industrialised food system has generated interest in localised food systems. Local food networks are regarded as more sustainable food provision systems since they are assumed to have high levels of social embeddedness and relations of regard. This paper explores the social relations between food…

Iron-Terephthalate Coordination Network Thin Films Through In-Situ Atomic/Molecular Layer Deposition.

PubMed

Tanskanen, A; Karppinen, M

2018-06-12

Iron terephthalate coordination network thin films can be fabricated using the state-of-the-art gas-phase atomic/molecular layer deposition (ALD/MLD) technique in a highly controlled manner. Iron is an Earth-abundant and nonhazardous transition metal, and with its rich variety of potential applications an interesting metal constituent for the inorganic-organic coordination network films. Our work underlines the role of the metal precursor used when aiming at in-situ ALD/MLD growth of crystalline inorganic-organic thin films. We obtain crystalline iron terephthalate films when FeCl 3 is employed as the iron source whereas depositions based on the bulkier Fe(acac) 3 precursor yield amorphous films. The chemical composition and structure of the films are investigated with GIXRD, XRR, FTIR and XPS.

Neural network system and methods for analysis of organic materials and structures using spectral data

DOEpatents

Meyer, Bernd J.; Sellers, Jeffrey P.; Thomsen, Jan U.

1993-01-01

Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

Error and attack tolerance of complex networks

NASA Astrophysics Data System (ADS)

Albert, Réka; Jeong, Hawoong; Barabási, Albert-László

2000-07-01

Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network. Complex communication networks display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web, the Internet, social networks and cells. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.

TimeXNet Web: Identifying cellular response networks from diverse omics time-course data.

PubMed

Tan, Phit Ling; López, Yosvany; Nakai, Kenta; Patil, Ashwini

2018-05-14

Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. http://txnet.hgc.jp/. ashwini@hgc.jp. Supplementary data are available at Bioinformatics online.

The Structure of Policy Networks for Injury and Violence Prevention in 15 US Cities.

PubMed

Harris, Jenine K; Jonson-Reid, Melissa; Carothers, Bobbi J; Fowler, Patrick

Changes in policy can reduce violence and injury; however, little is known about how partnerships among organizations influence policy development, adoption, and implementation. To understand partnerships among organizations working on injury and violence prevention (IVP) policy, we examined IVP policy networks in 15 large US cities. In summer 2014, we recruited 15 local health departments (LHDs) to participate in the study. They identified an average of 28.9 local partners (SD = 10.2) working on IVP policy. In late 2014, we sent survey questionnaires to 434 organizations, including the 15 LHDs and their local partners, about their partnerships and the importance of each organization to local IVP policy efforts; 319 participated. We used network methods to examine the composition and structure of the policy networks. Each IVP policy network included the LHD and an average of 21.3 (SD = 6.9) local partners. On average, nonprofit organizations constituted 50.7% of networks, followed by government agencies (26.3%), schools and universities (11.8%), coalitions (11.2%), voluntary organizations (9.6%), hospitals (8.5%), foundations (2.2%), and for-profit organizations (0.7%). Government agencies were perceived as important by the highest proportion of partners. Perceived importance was significantly associated with forming partnerships in most networks; odds ratios ranged from 1.07 (95% CI, 1.02-1.13) to 2.35 (95% CI, 1.68-3.28). Organization type was significantly associated with partnership formation in most networks after controlling for an organization's importance to the network. Several strategies could strengthen local IVP policy networks, including (1) developing connections with partners from sectors that are not well integrated into the networks and (2) encouraging indirect or less formal connections with important but missing partners and partner types.

42 CFR 405.2112 - ESRD network organizations.

Code of Federal Regulations, 2012 CFR

2012-10-01

...) Evaluating and resolving patient grievances. (h) Appointing a network council and a medical review board... 42 Public Health 2 2012-10-01 2012-10-01 false ESRD network organizations. 405.2112 Section 405... End-Stage Renal Disease (ESRD) Services § 405.2112 ESRD network organizations. CMS will designate an...

42 CFR 405.2112 - ESRD network organizations.

Code of Federal Regulations, 2014 CFR

2014-10-01

...) Evaluating and resolving patient grievances. (h) Appointing a network council and a medical review board... 42 Public Health 2 2014-10-01 2014-10-01 false ESRD network organizations. 405.2112 Section 405... End-Stage Renal Disease (ESRD) Services § 405.2112 ESRD network organizations. CMS will designate an...

42 CFR 405.2112 - ESRD network organizations.

Code of Federal Regulations, 2013 CFR

2013-10-01

...) Evaluating and resolving patient grievances. (h) Appointing a network council and a medical review board... 42 Public Health 2 2013-10-01 2013-10-01 false ESRD network organizations. 405.2112 Section 405... End-Stage Renal Disease (ESRD) Services § 405.2112 ESRD network organizations. CMS will designate an...

Religious networking organizations and social justice: an ethnographic case study.

PubMed

Todd, Nathan R

2012-09-01

The current study provides an innovative examination of how and why religious networking organizations work for social justice in their local community. Similar to a coalition or community coordinating council, religious networking organizations are formal organizations comprised of individuals from multiple religious congregations who consistently meet to organize around a common goal. Based on over a year and a half of ethnographic participation in two separate religious networking organizations focused on community betterment and social justice, this study reports on the purpose and structure of these organizations, how each used networking to create social capital, and how religion was integrated into the organizations' social justice work. Findings contribute to the growing literature on social capital, empowering community settings, and the unique role of religious settings in promoting social justice. Implications for future research and practice also are discussed.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria.

PubMed

Ibarra-Arellano, Miguel A; Campos-González, Adrián I; Treviño-Quintanilla, Luis G; Tauch, Andreas; Freyre-González, Julio A

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy ( A: cross- BA: cteria SY: stems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.Database URL: http://abasy.ccg.unam.mx. © The Author(s) 2016. Published by Oxford University Press.

An inorganic/organic hybrid magnetic network as a colorimetric fluorescent nanosensor and its recognizing behavior toward Hg2+

NASA Astrophysics Data System (ADS)

Zeng, Xianfei; Xu, Yaohui; Chen, Xiumin; Ma, Wenhui; Zhou, Yang

2017-11-01

An inorganic/organic hybrid magnetic Fe3O4@SiO2 network functionalized with rhodamine derivatives was devised as a nanosensor for selective detection and removal of Hg2+ in this work. The inorganic/organic hybrid composites showed naked-eye color change in water/methanol media. The distinct color change on the surface of functionalized composite network was observed by separating and drying from aqueous solution after adsorbing Hg2+. The fluorescence spectra indicated that the functionalized nanosensor was highly sensitive and selective to Hg2+ in aqueous solution. Density functional theory (DFT) calculation was performed, which revealed a mechanism of fluorescence generated from Hg2+ induced desulfurization of rhodamine derivatives via forming new five-membered ring structure. The as-obtained composites not only had an excellent adsorption capability for Hg2+, but also showed a strong magnetic sensitivity, which allowed one to separate the functionalized magnetic nanocomposites from the solution.

Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study.

PubMed

Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong

2011-01-01

We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition. Copyright © 2010 Elsevier Inc. All rights reserved.

The 'wired' universe of organic chemistry.

PubMed

Grzybowski, Bartosz A; Bishop, Kyle J M; Kowalczyk, Bartlomiej; Wilmer, Christopher E

2009-04-01

The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. The network evolves in time according to trends that have not changed since the inception of the discipline, and thus project into chemistry's future. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more.

Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

DTIC Science & Technology

2015-12-01

use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

Association between Organ Procurement Organization Social Network Centrality and Kidney Discard and Transplant Outcomes1

PubMed Central

Butala, Neel M.; King, Marissa D.; Reitsma, William; Formica, Richard N.; Abt, Peter L.; Reese, Peter P.; Parikh, Chirag R.

2015-01-01

Background Given growth in kidney transplant waitlists and discard rates, donor kidney acceptance is an important problem. We used network analysis to examine whether organ procurement organization (OPO) network centrality affects discard and outcomes. Methods We identified 106,160 deceased-donor kidneys recovered for transplant from 2000–2010 in SRTR. We constructed the transplant network by year with each OPO representing a node and each kidney-sharing relationship between OPOs representing a directed tie between nodes. Primary exposures were the number of different OPOs to which an OPO has given a kidney or from which an OPO has received a kidney in year preceding procurement year. Primary outcomes were discard, cold-ischemia time, delayed graft function, and 1-year graft loss. We used multivariable regression, restricting analysis to the 50% of OPOs with highest discard and stratifying remaining OPOs by kidney volume. Models controlled for kidney donor risk index, waitlist time, and kidney pumping. Results An increase in one additional OPO to which a kidney was given by a procuring OPO in a year was associated with 1.4% lower likelihood of discard for a given kidney (odds ratio, 0.986; 95% confidence interval, 0.974-0.998) among OPOs procuring high kidney volume, but 2% higher likelihood of discard (OR:1.021, CI:1.006, 1.037) among OPOs procuring low kidney volume, with mixed associations with recipient outcomes. Conclusions Our study highlights the value of network analysis in revealing how broader kidney sharing is associated with levels of organ acceptance. We conclude interventions to promote broader inter-OPO sharing could be developed to reduce discard for a subset of OPOs. PMID:26102610

Quantifying the Relationships among Drug Classes

PubMed Central

Hert, Jérôme; Keiser, Michael J.; Irwin, John J.; Oprea, Tudor I.; Shoichet, Brian K.

2009-01-01

The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale. PMID:18335977

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Autonomous distributed self-organization for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Tang, Hung-Kai

2009-01-01

This paper presents an adaptive combined-metrics-based clustering scheme for mobile wireless sensor networks, which manages the mobile sensors by utilizing the hierarchical network structure and allocates network resources efficiently A local criteria is used to help mobile sensors form a new cluster or join a current cluster. The messages transmitted during hierarchical clustering are applied to choose distributed gateways such that communication for adjacent clusters and distributed topology control can be achieved. In order to balance the load among clusters and govern the topology change, a cluster reformation scheme using localized criterions is implemented. The proposed scheme is simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithm provides efficient network topology management and achieves high scalability in mobile sensor networks.

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

Network Physiology: How Organ Systems Dynamically Interact

PubMed Central

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

2015-01-01

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

The large-scale organization of metabolic networks

NASA Astrophysics Data System (ADS)

Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L.

2000-10-01

In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.

Seamless growth of a supramolecular carpet

PubMed Central

Kim, Ju-Hyung; Ribierre, Jean-Charles; Yang, Yu Seok; Adachi, Chihaya; Kawai, Maki; Jung, Jaehoon; Fukushima, Takanori; Kim, Yousoo

2016-01-01

Organic/metal interfaces play crucial roles in the formation of intermolecular networks on metal surfaces and the performance of organic devices. Although their purity and uniformity have profound effects on the operation of organic devices, the formation of organic thin films with high interfacial uniformity on metal surfaces has suffered from the intrinsic limitation of molecular ordering imposed by irregular surface structures. Here we demonstrate a supramolecular carpet with widely uniform interfacial structure and high adaptability on a metal surface via a one-step process. The high uniformity is achieved with well-balanced interfacial interactions and site-specific molecular rearrangements, even on a pre-annealed amorphous gold surface. Co-existing electronic structures show selective availability corresponding to the energy region and the local position of the system. These findings provide not only a deeper insight into organic thin films with high structural integrity, but also a new way to tailor interfacial geometric and electronic structures. PMID:26839053

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Hierarchical self-assembly of a bow-shaped molecule bearing self-complementary hydrogen bonding sites into extended supramolecular assemblies.

PubMed

Ikeda, Masato; Nobori, Tadahito; Schmutz, Marc; Lehn, Jean-Marie

2005-01-07

The bow-shaped molecule 1 bearing a self-complementary DAAD-ADDA (D=donor A=acceptor) hydrogen-bonding array generates, in hydrocarbon solvents, highly ordered supramolecular sheet aggregates that subsequently give rise to gels by formation of an entangled network. The process of hierarchical self-assembly of compound 1 was investigated by the concentration and temperature dependence of UV-visible and (1)H NMR spectra, fluorescence spectra, and electron microscopy data. The temperature dependence of the UV-visible spectra indicates a highly cooperative process for the self-assembly of compound 1 in decaline. The electron micrograph of the decaline solution of compound 1 (1.0 mM) revealed supramolecular sheet aggregates forming an entangled network. The selected area electronic diffraction patterns of the supramolecular sheet aggregates were typical for single crystals, indicative of a highly ordered assembly. The results exemplify the generation, by hierarchical self-assembly, of highly organized supramolecular materials presenting novel collective properties at each level of organization.

The Structure of Policy Networks for Injury and Violence Prevention in 15 US Cities

PubMed Central

Jonson-Reid, Melissa; Carothers, Bobbi J.; Fowler, Patrick

2017-01-01

Objectives: Changes in policy can reduce violence and injury; however, little is known about how partnerships among organizations influence policy development, adoption, and implementation. To understand partnerships among organizations working on injury and violence prevention (IVP) policy, we examined IVP policy networks in 15 large US cities. Methods: In summer 2014, we recruited 15 local health departments (LHDs) to participate in the study. They identified an average of 28.9 local partners (SD = 10.2) working on IVP policy. In late 2014, we sent survey questionnaires to 434 organizations, including the 15 LHDs and their local partners, about their partnerships and the importance of each organization to local IVP policy efforts; 319 participated. We used network methods to examine the composition and structure of the policy networks. Results: Each IVP policy network included the LHD and an average of 21.3 (SD = 6.9) local partners. On average, nonprofit organizations constituted 50.7% of networks, followed by government agencies (26.3%), schools and universities (11.8%), coalitions (11.2%), voluntary organizations (9.6%), hospitals (8.5%), foundations (2.2%), and for-profit organizations (0.7%). Government agencies were perceived as important by the highest proportion of partners. Perceived importance was significantly associated with forming partnerships in most networks; odds ratios ranged from 1.07 (95% CI, 1.02-1.13) to 2.35 (95% CI, 1.68-3.28). Organization type was significantly associated with partnership formation in most networks after controlling for an organization’s importance to the network. Conclusions: Several strategies could strengthen local IVP policy networks, including (1) developing connections with partners from sectors that are not well integrated into the networks and (2) encouraging indirect or less formal connections with important but missing partners and partner types. PMID:28426291

The emergence of overlapping scale-free genetic architecture in digital organisms.

PubMed

Gerlee, P; Lundh, T

2008-01-01

We have studied the evolution of genetic architecture in digital organisms and found that the gene overlap follows a scale-free distribution, which is commonly found in metabolic networks of many organisms. Our results show that the slope of the scale-free distribution depends on the mutation rate and that the gene development is driven by expansion of already existing genes, which is in direct correspondence to the preferential growth algorithm that gives rise to scale-free networks. To further validate our results we have constructed a simple model of gene development, which recapitulates the results from the evolutionary process and shows that the mutation rate affects the tendency of genes to cluster. In addition we could relate the slope of the scale-free distribution to the genetic complexity of the organisms and show that a high mutation rate gives rise to a more complex genetic architecture.

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

PubMed

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

2016-04-08

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

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Influence of network topology on cooperative problem-solving systems.

PubMed

Fontanari, José F; Rodrigues, Francisco A

2016-09-01

The idea of a collective intelligence behind the complex natural structures built by organisms suggests that the organization of social networks is selected so as to optimize problem-solving competence at the group level. Here we study the influence of the social network topology on the performance of a group of agents whose task is to locate the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence networks. In the case those messages convey accurate information on the proximity of the solution (i.e., for smooth fitness landscapes), we find that high connectivity as well as centralization boosts the group performance. For rugged landscapes, however, these characteristics are beneficial for small groups only. For large groups, it is advantageous to slow down the information transmission through the network to avoid local maximum traps. Long-range links and modularity have marginal effects on the performance of the group, except for a very narrow region of the model parameters.

Analytical theory of polymer-network-mediated interaction between colloidal particles

PubMed Central

Di Michele, Lorenzo; Zaccone, Alessio; Eiser, Erika

2012-01-01

Nanostructured materials based on colloidal particles embedded in a polymer network are used in a variety of applications ranging from nanocomposite rubbers to organic-inorganic hybrid solar cells. Further, polymer-network-mediated colloidal interactions are highly relevant to biological studies whereby polymer hydrogels are commonly employed to probe the mechanical response of living cells, which can determine their biological function in physiological environments. The performance of nanomaterials crucially relies upon the spatial organization of the colloidal particles within the polymer network that depends, in turn, on the effective interactions between the particles in the medium. Existing models based on nonlocal equilibrium thermodynamics fail to clarify the nature of these interactions, precluding the way toward the rational design of polymer-composite materials. In this article, we present a predictive analytical theory of these interactions based on a coarse-grained model for polymer networks. We apply the theory to the case of colloids partially embedded in cross-linked polymer substrates and clarify the origin of attractive interactions recently observed experimentally. Monte Carlo simulation results that quantitatively confirm the theoretical predictions are also presented. PMID:22679289

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

PubMed Central

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

2016-01-01

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

GIANT API: an application programming interface for functional genomics.

PubMed

Roberts, Andrew M; Wong, Aaron K; Fisk, Ian; Troyanskaya, Olga G

2016-07-08

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Porous, Hyper-cross-linked, Three-Dimensional Polymer as Stable, High Rate Capability Electrode for Lithium-Ion Battery.

PubMed

Mukherjee, Debdyuti; Gowda Y K, Guruprasada; Makri Nimbegondi Kotresh, Harish; Sampath, S

2017-06-14

Organic materials containing active carbonyl groups have attracted considerable attention as electrodes in Li-ion batteries due to their reversible redox activity, ability to retain capacity, and, in addition, their ecofriendly nature. Introduction of porosity will help accommodate as well as store small ions and molecules reversibly. In the present work, we introduce a mesoporous triptycene-related, rigid network polymer with high specific surface area as an electrode material for rechargeable Li-ion battery. The designed polymer with a three-dimensional (3D), rigid porous network allows free movement of ions/electrolyte as well as helps in interacting with the active anhydride moieties (containing two carbonyl groups). Considerable intake of Li + ions giving rise to very high specific capacity of 1100 mA h g -1 at a discharge current of 50 mA g -1 and ∼120 mA h g -1 at a high discharge current of 3 A g -1 are observed with excellent cyclability up to 1000 cycles. This remarkable rate capability, which is one of the highest among the reported organic porous polymers to date, makes the triptycene-related rigid 3D network a very good choice for Li-ion batteries and opens up a new method to design polymer-based electrode materials for metal-ion battery technology.

The New Networkers: The Path to Hot IT Jobs Begins in High School.

ERIC Educational Resources Information Center

Bushweller, Kevin

2001-01-01

Some graduates of information-technology training programs are bypassing a traditional college education to take high-paying jobs straight out of high school. Others (both sexes) are taking IT skills to college and nabbing lucrative part-time jobs. Programs by Cisco Systems and other organizations are profiled. (MLH)

Variability in functional brain networks predicts expertise during action observation.

PubMed

Amoruso, Lucía; Ibáñez, Agustín; Fonseca, Bruno; Gadea, Sebastián; Sedeño, Lucas; Sigman, Mariano; García, Adolfo M; Fraiman, Ricardo; Fraiman, Daniel

2017-02-01

Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustained training in a particular motor domain leads to structural and functional changes in critical brain areas. Here, we capitalized on a novel graph-theory approach to electroencephalographic data (Fraiman et al., 2016) to test whether variability in functional brain networks implicated in Tango observation can discriminate between groups differing in their level of expertise. We found that experts and beginners significantly differed in the functional organization of task-relevant networks. Specifically, networks in expert Tango dancers exhibited less variability and a more robust functional architecture. Notably, these expertise-dependent effects were captured within networks derived from electrophysiological brain activity recorded in a very short time window (2s). In brief, variability in the organization of task-related networks seems to be a highly sensitive indicator of long-lasting training effects. This finding opens new methodological and theoretical windows to explore the impact of domain-specific expertise on brain plasticity, while highlighting variability as a fruitful measure in neuroimaging research. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks*

PubMed Central

Bandeira, Nuno

2016-01-01

Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software. PMID:27609420

Synthetic biology: Novel approaches for microbiology.

PubMed

Padilla-Vaca, Felipe; Anaya-Velázquez, Fernando; Franco, Bernardo

2015-06-01

In the past twenty years, molecular genetics has created powerful tools for genetic manipulation of living organisms. Whole genome sequencing has provided necessary information to assess knowledge on gene function and protein networks. In addition, new tools permit to modify organisms to perform desired tasks. Gene function analysis is speed up by novel approaches that couple both high throughput data generation and mining. Synthetic biology is an emerging field that uses tools for generating novel gene networks, whole genome synthesis and engineering. New applications in biotechnological, pharmaceutical and biomedical research are envisioned for synthetic biology. In recent years these new strategies have opened up the possibilities to study gene and genome editing, creation of novel tools for functional studies in virus, parasites and pathogenic bacteria. There is also the possibility to re-design organisms to generate vaccine subunits or produce new pharmaceuticals to combat multi-drug resistant pathogens. In this review we provide our opinion on the applicability of synthetic biology strategies for functional studies of pathogenic organisms and some applications such as genome editing and gene network studies to further comprehend virulence factors and determinants in pathogenic organisms. We also discuss what we consider important ethical issues for this field of molecular biology, especially for potential misuse of the new technologies. Copyright© by the Spanish Society for Microbiology and Institute for Catalan Studies.

A Self-organized MIMO-OFDM-based Cellular Network

NASA Astrophysics Data System (ADS)

Grünheid, Rainer; Fellenberg, Christian

2012-05-01

This paper presents a system proposal for a self-organized cellular network, which is based on the MIMO-OFDM transmission technique. Multicarrier transmission, combined with appropriate beamforming concepts, yields high bandwidth-efficiency and shows a robust behavior in multipath radio channels. Moreover, it provides a fine and tuneable granularity of space-time-frequency resources. Using a TDD approach and interference measurements in each cell, the Base Stations (BSs) decide autonomously which of the space-time-frequency resource blocks are allocated to the Mobile Terminals (MTs) in the cell, in order to fulfil certain Quality of Service (QoS) parameters. Since a synchronized Single Frequency Network (SFN), i.e., a re-use factor of one is applied, the resource blocks can be shared adaptively and flexibly among the cells, which is very advantageous in the case of a non-uniform MT distribution.

Neural network system and methods for analysis of organic materials and structures using spectral data

DOEpatents

Meyer, B.J.; Sellers, J.P.; Thomsen, J.U.

1993-06-08

Apparatus and processes are described for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Identifying PHM market and network opportunities.

PubMed

Grube, Mark E; Krishnaswamy, Anand; Poziemski, John; York, Robert W

2015-11-01

Two key processes for healthcare organizations seeking to assume a financially sustainable role in population health management (PHM), after laying the groundwork for the effort, are to identify potential PHM market opportunities and determine the scope of the PHM network. Key variables organizations should consider with respect to market opportunities include the patient population, the overall insurance/employer market, and available types of insurance products. Regarding the network's scope, organizations should consider both traditional strategic criteria for a viable network and at least five additional criteria: network essentiality and PHM care continuum, network adequacy, service distribution right-sizing, network growth strategy, and organizational agility.

Self-organization and solution of shortest-path optimization problems with memristive networks

NASA Astrophysics Data System (ADS)

Pershin, Yuriy V.; Di Ventra, Massimiliano

2013-07-01

We show that memristive networks, namely networks of resistors with memory, can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time nonlocality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.

Atomic switch networks-nanoarchitectonic design of a complex system for natural computing.

PubMed

Demis, E C; Aguilera, R; Sillin, H O; Scharnhorst, K; Sandouk, E J; Aono, M; Stieg, A Z; Gimzewski, J K

2015-05-22

Self-organized complex systems are ubiquitous in nature, and the structural complexity of these natural systems can be used as a model to design new classes of functional nanotechnology based on highly interconnected networks of interacting units. Conventional fabrication methods for electronic computing devices are subject to known scaling limits, confining the diversity of possible architectures. This work explores methods of fabricating a self-organized complex device known as an atomic switch network and discusses its potential utility in computing. Through a merger of top-down and bottom-up techniques guided by mathematical and nanoarchitectonic design principles, we have produced functional devices comprising nanoscale elements whose intrinsic nonlinear dynamics and memorization capabilities produce robust patterns of distributed activity and a capacity for nonlinear transformation of input signals when configured in the appropriate network architecture. Their operational characteristics represent a unique potential for hardware implementation of natural computation, specifically in the area of reservoir computing-a burgeoning field that investigates the computational aptitude of complex biologically inspired systems.

Organic loading rate and hydraulic retention time shape distinct ecological networks of anaerobic digestion related microbiome.

PubMed

Xu, Rui; Yang, Zhao-Hui; Zheng, Yue; Liu, Jian-Bo; Xiong, Wei-Ping; Zhang, Yan-Ru; Lu, Yue; Xue, Wen-Jing; Fan, Chang-Zheng

2018-04-22

Understanding of how anaerobic digestion (AD)-related microbiomes are constructed by operational parameters or their interactions within the biochemical process is limited. Using high-throughput sequencing and molecular ecological network analysis, this study shows the succession of AD-related microbiome hosting diverse members of the phylum Actinobacteria, Bacteroidetes, Euryarchaeota, and Firmicutes, which were affected by organic loading rate (OLR) and hydraulic retention time (HRT). OLR formed finer microbial network modules than HRT (12 vs. 6), suggesting the further subdivision of functional components. Biomarkers were also identified in OLR or HRT groups (e.g. the family Actinomycetaceae, Methanosaetaceae and Aminiphilaceae). The most pair-wise link between Firmicutes and biogas production indicates the keystone members based on network features can be considered as markers in the regulation of AD. A set of 40% species ("core microbiome") were similar across different digesters. Such noteworthy overlap of microbiomes indicates they are generalists in maintaining the ecological stability of digesters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Assembly Kinetics Determine the Architecture of α-actinin Crosslinked F-actin Networks

PubMed Central

Falzone, Tobias T.; Lenz, Martin; Kovar, David R.; Gardel, Margaret L.

2013-01-01

The actin cytoskeleton is organized into diverse meshworks and bundles that support many aspects of cell physiology. Understanding the self-assembly of these actin-based structures is essential for developing predictive models of cytoskeletal organization. Here we show that the competing kinetics of bundle formation with the onset of dynamic arrest arising from filament entanglements and cross-linking determine the architecture of reconstituted actin networks formed with α-actinin cross-links. Cross-link mediated bundle formation only occurs in dilute solutions of highly mobile actin filaments. As actin polymerization proceeds, filament mobility and bundle formation are arrested concomitantly. By controlling the onset of dynamic arrest, perturbations to actin assembly kinetics dramatically alter the architecture of biochemically identical samples. Thus, the morphology of reconstituted F-actin networks is a kinetically determined structure similar to those formed by physical gels and glasses. These results establish mechanisms controlling the structure and mechanics in diverse semi-flexible biopolymer networks. PMID:22643888

Network formation, governance, and evolution in public health: The North American Quitline Consortium case

PubMed Central

Provan, Keith G.; Beagles, Jonathan E.; Leischow, Scott J.

2014-01-01

Background Collaborative networks of health organizations have received a great deal of attention in recent years as a way of enhancing the flow of information and coordination of services. However, relatively little is known about how such networks are formed and evolve, especially outside a local, community-based setting. This article is an in-depth discussion of the evolution of the North American Quitline Consortium (NAQC). The NAQC is a network of U.S. and Canadian organizations that provide telephone-based counseling and related services to people trying to quit smoking. Methodology The research draws on data from interviews, documents, and a survey of NAQC members to assess how the network emerged, became formalized, and effectively governed. Findings The findings provide an understanding of how multiregional public health networks evolve, while building on and extending the broader literature on organizational networks in other sectors and settings. Specifically, we found that the network form that ultimately emerged was a product of the back-and-forth interplay between the internal needs and goals of those organizations that would ultimately become network members, in this case, state-, and provincial-level tobacco quitline organizations. We also found that network formation, and then governance through a network administrative organization, was driven by important events and shifts in the external environment, including the impact and influence of major national organizations. Practice Implications The results of the study provide health care leaders and policy officials an understanding of how the activities of a large number of organizations having a common health goal, but spanning multiple states and countries, might be coordinated and integrated through the establishment of a formal network. PMID:21712725

Network formation, governance, and evolution in public health: the North American Quitline Consortium case.

PubMed

Provan, Keith G; Beagles, Jonathan E; Leischow, Scott J

2011-01-01

Collaborative networks of health organizations have received a great deal of attention in recent years as a way of enhancing the flow of information and coordination of services. However, relatively little is known about how such networks are formed and evolve, especially outside a local, community-based setting. This article is an in-depth discussion of the evolution of the North American Quitline Consortium (NAQC). The NAQC is a network of U.S. and Canadian organizations that provide telephone-based counseling and related services to people trying to quit smoking. The research draws on data from interviews, documents, and a survey of NAQC members to assess how the network emerged, became formalized, and effectively governed. The findings provide an understanding of how multiregional public health networks evolve, while building on and extending the broader literature on organizational networks in other sectors and settings. Specifically, we found that the network form that ultimately emerged was a product of the back-and-forth interplay between the internal needs and goals of those organizations that would ultimately become network members, in this case, state-, and provincial-level tobacco quitline organizations. We also found that network formation, and then governance through a network administrative organization, was driven by important events and shifts in the external environment, including the impact and influence of major national organizations. The results of the study provide health care leaders and policy officials an understanding of how the activities of a large number of organizations having a common health goal, but spanning multiple states and countries, might be coordinated and integrated through the establishment of a formal network.

Forming a three-dimensional porous organic network via solid-state explosion of organic single crystals.

PubMed

Bae, Seo-Yoon; Kim, Dongwook; Shin, Dongbin; Mahmood, Javeed; Jeon, In-Yup; Jung, Sun-Min; Shin, Sun-Hee; Kim, Seok-Jin; Park, Noejung; Lah, Myoung Soo; Baek, Jong-Beom

2017-11-17

Solid-state reaction of organic molecules holds a considerable advantage over liquid-phase processes in the manufacturing industry. However, the research progress in exploring this benefit is largely staggering, which leaves few liquid-phase systems to work with. Here, we show a synthetic protocol for the formation of a three-dimensional porous organic network via solid-state explosion of organic single crystals. The explosive reaction is realized by the Bergman reaction (cycloaromatization) of three enediyne groups on 2,3,6,7,14,15-hexaethynyl-9,10-dihydro-9,10-[1,2]benzenoanthracene. The origin of the explosion is systematically studied using single-crystal X-ray diffraction and differential scanning calorimetry, along with high-speed camera and density functional theory calculations. The results suggest that the solid-state explosion is triggered by an abrupt change in lattice energy induced by release of primer molecules in the 2,3,6,7,14,15-hexaethynyl-9,10-dihydro-9,10-[1,2]benzenoanthracene crystal lattice.

Optimizing Organization Design for the Future.

ERIC Educational Resources Information Center

Creth, Sheila

2000-01-01

Discussion of planning organization design within the higher education environment stresses the goal of integrating structure and process to maintain stability while increasing organizational flexibility. Considers organization culture, organization structure and processes, networked organizations, a networked organization in action, and personal…

FACETS: multi-faceted functional decomposition of protein interaction networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2012-10-15

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Supplementary data are available at the Bioinformatics online. Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification.

PubMed

Ito, Eric Augusto; Katahira, Isaque; Vicente, Fábio Fernandes da Rocha; Pereira, Luiz Filipe Protasio; Lopes, Fabrício Martins

2018-06-05

With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.

Foundations of metabolic organization: coherence as a basis of computational properties in metabolic networks.

PubMed

Igamberdiev, A U

1999-04-01

Biological organization is based on the coherent energy transfer allowing for macromolecules to operate with high efficiency and realize computation. Computation is executed with virtually 100% efficiency via the coherent operation of molecular machines in which low-energy recognitions trigger energy-driven non-equilibrium dynamic processes. The recognition process is of quantum mechanical nature being a non-demolition measurement. It underlies the enzymatic conversion of a substrate into the product (an elementary metabolic phenomenon); the switching via separation of the direct and reverse routes in futile cycles provides the generation and complication of metabolic networks (coherence within cycles is maintained by the supramolecular organization of enzymes); the genetic level corresponding to the appearance of digital information is based on reflective arrows (catalysts realize their own self-reproduction) and operation of hypercycles. Every metabolic cycle via reciprocal regulation of both its halves can generate rhythms and spatial structures (resulting from the temporally organized depositions from the cycles). Via coherent events which percolate from the elementary submolecular level to organismic entities, self-assembly based on the molecular complementarity is realized and the dynamic informational field operating within the metabolic network is generated.

Trans-Pacific Astronomy Experiment Project Status

NASA Technical Reports Server (NTRS)

Hsu, Eddie

2000-01-01

The Trans-Pacific Astronomy Experiment is Phase 2 of the Trans-Pacific High Data Rate Satcom Experiments following the Trans-Pacific High Definition Video Experiment. It is a part of the Global Information Infrastructure-Global Interoperability for Broadband Networks Project (GII-GIBN). Provides global information infrastructure involving broadband satellites and terrestrial networks and access to information by anyone, anywhere, at any time. Collaboration of government, industry, and academic organizations demonstrate the use of broadband satellite links in a global information infrastructure with emphasis on astronomical observations, collaborative discussions and distance learning.

The Rita Network.: how the High Energy Tools can BE Used in Order to Transmit Clinical Hadrontherapic Data

NASA Astrophysics Data System (ADS)

Ferraris, M.; Risso, P.; Squarcia, S.

We present the realization done for the organization, selection, transmission of Radiotherapy's data and images. The choice of a standard healthcare records, based on the stereotactic and/or conformational radiotherapy, the implementation of the healthcare file into a distributed data-base using the World Wide Web platform for data presentation and transmission and the availability in the network is presented. The solution chosen is a good example of technology transfert from High Energy physics and Medicine and opens new interesting ways in this field.

Foldable interpenetrated metal-organic frameworks/carbon nanotubes thin film for lithium-sulfur batteries

NASA Astrophysics Data System (ADS)

Mao, Yiyin; Li, Gaoran; Guo, Yi; Li, Zhoupeng; Liang, Chengdu; Peng, Xinsheng; Lin, Zhan

2017-03-01

Lithium-sulfur batteries are promising technologies for powering flexible devices due to their high energy density, low cost and environmental friendliness, when the insulating nature, shuttle effect and volume expansion of sulfur electrodes are well addressed. Here, we report a strategy of using foldable interpenetrated metal-organic frameworks/carbon nanotubes thin film for binder-free advanced lithium-sulfur batteries through a facile confinement conversion. The carbon nanotubes interpenetrate through the metal-organic frameworks crystal and interweave the electrode into a stratified structure to provide both conductivity and structural integrity, while the highly porous metal-organic frameworks endow the electrode with strong sulfur confinement to achieve good cyclability. These hierarchical porous interpenetrated three-dimensional conductive networks with well confined S8 lead to high sulfur loading and utilization, as well as high volumetric energy density.

Foldable interpenetrated metal-organic frameworks/carbon nanotubes thin film for lithium–sulfur batteries

PubMed Central

Mao, Yiyin; Li, Gaoran; Guo, Yi; Li, Zhoupeng; Liang, Chengdu; Peng, Xinsheng; Lin, Zhan

2017-01-01

Lithium–sulfur batteries are promising technologies for powering flexible devices due to their high energy density, low cost and environmental friendliness, when the insulating nature, shuttle effect and volume expansion of sulfur electrodes are well addressed. Here, we report a strategy of using foldable interpenetrated metal-organic frameworks/carbon nanotubes thin film for binder-free advanced lithium–sulfur batteries through a facile confinement conversion. The carbon nanotubes interpenetrate through the metal-organic frameworks crystal and interweave the electrode into a stratified structure to provide both conductivity and structural integrity, while the highly porous metal-organic frameworks endow the electrode with strong sulfur confinement to achieve good cyclability. These hierarchical porous interpenetrated three-dimensional conductive networks with well confined S8 lead to high sulfur loading and utilization, as well as high volumetric energy density. PMID:28262801

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

PubMed

Peleato, Nicolas M; Legge, Raymond L; Andrews, Robert C

2018-06-01

The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to common dimensionality reduction techniques of parallel factors analysis (PARAFAC) and principal component analysis (PCA). The proposed method was assessed based on component interpretability as well as for prediction of organic matter reactivity to formation of DBPs. Optimal prediction accuracies on a validation dataset were observed with an autoencoder-neural network approach or by utilizing the full spectrum without pre-processing. Latent representation by an autoencoder appeared to mitigate overfitting when compared to other methods. Although DBP prediction error was minimized by other pre-processing techniques, PARAFAC yielded interpretable components which resemble fluorescence expected from individual organic fluorophores. Through analysis of the network weights, fluorescence regions associated with DBP formation can be identified, representing a potential method to distinguish reactivity between fluorophore groupings. However, distinct results due to the applied dimensionality reduction approaches were observed, dictating a need for considering the role of data pre-processing in the interpretability of the results. In comparison to common organic measures currently used for DBP formation prediction, fluorescence was shown to improve prediction accuracies, with improvements to DBP prediction best realized when appropriate pre-processing and regression techniques were applied. The results of this study show promise for the potential application of neural networks to best utilize fluorescence EEM data for prediction of organic matter reactivity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of the Spatial Organization of Pastures as a Contact Network, Implications for Potential Disease Spread and Biosecurity in Livestock, France, 2010.

PubMed

Palisson, Aurore; Courcoul, Aurélie; Durand, Benoit

2017-01-01

The use of pastures is part of common herd management practices for livestock animals, but contagion between animals located on neighbouring pastures is one of the major modes of infectious disease transmission between herds. At the population level, this transmission is strongly constrained by the spatial organization of pastures. The aim of this study was to answer two questions: (i) is the spatial configuration of pastures favourable to the spread of infectious diseases in France? (ii) would biosecurity measures allow decreasing this vulnerability? Based on GIS data, the spatial organization of pastures was represented using networks. Nodes were the 3,159,787 pastures reported in 2010 by the French breeders to claim the Common Agricultural Policy subsidies. Links connected pastures when the distance between them was below a predefined threshold. Premises networks were obtained by aggregating into a single node all the pastures under the same ownership. Although the pastures network was very fragmented when the distance threshold was short (1.5 meters, relevant for a directly-transmitted disease), it was not the case when the distance threshold was larger (500 m, relevant for a vector-borne disease: 97% of the nodes in the largest connected component). The premises network was highly connected as the largest connected component always included more than 83% of the nodes, whatever the distance threshold. Percolation analyses were performed to model the population-level efficacy of biosecurity measures. Percolation thresholds varied according to the modelled biosecurity measures and to the distance threshold. They were globally high (e.g. >17% of nodes had to be removed, mimicking the confinement of animals inside farm buildings, to obtain the disappearance of the large connected component). The network of pastures thus appeared vulnerable to the spread of diseases in France. Only a large acceptance of biosecurity measures by breeders would allow reducing this structural risk.

Analysis of the Spatial Organization of Pastures as a Contact Network, Implications for Potential Disease Spread and Biosecurity in Livestock, France, 2010

PubMed Central

Palisson, Aurore; Courcoul, Aurélie; Durand, Benoit

2017-01-01

The use of pastures is part of common herd management practices for livestock animals, but contagion between animals located on neighbouring pastures is one of the major modes of infectious disease transmission between herds. At the population level, this transmission is strongly constrained by the spatial organization of pastures. The aim of this study was to answer two questions: (i) is the spatial configuration of pastures favourable to the spread of infectious diseases in France? (ii) would biosecurity measures allow decreasing this vulnerability? Based on GIS data, the spatial organization of pastures was represented using networks. Nodes were the 3,159,787 pastures reported in 2010 by the French breeders to claim the Common Agricultural Policy subsidies. Links connected pastures when the distance between them was below a predefined threshold. Premises networks were obtained by aggregating into a single node all the pastures under the same ownership. Although the pastures network was very fragmented when the distance threshold was short (1.5 meters, relevant for a directly-transmitted disease), it was not the case when the distance threshold was larger (500 m, relevant for a vector-borne disease: 97% of the nodes in the largest connected component). The premises network was highly connected as the largest connected component always included more than 83% of the nodes, whatever the distance threshold. Percolation analyses were performed to model the population-level efficacy of biosecurity measures. Percolation thresholds varied according to the modelled biosecurity measures and to the distance threshold. They were globally high (e.g. >17% of nodes had to be removed, mimicking the confinement of animals inside farm buildings, to obtain the disappearance of the large connected component). The network of pastures thus appeared vulnerable to the spread of diseases in France. Only a large acceptance of biosecurity measures by breeders would allow reducing this structural risk. PMID:28060913

Internal and External Pressures: How Does an Organic Cotton Production Network Learn to Keep Its Hybrid Nature?

ERIC Educational Resources Information Center

Turcato, Carolina; Barin-Cruz, Luciano; Pedrozo, Eugenio Avila

2012-01-01

Purpose: This study aims to investigate how an organic cotton production network learns to maintain its hybrid network and its sustainability in the face of internal and external pressures. Design/methodology/approach: A qualitative case study was conducted in Justa Trama, a Brazilian-based organic cotton production network formed by six members…

Mesoscopic self-organization of a self-assembled supramolecular rectangle on highly oriented pyrolytic graphite and Au(111) surfaces.

PubMed

Gong, Jian-Ru; Wan, Li-Jun; Yuan, Qun-Hui; Bai, Chun-Li; Jude, Hershel; Stang, Peter J

2005-01-25

A self-assembled supramolecular metallacyclic rectangle was investigated with scanning tunneling microscopy on highly oriented pyrolytic graphite and Au(111) surfaces. The rectangles spontaneously adsorb on both surfaces and self-organize into well ordered adlayers. On highly oriented pyrolytic graphite, the long edge of the rectangle stands on the surface, forming a 2D molecular network. In contrast, the face of the rectangle lays flat on the Au(111) surface, forming linear chains. The structures and intramolecular features obtained through high-resolution scanning tunneling microscopy imaging are discussed.

Link predication based on matrix factorization by fusion of multi class organizations of the network.

PubMed

Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

2017-08-21

Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

Architectures and economics for pervasive broadband satellite networks

NASA Technical Reports Server (NTRS)

Staelin, D. H.; Harvey, R. L.

1979-01-01

The size of a satellite network necessary to provide pervasive high-data-rate business communications is estimated, and one possible configuration is described which could interconnect most organizations in the United States. Within an order of magnitude, such a network might reasonably have a capacity equivalent to 10,000 simultaneous 3-Mbps channels, and rely primarily upon a cluster of approximately 3-5 satellites in a single orbital slot. Nominal prices for 3-6 Mbps video conference services might then be approximately $2000 monthly lease charge plus perhaps 70 cents per minute one way.

Theoretical Neuroanatomy:Analyzing the Structure, Dynamics,and Function of Neuronal Networks

NASA Astrophysics Data System (ADS)

Seth, Anil K.; Edelman, Gerald M.

The mammalian brain is an extraordinary object: its networks give rise to our conscious experiences as well as to the generation of adaptive behavior for the organism within its environment. Progress in understanding the structure, dynamics and function of the brain faces many challenges. Biological neural networks change over time, their detailed structure is difficult to elucidate, and they are highly heterogeneous both in their neuronal units and synaptic connections. In facing these challenges, graph-theoretic and information-theoretic approaches have yielded a number of useful insights and promise many more.

Regional brain network organization distinguishes the combined and inattentive subtypes of Attention Deficit Hyperactivity Disorder.

PubMed

Saad, Jacqueline F; Griffiths, Kristi R; Kohn, Michael R; Clarke, Simon; Williams, Leanne M; Korgaonkar, Mayuresh S

2017-01-01

Attention Deficit Hyperactivity Disorder (ADHD) is characterized clinically by hyperactive/impulsive and/or inattentive symptoms which determine diagnostic subtypes as Predominantly Hyperactive-Impulsive (ADHD-HI), Predominantly Inattentive (ADHD-I), and Combined (ADHD-C). Neuroanatomically though we do not yet know if these clinical subtypes reflect distinct aberrations in underlying brain organization. We imaged 34 ADHD participants defined using DSM-IV criteria as ADHD-I ( n  = 16) or as ADHD-C ( n  = 18) and 28 matched typically developing controls, aged 8-17 years, using high-resolution T1 MRI. To quantify neuroanatomical organization we used graph theoretical analysis to assess properties of structural covariance between ADHD subtypes and controls (global network measures: path length, clustering coefficient, and regional network measures: nodal degree). As a context for interpreting network organization differences, we also quantified gray matter volume using voxel-based morphometry. Each ADHD subtype was distinguished by a different organizational profile of the degree to which specific regions were anatomically connected with other regions (i.e., in "nodal degree"). For ADHD-I (compared to both ADHD-C and controls) the nodal degree was higher in the hippocampus. ADHD-I also had a higher nodal degree in the supramarginal gyrus, calcarine sulcus, and superior occipital cortex compared to ADHD-C and in the amygdala compared to controls. By contrast, the nodal degree was higher in the cerebellum for ADHD-C compared to ADHD-I and in the anterior cingulate, middle frontal gyrus and putamen compared to controls. ADHD-C also had reduced nodal degree in the rolandic operculum and middle temporal pole compared to controls. These regional profiles were observed in the context of no differences in gray matter volume or global network organization. Our results suggest that the clinical distinction between the Inattentive and Combined subtypes of ADHD may also be reflected in distinct aberrations in underlying brain organization.

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Dynamic social networks promote cooperation in experiments with humans

PubMed Central

Rand, David G.; Arbesman, Samuel; Christakis, Nicholas A.

2011-01-01

Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one's social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups. Our experiments explore large-scale cooperation, where subjects’ cooperative actions are equally beneficial to all those with whom they interact. Consistent with previous research, we find that cooperation decays over time when social networks are shuffled randomly every round or are fixed across all rounds. We also find that, when networks are dynamic but are updated only infrequently, cooperation again fails. However, when subjects can update their network connections frequently, we see a qualitatively different outcome: Cooperation is maintained at a high level through network rewiring. Subjects preferentially break links with defectors and form new links with cooperators, creating an incentive to cooperate and leading to substantial changes in network structure. Our experiments confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation. PMID:22084103

Trapping in scale-free networks with hierarchical organization of modularity.

PubMed

Zhang, Zhongzhi; Lin, Yuan; Gao, Shuyang; Zhou, Shuigeng; Guan, Jihong; Li, Mo

2009-11-01

A wide variety of real-life networks share two remarkable generic topological properties: scale-free behavior and modular organization, and it is natural and important to study how these two features affect the dynamical processes taking place on such networks. In this paper, we investigate a simple stochastic process--trapping problem, a random walk with a perfect trap fixed at a given location, performed on a family of hierarchical networks that exhibit simultaneously striking scale-free and modular structure. We focus on a particular case with the immobile trap positioned at the hub node having the largest degree. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping problem, which is the mean of the node-to-trap first-passage time over the entire network. The exact expression for the MFPT is calculated through the recurrence relations derived from the special construction of the hierarchical networks. The obtained rigorous formula corroborated by extensive direct numerical calculations exhibits that the MFPT grows algebraically with the network order. Concretely, the MFPT increases as a power-law function of the number of nodes with the exponent much less than 1. We demonstrate that the hierarchical networks under consideration have more efficient structure for transport by diffusion in contrast with other analytically soluble media including some previously studied scale-free networks. We argue that the scale-free and modular topologies are responsible for the high efficiency of the trapping process on the hierarchical networks.

Network design and analysis for multi-enzyme biocatalysis.

PubMed

Blaß, Lisa Katharina; Weyler, Christian; Heinzle, Elmar

2017-08-10

As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.

Evidence for Functional Networks within the Human Brain's White Matter.

PubMed

Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar

2017-07-05

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry. SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders. Copyright © 2017 the authors 0270-6474/17/376394-14$15.00/0.

A multi-user real time inventorying system for radioactive materials: a networking approach.

PubMed

Mehta, S; Bandyopadhyay, D; Hoory, S

1998-01-01

A computerized system for radioisotope management and real time inventory coordinated across a large organization is reported. It handles hundreds of individual users and their separate inventory records. Use of highly efficient computer network and database technologies makes it possible to accept, maintain, and furnish all records related to receipt, usage, and disposal of the radioactive materials for the users separately and collectively. The system's central processor is an HP-9000/800 G60 RISC server and users from across the organization use their personal computers to login to this server using the TCP/IP networking protocol, which makes distributed use of the system possible. Radioisotope decay is automatically calculated by the program, so that it can make the up-to-date radioisotope inventory data of an entire institution available immediately. The system is specifically designed to allow use by large numbers of users (about 300) and accommodates high volumes of data input and retrieval without compromising simplicity and accuracy. Overall, it is an example of a true multi-user, on-line, relational database information system that makes the functioning of a radiation safety department efficient.

Developing Countries Vaccine Manufacturers Network: doing good by making high-quality vaccines affordable for all.

PubMed

Pagliusi, Sonia; Leite, Luciana C C; Datla, Mahima; Makhoana, Morena; Gao, Yongzhong; Suhardono, Mahendra; Jadhav, Suresh; Harshavardhan, Gutla V J A; Homma, Akira

2013-04-18

The Developing Countries Vaccine Manufacturers Network (DCVMN) is a unique model of a public and private international alliance. It assembles governmental and private organizations to work toward a common goal of manufacturing and supplying high-quality vaccines at affordable prices to protect people around the world from known and emerging infectious diseases. Together, this group of manufacturers has decades of experience in manufacturing vaccines, with technologies, know-how, and capacity to produce more than 40 vaccines types. These manufacturers have already contributed more than 30 vaccines in various presentations that have been prequalified by the World Health Organization for use by global immunization programmes. Furthermore, more than 45 vaccines are in the pipeline. Recent areas of focus include vaccines to protect against rotavirus, human papillomavirus (HPV), Japanese encephalitis, meningitis, hepatitis E, poliovirus, influenza, and pertussis, as well as combined pentavalent vaccines for children. The network has a growing number of manufacturers that produce a growing number of products to supply the growing demand for vaccines in developing countries. Copyright © 2013. Published by Elsevier Ltd.

Proton conducting membranes for high temperature fuel cells with solid state water free membranes

NASA Technical Reports Server (NTRS)

Narayanan, Sekharipuram R. (Inventor); Yen, Shiao-Pin S. (Inventor)

2006-01-01

A water free, proton conducting membrane for use in a fuel cell is fabricated as a highly conducting sheet of converted solid state organic amine salt, such as converted acid salt of triethylenediamine with two quaternized tertiary nitrogen atoms, combined with a nanoparticulate oxide and a stable binder combined with the converted solid state organic amine salt to form a polymeric electrolyte membrane. In one embodiment the membrane is derived from triethylenediamine sulfate, hydrogen phosphate or trifiate, an oxoanion with at least one ionizable hydrogen, organic tertiary amine bisulfate, polymeric quaternized amine bisulfate or phosphate, or polymeric organic compounds with quaternizable nitrogen combined with Nafion to form an intimate network with ionic interactions.

Two-layer wireless distributed sensor/control network based on RF

NASA Astrophysics Data System (ADS)

Feng, Li; Lin, Yuchi; Zhou, Jingjing; Dong, Guimei; Xia, Guisuo

2006-11-01

A project of embedded Wireless Distributed Sensor/Control Network (WDSCN) based on RF is presented after analyzing the disadvantages of traditional measure and control system. Because of high-cost and complexity, such wireless techniques as Bluetooth and WiFi can't meet the needs of WDSCN. The two-layer WDSCN is designed based on RF technique, which operates in the ISM free frequency channel with low power and high transmission speed. Also the network is low cost, portable and moveable, integrated with the technologies of computer network, sensor, microprocessor and wireless communications. The two-layer network topology is selected in the system; a simple but efficient self-organization net protocol is designed to fit the periodic data collection, event-driven and store-and-forward. Furthermore, adaptive frequency hopping technique is adopted for anti-jamming apparently. The problems about power reduction and synchronization of data in wireless system are solved efficiently. Based on the discussion above, a measure and control network is set up to control such typical instruments and sensors as temperature sensor and signal converter, collect data, and monitor environmental parameters around. This system works well in different rooms. Experiment results show that the system provides an efficient solution to WDSCN through wireless links, with high efficiency, low power, high stability, flexibility and wide working range.

Achieving high aspect ratio wrinkles by modifying material network stress.

PubMed

Chen, Yu-Cheng; Wang, Yan; McCarthy, Thomas J; Crosby, Alfred J

2017-06-07

Wrinkle aspect ratio, or the amplitude divided by the wavelength, is hindered by strain localization transitions when an increasing global compressive stress is applied to synthetic material systems. However, many examples from living organisms show extremely high aspect ratios, such as gut villi and flower petals. We use three experimental approaches to demonstrate that these high aspect ratio structures can be achieved by modifying the network stress in the wrinkle substrate. We modify the wrinkle stress and effectively delay the strain localization transition, such as folding, to larger aspect ratios by using a zero-stress initial wavy substrate, creating a secondary network with post-curing, or using chemical stress relaxation materials. A wrinkle aspect ratio as high as 0.85, almost three times higher than common values of synthetic wrinkles, is achieved, and a quantitative framework is presented to provide understanding the different strategies and predictions for future investigations.

Long-Term Evolution of Email Networks: Statistical Regularities, Predictability and Stability of Social Behaviors.

PubMed

Godoy-Lorite, Antonia; Guimerà, Roger; Sales-Pardo, Marta

2016-01-01

In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical patterns, characterized by exponentially decaying log-variations of the weight of social ties and of individuals' social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years.

Creativity and the default network: A functional connectivity analysis of the creative brain at rest☆

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Wilkins, Robin W.; Jauk, Emanuel; Fink, Andreas; Silvia, Paul J.; Hodges, Donald A.; Koschutnig, Karl; Neubauer, Aljoscha C.

2014-01-01

The present research used resting-state functional magnetic resonance imaging (fMRI) to examine whether the ability to generate creative ideas corresponds to differences in the intrinsic organization of functional networks in the brain. We examined the functional connectivity between regions commonly implicated in neuroimaging studies of divergent thinking, including the inferior prefrontal cortex and the core hubs of the default network. Participants were prescreened on a battery of divergent thinking tests and assigned to high- and low-creative groups based on task performance. Seed-based functional connectivity analysis revealed greater connectivity between the left inferior frontal gyrus (IFG) and the entire default mode network in the high-creative group. The right IFG also showed greater functional connectivity with bilateral inferior parietal cortex and the left dorsolateral prefrontal cortex in the high-creative group. The results suggest that the ability to generate creative ideas is characterized by increased functional connectivity between the inferior prefrontal cortex and the default network, pointing to a greater cooperation between brain regions associated with cognitive control and low-level imaginative processes. PMID:25245940

Signatures of Currency Vertices

NASA Astrophysics Data System (ADS)

Holme, Petter

2009-03-01

Many real-world networks have broad degree distributions. For some systems, this means that the functional significance of the vertices is also broadly distributed, in other cases the vertices are equally significant, but in different ways. One example of the latter case is metabolic networks, where the high-degree vertices — the currency metabolites — supply the molecular groups to the low-degree metabolites, and the latter are responsible for the higher-order biological function, of vital importance to the organism. In this paper, we propose a generalization of currency metabolites to currency vertices. We investigate the network structural characteristics of such systems, both in model networks and in some empirical systems. In addition to metabolic networks, we find that a network of music collaborations and a network of e-mail exchange could be described by a division of the vertices into currency vertices and others.

Discovering Network Structure Beyond Communities

NASA Astrophysics Data System (ADS)

Nishikawa, Takashi; Motter, Adilson E.

2011-11-01

To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease

NASA Astrophysics Data System (ADS)

Daianu, Madelaine; Jahanshad, Neda; Mendez, Mario F.; Bartzokis, George; Jimenez, Elvira E.; Thompson, Paul M.

2015-03-01

Diffusion imaging and brain connectivity analyses can assess white matter deterioration in the brain, revealing the underlying patterns of how brain structure declines. Fiber tractography methods can infer neural pathways and connectivity patterns, yielding sensitive mathematical metrics of network integrity. Here, we analyzed 1.5-Tesla wholebrain diffusion-weighted images from 64 participants - 15 patients with behavioral variant frontotemporal dementia (bvFTD), 19 with early-onset Alzheimer's disease (EOAD), and 30 healthy elderly controls. Using whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We evaluated the brain's networks focusing on the most highly central and connected regions, also known as hubs, in each diagnostic group - specifically the "high-cost" structural backbone used in global and regional communication. The high-cost backbone of the brain, predicted by fiber density and minimally short pathways between brain regions, accounted for 81-92% of the overall brain communication metric in all diagnostic groups. Furthermore, we found that the set of pathways interconnecting high-cost and high-capacity regions of the brain's communication network are globally and regionally altered in bvFTD, compared to healthy participants; however, the overall organization of the high-cost and high-capacity networks were relatively preserved in EOAD participants, relative to controls. Disruption of the major central hubs that transfer information between brain regions may impair neural communication and functional integrity in characteristic ways typical of each subtype of dementia.

Fetal brain extracellular matrix boosts neuronal network formation in 3D bioengineered model of cortical brain tissue.

PubMed

Sood, Disha; Chwalek, Karolina; Stuntz, Emily; Pouli, Dimitra; Du, Chuang; Tang-Schomer, Min; Georgakoudi, Irene; Black, Lauren D; Kaplan, David L

2016-01-01

The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Since fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling and electrophysiology. Both, adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.

The virtual library: Coming of age

NASA Technical Reports Server (NTRS)

Hunter, Judy F.; Cotter, Gladys A.

1994-01-01

With the high speed networking capabilities, multiple media options, and massive amounts of information that exist in electronic format today, the concept of a 'virtual' library or 'library without walls' is becoming viable. In virtual library environment, the information processed goes beyond the traditional definition of documents to include the results of scientific and technical research and development (reports, software, data) recorded in any format or media: electronic, audio, video, or scanned images. Network access to information must include tools to help locate information sources and navigate the networks to connect to the sources, as well as methods to extract the relevant information. Graphical User Interfaces (GUI's) that are intuitive and navigational tools such as Intelligent Gateway Processors (IGP) will provide users with seamless and transparent use of high speed networks to access, organize, and manage information. Traditional libraries will become points of electronic access to information on multiple medias. The emphasis will be towards unique collections of information at each library rather than entire collections at every library. It is no longer a question of whether there is enough information available; it is more a question of how to manage the vast volumes of information. The future equation will involve being able to organize knowledge, manage information, and provide access at the point of origin.

Emotional intelligence skills for maintaining social networks in healthcare organizations.

PubMed

Freshman, Brenda; Rubino, Louis

2004-01-01

For healthcare organizations to survive in these increasingly challenging times, leadership and management must face mounting interpersonal concerns. The authors present the boundaries of internal and external social networks with respect to leadership and managerial functions: Social networks within the organization are stretched by reductions in available resources and structural ambiguity, whereas external social networks are stressed by interorganizational competitive pressures. The authors present the development of emotional intelligence skills in employees as a strategic training objective that can strengthen the internal and external social networks of healthcare organizations. The authors delineate the unique functions of leadership and management with respect to the application of emotional intelligence skills and discuss training and future research implications for emotional intelligence skill sets and social networks.

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

42 CFR 486.320 - Condition: Participation in Organ Procurement and Transplantation Network.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Condition: Participation in Organ Procurement and Transplantation Network. 486.320 Section 486.320 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES....320 Condition: Participation in Organ Procurement and Transplantation Network. After being designated...

42 CFR 486.320 - Condition: Participation in Organ Procurement and Transplantation Network.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 5 2011-10-01 2011-10-01 false Condition: Participation in Organ Procurement and Transplantation Network. 486.320 Section 486.320 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES....320 Condition: Participation in Organ Procurement and Transplantation Network. After being designated...

Metal-Organic Framework-Derived Metal Oxide Embedded in Nitrogen-Doped Graphene Network for High-Performance Lithium-Ion Batteries.

PubMed

Sui, Zhu-Yin; Zhang, Pei-Ying; Xu, Meng-Ying; Liu, Yu-Wen; Wei, Zhi-Xiang; Han, Bao-Hang

2017-12-13

Metal-organic frameworks (MOFs) are hybrid inorganic-organic materials that can be used as effective precursors to prepare various functional nanomaterials for energy-related applications. Nevertheless, most MOF-derived metal oxides exhibit low electrical conductivity and mechanical strain. These characteristics limit their electrochemical performance and hamper their practical application. Herein, we report a rational strategy for enhancing the lithium storage performance of MOF-derived metal oxide. The hierarchically porous Co 3 O 4 @NGN is successfully prepared by embedding ZIF-67-derived Co 3 O 4 particles in a nitrogen-doped graphene network (NGN). The high electrical conductivity and porous structure of the NGN accelerates the diffusion of electrolyte ions and buffers stress resulting from the volume changes of Co 3 O 4 . As an anode material, the Co 3 O 4 @NGN shows high capacity (1030 mA h g -1 at 100 mA g -1 ), outstanding rate performance (681 mA h g -1 at 1000 mA g -1 ), and good cycling stability (676 mA h g -1 at 1000 mA g -1 after 400 cycles).

Integrated genomic and interfacility patient-transfer data reveal the transmission pathways of multidrug-resistant Klebsiella pneumoniae in a regional outbreak.

PubMed

Snitkin, Evan S; Won, Sarah; Pirani, Ali; Lapp, Zena; Weinstein, Robert A; Lolans, Karen; Hayden, Mary K

2017-11-22

Development of effective strategies to limit the proliferation of multidrug-resistant organisms requires a thorough understanding of how such organisms spread among health care facilities. We sought to uncover the chains of transmission underlying a 2008 U.S. regional outbreak of carbapenem-resistant Klebsiella pneumoniae by performing an integrated analysis of genomic and interfacility patient-transfer data. Genomic analysis yielded a high-resolution transmission network that assigned directionality to regional transmission events and discriminated between intra- and interfacility transmission when epidemiologic data were ambiguous or misleading. Examining the genomic transmission network in the context of interfacility patient transfers (patient-sharing networks) supported the role of patient transfers in driving the outbreak, with genomic analysis revealing that a small subset of patient-transfer events was sufficient to explain regional spread. Further integration of the genomic and patient-sharing networks identified one nursing home as an important bridge facility early in the outbreak-a role that was not apparent from analysis of genomic or patient-transfer data alone. Last, we found that when simulating a real-time regional outbreak, our methodology was able to accurately infer the facility at which patients acquired their infections. This approach has the potential to identify facilities with high rates of intra- or interfacility transmission, data that will be useful for triggering targeted interventions to prevent further spread of multidrug-resistant organisms. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Conducting-insulating transition in adiabatic memristive networks

NASA Astrophysics Data System (ADS)

Sheldon, Forrest C.; Di Ventra, Massimiliano

2017-01-01

The development of neuromorphic systems based on memristive elements—resistors with memory—requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.

High-gain AlGaAs/GaAs double heterojunction Darlington phototransistors for optical neural networks

NASA Technical Reports Server (NTRS)

Kim, Jae H. (Inventor); Lin, Steven H. (Inventor)

1991-01-01

High-gain MOCVD-grown (metal-organic chemical vapor deposition) AlGaAs/GaAs/AlGaAs n-p-n double heterojunction bipolar transistors (DHBTs) and Darlington phototransistor pairs are provided for use in optical neural networks and other optoelectronic integrated circuit applications. The reduced base doping level used results in effective blockage of Zn out-diffusion, enabling a current gain of 500, higher than most previously reported values for Zn-diffused-base DHBTs. Darlington phototransitor pairs of this material can achieve a current gain of over 6000, which satisfies the gain requirement for optical neural network designs, which advantageously may employ neurons comprising the Darlington phototransistor pairs in series with a light source.

Wireless mesh networks.

PubMed

Wang, Xinheng

2008-01-01

Wireless telemedicine using GSM and GPRS technologies can only provide low bandwidth connections, which makes it difficult to transmit images and video. Satellite or 3G wireless transmission provides greater bandwidth, but the running costs are high. Wireless networks (WLANs) appear promising, since they can supply high bandwidth at low cost. However, the WLAN technology has limitations, such as coverage. A new wireless networking technology named the wireless mesh network (WMN) overcomes some of the limitations of the WLAN. A WMN combines the characteristics of both a WLAN and ad hoc networks, thus forming an intelligent, large scale and broadband wireless network. These features are attractive for telemedicine and telecare because of the ability to provide data, voice and video communications over a large area. One successful wireless telemedicine project which uses wireless mesh technology is the Emergency Room Link (ER-LINK) in Tucson, Arizona, USA. There are three key characteristics of a WMN: self-organization, including self-management and self-healing; dynamic changes in network topology; and scalability. What we may now see is a shift from mobile communication and satellite systems for wireless telemedicine to the use of wireless networks based on mesh technology, since the latter are very attractive in terms of cost, reliability and speed.

In Situ-Formed Hierarchical Metal-Organic Flexible Cathode for High-Energy Sodium-Ion Batteries.

PubMed

Huang, Ying; Fang, Chun; Zeng, Rui; Liu, Yaojun; Zhang, Wang; Wang, Yanjie; Liu, Qingju; Huang, Yunhui

2017-12-08

Metal-organic compounds are a family of electrode materials with structural diversity and excellent thermal stability for rechargeable batteries. Here, we fabricated a hierarchical nanocomposite with metal-organic cuprous tetracyanoquinodimethane (CuTCNQ) in a 3 D conductive carbon nanofibers (CNFs) network by in situ growth, and evaluated it as flexible cathode for sodium-ion batteries (SIBs). CuTCNQ in such flexible composite electrode is able to exhibit a high capacity of 252 mAh g -1 at 0.1 C and highly reversible stability for 1200 cycles within the voltage range of 2.5-4.1 V (vs. Na + /Na). A high specific energy of 762 Wh kg -1 was obtained with high average potential of 3.2 V (vs. Na + /Na). The in situ-formed electroactive metal-organic composites with tailored nanoarchitecture provide a promising alternative choice for high-performance cathode materials in SIBs with high energy. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Social network analysis of sustainable transportation organizations.

DOT National Transportation Integrated Search

2012-07-15

Studying how organizations communicate with each other can provide important insights into the influence, and policy success of different types of organizations. This study examines the communication networks of 121 organizations promoting sustainabl...

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

Development of coherent neuronal activity patterns in mammalian cortical networks: common principles and local hetereogeneity.

PubMed

Egorov, Alexei V; Draguhn, Andreas

2013-01-01

Many mammals are born in a very immature state and develop their rich repertoire of behavioral and cognitive functions postnatally. This development goes in parallel with changes in the anatomical and functional organization of cortical structures which are involved in most complex activities. The emerging spatiotemporal activity patterns in multi-neuronal cortical networks may indeed form a direct neuronal correlate of systemic functions like perception, sensorimotor integration, decision making or memory formation. During recent years, several studies--mostly in rodents--have shed light on the ontogenesis of such highly organized patterns of network activity. While each local network has its own peculiar properties, some general rules can be derived. We therefore review and compare data from the developing hippocampus, neocortex and--as an intermediate region--entorhinal cortex. All cortices seem to follow a characteristic sequence starting with uncorrelated activity in uncoupled single neurons where transient activity seems to have mostly trophic effects. In rodents, before and shortly after birth, cortical networks develop weakly coordinated multineuronal discharges which have been termed synchronous plateau assemblies (SPAs). While these patterns rely mostly on electrical coupling by gap junctions, the subsequent increase in number and maturation of chemical synapses leads to the generation of large-scale coherent discharges. These patterns have been termed giant depolarizing potentials (GDPs) for predominantly GABA-induced events or early network oscillations (ENOs) for mostly glutamatergic bursts, respectively. During the third to fourth postnatal week, cortical areas reach their final activity patterns with distinct network oscillations and highly specific neuronal discharge sequences which support adult behavior. While some of the mechanisms underlying maturation of network activity have been elucidated much work remains to be done in order to fully understand the rules governing transition from immature to mature patterns of network activity. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Platelet lysate gel and endothelial progenitors stimulate microvascular network formation in vitro: tissue engineering implications.

PubMed

Fortunato, Tiago M; Beltrami, Cristina; Emanueli, Costanza; De Bank, Paul A; Pula, Giordano

2016-05-04

Revascularisation is a key step for tissue regeneration and complete organ engineering. We describe the generation of human platelet lysate gel (hPLG), an extracellular matrix preparation from human platelets able to support the proliferation of endothelial colony forming cells (ECFCs) in 2D cultures and the formation of a complete microvascular network in vitro in 3D cultures. Existing extracellular matrix preparations require addition of high concentrations of recombinant growth factors and allow only limited formation of capillary-like structures. Additional advantages of our approach over existing extracellular matrices are the absence of any animal product in the composition hPLG and the possibility of obtaining hPLG from patients to generate homologous scaffolds for re-implantation. This discovery has the potential to accelerate the development of regenerative medicine applications based on implantation of microvascular networks expanded ex vivo or the generation of fully vascularised organs.

Platelet lysate gel and endothelial progenitors stimulate microvascular network formation in vitro: tissue engineering implications

PubMed Central

Fortunato, Tiago M.; Beltrami, Cristina; Emanueli, Costanza; De Bank, Paul A.; Pula, Giordano

2016-01-01

Revascularisation is a key step for tissue regeneration and complete organ engineering. We describe the generation of human platelet lysate gel (hPLG), an extracellular matrix preparation from human platelets able to support the proliferation of endothelial colony forming cells (ECFCs) in 2D cultures and the formation of a complete microvascular network in vitro in 3D cultures. Existing extracellular matrix preparations require addition of high concentrations of recombinant growth factors and allow only limited formation of capillary-like structures. Additional advantages of our approach over existing extracellular matrices are the absence of any animal product in the composition hPLG and the possibility of obtaining hPLG from patients to generate homologous scaffolds for re-implantation. This discovery has the potential to accelerate the development of regenerative medicine applications based on implantation of microvascular networks expanded ex vivo or the generation of fully vascularised organs. PMID:27141997

Emerging Leader for Education and Outreach

NASA Astrophysics Data System (ADS)

Bartholow, S.

2013-12-01

Polar Educators International (PEI) is a global professional network for those who educate in, for, and about the polar regions. Our goal is to connect educators, scientists, and community members to share expertise around the world and to rekindle student and public engagement with global environmental change. The growing membership in over 30 countries is now recognized as a leading organization capable of fulfilling E&O goals of international science organizations and training educators to facilitate outstanding polar science and climate change education in classrooms. This session will address the importance of dedicated, high-caliber, interpersonal professional networks that are linked directly to the expert science community to better serve science goals and education in classrooms. Discover that the educators and scientists in the network are resources themselves to help you become a leader in polar and climate education; arguably our most important content at the international level.

Percolation and criticality in a mitochondrial network

PubMed Central

Aon, Miguel A.; Cortassa, Sonia; O'Rourke, Brian

2004-01-01

Synchronization of mitochondrial function is an important determinant of cell physiology and survival, yet little is known about the mechanism of interorganellar communication. We have recently observed that coordinated cell-wide oscillations in the mitochondrial energy state of heart cells can be induced by a highly localized perturbation of a few elements of the mitochondrial network, indicating that mitochondria represent a complex, self-organized system. Here, we apply percolation theory to explain the mechanism of intermitochondrial signal propagation in response to oxidative stress. A global phase transition (mitochondrial depolarization) is shown to occur when a critical density of mitochondria accumulate reactive oxygen species above a threshold to form an extended spanning cluster. The scaling and fractal properties of the mitochondrial network at the edge of instability agree remarkably well with the idea that mitochondria are organized as a percolation matrix, with reactive oxygen species as a key messenger. PMID:15070738

Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior.

PubMed

Portugues, Ruben; Feierstein, Claudia E; Engert, Florian; Orger, Michael B

2014-03-19

Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate but ordered pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments systematically reveal the functional architecture of neural circuits underlying a sensorimotor behavior in a vertebrate brain. Copyright © 2014 Elsevier Inc. All rights reserved.

Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior

PubMed Central

Portugues, Ruben; Feierstein, Claudia E.; Engert, Florian; Orger, Michael B.

2014-01-01

Summary Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate, but ordered, pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments reveal, for the first time in a vertebrate, the comprehensive functional architecture of the neural circuits underlying a sensorimotor behavior. PMID:24656252

Controllable Hysteresis and Threshold Voltage of Single-Walled Carbon Nano-tube Transistors with Ferroelectric Polymer Top-Gate Insulators

PubMed Central

Sun, Yi-Lin; Xie, Dan; Xu, Jian-Long; Zhang, Cheng; Dai, Rui-Xuan; Li, Xian; Meng, Xiang-Jian; Zhu, Hong-Wei

2016-01-01

Double-gated field effect transistors have been fabricated using the SWCNT networks as channel layer and the organic ferroelectric P(VDF-TrFE) film spin-coated as top gate insulators. Standard photolithography process has been adopted to achieve the patterning of organic P(VDF-TrFE) films and top-gate electrodes, which is compatible with conventional CMOS process technology. An effective way for modulating the threshold voltage in the channel of P(VDF-TrFE) top-gate transistors under polarization has been reported. The introduction of functional P(VDF-TrFE) gate dielectric also provides us an alternative method to suppress the initial hysteresis of SWCNT networks and obtain a controllable ferroelectric hysteresis behavior. Applied bottom gate voltage has been found to be another effective way to highly control the threshold voltage of the networked SWCNTs based FETs by electrostatic doping effect. PMID:26980284

Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

PubMed Central

Ren, Jun; Zhou, Wei; Wang, Jianxin

2014-01-01

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

One-to-one neuron-electrode interfacing.

PubMed

Greenbaum, Alon; Anava, Sarit; Ayali, Amir; Shein, Mark; David-Pur, Moshe; Ben-Jacob, Eshel; Hanein, Yael

2009-09-15

The question of neuronal network development and organization is a principle one, which is closely related to aspects of neuronal and network form-function interactions. In-vitro two-dimensional neuronal cultures have proved to be an attractive and successful model for the study of these questions. Research is constraint however by the search for techniques aimed at culturing stable networks, whose electrical activity can be reliably and consistently monitored. A simple approach to form small interconnected neuronal circuits while achieving one-to-one neuron-electrode interfacing is presented. Locust neurons were cultured on a novel bio-chip consisting of carbon-nanotube multi-electrode-arrays. The cells self-organized to position themselves in close proximity to the bio-chip electrodes. The organization of the cells on the electrodes was analyzed using time lapse microscopy, fluorescence imaging and scanning electron microscopy. Electrical recordings from well identified cells is presented and discussed. The unique properties of the bio-chip and the specific neuron-nanotube interactions, together with the use of relatively large insect ganglion cells, allowed long-term stabilization (as long as 10 days) of predefined neural network topology as well as high fidelity electrical recording of individual neuron firing. This novel preparation opens ample opportunity for future investigation into key neurobiological questions and principles.

Acting discursively: the development of UK organic food and farming policy networks.

PubMed

TOMLINSON, Isobel Jane

2010-01-01

This paper documents the early evolution of UK organic food and farming policy networks and locates this empirical focus in a theoretical context concerned with understanding the contemporary policy-making process. While policy networks have emerged as a widely acknowledged empirical manifestation of governance, debate continues as to the concept's explanatory utility and usefulness in situations of network and policy transformation since, historically, policy networks have been applied to "static" circumstances. Recognizing this criticism, and in drawing on an interpretivist perspective, this paper sees policy networks as enacted by individual actors whose beliefs and actions construct the nature of the network. It seeks to make links between the characteristics of the policy network and the policy outcomes through the identification of discursively constructed "storylines" that form a tool for consensus building in networks. This study analyses the functioning of the organic policy networks through the discursive actions of policy-network actors.

The small world of osteocytes: connectomics of the lacuno-canalicular network in bone

NASA Astrophysics Data System (ADS)

Kollmannsberger, Philip; Kerschnitzki, Michael; Repp, Felix; Wagermaier, Wolfgang; Weinkamer, Richard; Fratzl, Peter

2017-07-01

Osteocytes and their cell processes reside in a large, interconnected network of voids pervading the mineralized bone matrix of most vertebrates. This osteocyte lacuno-canalicular network (OLCN) is believed to play important roles in mechanosensing, mineral homeostasis, and for the mechanical properties of bone. While the extracellular matrix structure of bone is extensively studied on ultrastructural and macroscopic scales, there is a lack of quantitative knowledge on how the cellular network is organized. Using a recently introduced imaging and quantification approach, we analyze the OLCN in different bone types from mouse and sheep that exhibit different degrees of structural organization not only of the cell network but also of the fibrous matrix deposited by the cells. We define a number of robust, quantitative measures that are derived from the theory of complex networks. These measures enable us to gain insights into how efficient the network is organized with regard to intercellular transport and communication. Our analysis shows that the cell network in regularly organized, slow-growing bone tissue from sheep is less connected, but more efficiently organized compared to irregular and fast-growing bone tissue from mice. On the level of statistical topological properties (edges per node, edge length and degree distribution), both network types are indistinguishable, highlighting that despite pronounced differences at the tissue level, the topological architecture of the osteocyte canalicular network at the subcellular level may be independent of species and bone type. Our results suggest a universal mechanism underlying the self-organization of individual cells into a large, interconnected network during bone formation and mineralization.

Network analysis of Bogotá's Ciclovía Recreativa, a self-organized multisectorial community program to promote physical activity in a middle-income country.

PubMed

Meisel, Jose D; Sarmiento, Olga L; Montes, Felipe; Martinez, Edwin O; Lemoine, Pablo D; Valdivia, Juan A; Brownson, Ross C; Zarama, Roberto

2014-01-01

Conduct a social network analysis of the health and non-health related organizations that participate in Bogotá's Ciclovía Recreativa (Ciclovía). Cross-sectional study. Ciclovía is a multisectoral community-based mass program in which streets are temporarily closed to motorized transport, allowing exclusive access to individuals for leisure activities and physical activity. Twenty-five organizations that participate in the Ciclovía. Seven variables were examined by using network analytic methods: relationship, link attributes (integration, contact, and importance), and node attributes (leadership, years in the program, and the sector of the organization). The network analytic methods were based on a visual descriptive analysis and an exponential random graph model. Analysis shows that the most central organizations in the network were outside of the Health sector and include Sports and Recreation, Government, and Security sectors. The organizations work in clusters formed by organizations of different sectors. Organization importance and structural predictors were positively related to integration, while the number of years working with Ciclovía was negatively associated with integration. Ciclovía is a network whose structure emerged as a self-organized complex system. Ciclovía of Bogotá is an example of a program with public health potential formed by organizations of multiple sectors with Sports and Recreation as the most central.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

PubMed Central

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing. PMID:21852971

Visual pathways from the perspective of cost functions and multi-task deep neural networks.

PubMed

Scholte, H Steven; Losch, Max M; Ramakrishnan, Kandan; de Haan, Edward H F; Bohte, Sander M

2018-01-01

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

PubMed Central

Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

2016-01-01

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

Sparse short-distance connections enhance calcium wave propagation in a 3D model of astrocyte networks

PubMed Central

Lallouette, Jules; De Pittà, Maurizio; Ben-Jacob, Eshel; Berry, Hugues

2014-01-01

Traditionally, astrocytes have been considered to couple via gap-junctions into a syncytium with only rudimentary spatial organization. However, this view is challenged by growing experimental evidence that astrocytes organize as a proper gap-junction mediated network with more complex region-dependent properties. On the other hand, the propagation range of intercellular calcium waves (ICW) within astrocyte populations is as well highly variable, depending on the brain region considered. This suggests that the variability of the topology of gap-junction couplings could play a role in the variability of the ICW propagation range. Since this hypothesis is very difficult to investigate with current experimental approaches, we explore it here using a biophysically realistic model of three-dimensional astrocyte networks in which we varied the topology of the astrocyte network, while keeping intracellular properties and spatial cell distribution and density constant. Computer simulations of the model suggest that changing the topology of the network is indeed sufficient to reproduce the distinct ranges of ICW propagation reported experimentally. Unexpectedly, our simulations also predict that sparse connectivity and restriction of gap-junction couplings to short distances should favor propagation while long–distance or dense connectivity should impair it. Altogether, our results provide support to recent experimental findings that point toward a significant functional role of the organization of gap-junction couplings into proper astroglial networks. Dynamic control of this topology by neurons and signaling molecules could thus constitute a new type of regulation of neuron-glia and glia-glia interactions. PMID:24795613

Evolution of quality at the Organ Center of the Organ Procurement and Transplantation Network/United Network for Organ Sharing.

PubMed

Brown, Roger S; Belton, A Matthew; Martin, Judith M; Simmons, Dee Dee; Taylor, Gloria J; Willard, Ellie

2009-09-01

One of the goals of the Organ Center of the Organ Procurement and Transplantation Network/United Network for Organ Sharing is to increase the efficiency of equitable organ allocation in the United States. Recognizing the ever-growing need for organ donors and transplants, leaders at the Organ Center increased its commitment to quality improvement initiatives through the development of a quality management team in 2001. The Organ Center began to focus on ways to capture data on processes and pinpoint areas for improvement. As the collection and analysis of data evolved, the Organ Center embraced formal quality standards, such as improvement cycles. Using these cycles, the Organ Center has seen significant improvement. One initiative involving lifesaving heart, lung, and liver placement showed success by doubling the Organ Center's organ placement rate. Another project involving the validation of donor information demonstrated that the accuracy of organ allocation can be improved by 5% on a consistent basis. As stewards for the gift of life and leaders in organ allocation, the Organ Center uses continuous quality improvement to achieve the goal of increasing the efficiency of equitable organ allocation.

Flexible Organic Electronics for Use in Neural Sensing

PubMed Central

Bink, Hank; Lai, Yuming; Saudari, Sangameshwar R.; Helfer, Brian; Viventi, Jonathan; Van der Spiegel, Jan; Litt, Brian; Kagan, Cherie

2016-01-01

Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. High density, active electrode arrays hold great promise in enabling high-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface can enable thousands of multiplexed electrodes to be connected using many fewer wires. Active electrode arrays have been demonstrated using flexible, inorganic silicon transistors. However, these approaches may be limited in their ability to be cost-effectively scaled to large array sizes (8×8 cm). Here we show amplifiers built using flexible organic transistors with sufficient performance for neural signal recording. We also demonstrate a pathway for a fully integrated, amplified and multiplexed electrode array built from these devices. PMID:22255558

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

PubMed Central

Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing

2016-01-01

Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371

Self-organization of network dynamics into local quantized states.

PubMed

Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

2016-02-17

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of the Swift-Hohenberg continuum model-a minimal-ingredients model of nodal activation and interaction within a complex network-is able to produce a complex suite of localized patterns. Hence, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.

Sleep: A synchrony of cell activity-driven small network states

PubMed Central

Krueger, James M.; Huang, Yanhua; Rector, David M.; Buysse, Daniel J.

2013-01-01

We posit a bottom-up sleep regulatory paradigm in which state changes are initiated within small networks as a consequence of local cell activity. Bottom-up regulatory mechanisms are prevalent throughout nature, occurring in vastly different systems and levels of organization. Synchronization of state without top-down regulation is a fundamental property of large collections of small semi-autonomous entities. We posit that such synchronization mechanisms are sufficient and necessary for whole organism sleep onset. Within brain we posit that small networks of highly interconnected neurons and glia, e.g. cortical columns, are semi-autonomous units oscillating between sleep-like and wake-like states. We review evidence showing that cells, small networks, and regional areas of brain share sleep-like properties with whole animal sleep. A testable hypothesis focused on how sleep is initiated within local networks is presented. We posit that the release of cell activity-dependent molecules, such as ATP and nitric oxide, into the extracellular space initiates state changes within the local networks where they are produced. We review mechanisms of ATP induction of sleep regulatory substances (SRS) and their actions on receptor trafficking. Finally, we provide an example of how such local metabolic and state changes provide mechanistic explanations for clinical conditions such as insomnia. PMID:23651209

Self-organizing feature maps for dynamic control of radio resources in CDMA microcellular networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

1998-03-01

The application of artificial neural networks to the channel assignment problem for cellular code-division multiple access (CDMA) cellular networks has previously been investigated. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth-limited. Any reduction in interference in CDMA translates linearly into increased capacity. To satisfy the high demands for new services and improved connectivity for mobile communications, microcellular and picocellular systems are being introduced. For these systems, there is a need to develop robust and efficient management procedures for the allocation of power and spectrum to maximize radio capacity. Topology-conserving mappings play an important role in the biological processing of sensory inputs. The same principles underlying Kohonen's self-organizing feature maps (SOFMs) are applied to the adaptive control of radio resources to minimize interference, hence, maximize capacity in direct-sequence (DS) CDMA networks. The approach based on SOFMs is applied to some published examples of both theoretical and empirical models of DS/CDMA microcellular networks in metropolitan areas. The results of the approach for these examples are informally compared to the performance of algorithms, based on Hopfield- Tank neural networks and on genetic algorithms, for the channel assignment problem.

The Social Networks and Paradoxes of the Opt-out Movement Amid the Common Core State Standards Implementation: The Case of New York

ERIC Educational Resources Information Center

Wang, Yinying

2017-01-01

Opting out of state standardized tests has recently become a movement--a series of grassroots, organized efforts to refuse to take high-stakes state standardized tests. In particular, the opt-out rates in the state of New York reached 20% in 2015 and 21% in 2016. This study aims to illustrate the social networks and examine the paradoxes that have…

Data-driven integration of genome-scale regulatory and metabolic network models

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

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Data-driven integration of genome-scale regulatory and metabolic network models

DOE PAGES

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; ...

2015-05-05

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Patterns in PARTNERing across Public Health Collaboratives.

PubMed

Bevc, Christine A; Retrum, Jessica H; Varda, Danielle M

2015-10-05

Inter-organizational networks represent one of the most promising practice-based approaches in public health as a way to attain resources, share knowledge, and, in turn, improve population health outcomes. However, the interdependencies and effectiveness related to the structure, management, and costs of these networks represents a critical item to be addressed. The objective of this research is to identify and determine the extent to which potential partnering patterns influence the structure of collaborative networks. This study examines data collected by PARTNER, specifically public health networks (n = 162), to better understand the structured relationships and interactions among public health organizations and their partners, in relation to collaborative activities. Combined with descriptive analysis, we focus on the composition of public health collaboratives in a series of Exponential Random Graph (ERG) models to examine the partnerships between different organization types to identify the attribute-based effects promoting the formation of network ties within and across collaboratives. We found high variation within and between these collaboratives including composition, diversity, and interactions. The findings of this research suggest common and frequent types of partnerships, as well as opportunities to develop new collaborations. The result of this analysis offer additional evidence to inform and strengthen public health practice partnerships.

Feasibility of Using Distributed Wireless Mesh Networks for Medical Emergency Response

PubMed Central

Braunstein, Brian; Trimble, Troy; Mishra, Rajesh; Manoj, B. S.; Rao, Ramesh; Lenert, Leslie

2006-01-01

Achieving reliable, efficient data communications networks at a disaster site is a difficult task. Network paradigms, such as Wireless Mesh Network (WMN) architectures, form one exemplar for providing high-bandwidth, scalable data communication for medical emergency response activity. WMNs are created by self-organized wireless nodes that use multi-hop wireless relaying for data transfer. In this paper, we describe our experience using a mesh network architecture we developed for homeland security and medical emergency applications. We briefly discuss the architecture and present the traffic behavioral observations made by a client-server medical emergency application tested during a large-scale homeland security drill. We present our traffic measurements, describe lessons learned, and offer functional requirements (based on field testing) for practical 802.11 mesh medical emergency response networks. With certain caveats, the results suggest that 802.11 mesh networks are feasible and scalable systems for field communications in disaster settings. PMID:17238308

Insights into the fold organization of TIM barrel from interaction energy based structure networks.

PubMed

Vijayabaskar, M S; Vishveshwara, Saraswathi

2012-01-01

There are many well-known examples of proteins with low sequence similarity, adopting the same structural fold. This aspect of sequence-structure relationship has been extensively studied both experimentally and theoretically, however with limited success. Most of the studies consider remote homology or "sequence conservation" as the basis for their understanding. Recently "interaction energy" based network formalism (Protein Energy Networks (PENs)) was developed to understand the determinants of protein structures. In this paper we have used these PENs to investigate the common non-covalent interactions and their collective features which stabilize the TIM barrel fold. We have also developed a method of aligning PENs in order to understand the spatial conservation of interactions in the fold. We have identified key common interactions responsible for the conservation of the TIM fold, despite high sequence dissimilarity. For instance, the central beta barrel of the TIM fold is stabilized by long-range high energy electrostatic interactions and low-energy contiguous vdW interactions in certain families. The other interfaces like the helix-sheet or the helix-helix seem to be devoid of any high energy conserved interactions. Conserved interactions in the loop regions around the catalytic site of the TIM fold have also been identified, pointing out their significance in both structural and functional evolution. Based on these investigations, we have developed a novel network based phylogenetic analysis for remote homologues, which can perform better than sequence based phylogeny. Such an analysis is more meaningful from both structural and functional evolutionary perspective. We believe that the information obtained through the "interaction conservation" viewpoint and the subsequently developed method of structure network alignment, can shed new light in the fields of fold organization and de novo computational protein design.

Polarity-specific high-level information propagation in neural networks.

PubMed

Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals.

Polarity-specific high-level information propagation in neural networks

PubMed Central

Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals. PMID:24672472

Synthesis of ZnO nanowires for thin film network transistors

NASA Astrophysics Data System (ADS)

Dalal, S. H.; Unalan, H. E.; Zhang, Y.; Hiralal, Pritesh; Gangloff, L.; Flewitt, Andrew J.; Amaratunga, Gehan A. J.; Milne, William I.

2008-08-01

Zinc oxide nanowire networks are attractive as alternatives to organic and amorphous semiconductors due to their wide bandgap, flexibility and transparency. We demonstrate the fabrication of thin film transistors (TFT)s which utilize ZnO nanowires as the semiconducting channel. These thin film transistors can be transparent and flexible and processed at low temperatures on to a variety of substrates. The nanowire networks are created using a simple contact transfer method that is easily scalable. Apparent nanowire network mobility values can be as high as 3.8 cm2/Vs (effective thin film mobility: 0.03 cm2/Vs) in devices with 20μm channel lengths and ON/OFF ratios of up to 104.

Spatially-Interactive Biomolecular Networks Organized by Nucleic Acid Nanostructures

PubMed Central

Fu, Jinglin; Liu, Minghui; Liu, Yan; Yan, Hao

2013-01-01

Conspectus Living systems have evolved a variety of nanostructures to control the molecular interactions that mediate many functions including the recognition of targets by receptors, the binding of enzymes to substrates, and the regulation of enzymatic activity. Mimicking these structures outside of the cell requires methods that offer nanoscale control over the organization of individual network components. Advances in DNA nanotechnology have enabled the design and fabrication of sophisticated one-, two- and three-dimensional (1D, 2D and 3D) nanostructures that utilize spontaneous and sequence specific DNA hybridization. Compared to other self-assembling biopolymers, DNA nanostructures offer predictable and programmable interactions, and surface features to which other nanoparticles and bio-molecules can be precisely positioned. The ability to control the spatial arrangement of the components while constructing highly-organized networks will lead to various applications of these systems. For example, DNA nanoarrays with surface displays of molecular probes can sense noncovalent hybridization interactions with DNA, RNA, and proteins and covalent chemical reactions. DNA nanostructures can also align external molecules into well-defined arrays, which may improve the resolution of many structural determination methods, such as X-ray diffraction, cryo-EM, NMR, and super-resolution fluorescence. Moreover, by constraining target entities to specific conformations, self-assembled DNA nanostructures can serve as molecular rulers to evaluate conformation-dependent activities. This Account describes the most recent advances in the DNA nanostructure directed assembly of biomolecular networks and explores the possibility of applying this technology to other fields of study. Recently, several reports have demonstrated the DNA nanostructure directed assembly of spatially-interactive biomolecular networks. For example, researchers have constructed synthetic multi-enzyme cascades by organizing the position of the components using DNA nanoscaffolds in vitro, or by utilizing RNA matrices in vivo. These structures display enhanced efficiency compared to the corresponding unstructured enzyme mixtures. Such systems are designed to mimic cellular function, where substrate diffusion between enzymes is facilitated and reactions are catalyzed with high efficiency and specificity. In addition, researchers have assembled multiple choromophores into arrays using a DNA nanoscaffold that optimizes the relative distance between the dyes and their spatial organization. The resulting artificial light harvesting system exhibits efficient cascading energy transfers. Finally, DNA nanostructures have been used as assembly templates to construct nanodevices that execute rationally-designed behaviors, including cargo loading, transportation and route control. PMID:22642503

Experimental evolution of protein–protein interaction networks

PubMed Central

Kaçar, Betül; Gaucher, Eric A.

2013-01-01

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

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.

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

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

International migration network: Topology and modeling

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

International migration network: topology and modeling.

PubMed

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

Social Networks and Adaptation to Environmental Change: The Case of Central Oregon's Fire-Prone Forest Landscape

NASA Astrophysics Data System (ADS)

Fischer, A.

2012-12-01

Social networks are the patterned interactions among individuals and organizations through which people refine their beliefs and values, negotiate meanings for things and develop behavioral intentions. The structure of social networks has bearing on how people communicate information, generate and retain knowledge, make decisions and act collectively. Thus, social network structure is important for how people perceive, shape and adapt to the environment. We investigated the relationship between social network structure and human adaptation to wildfire risk in the fire-prone forested landscape of Central Oregon. We conducted descriptive and non-parametric social network analysis on data gathered through interviews to 1) characterize the structure of the network of organizations involved in forest and wildfire issues and 2) determine whether network structure is associated with organizations' beliefs, values and behaviors regarding fire and forest management. Preliminary findings indicate that fire protection and forest-related organizations do not frequently communicate or cooperate, suggesting that opportunities for joint problem-solving, innovation and collective action are limited. Preliminary findings also suggest that organizations with diverse partners are more likely to hold adaptive beliefs about wildfire and work cooperatively. We discuss the implications of social network structure for adaptation to changing environmental conditions such as wildfire risk.

Physics textbooks from the viewpoint of network structures

NASA Astrophysics Data System (ADS)

Králiková, Petra; Teleki, Aba

2017-01-01

We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

Self-Organizing Maps and Parton Distribution Functions

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

K. Holcomb, Simonetta Liuti, D. Z. Perry

2011-05-01

We present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly useful for an anaysis directed at extracting generalized parton distributions from data. We show quantitative results of our initial analysis of the parton distribution functions from inclusive deep inelastic scattering.

"Master-Slave" Biological Network Alignment

NASA Astrophysics Data System (ADS)

Ferraro, Nicola; Palopoli, Luigi; Panni, Simona; Rombo, Simona E.

Performing global alignment between protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform this task operate symmetrically, that is to say, they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how well the corresponding organism is biologically well-characterized. For well-characterized organisms the associated PPI network supposedly encode in a sound manner all the information about their proteins and associated interactions, which is far from being the case for not well characterized ones. Here the new idea is developed to devise a method for global alignment of PPI networks that in fact exploit differences in the characterization of organisms at hand. We assume that the PPI network (called Master) of the best characterized is used as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master (and using the Slave) network. We tested our method showing that the results it returns are biologically relevant.

The neural network classification of false killer whale (Pseudorca crassidens) vocalizations.

PubMed

Murray, S O; Mercado, E; Roitblat, H L

1998-12-01

This study reports the use of unsupervised, self-organizing neural network to categorize the repertoire of false killer whale vocalizations. Self-organizing networks are capable of detecting patterns in their input and partitioning those patterns into categories without requiring that the number or types of categories be predefined. The inputs for the neural networks were two-dimensional characterization of false killer whale vocalization, where each vocalization was characterized by a sequence of short-time measurements of duty cycle and peak frequency. The first neural network used competitive learning, where units in a competitive layer distributed themselves to recognize frequently presented input vectors. This network resulted in classes representing typical patterns in the vocalizations. The second network was a Kohonen feature map which organized the outputs topologically, providing a graphical organization of pattern relationships. The networks performed well as measured by (1) the average correlation between the input vectors and the weight vectors for each category, and (2) the ability of the networks to classify novel vocalizations. The techniques used in this study could easily be applied to other species and facilitate the development of objective, comprehensive repertoire models.

Auditing Albaha University Network Security using in-house Developed Penetration Tool

NASA Astrophysics Data System (ADS)

Alzahrani, M. E.

2018-03-01

Network security becomes very important aspect in any enterprise/organization computer network. If important information of the organization can be accessed by anyone it may be used against the organization for further own interest. Thus, network security comes into it roles. One of important aspect of security management is security audit. Security performance of Albaha university network is relatively low (in term of the total controls outlined in the ISO 27002 security control framework). This paper proposes network security audit tool to address issues in Albaha University network. The proposed penetration tool uses Nessus and Metasploit tool to find out the vulnerability of a site. A regular self-audit using inhouse developed tool will increase the overall security and performance of Albaha university network. Important results of the penetration test are discussed.

Trophic level, successional age and trait matching determine specialization of deadwood-based interaction networks of saproxylic beetles

PubMed Central

Gossner, Martin M.; Grass, Ingo; Arnstadt, Tobias; Hofrichter, Martin; Floren, Andreas; Linsenmair, Karl Eduard; Weisser, Wolfgang W.; Steffan-Dewenter, Ingolf

2017-01-01

The specialization of ecological networks provides important insights into possible consequences of biodiversity loss for ecosystem functioning. However, mostly mutualistic and antagonistic interactions of living organisms have been studied, whereas detritivore networks and their successional changes are largely unexplored. We studied the interactions of saproxylic (deadwood-dependent) beetles with their dead host trees. In a large-scale experiment, 764 logs of 13 tree species were exposed to analyse network structure of three trophic groups of saproxylic beetles over 3 successional years. We found remarkably high specialization of deadwood-feeding xylophages and lower specialization of fungivorous and predatory species. During deadwood succession, community composition, network specialization and network robustness changed differently for the functional groups. To reveal potential drivers of network specialization, we linked species' functional traits to their network roles, and tested for trait matching between plant (i.e. chemical compounds) and beetle (i.e. body size) traits. We found that both plant and animal traits are major drivers of species specialization, and that trait matching can be more important in explaining interactions than neutral processes reflecting species abundance distributions. High network specialization in the early successional stage and decreasing network robustness during succession indicate vulnerability of detritivore networks to reduced tree species diversity and beetle extinctions, with unknown consequences for wood decomposition and nutrient cycling. PMID:28469020

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.

The Contribution of Network Organization and Integration to the Development of Cognitive Control

PubMed Central

Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N.; Luna, Beatriz

2015-01-01

Abstract Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10–26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control. PMID:26713863

The Contribution of Network Organization and Integration to the Development of Cognitive Control.

PubMed

Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N; Luna, Beatriz

2015-12-01

Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

Ground-water-quality assessment of the Central Oklahoma aquifer, Oklahoma; hydrologic, water-quality, and quality-assurance data 1987-90

USGS Publications Warehouse

Ferree, D.M.; Christenson, S.C.; Rea, A.H.; Mesander, B.A.

1992-01-01

This report presents data collected from 202 wells between June 1987 and September 1990 as part of the Central Oklahoma aquifer pilot study of the National Water-Quality Assessment Program. The report describes the sampling networks, the sampling procedures, and the results of the ground-water quality and quality-assurance sample analyses. The data tables consist of information about the wells sampled and the results of the chemical analyses of ground water and quality-assurance sampling. Chemical analyses of ground-water samples in four sampling networks are presented: A geochemical network, a low-density survey bedrock network, a low-density survey alluvium and terrace deposits network, and a targeted urban network. The analyses generally included physical properties, major ions, nutrients, trace substances, radionuclides, and organic constituents. The chemical analyses of the ground-water samples are presented in five tables: (1) Physical properties and concentrations of major ions, nutrients, and trace substances; (2) concentrations of radionuclides and radioactivities; (3) carbon isotope ratios and delta values (d-values) of selected isotopes; (4) concentrations of organic constituents; and (5) organic constituents not reported in ground-water samples. The quality of the ground water sampled varied substantially. The sum of constituents (dissolved solids) concentrations ranged from 71 to 5,610 milligrams per liter, with 38 percent of the wells sampled exceeding the Secondary Maximum Contaminant Level of 500 milligrams per liter established under the Safe Drinking Water Act. Values of pH ranged from 5.7 to 9.2 units with 20 percent of the wells outside the Secondary Maximum Contaminant Level of 6.5 to 8.5 units. Nitrite plus nitrate concentrations ranged from less than 0.1 to 85 milligrams per liter with 8 percent of the wells exceeding the proposed Maximum Contaminant Level of 10 milligrams per liter. Concentrations of trace substances were highly variable, ranging from below the reporting level to concentrations over the Maximum Contaminant Levels for several constituents (arsenic, barium, cadmium, chromium, lead, and selenium). Radionuclide activities also were highly variable. Gross alpha radioactivity ranged from 0.1 to 210 picocuries per liter as 230thorium. Of the wells sampled, 20 percent exceeded the proposed Maximum Contaminant Level of 15 picocuries per liter for gross alpha radioactivity. Organic constituents were detected in 39 percent of the 170 wells sampled for organic constituents; in most cases concentrations were at or near the laboratory minimum reporting levels. Ten of the wells sampled for organic constituents had one or more constituents (chlordane, dieldrin, heptachlor epoxide, trichloroethylene, 1,1-dichloroethylene, 1,1,1-trichloroethane) at concentrations equal to or greater than the Maximum Contaminant Level or acceptable concentrations as suggested in the Environmental Protection Agency's Health Advisory Summaries. Quality-assurance sampling included duplicate samples, repeated samples, blanks, spikes, and blind samples. These samples proved to be essential in evaluating the accuracy of the data, particularly in the case of volatile organic constituents.

Ethnic diversity and value sharing: A longitudinal social network perspective on interactive group processes.

PubMed

Meeussen, Loes; Agneessens, Filip; Delvaux, Ellen; Phalet, Karen

2018-04-01

People often collaborate in groups that are increasingly diverse. As research predominantly investigated effects of diversity, the processes behind these effects remain understudied. We follow recent research that shows creating shared values is important for group functioning but seems hindered in high diversity groups - and use longitudinal social network analyses to study two interpersonal processes behind value sharing: creating relations between members or 'social bonding' (network tie formation and homophily) and sharing values - potentially through these relationships - or 'social norming' (network convergence and influence). We investigate these processes in small interactive groups with low and high ethnic diversity as they collaborate over time. In both low and high diversity groups, members showed social bonding and this creation of relations between members was not organized along ethnic lines. Low diversity groups also showed social norming: Members adjusted their relational values to others they liked and achievement values converged regardless of liking. In high diversity groups, however, there was no evidence for social norming. Thus, ethnic diversity seems to especially affect processes of social norming in groups, suggesting that targeted interventions should focus on facilitating social norming to stimulate value sharing in high diversity groups. © 2018 The British Psychological Society.

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

Network analysis of Bogotá’s Ciclovía Recreativa, a self-organized multisectoral community program to promote physical activity in a middle-income country

PubMed Central

Meisel, Jose D; Sarmiento, Olga; Montes, Felipe; Martinez, Edwin O.; Lemoine, Pablo D; Valdivia, Juan A; Brownson, RC; Zarama, Robert

2016-01-01

Purpose Conduct a social network analysis of the health and non-health related organizations that participate in the Bogotá’s Ciclovía Recreativa (Ciclovía). Design Cross sectional study. Setting Ciclovía is a multisectoral community-based mass program in which streets are temporarily closed to motorized transport, allowing exclusive access to individuals for leisure activities and PA. Subjects 25 organizations that participate in the Ciclovía. Measures Seven variables were examined using network analytic methods: relationship, link attributes (integration, contact, and importance), and node attributes (leadership, years in the program, and the sector of the organization). Analysis The network analytic methods were based on a visual descriptive analysis and an exponential random graph model. Results Analysis shows that the most central organizations in the network were outside of the health sector and includes Sports and Recreation, Government, and Security sectors. The organizations work in clusters formed by organizations of different sectors. Organization importance and structural predictors were positively related to integration, while the number of years working with Ciclovía was negatively associated with integration. Conclusion Ciclovía is a network whose structure emerged as a self-organized complex system. Ciclovía of Bogotá is an example of a program with public health potential formed by organizations of multiple sectors with Sports and Recreation as the most central. PMID:23971523

Executive Leadership in School Improvement Networks: A Conceptual Framework and Agenda for Research

ERIC Educational Resources Information Center

Peurach, Donald J.; Gumus, Emine

2011-01-01

The purpose of this analysis is to improve understanding of executive leadership in school improvement networks: for example, networks supported by comprehensive school reform providers, charter management organizations, and education management organizations. In this analysis, we review the literature on networks and executive leadership. We draw…

GENASIS national and international monitoring networks for persistent organic pollutants

NASA Astrophysics Data System (ADS)

Brabec, Karel; Dušek, Ladislav; Holoubek, Ivan; Hřebíček, Jiří; Kubásek, Miroslav; Urbánek, Jaroslav

2010-05-01

Persistent organic pollutants (POPs) remain in the centre of scientific attention due to their slow rates of degradation, their toxicity, and potential for both long-range transport and bioaccumulation in living organisms. This group of compounds covers large number of various chemicals from industrial products, such as polychlorinated biphenyls, etc. The GENASIS (Global Environmental Assessment and Information System) information system utilizes data from national and international monitoring networks to obtain as-complete-as-possible set of information and a representative picture of environmental contamination by persistent organic pollutants (POPs). There are data from two main datasets on POPs monitoring: 1.Integrated monitoring of POPs in Košetice Observatory (Czech Republic) which is a long term background site of the European Monitoring and Evaluation Programme (EMEP) for the Central Europe; the data reveals long term trends of POPs in all environmental matrices. The Observatory is the only one in Europe where POPs have been monitored not only in ambient air, but also in wet atmospheric deposition, surface waters, sediments, soil, mosses and needles (integrated monitoring). Consistent data since the year 1996 are available, earlier data (up to 1998) are burdened by high variability and high detection limits. 2.MONET network is ambient air monitoring activities in the Central and Eastern European region (CEEC), Central Asia, Africa and Pacific Islands driven by RECETOX as the Regional Centre of the Stockholm Convention for the region of Central and Eastern Europe under the common name of the MONET networks (MONitoring NETwork). For many of the participating countries these activities generated first data on the atmospheric levels of POPs. The MONET network uses new technologies of air passive sampling, which was developed, tested, and calibrated by RECETOX in cooperation with Environment Canada and Lancaster University, and was originally launched as a model monitoring network providing public administration, private subject, and general public information about air pollution by POPs that had not been previously regularly monitored and whose measurement is further required by global monitoring plan of the Stockholm Convention. The MONET network is international project with many participants. Monitoring in the MONET-CZ network started in 2004 with the pilot project and continues to the current days, MONET CEEC started in 2006 and continues nowadays, MONET Africa started in 2008. The database of the GENASIS systems currently covers MONET-CZ data until the year 2008. The MONET network currently covers 37 countries in the Europe, Asia and Africa with more than 350 sampling sites. The paper will discuss about following topics * Data Fusion in GENASIS: how can GENASIS maximize the value and accuracy of the information gathered from heterogeneous data sources? * Sensor types in GENASIS: which POPs can be measured; what are the physical limitations to achievable accuracy, reliability, and long-term stability of miniaturized sensors; which applications can (not) be realized within these limitations?

How to Identify Success Among Networks That Promote Active Living

PubMed Central

Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-01-01

Objectives. We evaluated organization- and network-level factors that influence organizations’ perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. Methods. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. Results. A total of 53 of 59 “whole networks” met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%–100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Conclusions. Organizations’ perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities. PMID:26378863

Function approximation using combined unsupervised and supervised learning.

PubMed

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Adding the 'heart' to hanging drop networks for microphysiological multi-tissue experiments.

PubMed

Rismani Yazdi, Saeed; Shadmani, Amir; Bürgel, Sebastian C; Misun, Patrick M; Hierlemann, Andreas; Frey, Olivier

2015-11-07

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid-air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip.

Networking Omic Data to Envisage Systems Biological Regulation.

PubMed

Kalapanulak, Saowalak; Saithong, Treenut; Thammarongtham, Chinae

To understand how biological processes work, it is necessary to explore the systematic regulation governing the behaviour of the processes. Not only driving the normal behavior of organisms, the systematic regulation evidently underlies the temporal responses to surrounding environments (dynamics) and long-term phenotypic adaptation (evolution). The systematic regulation is, in effect, formulated from the regulatory components which collaboratively work together as a network. In the drive to decipher such a code of lives, a spectrum of technologies has continuously been developed in the post-genomic era. With current advances, high-throughput sequencing technologies are tremendously powerful for facilitating genomics and systems biology studies in the attempt to understand system regulation inside the cells. The ability to explore relevant regulatory components which infer transcriptional and signaling regulation, driving core cellular processes, is thus enhanced. This chapter reviews high-throughput sequencing technologies, including second and third generation sequencing technologies, which support the investigation of genomics and transcriptomics data. Utilization of this high-throughput data to form the virtual network of systems regulation is explained, particularly transcriptional regulatory networks. Analysis of the resulting regulatory networks could lead to an understanding of cellular systems regulation at the mechanistic and dynamics levels. The great contribution of the biological networking approach to envisage systems regulation is finally demonstrated by a broad range of examples.

Long-term reorganization of structural brain networks in a rabbit model of intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Arbat-Plana, Ariadna; Illa, Miriam; Figueras, Francesc; Eixarch, Elisenda; Gratacos, Eduard

2014-10-15

Characterization of brain changes produced by intrauterine growth restriction (IUGR) is among the main challenges of modern fetal medicine and pediatrics. This condition affects 5-10% of all pregnancies and is associated with a wide range of neurodevelopmental disorders. Better understanding of the brain reorganization produced by IUGR opens a window of opportunity to find potential imaging biomarkers in order to identify the infants with a high risk of having neurodevelopmental problems and apply therapies to improve their outcomes. Structural brain networks obtained from diffusion magnetic resonance imaging (MRI) is a promising tool to study brain reorganization and to be used as a biomarker of neurodevelopmental alterations. In the present study this technique is applied to a rabbit animal model of IUGR, which presents some advantages including a controlled environment and the possibility to obtain high quality MRI with long acquisition times. Using a Q-Ball diffusion model, and a previously published rabbit brain MRI atlas, structural brain networks of 15 IUGR and 14 control rabbits at 70 days of age (equivalent to pre-adolescence human age) were obtained. The analysis of graph theory features showed a decreased network infrastructure (degree and binary global efficiency) associated with IUGR condition and a set of generalized fractional anisotropy (GFA) weighted measures associated with abnormal neurobehavior. Interestingly, when assessing the brain network organization independently of network infrastructure by means of normalized networks, IUGR showed increased global and local efficiencies. We hypothesize that this effect could reflect a compensatory response to reduced infrastructure in IUGR. These results present new evidence on the long-term persistence of the brain reorganization produced by IUGR that could underlie behavioral and developmental alterations previously described. The described changes in network organization have the potential to be used as biomarkers to monitor brain changes produced by experimental therapies in IUGR animal model. Copyright © 2014 Elsevier Inc. All rights reserved.

Space-based Science Operations Grid Prototype

NASA Technical Reports Server (NTRS)

Bradford, Robert N.; Welch, Clara L.; Redman, Sandra

2004-01-01

Grid technology is the up and coming technology that is enabling widely disparate services to be offered to users that is very economical, easy to use and not available on a wide basis. Under the Grid concept disparate organizations generally defined as "virtual organizations" can share services i.e. sharing discipline specific computer applications, required to accomplish the specific scientific and engineering organizational goals and objectives. Grids are emerging as the new technology of the future. Grid technology has been enabled by the evolution of increasingly high speed networking. Without the evolution of high speed networking Grid technology would not have emerged. NASA/Marshall Space Flight Center's (MSFC) Flight Projects Directorate, Ground Systems Department is developing a Space-based Science Operations Grid prototype to provide to scientists and engineers the tools necessary to operate space-based science payloads/experiments and for scientists to conduct public and educational outreach. In addition Grid technology can provide new services not currently available to users. These services include mission voice and video, application sharing, telemetry management and display, payload and experiment commanding, data mining, high order data processing, discipline specific application sharing and data storage, all from a single grid portal. The Prototype will provide most of these services in a first step demonstration of integrated Grid and space-based science operations technologies. It will initially be based on the International Space Station science operational services located at the Payload Operations Integration Center at MSFC, but can be applied to many NASA projects including free flying satellites and future projects. The Prototype will use the Internet2 Abilene Research and Education Network that is currently a 10 Gb backbone network to reach the University of Alabama at Huntsville and several other, as yet unidentified, Space Station based science experimenters. There is an international aspect to the Grid involving the America's Pathway (AMPath) network, the Chilean REUNA Research and Education Network and the University of Chile in Santiago that will further demonstrate how extensive these services can be used. From the user's perspective, the Prototype will provide a single interface and logon to these varied services without the complexity of knowing the where's and how's of each service. There is a separate and deliberate emphasis on security. Security will be addressed by specifically outlining the different approaches and tools used. Grid technology, unlike the Internet, is being designed with security in mind. In addition we will show the locations, configurations and network paths associated with each service and virtual organization. We will discuss the separate virtual organizations that we define for the varied user communities. These will include certain, as yet undetermined, space-based science functions and/or processes and will include specific virtual organizations required for public and educational outreach and science and engineering collaboration. We will also discuss the Grid Prototype performance and the potential for further Grid applications both space-based and ground based projects and processes. In this paper and presentation we will detail each service and how they are integrated using Grid

Modeling self-organization of novel organic materials

NASA Astrophysics Data System (ADS)

Sayar, Mehmet

In this thesis, the structural organization of oligomeric multi-block molecules is analyzed by computational analysis of coarse-grained models. These molecules form nanostructures with different dimensionalities, and the nanostructured nature of these materials leads to novel structural properties at different length scales. Previously, a number of oligomeric triblock rodcoil molecules have been shown to self-organize into mushroom shaped noncentrosymmetric nanostructures. Interestingly, thin films of these molecules contain polar domains and a finite macroscopic polarization. However, the fully polarized state is not the equilibrium state. In the first chapter, by solving a model with dipolar and Ising-like short range interactions, we show that polar domains are stable in films composed of aggregates as opposed to isolated molecules. Unlike classical molecular systems, these nanoaggregates have large intralayer spacings (a ≈ 6 nm), leading to a reduction in the repulsive dipolar interactions that oppose polar order within layers. This enables the formation of a striped pattern with polar domains of alternating directions. The energies of the possible structures at zero temperature are computed exactly and results of Monte Carlo simulations are provided at non-zero temperatures. In the second chapter, the macroscopic polarization of such nanostructured films is analyzed in the presence of a short range surface interaction. The surface interaction leads to a periodic domain structure where the balance between the up and down domains is broken, and therefore films of finite thickness have a net macroscopic polarization. The polarization per unit volume is a function of film thickness and strength of the surface interaction. Finally, in chapter three, self-organization of organic molecules into a network of one dimensional objects is analyzed. Multi-block organic dendron rodcoil molecules were found to self-organize into supramolecular nanoribbons (threads) and form gels at very low concentrations. Here, the formation and structural properties of these networks are studied with Monte Carlo simulations. The model gelators can form intra and inter-thread bonds, and the threads have a finite stiffness. The results suggest that the high persistence length is a result of the interplay of thread stiffness and inter-thread interactions. Furthermore, this high persistence length enables the formation of networks at low concentrations.

Toward Developmental Connectomics of the Human Brain

PubMed Central

Cao, Miao; Huang, Hao; Peng, Yun; Dong, Qi; He, Yong

2016-01-01

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders. PMID:27064378

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

PubMed

Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

2014-01-27

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W(k)(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).

A network perspective on the processes of empowered organizations.

PubMed

Neal, Zachary P

2014-06-01

Organizational empowerment is a multi-faceted concept that involves processes occurring both within and between organizations that facilitate achievement of their goals. This paper takes a closer look at three interorganizational processes that lead to empowered organizations: building alliances, getting the word out, and capturing others' attention. These processes are located within the broader nomological network of empowerment and organizational empowerment, and are linked to particular patterns of interorganizational relationships that facilitate organizations' ability to engage in them. A new network-based measure, γ-centrality, is introduced to capture the particular network structure associated with each process to be assessed. It is demonstrated first in a hypothetical organizational network, then applied to take a closer look at organizational empowerment in the context of a coordinating council composed of human service agencies. The paper concludes with a discussion of the implications of relationships between these processes, and the potential for unintended consequences in the empowerment of organizations.

Creativity and the default network: A functional connectivity analysis of the creative brain at rest.

PubMed

Beaty, Roger E; Benedek, Mathias; Wilkins, Robin W; Jauk, Emanuel; Fink, Andreas; Silvia, Paul J; Hodges, Donald A; Koschutnig, Karl; Neubauer, Aljoscha C

2014-11-01

The present research used resting-state functional magnetic resonance imaging (fMRI) to examine whether the ability to generate creative ideas corresponds to differences in the intrinsic organization of functional networks in the brain. We examined the functional connectivity between regions commonly implicated in neuroimaging studies of divergent thinking, including the inferior prefrontal cortex and the core hubs of the default network. Participants were prescreened on a battery of divergent thinking tests and assigned to high- and low-creative groups based on task performance. Seed-based functional connectivity analysis revealed greater connectivity between the left inferior frontal gyrus (IFG) and the entire default mode network in the high-creative group. The right IFG also showed greater functional connectivity with bilateral inferior parietal cortex and the left dorsolateral prefrontal cortex in the high-creative group. The results suggest that the ability to generate creative ideas is characterized by increased functional connectivity between the inferior prefrontal cortex and the default network, pointing to a greater cooperation between brain regions associated with cognitive control and low-level imaginative processes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Histological and three dimensional organizations of lymphoid tubules in normal lymphoid organ of Penaeus monodon.

PubMed

Duangsuwan, Pornsawan; Phoungpetchara, Ittipon; Tinikul, Yotsawan; Poljaroen, Jaruwan; Wanichanon, Chaitip; Sobhon, Prasert

2008-04-01

The normal lymphoid organ of Penaeus monodon (which tested negative for WSSV and YHV) was composed of two parts: lymphoid tubules and interstitial spaces, which were permeated with haemal sinuses filled with large numbers of haemocytes. There were three permanent types of cells present in the wall of lymphoid tubules: endothelial, stromal and capsular cells. Haemocytes penetrated the endothelium of the lymphoid tubule's wall to reside among the fixed cells. The outermost layer of the lymphoid tubule was covered by a network of fibers embedded in a PAS-positive extracellular matrix, which corresponded to a basket-like network that covered all the lymphoid tubules as visualized by a scanning electron microscope (SEM). Argyrophilic reticular fibers surrounded haemal sinuses and lymphoid tubules. Together they formed the scaffold that supported the lymphoid tubule. Using vascular cast and SEM, the three dimensional structure of the subgastric artery that supplies each lobe of the lymphoid organ was reconstructed. This artery branched into highly convoluted and blind-ending terminal capillaries, each forming the lumen of a lymphoid tubule around which haemocytes and other cells aggregated to form a cuff-like wall. Stromal cells which form part of the tubular scaffold were immunostained for vimentin. Examination of the whole-mounted lymphoid organ, immunostained for vimentin, by confocal microscopy exhibited the highly branching and convoluted lymphoid tubules matching the pattern of the vascular cast observed in SEM.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

PubMed

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

Collective dynamics of 'small-world' networks.

PubMed

Watts, D J; Strogatz, S H

1998-06-04

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

Self-organization of network dynamics into local quantized states

DOE PAGES

Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

2016-02-17

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of themore » Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Thus, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.« less

Self-organization of network dynamics into local quantized states

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

Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of themore » Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Thus, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.« less

Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning

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

Erokhina, Svetlana; Sorokin, Vladimir; Erokhin, Victor, E-mail: victor.erokhin@fis.unipr.it

Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers.

Assessing and comparing relationships between urban environmental stewardship networks and land cover in Baltimore and Seattle

Treesearch

Michele Romolini; J. Morgan Grove; Dexter H. Locke

2013-01-01

Implementation of urban sustainability policies often requires collaborations between organizations across sectors. Indeed, it is commonly agreed that governance by environmental networks is preferred to individual organizations acting alone. Yet research shows that network structures vary widely, and that these variations can impact network effectiveness. However,...

Intrinsic and task-evoked network architectures of the human brain

PubMed Central

Cole, Michael W.; Bassett, Danielle S.; Power, Jonathan D.; Braver, Todd S.; Petersen, Steven E.

2014-01-01

Summary Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic”, standard architecture of functional brain organization. Further, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity – areas of neuroscientific inquiry typically considered separately. PMID:24991964

The Strength of the Strongest Ties in Collaborative Problem Solving

NASA Astrophysics Data System (ADS)

de Montjoye, Yves-Alexandre; Stopczynski, Arkadiusz; Shmueli, Erez; Pentland, Alex; Lehmann, Sune

2014-06-01

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.

The strength of the strongest ties in collaborative problem solving.

PubMed

de Montjoye, Yves-Alexandre; Stopczynski, Arkadiusz; Shmueli, Erez; Pentland, Alex; Lehmann, Sune

2014-06-20

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.

Internet firewalls: questions and answers

NASA Astrophysics Data System (ADS)

Ker, Keith

1996-03-01

As organizations consider connecting to the Internet, the issue of internetwork security becomes more important. There are many tools and components that can be used to secure a network, one of which is a firewall. Modern firewalls offer highly flexible private network security by controlling and monitoring all communications passing into or out of the private network. Specifically designed for security, firewalls become the private network's single point of attack from Internet intruders. Application gateways (or proxies) that have been written to be secure against even the most persistent attacks ensure that only authorized users and services access the private network. One-time passwords prevent intruders from `sniffing' and replaying the usernames and passwords of authorized users to gain access to the private network. Comprehensive logging permits constant and uniform system monitoring. `Address spoofing' attacks are prevented. The private network may use registered or unregistered IP addresses behind the firewall. Firewall-to-firewall encryption establishes a `virtual private network' across the Internet, preventing intruders from eavesdropping on private communications, eliminating the need for costly dedicated lines.

Introduction to Social Network Analysis

NASA Astrophysics Data System (ADS)

Zaphiris, Panayiotis; Ang, Chee Siang

Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

How Can Community Organizations Support Urban Transformative Teacher Leadership? Lessons from Three Successful Alliances

ERIC Educational Resources Information Center

Baker-Doyle, Kira J.

2017-01-01

Teachers who organize for educational equity and social justice generally do so through teacher-led professional networks. Community organizations (COs) that seek to support such teacher leaders can face challenges in working with their organic and often horizontally organized networks. This article examines three case studies of COs that…

Evolving gene regulation networks into cellular networks guiding adaptive behavior: an outline how single cells could have evolved into a centralized neurosensory system

PubMed Central

Fritzsch, Bernd; Jahan, Israt; Pan, Ning; Elliott, Karen L.

2014-01-01

Understanding the evolution of the neurosensory system of man, able to reflect on its own origin, is one of the major goals of comparative neurobiology. Details of the origin of neurosensory cells, their aggregation into central nervous systems and associated sensory organs, their localized patterning into remarkably different cell types aggregated into variably sized parts of the central nervous system begin to emerge. Insights at the cellular and molecular level begin to shed some light on the evolution of neurosensory cells, partially covered in this review. Molecular evidence suggests that high mobility group (HMG) proteins of pre-metazoans evolved into the definitive Sox [SRY (sex determining region Y)-box] genes used for neurosensory precursor specification in metazoans. Likewise, pre-metazoan basic helix-loop-helix (bHLH) genes evolved in metazoans into the group A bHLH genes dedicated to neurosensory differentiation in bilaterians. Available evidence suggests that the Sox and bHLH genes evolved a cross-regulatory network able to synchronize expansion of precursor populations and their subsequent differentiation into novel parts of the brain or sensory organs. Molecular evidence suggests metazoans evolved patterning gene networks early and not dedicated to neuronal development. Only later in evolution were these patterning gene networks tied into the increasing complexity of diffusible factors, many of which were already present in pre-metazoans, to drive local patterning events. It appears that the evolving molecular basis of neurosensory cell development may have led, in interaction with differentially expressed patterning genes, to local network modifications guiding unique specializations of neurosensory cells into sensory organs and various areas of the central nervous system. PMID:25416504

Evolving gene regulatory networks into cellular networks guiding adaptive behavior: an outline how single cells could have evolved into a centralized neurosensory system.

PubMed

Fritzsch, Bernd; Jahan, Israt; Pan, Ning; Elliott, Karen L

2015-01-01

Understanding the evolution of the neurosensory system of man, able to reflect on its own origin, is one of the major goals of comparative neurobiology. Details of the origin of neurosensory cells, their aggregation into central nervous systems and associated sensory organs and their localized patterning leading to remarkably different cell types aggregated into variably sized parts of the central nervous system have begun to emerge. Insights at the cellular and molecular level have begun to shed some light on the evolution of neurosensory cells, partially covered in this review. Molecular evidence suggests that high mobility group (HMG) proteins of pre-metazoans evolved into the definitive Sox [SRY (sex determining region Y)-box] genes used for neurosensory precursor specification in metazoans. Likewise, pre-metazoan basic helix-loop-helix (bHLH) genes evolved in metazoans into the group A bHLH genes dedicated to neurosensory differentiation in bilaterians. Available evidence suggests that the Sox and bHLH genes evolved a cross-regulatory network able to synchronize expansion of precursor populations and their subsequent differentiation into novel parts of the brain or sensory organs. Molecular evidence suggests metazoans evolved patterning gene networks early, which were not dedicated to neuronal development. Only later in evolution were these patterning gene networks tied into the increasing complexity of diffusible factors, many of which were already present in pre-metazoans, to drive local patterning events. It appears that the evolving molecular basis of neurosensory cell development may have led, in interaction with differentially expressed patterning genes, to local network modifications guiding unique specializations of neurosensory cells into sensory organs and various areas of the central nervous system.

Expansion of Surveillance for Vaccine-preventable Diseases: Building on the Global Polio Laboratory Network and the Global Measles and Rubella Laboratory Network Platforms.

PubMed

Mulders, Mick N; Serhan, Fatima; Goodson, James L; Icenogle, Joseph; Johnson, Barbara W; Rota, Paul A

2017-07-01

Laboratory networks were established to provide accurate and timely laboratory confirmation of infections, an essential component of disease surveillance systems. The World Health Organization (WHO) coordinates global laboratory surveillance of vaccine-preventable diseases (VPDs), including polio, measles and rubella, yellow fever, Japanese encephalitis, rotavirus, and invasive bacterial diseases. In addition to providing high-quality laboratory surveillance data to help guide disease control, elimination, and eradication programs, these global networks provide capacity-building and an infrastructure for public health laboratories. There are major challenges with sustaining and expanding the global laboratory surveillance capacity: limited resources and the need for expansion to meet programmatic goals. Here, we describe the WHO-coordinated laboratory networks supporting VPD surveillance and present a plan for the further development of these networks. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

Advanced Scientific Computing Research Network Requirements: ASCR Network Requirements Review Final Report

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

Bacon, Charles; Bell, Greg; Canon, Shane

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In October 2012, ESnet and the Office of Advanced Scientific Computing Research (ASCR) of the DOE SCmore » organized a review to characterize the networking requirements of the programs funded by the ASCR program office. The requirements identified at the review are summarized in the Findings section, and are described in more detail in the body of the report.« less

Seeing Eye to Eye: Predicting Teacher-Student Agreement on Classroom Social Networks

PubMed Central

Neal, Jennifer Watling; Cappella, Elise; Wagner, Caroline; Atkins, Marc S.

2010-01-01

This study examines the association between classroom characteristics and teacher-student agreement in perceptions of students’ classroom peer networks. Social network, peer nomination, and observational data were collected from a sample of second through fourth grade teachers (N=33) and students (N=669) in 33 classrooms across five high poverty urban schools. Results demonstrate that variation in teacher-student agreement on the structure of students’ peer networks can be explained, in part, by developmental factors and classroom characteristics. Developmental increases in network density partially mediated the positive relationship between grade level and teacher-student agreement. Larger class sizes and higher levels of normative aggressive behavior resulted in lower levels of teacher-student agreement. Teachers’ levels of classroom organization had mixed influences, with behavior management negatively predicting agreement, and productivity positively predicting agreement. These results underscore the importance of the classroom context in shaping teacher and student perceptions of peer networks. PMID:21666768

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

PubMed Central

Chang, Roger L; Ghamsari, Lila; Manichaikul, Ani; Hom, Erik F Y; Balaji, Santhanam; Fu, Weiqi; Shen, Yun; Hao, Tong; Palsson, Bernhard Ø; Salehi-Ashtiani, Kourosh; Papin, Jason A

2011-01-01

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. PMID:21811229

Social networks and links to isolation and loneliness among elderly HCBS clients.

PubMed

Medvene, Louis J; Nilsen, Kari M; Smith, Rachel; Ofei-Dodoo, Samuel; DiLollo, Anthony; Webster, Noah; Graham, Annette; Nance, Anita

2016-01-01

The purpose of this study was to explore the network types of HCBS clients based on the structural characteristics of their social networks. We also examined how the network types were associated with social isolation, relationship quality and loneliness. Forty personal interviews were carried out with HCBS clients to assess the structure of their social networks as indicated by frequency of contact with children, friends, family and participation in religious and community organizations. Hierarchical cluster analysis was conducted to identify network types. Four network types were found including: family (n = 16), diverse (n = 8), restricted (n = 8) and religious (n = 7). Family members comprised almost half of participants' social networks, and friends comprised less than one-third. Clients embedded in family, diverse and religious networks had significantly more positive relationships than clients embedded in restricted networks. Clients embedded in restricted networks had significantly higher social isolation scores and were lonelier than clients in diverse and family networks. The findings suggest that HCBS clients' isolation and loneliness are linked to the types of social networks in which they are embedded. The findings also suggest that clients embedded in restricted networks are at high risk for negative outcomes.

Effects of Peer-Tutor Competences on Learner Cognitive Load and Learning Performance during Knowledge Sharing

ERIC Educational Resources Information Center

Hsiao, Ya-Ping; Brouns, Francis; van Bruggen, Jan; Sloep, Peter B.

2012-01-01

In Learning Networks, learners need to share knowledge with others to build knowledge. In particular, when working on complex tasks, they often need to acquire extra cognitive resources from others to process a high task load. However, without support high task load and organizing knowledge sharing themselves might easily overload learners'…

Grower Communication Networks: Information Sources for Organic Farmers

ERIC Educational Resources Information Center

Crawford, Chelsi; Grossman, Julie; Warren, Sarah T.; Cubbage, Fred

2015-01-01

This article reports on a study to determine which information sources organic growers use to inform farming practices by conducting in-depth semi-structured interviews with 23 organic farmers across 17 North Carolina counties. Effective information sources included: networking, agricultural organizations, universities, conferences, Extension, Web…

Organ Preservation: Current Concepts and New Strategies for the Next Decade

PubMed Central

Guibert, Edgardo E.; Petrenko, Alexander Y.; Balaban, Cecilia L.; Somov, Alexander Y.; Rodriguez, Joaquín V.; Fuller, Barry J.

2011-01-01

Summary Organ transplantation has developed over the past 50 years to reach the sophisticated and integrated clinical service of today through several advances in science. One of the most important of these has been the ability to apply organ preservation protocols to deliver donor organs of high quality, via a network of organ exchange to match the most suitable recipient patient to the best available organ, capable of rapid resumption of life-sustaining function in the recipient patient. This has only been possible by amassing a good understanding of the potential effects of hypoxic injury on donated organs, and how to prevent these by applying organ preservation. This review sets out the history of organ preservation, how applications of hypothermia have become central to the process, and what the current status is for the range of solid organs commonly transplanted. The science of organ preservation is constantly being updated with new knowledge and ideas, and the review also discusses what innovations are coming close to clinical reality to meet the growing demands for high quality organs in transplantation over the next few years. PMID:21566713

Network traffic intelligence using a low interaction honeypot

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington’s disease

PubMed Central

Seunarine, Kiran K.; Razi, Adeel; Cole, James H.; Gregory, Sarah; Durr, Alexandra; Roos, Raymund A. C.; Stout, Julie C.; Landwehrmeyer, Bernhard; Scahill, Rachael I.; Clark, Chris A.; Rees, Geraint

2015-01-01

Huntington’s disease can be predicted many years before symptom onset, and thus makes an ideal model for studying the earliest mechanisms of neurodegeneration. Diffuse patterns of structural connectivity loss occur in the basal ganglia and cortex early in the disease. However, the organizational principles that underlie these changes are unclear. By understanding such principles we can gain insight into the link between the cellular pathology caused by mutant huntingtin and its downstream effect at the macroscopic level. The ‘rich club’ is a pattern of organization established in healthy human brains, where specific hub ‘rich club’ brain regions are more highly connected to each other than other brain regions. We hypothesized that selective loss of rich club connectivity might represent an organizing principle underlying the distributed pattern of structural connectivity loss seen in Huntington’s disease. To test this hypothesis we performed diffusion tractography and graph theoretical analysis in a pseudo-longitudinal study of 50 premanifest and 38 manifest Huntington’s disease participants compared with 47 healthy controls. Consistent with our hypothesis we found that structural connectivity loss selectively affected rich club brain regions in premanifest and manifest Huntington’s disease participants compared with controls. We found progressive network changes across controls, premanifest Huntington’s disease and manifest Huntington’s disease characterized by increased network segregation in the premanifest stage and loss of network integration in manifest disease. These regional and whole brain network differences were highly correlated with cognitive and motor deficits suggesting they have pathophysiological relevance. We also observed greater reductions in the connectivity of brain regions that have higher network traffic and lower clustering of neighbouring regions. This provides a potential mechanism that results in a characteristic pattern of structural connectivity loss targeting highly connected brain regions with high network traffic and low clustering of neighbouring regions. Our findings highlight the role of the rich club as a substrate for the structural connectivity loss seen in Huntington’s disease and have broader implications for understanding the connection between molecular and systems level pathology in neurodegenerative disease. PMID:26384928

Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington's disease.

PubMed

McColgan, Peter; Seunarine, Kiran K; Razi, Adeel; Cole, James H; Gregory, Sarah; Durr, Alexandra; Roos, Raymund A C; Stout, Julie C; Landwehrmeyer, Bernhard; Scahill, Rachael I; Clark, Chris A; Rees, Geraint; Tabrizi, Sarah J

2015-11-01

Huntington's disease can be predicted many years before symptom onset, and thus makes an ideal model for studying the earliest mechanisms of neurodegeneration. Diffuse patterns of structural connectivity loss occur in the basal ganglia and cortex early in the disease. However, the organizational principles that underlie these changes are unclear. By understanding such principles we can gain insight into the link between the cellular pathology caused by mutant huntingtin and its downstream effect at the macroscopic level. The 'rich club' is a pattern of organization established in healthy human brains, where specific hub 'rich club' brain regions are more highly connected to each other than other brain regions. We hypothesized that selective loss of rich club connectivity might represent an organizing principle underlying the distributed pattern of structural connectivity loss seen in Huntington's disease. To test this hypothesis we performed diffusion tractography and graph theoretical analysis in a pseudo-longitudinal study of 50 premanifest and 38 manifest Huntington's disease participants compared with 47 healthy controls. Consistent with our hypothesis we found that structural connectivity loss selectively affected rich club brain regions in premanifest and manifest Huntington's disease participants compared with controls. We found progressive network changes across controls, premanifest Huntington's disease and manifest Huntington's disease characterized by increased network segregation in the premanifest stage and loss of network integration in manifest disease. These regional and whole brain network differences were highly correlated with cognitive and motor deficits suggesting they have pathophysiological relevance. We also observed greater reductions in the connectivity of brain regions that have higher network traffic and lower clustering of neighbouring regions. This provides a potential mechanism that results in a characteristic pattern of structural connectivity loss targeting highly connected brain regions with high network traffic and low clustering of neighbouring regions. Our findings highlight the role of the rich club as a substrate for the structural connectivity loss seen in Huntington's disease and have broader implications for understanding the connection between molecular and systems level pathology in neurodegenerative disease. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

A flexible, open, decentralized system for digital pathology networks.

PubMed

Schuler, Robert; Smith, David E; Kumaraguruparan, Gowri; Chervenak, Ann; Lewis, Anne D; Hyde, Dallas M; Kesselman, Carl

2012-01-01

High-resolution digital imaging is enabling digital archiving and sharing of digitized microscopy slides and new methods for digital pathology. Collaborative research centers, outsourced medical services, and multi-site organizations stand to benefit from sharing pathology data in a digital pathology network. Yet significant technological challenges remain due to the large size and volume of digitized whole slide images. While information systems do exist for managing local pathology laboratories, they tend to be oriented toward narrow clinical use cases or offer closed ecosystems around proprietary formats. Few solutions exist for networking digital pathology operations. Here we present a system architecture and implementation of a digital pathology network and share results from a production system that federates major research centers.

Robust C–C bonded porous networks with chemically designed functionalities for improved CO2 capture from flue gas

PubMed Central

Thirion, Damien; Lee, Joo S; Özdemir, Ercan

2016-01-01

Effective carbon dioxide (CO2) capture requires solid, porous sorbents with chemically and thermally stable frameworks. Herein, we report two new carbon–carbon bonded porous networks that were synthesized through metal-free Knoevenagel nitrile–aldol condensation, namely the covalent organic polymer, COP-156 and 157. COP-156, due to high specific surface area (650 m2/g) and easily interchangeable nitrile groups, was modified post-synthetically into free amine- or amidoxime-containing networks. The modified COP-156-amine showed fast and increased CO2 uptake under simulated moist flue gas conditions compared to the starting network and usual industrial CO2 solvents, reaching up to 7.8 wt % uptake at 40 °C. PMID:28144294

A Flexible, Open, Decentralized System for Digital Pathology Networks

PubMed Central

SMITH, David E.; KUMARAGURUPARAN, Gowri; CHERVENAK, Ann; LEWIS, Anne D.; HYDE, Dallas M.; KESSELMAN, Carl

2014-01-01

High-resolution digital imaging is enabling digital archiving and sharing of digitized microscopy slides and new methods for digital pathology. Collaborative research centers, outsourced medical services, and multi-site organizations stand to benefit from sharing pathology data in a digital pathology network. Yet significant technological challenges remain due to the large size and volume of digitized whole slide images. While information systems do exist for managing local pathology laboratories, they tend to be oriented toward narrow clinical use cases or offer closed ecosystems around proprietary formats. Few solutions exist for networking digital pathology operations. Here we present a system architecture and implementation of a digital pathology network and share results from a production system that federates major research centers. PMID:22941985

GENIUS: web server to predict local gene networks and key genes for biological functions.

PubMed

Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

2017-03-01

GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods. GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius . genius.psbl@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Identifying Opinion Leaders to Promote Organ Donation on Social Media: Network Study.

PubMed

Shi, Jingyuan; Salmon, Charles T

2018-01-09

In the recent years, social networking sites (SNSs, also called social media) have been adopted in organ donation campaigns, and recruiting opinion leaders for such campaigns has been found effective in promoting behavioral changes. The aim of this paper was to focus on the dissemination of organ donation tweets on Weibo, the Chinese equivalent of Twitter, and to examine the opinion leadership in the retweet network of popular organ donation messages using social network analysis. It also aimed to investigate how personal and social attributes contribute to a user's opinion leadership on the topic of organ donation. All messages about organ donation posted on Weibo from January 1, 2015 to December 31, 2015 were extracted using Python Web crawler. A retweet network with 505,047 nodes and 545,312 edges of the popular messages (n=206) was constructed and analyzed. The local and global opinion leaderships were measured using network metrics, and the roles of personal attributes, professional knowledge, and social positions in obtaining the opinion leadership were examined using general linear model. The findings revealed that personal attributes, professional knowledge, and social positions predicted individual's local opinion leadership in the retweet network of popular organ donation messages. Alternatively, personal attributes and social positions, but not professional knowledge, were significantly associated with global opinion leadership. The findings of this study indicate that health campaign designers may recruit peer leaders in SNS organ donation promotions to facilitate information sharing among the target audience. Users who are unverified, active, well connected, and experienced with information and communications technology (ICT) will accelerate the sharing of organ donation messages in the global environment. Medical professionals such as organ transplant surgeons who can wield a great amount of influence on their direct connections could also effectively participate in promoting organ donation on social media. ©Jingyuan Shi, Charles T Salmon. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.01.2018.

Identifying Opinion Leaders to Promote Organ Donation on Social Media: Network Study

PubMed Central

Salmon, Charles T

2018-01-01

Background In the recent years, social networking sites (SNSs, also called social media) have been adopted in organ donation campaigns, and recruiting opinion leaders for such campaigns has been found effective in promoting behavioral changes. Objective The aim of this paper was to focus on the dissemination of organ donation tweets on Weibo, the Chinese equivalent of Twitter, and to examine the opinion leadership in the retweet network of popular organ donation messages using social network analysis. It also aimed to investigate how personal and social attributes contribute to a user’s opinion leadership on the topic of organ donation. Methods All messages about organ donation posted on Weibo from January 1, 2015 to December 31, 2015 were extracted using Python Web crawler. A retweet network with 505,047 nodes and 545,312 edges of the popular messages (n=206) was constructed and analyzed. The local and global opinion leaderships were measured using network metrics, and the roles of personal attributes, professional knowledge, and social positions in obtaining the opinion leadership were examined using general linear model. Results The findings revealed that personal attributes, professional knowledge, and social positions predicted individual’s local opinion leadership in the retweet network of popular organ donation messages. Alternatively, personal attributes and social positions, but not professional knowledge, were significantly associated with global opinion leadership. Conclusions The findings of this study indicate that health campaign designers may recruit peer leaders in SNS organ donation promotions to facilitate information sharing among the target audience. Users who are unverified, active, well connected, and experienced with information and communications technology (ICT) will accelerate the sharing of organ donation messages in the global environment. Medical professionals such as organ transplant surgeons who can wield a great amount of influence on their direct connections could also effectively participate in promoting organ donation on social media. PMID:29317384

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

Cascading network failure across the Alzheimer’s disease spectrum

PubMed Central

Knopman, David S.; Gunter, Jeffrey L.; Graff-Radford, Jonathan; Vemuri, Prashanthi; Boeve, Bradley F.; Petersen, Ronald C.; Weiner, Michael W.; Jack, Clifford R.

2016-01-01

Abstract Complex biological systems are organized across various spatiotemporal scales with particular scientific disciplines dedicated to the study of each scale (e.g. genetics, molecular biology and cognitive neuroscience). When considering disease pathophysiology, one must contemplate the scale at which the disease process is being observed and how these processes impact other levels of organization. Historically Alzheimer’s disease has been viewed as a disease of abnormally aggregated proteins by pathologists and molecular biologists and a disease of clinical symptoms by neurologists and psychologists. Bridging the divide between these scales has been elusive, but the study of brain networks appears to be a pivotal inroad to accomplish this task. In this study, we were guided by an emerging systems-based conceptualization of Alzheimer’s disease and investigated changes in brain networks across the disease spectrum. The default mode network has distinct subsystems with unique functional-anatomic connectivity, cognitive associations, and responses to Alzheimer’s pathophysiology. These distinctions provide a window into the systems-level pathophysiology of Alzheimer’s disease. Using clinical phenotyping, metadata, and multimodal neuroimaging data from the Alzheimer’s Disease Neuroimaging Initiative, we characterized the pattern of default mode network subsystem connectivity changes across the entire disease spectrum (n = 128). The two main findings of this paper are (i) the posterior default mode network fails before measurable amyloid plaques and appears to initiate a connectivity cascade that continues throughout the disease spectrum; and (ii) high connectivity between the posterior default mode network and hubs of high connectivity (many located in the frontal lobe) is associated with amyloid accumulation. These findings support a system model best characterized by a cascading network failure—analogous to cascading failures seen in power grids triggered by local overloads proliferating to downstream nodes eventually leading to widespread power outages, or systems failures. The failure begins in the posterior default mode network, which then shifts processing burden to other systems containing prominent connectivity hubs. This model predicts a connectivity ‘overload’ that precedes structural and functional declines and recasts the interpretation of high connectivity from that of a positive compensatory phenomenon to that of a load-shifting process transiently serving a compensatory role. It is unknown whether this systems-level pathophysiology is the inciting event driving downstream molecular events related to synaptic activity embedded in these systems. Possible interpretations include that the molecular-level events drive the network failure, a pathological interaction between the network-level and the molecular-level, or other upstream factors are driving both. PMID:26586695

A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities

NASA Astrophysics Data System (ADS)

Muscoloni, Alessandro; Vittorio Cannistraci, Carlo

2018-05-01

The investigation of the hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The popularity-similarity-optimization (PSO) model simulates how random geometric graphs grow in the hyperbolic space, generating realistic networks with clustering, small-worldness, scale-freeness and rich-clubness. However, it misses to reproduce an important feature of real complex networks, which is the community organization. The geometrical-preferential-attachment (GPA) model was recently developed in order to confer to the PSO also a soft community structure, which is obtained by forcing different angular regions of the hyperbolic disk to have a variable level of attractiveness. However, the number and size of the communities cannot be explicitly controlled in the GPA, which is a clear limitation for real applications. Here, we introduce the nonuniform PSO (nPSO) model. Differently from GPA, the nPSO generates synthetic networks in the hyperbolic space where heterogeneous angular node attractiveness is forced by sampling the angular coordinates from a tailored nonuniform probability distribution (for instance a mixture of Gaussians). The nPSO differs from GPA in other three aspects: it allows one to explicitly fix the number and size of communities; it allows one to tune their mixing property by means of the network temperature; it is efficient to generate networks with high clustering. Several tests on the detectability of the community structure in nPSO synthetic networks and wide investigations on their structural properties confirm that the nPSO is a valid and efficient model to generate realistic complex networks with communities.

Intrinsically organized network for word processing during the resting state.

PubMed

Zhao, Jizheng; Liu, Jiangang; Li, Jun; Liang, Jimin; Feng, Lu; Ai, Lin; Lee, Kang; Tian, Jie

2011-01-03

Neural mechanisms underlying word processing have been extensively studied. It has been revealed that when individuals are engaged in active word processing, a complex network of cortical regions is activated. However, it is entirely unknown whether the word-processing regions are intrinsically organized without any explicit processing tasks during the resting state. The present study investigated the intrinsic functional connectivity between word-processing regions during the resting state with the use of fMRI methodology. The low-frequency fluctuations were observed between the left middle fusiform gyrus and a number of cortical regions. They included the left angular gyrus, left supramarginal gyrus, bilateral pars opercularis, and left pars triangularis of the inferior frontal gyrus, which have been implicated in phonological and semantic processing. Additionally, the activations were also observed in the bilateral superior parietal lobule and dorsal lateral prefrontal cortex, which have been suggested to provide top-down monitoring on the visual-spatial processing of words. The findings of our study indicate an intrinsically organized network during the resting state that likely prepares the visual system to anticipate the highly probable word input for ready and effective processing. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Reflections from organization science on the development of primary health care research networks.

PubMed

Fenton, E; Harvey, J; Griffiths, F; Wild, A; Sturt, J

2001-10-01

In the UK, policy changes in primary health care research and development have led to the establishment of primary care research networks. These organizations aim to increase research culture, capacity and evidence base in primary care. As publicly funded bodies, these networks need to be accountable. Organizational science has studied network organizations including why and how they develop and how they function most effectively. This paper draws on organizational science to reflect on why primary care research networks appear to be appropriate for primary care research and how their structures and processes can best enable the achievement of their aims.

Networking: OFFLU example

USDA-ARS?s Scientific Manuscript database

World Organization for Animal Health (OIE) and Food and Agriculture Organization (FAO) for the United Nations Influenza Network (OFFLU) is the joint OIE-FAO global network of expertise on animal influenzas: equine, swine, poultry and wild birds. OFFLU aims to reduce negative impacts of animal influ...

Self organization of wireless sensor networks using ultra-wideband radios

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

Dowla, Farid U; Nekoogar, Franak; Spiridon, Alex

A novel UWB communications method and system that provides self-organization for wireless sensor networks is introduced. The self-organization is in terms of scalability, power conservation, channel estimation, and node synchronization in wireless sensor networks. The UWB receiver in the present invention adds two new tasks to conventional TR receivers. The two additional units are SNR enhancing unit and timing acquisition and tracking unit.

Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

ERIC Educational Resources Information Center

Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

2015-01-01

The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

Creating a Global Dialogue on Infectious Disease Surveillance: Connecting Organizations for Regional Disease Surveillance (CORDS)

PubMed Central

Gresham, Louise S.; Smolinski, Mark S.; Suphanchaimat, Rapeepong; Kimball, Ann Marie; Wibulpolprasert, Suwit

2013-01-01

Connecting Organizations for Regional Disease Surveillance (CORDS) is an international non-governmental organization focused on information exchange between disease surveillance networks in different areas of the world. By linking regional disease surveillance networks, CORDS builds a trust-based social fabric of experts who share best practices, surveillance tools and strategies, training courses, and innovations. CORDS exemplifies the shifting patterns of international collaboration needed to prevent, detect, and counter all types of biological dangers – not just naturally occurring infectious diseases, but also terrorist threats. Representing a network-of-networks approach, the mission of CORDS is to link regional disease surveillance networks to improve global capacity to respond to infectious diseases. CORDS is an informal governance cooperative with six founding regional disease surveillance networks, with plans to expand; it works in complement and cooperatively with the World Health Organization (WHO), the World Organization for Animal Health (OIE), and the Food and Animal Organization of the United Nations (FAO). As described in detail elsewhere in this special issue of Emerging Health Threats, each regional network is an alliance of a small number of neighboring countries working across national borders to tackle emerging infectious diseases that require unified regional efforts. Here we describe the history, culture and commitment of CORDS; and the novel and necessary role that CORDS serves in the existing international infectious disease surveillance framework. PMID:23362412

Creating a global dialogue on infectious disease surveillance: connecting organizations for regional disease surveillance (CORDS).

PubMed

Gresham, Louise S; Smolinski, Mark S; Suphanchaimat, Rapeepong; Kimball, Ann Marie; Wibulpolprasert, Suwit

2013-01-01

Connecting Organizations for Regional Disease Surveillance (CORDS) is an international non-governmental organization focused on information exchange between disease surveillance networks in different areas of the world. By linking regional disease surveillance networks, CORDS builds a trust-based social fabric of experts who share best practices, surveillance tools and strategies, training courses, and innovations. CORDS exemplifies the shifting patterns of international collaboration needed to prevent, detect, and counter all types of biological dangers - not just naturally occurring infectious diseases, but also terrorist threats. Representing a network-of-networks approach, the mission of CORDS is to link regional disease surveillance networks to improve global capacity to respond to infectious diseases. CORDS is an informal governance cooperative with six founding regional disease surveillance networks, with plans to expand; it works in complement and cooperatively with the World Health Organization (WHO), the World Organization for Animal Health (OIE), and the Food and Animal Organization of the United Nations (FAO). As described in detail elsewhere in this special issue of Emerging Health Threats, each regional network is an alliance of a small number of neighboring countries working across national borders to tackle emerging infectious diseases that require unified regional efforts. Here we describe the history, culture and commitment of CORDS; and the novel and necessary role that CORDS serves in the existing international infectious disease surveillance framework.

High-end Home Firewalls CIAC-2326

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

Orvis, W

Networking in most large organizations is protected with corporate firewalls and managed by seasoned security professionals. Attempts to break into systems at these organizations are extremely difficult to impossible for an external intruder. With the growth in networking and the options that it makes possible, new avenues of intrusion are opening up. Corporate machines exist that are completely unprotected against intrusions, that are not managed by a security professional, and that are regularly connected to the company network. People have the option of and are encouraged to work at home using a home computer linked to the company network. Managersmore » have home computers linked to internal machines so they can keep an eye on internal processes while not physically at work. Researchers do research or writing at home and connect to the company network to download information and upload results. In most cases, these home computers are completely unprotected, except for any protection that the home user might have installed. Unfortunately, most home users are not security professionals and home computers are often used by other family members, such as children downloading music, who are completely unconcerned about security precautions. When these computers are connected to the company network, they can easily introduce viruses, worms, and other malicious code or open a channel behind the company firewall for an external intruder.« less

Network architecture of the cerebral nuclei (basal ganglia) association and commissural connectome.

PubMed

Swanson, Larry W; Sporns, Olaf; Hahn, Joel D

2016-10-04

The cerebral nuclei form the ventral division of the cerebral hemisphere and are thought to play an important role in neural systems controlling somatic movement and motivation. Network analysis was used to define global architectural features of intrinsic cerebral nuclei circuitry in one hemisphere (association connections) and between hemispheres (commissural connections). The analysis was based on more than 4,000 reports of histologically defined axonal connections involving all 45 gray matter regions of the rat cerebral nuclei and revealed the existence of four asymmetrically interconnected modules. The modules form four topographically distinct longitudinal columns that only partly correspond to previous interpretations of cerebral nuclei structure-function organization. The network of connections within and between modules in one hemisphere or the other is quite dense (about 40% of all possible connections), whereas the network of connections between hemispheres is weak and sparse (only about 5% of all possible connections). Particularly highly interconnected regions (rich club and hubs within it) form a topologically continuous band extending through two of the modules. Connection path lengths among numerous pairs of regions, and among some of the network's modules, are relatively long, thus accounting for low global efficiency in network communication. These results provide a starting point for reexamining the connectional organization of the cerebral hemispheres as a whole (right and left cerebral cortex and cerebral nuclei together) and their relation to the rest of the nervous system.

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Fast Distributed Dynamics of Semantic Networks via Social Media.

PubMed

Carrillo, Facundo; Cecchi, Guillermo A; Sigman, Mariano; Slezak, Diego Fernández

2015-01-01

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.

Fast Distributed Dynamics of Semantic Networks via Social Media

PubMed Central

Carrillo, Facundo; Cecchi, Guillermo A.; Sigman, Mariano; Fernández Slezak, Diego

2015-01-01

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network. PMID:26074953

Emergence of fractal scaling in complex networks

NASA Astrophysics Data System (ADS)

Wei, Zong-Wen; Wang, Bing-Hong

2016-09-01

Some real-world networks are shown to be fractal or self-similar. It is widespread that such a phenomenon originates from the repulsion between hubs or disassortativity. Here we show that this common belief fails to capture the causality. Our key insight to address it is to pinpoint links critical to fractality. Those links with small edge betweenness centrality (BC) constitute a special architecture called fractal reference system, which gives birth to the fractal structure of those reported networks. In contrast, a small amount of links with high BC enable small-world effects, hiding the intrinsic fractality. With enough of such links removed, fractal scaling spontaneously arises from nonfractal networks. Our results provide a multiple-scale view on the structure and dynamics and place fractality as a generic organizing principle of complex networks on a firmer ground.

Musculoskeletal networks reveal topological disparity in mammalian neck evolution.

PubMed

Arnold, Patrick; Esteve-Altava, Borja; Fischer, Martin S

2017-12-13

The increase in locomotor and metabolic performance during mammalian evolution was accompanied by the limitation of the number of cervical vertebrae to only seven. In turn, nuchal muscles underwent a reorganization while forelimb muscles expanded into the neck region. As variation in the cervical spine is low, the variation in the arrangement of the neck muscles and their attachment sites (i.e., the variability of the neck's musculoskeletal organization) is thus proposed to be an important source of neck disparity across mammals. Anatomical network analysis provides a novel framework to study the organization of the anatomical arrangement, or connectivity pattern, of the bones and muscles that constitute the mammalian neck in an evolutionary context. Neck organization in mammals is characterized by a combination of conserved and highly variable network properties. We uncovered a conserved regionalization of the musculoskeletal organization of the neck into upper, mid and lower cervical modules. In contrast, there is a varying degree of complexity or specialization and of the integration of the pectoral elements. The musculoskeletal organization of the monotreme neck is distinctively different from that of therian mammals. Our findings reveal that the limited number of vertebrae in the mammalian neck does not result in a low musculoskeletal disparity when examined in an evolutionary context. However, this disparity evolved late in mammalian history in parallel with the radiation of certain lineages (e.g., cetartiodactyls, xenarthrans). Disparity is further facilitated by the enhanced incorporation of forelimb muscles into the neck and their variability in attachment sites.

Electrochemically active, crystalline, mesoporous covalent organic frameworks on carbon nanotubes for synergistic lithium-ion battery energy storage

PubMed Central

Xu, Fei; Jin, Shangbin; Zhong, Hui; Wu, Dingcai; Yang, Xiaoqing; Chen, Xiong; Wei, Hao; Fu, Ruowen; Jiang, Donglin

2015-01-01

Organic batteries free of toxic metal species could lead to a new generation of consumer energy storage devices that are safe and environmentally benign. However, the conventional organic electrodes remain problematic because of their structural instability, slow ion-diffusion dynamics, and poor electrical conductivity. Here, we report on the development of a redox-active, crystalline, mesoporous covalent organic framework (COF) on carbon nanotubes for use as electrodes; the electrode stability is enhanced by the covalent network, the ion transport is facilitated by the open meso-channels, and the electron conductivity is boosted by the carbon nanotube wires. These effects work synergistically for the storage of energy and provide lithium-ion batteries with high efficiency, robust cycle stability, and high rate capability. Our results suggest that redox-active COFs on conducting carbons could serve as a unique platform for energy storage and may facilitate the design of new organic electrodes for high-performance and environmentally benign battery devices. PMID:25650133

Electrochemically active, crystalline, mesoporous covalent organic frameworks on carbon nanotubes for synergistic lithium-ion battery energy storage.

PubMed

Xu, Fei; Jin, Shangbin; Zhong, Hui; Wu, Dingcai; Yang, Xiaoqing; Chen, Xiong; Wei, Hao; Fu, Ruowen; Jiang, Donglin

2015-02-04

Organic batteries free of toxic metal species could lead to a new generation of consumer energy storage devices that are safe and environmentally benign. However, the conventional organic electrodes remain problematic because of their structural instability, slow ion-diffusion dynamics, and poor electrical conductivity. Here, we report on the development of a redox-active, crystalline, mesoporous covalent organic framework (COF) on carbon nanotubes for use as electrodes; the electrode stability is enhanced by the covalent network, the ion transport is facilitated by the open meso-channels, and the electron conductivity is boosted by the carbon nanotube wires. These effects work synergistically for the storage of energy and provide lithium-ion batteries with high efficiency, robust cycle stability, and high rate capability. Our results suggest that redox-active COFs on conducting carbons could serve as a unique platform for energy storage and may facilitate the design of new organic electrodes for high-performance and environmentally benign battery devices.

Statistical mechanics of scale-free gene expression networks

NASA Astrophysics Data System (ADS)

Gross, Eitan

2012-12-01

The gene co-expression networks of many organisms including bacteria, mice and man exhibit scale-free distribution. This heterogeneous distribution of connections decreases the vulnerability of the network to random attacks and thus may confer the genetic replication machinery an intrinsic resilience to such attacks, triggered by changing environmental conditions that the organism may be subject to during evolution. This resilience to random attacks comes at an energetic cost, however, reflected by the lower entropy of the scale-free distribution compared to the more homogenous, random network. In this study we found that the cell cycle-regulated gene expression pattern of the yeast Saccharomyces cerevisiae obeys a power-law distribution with an exponent α = 2.1 and an entropy of 1.58. The latter is very close to the maximal value of 1.65 obtained from linear optimization of the entropy function under the constraint of a constant cost function, determined by the average degree connectivity . We further show that the yeast's gene expression network can achieve scale-free distribution in a process that does not involve growth but rather via re-wiring of the connections between nodes of an ordered network. Our results support the idea of an evolutionary selection, which acts at the level of the protein sequence, and is compatible with the notion of greater biological importance of highly connected nodes in the protein interaction network. Our constrained re-wiring model provides a theoretical framework for a putative thermodynamically driven evolutionary selection process.

FACETS: multi-faceted functional decomposition of protein interaction networks

PubMed Central

Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

2012-01-01

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks.

PubMed

Abedpour, Nima; Kollmann, Markus

2015-11-23

A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection. We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle. We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

PubMed

Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

2015-05-01

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

Structural Preferential Attachment: Network Organization beyond the Link

NASA Astrophysics Data System (ADS)

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

2011-10-01

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

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

Joe Mambretti

This is the summary report of the third annual Optical Networking Testbed Workshop (ONT3), which brought together leading members of the international advanced research community to address major challenges in creating next generation communication services and technologies. Networking research and development (R&D) communities throughout the world continue to discover new methods and technologies that are enabling breakthroughs in advanced communications. These discoveries are keystones for building the foundation of the future economy, which requires the sophisticated management of extremely large qualities of digital information through high performance communications. This innovation is made possible by basic research and experiments within laboratoriesmore » and on specialized testbeds. Initial network research and development initiatives are driven by diverse motives, including attempts to solve existing complex problems, the desire to create powerful new technologies that do not exist using traditional methods, and the need to create tools to address specific challenges, including those mandated by large scale science or government agency mission agendas. Many new discoveries related to communications technologies transition to wide-spread deployment through standards organizations and commercialization. These transition paths allow for new communications capabilities that drive many sectors of the digital economy. In the last few years, networking R&D has increasingly focused on advancing multiple new capabilities enabled by next generation optical networking. Both US Federal networking R&D and other national R&D initiatives, such as those organized by the National Institute of Information and Communications Technology (NICT) of Japan are creating optical networking technologies that allow for new, powerful communication services. Among the most promising services are those based on new types of multi-service or hybrid networks, which use new optical networking technologies. Several years ago, when many of these optical networking research topics were first being investigated, they were the subject of controversial debate. The new techniques challenged many long-held concepts related to architecture and technology. However, today all major networking organizations are transitioning toward infrastructure that incorporates these new concepts. This progress has been assisted through the series of Optical Networking Testbed Workshops (ONT). The first (ONT1) outlined a general framework of key issues and topics and developed a series of recommendations (www.nren.nasa.gov/workshop7). The second (ONT2) developed a common vision of optical network technologies, services, infrastructure, and organizations (www.nren.nasa.gov/workshop8). Processes that allow for a common vision encourage widespread deployment of these types of resources among advanced networking communities. Also, such a shared vision enables key concepts and technologies to migrate from basic research testbeds to wider networking communities. The ONT-3 workshop built on these earlier activities by expanding discussion to include additional considerations of the international interoperability and of greater impact of optical networking technology on networking in general. In accordance with this recognition, the workshop confirmed that future-oriented research and development is indispensable to fundamentally change the current Internet architecture to create a global network incorporating completely new concepts. The workshop also recognized that the first priority to allow for this progress is basic research and development, including international collaborative activities, which are important for the global realization of interoperability of a new generation architecture.« less

Participation and coordination in Dutch health care policy-making. A network analysis of the system of intermediate organizations in Dutch health care.

PubMed

Lamping, Antonie J; Raab, Jörg; Kenis, Patrick

2013-06-01

This study explores the system of intermediate organizations in Dutch health care as the crucial system to understand health care policy-making in the Netherlands. We argue that the Dutch health care system can be understood as a system consisting of distinct but inter-related policy domains. In this study, we analyze four such policy domains: Finances, quality of care, manpower planning and pharmaceuticals. With the help of network analytic techniques, we describe how this highly differentiated system of >200 intermediate organizations is structured and coordinated and what (policy) consequences can be observed with regard to its particular structure and coordination mechanisms. We further analyze the extent to which this system of intermediate organizations enables participation of stakeholders in policy-making using network visualization tools. The results indicate that coordination between the different policy domains within the health care sector takes place not as one would expect through governmental agencies, but through representative organizations such as the representative organizations of the (general) hospitals, the health care consumers and the employers' association. We further conclude that the system allows as well as denies a large number of potential participants access to the policy-making process. As a consequence, the representation of interests is not necessarily balanced, which in turn affects health care policy. We find that the interests of the Dutch health care consumers are well accommodated with the national umbrella organization NPCF in the lead. However, this is no safeguard for the overall community values of good health care since, for example, the interests of the public health sector are likely to be marginalized.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

PubMed

Podobnik, Boris; Jusup, Marko; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M; Stanley, H Eugene

2017-11-07

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Biological conservation law as an emerging functionality in dynamical neuronal networks

PubMed Central

Podobnik, Boris; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M.

2017-01-01

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law—the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective. PMID:29078286

Random Time Identity Based Firewall In Mobile Ad hoc Networks

NASA Astrophysics Data System (ADS)

Suman, Patel, R. B.; Singh, Parvinder

2010-11-01

A mobile ad hoc network (MANET) is a self-organizing network of mobile routers and associated hosts connected by wireless links. MANETs are highly flexible and adaptable but at the same time are highly prone to security risks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized control. Firewall is an effective means of protecting a local network from network-based security threats and forms a key component in MANET security architecture. This paper presents a review of firewall implementation techniques in MANETs and their relative merits and demerits. A new approach is proposed to select MANET nodes at random for firewall implementation. This approach randomly select a new node as firewall after fixed time and based on critical value of certain parameters like power backup. This approach effectively balances power and resource utilization of entire MANET because responsibility of implementing firewall is equally shared among all the nodes. At the same time it ensures improved security for MANETs from outside attacks as intruder will not be able to find out the entry point in MANET due to the random selection of nodes for firewall implementation.

Social media and flu: Media Twitter accounts as agenda setters.

PubMed

Yun, Gi Woong; Morin, David; Park, Sanghee; Joa, Claire Youngnyo; Labbe, Brett; Lim, Jongsoo; Lee, Sooyoung; Hyun, Daewon

2016-07-01

This paper has two objectives. First, it categorizes the Twitter handles tweeted flu related information based on the amount of replies and mentions within the Twitter network. The collected Twitter accounts are categorized as media, health related individuals, organizations, government, individuals with no background with media or medical field, in order to test the relationship between centrality measures of the accounts and their categories. The second objective is to examine the relationship between the importance of the Twitter accounts in the network, centrality measures, and specific characteristics of each account, including the number of tweets and followers as well as the number of accounts followed and liked. Using Twitter search network API, tweets with "flu" keyword were collected and tabulated. Network centralities were calculated with network analysis tool, NodeXL. The collected Twitters accounts were content analyzed and categorized by multiple coders. When the media or organizational Twitter accounts were present in the list of important Twitter accounts, they were highly effective disseminating flu-related information. Also, they were more likely to stay active one year after the data collection period compared to other influential individual accounts. Health campaigns are recommended to focus on recruiting influential Twitter accounts and encouraging them to retweet or mention in order to produce better results in disseminating information. Although some individual social media users were valuable assets in terms of spreading information about flu, media and organization handles were more reliable information distributors. Thus, health information practitioners are advised to design health campaigns better utilizing media and organizations rather than individuals to achieve consistent and efficient campaign outcomes. Published by Elsevier Ireland Ltd.

Network modules and hubs in plant-root fungal biomes

PubMed Central

Toju, Hirokazu; Yamamoto, Satoshi; Tanabe, Akifumi S.; Hayakawa, Takashi; Ishii, Hiroshi S.

2016-01-01

Terrestrial plants host phylogenetically and functionally diverse groups of below-ground microbes, whose community structure controls plant growth/survival in both natural and agricultural ecosystems. Therefore, understanding the processes by which whole root-associated microbiomes are organized is one of the major challenges in ecology and plant science. We here report that diverse root-associated fungi can form highly compartmentalized networks of coexistence within host roots and that the structure of the fungal symbiont communities can be partitioned into semi-discrete types even within a single host plant population. Illumina sequencing of root-associated fungi in a monodominant south beech forest revealed that the network representing symbiont–symbiont co-occurrence patterns was compartmentalized into clear modules, which consisted of diverse functional groups of mycorrhizal and endophytic fungi. Consequently, terminal roots of the plant were colonized by either of the two largest fungal species sets (represented by Oidiodendron or Cenococcum). Thus, species-rich root microbiomes can have alternative community structures, as recently shown in the relationships between human gut microbiome type (i.e. ‘enterotype’) and host individual health. This study also shows an analytical framework for pinpointing network hubs in symbiont–symbiont networks, leading to the working hypothesis that a small number of microbial species organize the overall root–microbiome dynamics. PMID:26962029

Metabolism and evolution: A comparative study of reconstructed genome-level metabolic networks

NASA Astrophysics Data System (ADS)

Almaas, Eivind

2008-03-01

The availability of high-quality annotations of sequenced genomes has made it possible to generate organism-specific comprehensive maps of cellular metabolism. Currently, more than twenty such metabolic reconstructions are publicly available, with the majority focused on bacteria. A typical metabolic reconstruction for a bacterium results in a complex network containing hundreds of metabolites (nodes) and reactions (links), while some even contain more than a thousand. The constrain-based optimization approach of flux-balance analysis (FBA) is used to investigate the functional characteristics of such large-scale metabolic networks, making it possible to estimate an organism's growth behavior in a wide variety of nutrient environments, as well as its robustness to gene loss. We have recently completed the genome-level metabolic reconstruction of Yersinia pseudotuberculosis, as well as the three Yersinia pestis biovars Antiqua, Mediaevalis, and Orientalis. While Y. pseudotuberculosis typically only causes fever and abdominal pain that can mimic appendicitis, the evolutionary closely related Y. pestis strains are the aetiological agents of the bubonic plague. In this presentation, I will discuss our results and conclusions from a comparative study on the evolution of metabolic function in the four Yersiniae networks using FBA and related techniques, and I will give particular focus to the interplay between metabolic network topology and evolutionary flexibility.

[Evaluation of a program to promote network building between disciplinary agencies and informal community organizations: trial in a community comprehensive support center].

PubMed

Murayama, Hiroshi; Kojima, Tomoko; Tomaru, Meiko; Narabu, Harumi; Tachibana, Reiko; Yamaguchi, Takuhiro; Murashima, Sachiyo

2010-10-01

To examine the effectiveness of a program promoting network building between disciplinary agencies and informal community organizations (IGOs) comprising community residents, by implemention with staff of a community comprehensive support center (CJCSG). The program was implemented for nine staff of a GGSG in Setagaya Ward, Tokyo for a year. For process evaluation, items were assessed concerning the contents of the program such as satisfaction and understandability, participants' goal attainment level at each period of the program, and program satisfaction as a whole. Outcome evaluation included measurement of participants' self-efficacy regarding network building with ICOs before and after the program, using interviews of the members who completed the program. Eight out of the nine participants completed the program. All positively evaluated the contents of the program and their own goal attainment at each period of the program. After its completion, they felt highly satisfied. Moreover, there was an improvement in the cognition of the participants, including self-efficacy on network building with IGOs and the atmosphere in the GGSG with regard to network building. The efficacy of this program could be confirmed as demonstrated by the staff of the CCSC, although a more detailed assessment of validity and effectiveness will be necessary in the future.

CytoViz: an artistic mapping of network measurements as living organisms in a VR application

NASA Astrophysics Data System (ADS)

López Silva, Brenda A.; Renambot, Luc

2007-02-01

CytoViz is an artistic, real-time information visualization driven by statistical information gathered during gigabit network transfers to the Scalable Adaptive Graphical Environment (SAGE) at various events. Data streams are mapped to cellular organisms defining their structure and behavior as autonomous agents. Network bandwidth drives the growth of each entity and the latency defines its physics-based independent movements. The collection of entity is bound within the 3D representation of the local venue. This visual and animated metaphor allows the public to experience the complexity of high-speed network streams that are used in the scientific community. Moreover, CytoViz displays the presence of discoverable Bluetooth devices carried by nearby persons. The concept is to generate an event-specific, real-time visualization that creates informational 3D patterns based on actual local presence. The observed Bluetooth traffic is put in opposition of the wide-area networking traffic by overlaying 2D animations on top of the 3D world. Each device is mapped to an animation fading over time while displaying the name of the detected device and its unique physical address. CytoViz was publicly presented at two major international conferences in 2005 (iGrid2005 in San Diego, CA and SC05 in Seattle, WA).

How does network structure affect partnerships for promoting physical activity? Evidence from Brazil and Colombia.

PubMed

Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Reyes, Lissette; Malta, Deborah C; Brownson, Ross C; Quintero, Mario A; Pratt, Michael

2011-11-01

The objective of this study was to describe the network structure and factors associated with collaboration in two networks that promote physical activity (PA) in Brazil and Colombia. Organizations that focus on studying and promoting PA in Brazil (35) and Colombia (53) were identified using a modified one-step reputational snowball sampling process. Participants completed an on-line survey between December 2008 and March 2009 for the Brazil network, and between April and June 2009 for the Colombia network. Network stochastic modeling was used to investigate the likelihood of reported inter-organizational collaboration. While structural features of networks were significant predictors of collaboration within each network, the coefficients and other network characteristics differed. Brazil's PA network was decentralized with a larger number of shared partnerships. Colombia's PA network was centralized and collaboration was influenced by perceived importance of peer organizations. On average, organizations in the PA network of Colombia reported facing more barriers (1.5 vs. 2.5 barriers) for collaboration. Future studies should focus on how these different network structures affect the implementation and uptake of evidence-based PA interventions. Copyright © 2011 Elsevier Ltd. All rights reserved.

Lexical Processing in School-Age Children with Autism Spectrum Disorder and Children with Specific Language Impairment: The Role of Semantics.

PubMed

Haebig, Eileen; Kaushanskaya, Margarita; Ellis Weismer, Susan

2015-12-01

Children with autism spectrum disorder (ASD) and specific language impairment (SLI) often have immature lexical-semantic knowledge; however, the organization of lexical-semantic knowledge is poorly understood. This study examined lexical processing in school-age children with ASD, SLI, and typical development, who were matched on receptive vocabulary. Children completed a lexical decision task, involving words with high and low semantic network sizes and nonwords. Children also completed nonverbal updating and shifting tasks. Children responded more accurately to words from high than from low semantic networks; however, follow-up analyses identified weaker semantic network effects in the SLI group. Additionally, updating and shifting abilities predicted lexical processing, demonstrating similarity in the mechanisms which underlie semantic processing in children with ASD, SLI, and typical development.

Lexical Processing in School-Age Children with Autism Spectrum Disorder and Children with Specific Language Impairment: The Role of Semantics

PubMed Central

Haebig, Eileen; Kaushanskaya, Margarita; Weismer, Susan Ellis

2016-01-01

Children with autism spectrum disorder (ASD) and specific language impairment (SLI) often have immature lexical-semantic knowledge; however, the organization of lexical-semantic knowledge is poorly understood. This study examined lexical processing in school-age children with ASD, SLI, and typical development, who were matched on receptive vocabulary. Children completed a lexical decision task, involving words with high and low semantic network sizes and nonwords. Children also completed nonverbal updating and shifting tasks. Children responded more accurately to words from high than from low semantic networks; however, follow-up analyses identified weaker semantic network effects in the SLI group. Additionally, updating and shifting abilities predicted lexical processing, demonstrating similarity in the mechanisms which underlie semantic processing in children with ASD, SLI, and typical development. PMID:26210517

Atomic switch networks—nanoarchitectonic design of a complex system for natural computing

NASA Astrophysics Data System (ADS)

Demis, E. C.; Aguilera, R.; Sillin, H. O.; Scharnhorst, K.; Sandouk, E. J.; Aono, M.; Stieg, A. Z.; Gimzewski, J. K.

2015-05-01

Self-organized complex systems are ubiquitous in nature, and the structural complexity of these natural systems can be used as a model to design new classes of functional nanotechnology based on highly interconnected networks of interacting units. Conventional fabrication methods for electronic computing devices are subject to known scaling limits, confining the diversity of possible architectures. This work explores methods of fabricating a self-organized complex device known as an atomic switch network and discusses its potential utility in computing. Through a merger of top-down and bottom-up techniques guided by mathematical and nanoarchitectonic design principles, we have produced functional devices comprising nanoscale elements whose intrinsic nonlinear dynamics and memorization capabilities produce robust patterns of distributed activity and a capacity for nonlinear transformation of input signals when configured in the appropriate network architecture. Their operational characteristics represent a unique potential for hardware implementation of natural computation, specifically in the area of reservoir computing—a burgeoning field that investigates the computational aptitude of complex biologically inspired systems.

From Saccharomyces cerevisiae to human: The important gene co-expression modules.

PubMed

Liu, Wei; Li, Li; Ye, Hua; Chen, Haiwei; Shen, Weibiao; Zhong, Yuexian; Tian, Tian; He, Huaqin

2017-08-01

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae . Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.

Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis

PubMed Central

2014-01-01

Background Public health resources are often deployed in developing countries by foreign governments, national governments, civil society and the private health clinics, but seldom in ways that are coordinated within a particular community or population. The lack of coordination results in inefficiencies and suboptimal results. Organizational network analysis can reveal how organizations interact with each other and provide insights into means of realizing better public health results from the resources already deployed. Our objective in this study was to identify the missed opportunities for the integration of HIV care and family planning services and to inform future network strengthening. Methods In two sub-cities of Addis Ababa, we identified each organization providing either HIV care or family planning services. We interviewed representatives of each of them about exchanges of clients with each of the others. With network analysis, we identified network characteristics in each sub-city network, such as referral density and centrality; and gaps in the referral patterns. The results were shared with representatives from the organizations. Results The two networks were of similar size (25 and 26 organizations) and had referral densities of 0.115 and 0.155 out of a possible range from 0 (none) to 1.0 (all possible connections). Two organizations in one sub-city did not refer HIV clients to a family planning organization. One organization in one sub-city and seven in the other offered few HIV services and did not refer clients to any other HIV service provider. Representatives from the networks confirmed the results reflected their experience and expressed an interest in establishing more links between organizations. Conclusions Because of organizations not working together, women in the two sub-cities were at risk of not receiving needed family planning or HIV care services. Facilitating referrals among a few organizations that are most often working in isolation could remediate the problem, but the overall referral densities suggests that improved connections throughout might benefit conditions in addition to HIV and family planning that need service integration. PMID:24438522

Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis.

PubMed

Thomas, James C; Reynolds, Heidi; Bevc, Christine; Tsegaye, Ademe

2014-01-18

Public health resources are often deployed in developing countries by foreign governments, national governments, civil society and the private health clinics, but seldom in ways that are coordinated within a particular community or population. The lack of coordination results in inefficiencies and suboptimal results. Organizational network analysis can reveal how organizations interact with each other and provide insights into means of realizing better public health results from the resources already deployed. Our objective in this study was to identify the missed opportunities for the integration of HIV care and family planning services and to inform future network strengthening. In two sub-cities of Addis Ababa, we identified each organization providing either HIV care or family planning services. We interviewed representatives of each of them about exchanges of clients with each of the others. With network analysis, we identified network characteristics in each sub-city network, such as referral density and centrality; and gaps in the referral patterns. The results were shared with representatives from the organizations. The two networks were of similar size (25 and 26 organizations) and had referral densities of 0.115 and 0.155 out of a possible range from 0 (none) to 1.0 (all possible connections). Two organizations in one sub-city did not refer HIV clients to a family planning organization. One organization in one sub-city and seven in the other offered few HIV services and did not refer clients to any other HIV service provider. Representatives from the networks confirmed the results reflected their experience and expressed an interest in establishing more links between organizations. Because of organizations not working together, women in the two sub-cities were at risk of not receiving needed family planning or HIV care services. Facilitating referrals among a few organizations that are most often working in isolation could remediate the problem, but the overall referral densities suggests that improved connections throughout might benefit conditions in addition to HIV and family planning that need service integration.

Challenges in network science: Applications to infrastructures, climate, social systems and economics

NASA Astrophysics Data System (ADS)

Havlin, S.; Kenett, D. Y.; Ben-Jacob, E.; Bunde, A.; Cohen, R.; Hermann, H.; Kantelhardt, J. W.; Kertész, J.; Kirkpatrick, S.; Kurths, J.; Portugali, J.; Solomon, S.

2012-11-01

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected.

Network configuration management : paving the way to network agility.

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

Maestas, Joseph H.

2007-08-01

Sandia networks consist of nearly nine hundred routers and switches and nearly one million lines of command code, and each line ideally contributes to the capabilities of the network to convey information from one location to another. Sandia's Cyber Infrastructure Development and Deployment organizations recognize that it is therefore essential to standardize network configurations and enforce conformance to industry best business practices and documented internal configuration standards to provide a network that is agile, adaptable, and highly available. This is especially important in times of constrained budgets as members of the workforce are called upon to improve efficiency, effectiveness, andmore » customer focus. Best business practices recommend using the standardized configurations in the enforcement process so that when root cause analysis results in recommended configuration changes, subsequent configuration auditing will improve compliance to the standard. Ultimately, this minimizes mean time to repair, maintains the network security posture, improves network availability, and enables efficient transition to new technologies. Network standardization brings improved network agility, which in turn enables enterprise agility, because the network touches all facets of corporate business. Improved network agility improves the business enterprise as a whole.« less

Information Sharing and Interoperability in Law Enforcement: An Investigation of Federal Criminal Justice Information Systems Use by State/Local Law Enforcement Organizations

DTIC Science & Technology

2003-04-09

reduction, marijuana eradication, mobile enforcement teams, and the Organized Crime Drug Enforcement Task Force. The DEA also operates eight laboratories...Owens’ Columbine Review Commission. Denver , CO: State of Colorado . Council on Foreign Relations. 2002. FBI and Law Enforcement. Washington DC...results of this research indicate high usage and perceived usefulness of the National Crime Information Center Network (NCIC Net), National Law

Scaling theory for information networks.

PubMed

Moses, Melanie E; Forrest, Stephanie; Davis, Alan L; Lodder, Mike A; Brown, James H

2008-12-06

Networks distribute energy, materials and information to the components of a variety of natural and human-engineered systems, including organisms, brains, the Internet and microprocessors. Distribution networks enable the integrated and coordinated functioning of these systems, and they also constrain their design. The similar hierarchical branching networks observed in organisms and microprocessors are striking, given that the structure of organisms has evolved via natural selection, while microprocessors are designed by engineers. Metabolic scaling theory (MST) shows that the rate at which networks deliver energy to an organism is proportional to its mass raised to the 3/4 power. We show that computational systems are also characterized by nonlinear network scaling and use MST principles to characterize how information networks scale, focusing on how MST predicts properties of clock distribution networks in microprocessors. The MST equations are modified to account for variation in the size and density of transistors and terminal wires in microprocessors. Based on the scaling of the clock distribution network, we predict a set of trade-offs and performance properties that scale with chip size and the number of transistors. However, there are systematic deviations between power requirements on microprocessors and predictions derived directly from MST. These deviations are addressed by augmenting the model to account for decentralized flow in some microprocessor networks (e.g. in logic networks). More generally, we hypothesize a set of constraints between the size, power and performance of networked information systems including transistors on chips, hosts on the Internet and neurons in the brain.

Brain anatomical networks in early human brain development.

PubMed

Fan, Yong; Shi, Feng; Smith, Jeffrey Keith; Lin, Weili; Gilmore, John H; Shen, Dinggang

2011-02-01

Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1 month, 1 year, and 2 years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1 year and 2 years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. Copyright © 2010. Published by Elsevier Inc.

Nonuniform traffic spots (NUTS) in multistage interconnection networks

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

Lang, T.; Kurisaki, L.

1990-09-01

The performance of multistage interconnection networks for multiprocessors is degraded when the traffic pattern produces nonuniform congestion in the blocking switches, that is, when there exist nonuniform traffic spots. For some specific patterns the authors evaluate this degradation in performance and propose modifications to the network organization and operation to reduce the degradation. Successful modifications are the use of diverting switches and the extension of the network with additional links. The use of these modifications makes the network more effective for a larger variety of traffic patterns. The authors also consider the case in which the network carries the superpositionmore » of two types of traffic. One type is the high throughput data and instruction traffic, while the other consists of control and I/O packets which are of low throughput but have severe real-time constraints. The authors conclude that diverting switches and networks with additional links are also suitable for assuring low latency for the real-time traffic, especially when using the displacing mode.« less

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

NASA Astrophysics Data System (ADS)

Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

2013-11-01

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

A dual-scale metal nanowire network transparent conductor for highly efficient and flexible organic light emitting diodes.

PubMed

Lee, Jinhwan; An, Kunsik; Won, Phillip; Ka, Yoonseok; Hwang, Hyejin; Moon, Hyunjin; Kwon, Yongwon; Hong, Sukjoon; Kim, Changsoon; Lee, Changhee; Ko, Seung Hwan

2017-02-02

Although solution processed metal nanowire (NW) percolation networks are a strong candidate to replace commercial indium tin oxide, their performance is limited in thin film device applications due to reduced effective electrical areas arising from the dimple structure and percolative voids that single size metal NW percolation networks inevitably possess. Here, we present a transparent electrode based on a dual-scale silver nanowire (AgNW) percolation network embedded in a flexible substrate to demonstrate a significant enhancement in the effective electrical area by filling the large percolative voids present in a long/thick AgNW network with short/thin AgNWs. As a proof of concept, the performance enhancement of a flexible phosphorescent OLED is demonstrated with the dual-scale AgNW percolation network compared to the previous mono-scale AgNWs. Moreover, we report that mechanical and oxidative robustness, which are critical for flexible OLEDs, are greatly increased by embedding the dual-scale AgNW network in a resin layer.

Face recognition: a convolutional neural-network approach.

PubMed

Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

1997-01-01

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

3D Bioprinting for Organ Regeneration

PubMed Central

Cui, Haitao; Nowicki, Margaret; Fisher, John P.; Zhang, Lijie Grace

2017-01-01

Regenerative medicine holds the promise of engineering functional tissues or organs to heal or replace abnormal and necrotic tissues/organs, offering hope for filling the gap between organ shortage and transplantation needs. Three-dimensional (3D) bioprinting is evolving into an unparalleled bio-manufacturing technology due to its high-integration potential for patient-specific designs, precise and rapid manufacturing capabilities with high resolution, and unprecedented versatility. It enables precise control over multiple compositions, spatial distributions, and architectural accuracy/complexity, therefore achieving effective recapitulation of microstructure, architecture, mechanical properties, and biological functions of target tissues and organs. Here we provide an overview of recent advances in 3D bioprinting technology, as well as design concepts of bioinks suitable for the bioprinting process. We focus on the applications of this technology for engineering living organs, focusing more specifically on vasculature, neural networks, the heart and liver. We conclude with current challenges and the technical perspective for further development of 3D organ bioprinting. PMID:27995751

3D Bioprinting for Organ Regeneration.

PubMed

Cui, Haitao; Nowicki, Margaret; Fisher, John P; Zhang, Lijie Grace

2017-01-01

Regenerative medicine holds the promise of engineering functional tissues or organs to heal or replace abnormal and necrotic tissues/organs, offering hope for filling the gap between organ shortage and transplantation needs. Three-dimensional (3D) bioprinting is evolving into an unparalleled biomanufacturing technology due to its high-integration potential for patient-specific designs, precise and rapid manufacturing capabilities with high resolution, and unprecedented versatility. It enables precise control over multiple compositions, spatial distributions, and architectural accuracy/complexity, therefore achieving effective recapitulation of microstructure, architecture, mechanical properties, and biological functions of target tissues and organs. Here we provide an overview of recent advances in 3D bioprinting technology, as well as design concepts of bioinks suitable for the bioprinting process. We focus on the applications of this technology for engineering living organs, focusing more specifically on vasculature, neural networks, the heart and liver. We conclude with current challenges and the technical perspective for further development of 3D organ bioprinting. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Next-generation mammalian genetics toward organism-level systems biology.

PubMed

Susaki, Etsuo A; Ukai, Hideki; Ueda, Hiroki R

2017-01-01

Organism-level systems biology in mammals aims to identify, analyze, control, and design molecular and cellular networks executing various biological functions in mammals. In particular, system-level identification and analysis of molecular and cellular networks can be accelerated by next-generation mammalian genetics. Mammalian genetics without crossing, where all production and phenotyping studies of genome-edited animals are completed within a single generation drastically reduce the time, space, and effort of conducting the systems research. Next-generation mammalian genetics is based on recent technological advancements in genome editing and developmental engineering. The process begins with introduction of double-strand breaks into genomic DNA by using site-specific endonucleases, which results in highly efficient genome editing in mammalian zygotes or embryonic stem cells. By using nuclease-mediated genome editing in zygotes, or ~100% embryonic stem cell-derived mouse technology, whole-body knock-out and knock-in mice can be produced within a single generation. These emerging technologies allow us to produce multiple knock-out or knock-in strains in high-throughput manner. In this review, we discuss the basic concepts and related technologies as well as current challenges and future opportunities for next-generation mammalian genetics in organism-level systems biology.

Two-dimensional self-organization of an ordered Au silicide nanowire network on a Si(110)-16 x 2 surface.

PubMed

Hong, Ie-Hong; Yen, Shang-Chieh; Lin, Fu-Shiang

2009-08-17

A well-ordered two-dimensional (2D) network consisting of two crossed Au silicide nanowire (NW) arrays is self-organized on a Si(110)-16 x 2 surface by the direct-current heating of approximately 1.5 monolayers of Au on the surface at 1100 K. Such a highly regular crossbar nanomesh exhibits both a perfect long-range spatial order and a high integration density over a mesoscopic area, and these two self-ordering crossed arrays of parallel-aligned NWs have distinctly different sizes and conductivities. NWs are fabricated with widths and pitches as small as approximately 2 and approximately 5 nm, respectively. The difference in the conductivities of two crossed-NW arrays opens up the possibility for their utilization in nanodevices of crossbar architecture. Scanning tunneling microscopy/spectroscopy studies show that the 2D self-organization of this perfect Au silicide nanomesh can be achieved through two different directional electromigrations of Au silicide NWs along different orientations of two nonorthogonal 16 x 2 domains, which are driven by the electrical field of direct-current heating. Prospects for this Au silicide nanomesh are also discussed.

A Large-Surface-Area Boracite-Network-Topology Porous MOF Constructed from a Conjugated Ligand Exhibiting a High Hydrogen Uptake Capacity

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

Wang, Xi-Sen; Ma, Shengqian; Yuan, Daqiang

2009-01-01

A new porous metal-organic framework, PCN-20 with a twisted boracite net topology, was constructed based on a highly conjugated planar tricarboxylate ligand; PCN-20 possesses a large Langmuir surface area of over 4200 m(2)/g as well as demonstrates a high hydrogen uptake capacity of 6.2 wt % at 77 K and 50 bar.

The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

NASA Technical Reports Server (NTRS)

Ansari, Nirwan; Liu, Dequan

1991-01-01

A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

[Study on dilemma and strategy of community support network by community organization for prevention of isolated death in an urban area].

PubMed

Masuda, Yuzuri; Tadaka, Etsuko; Dai, Yuka; Itoi, Waka; Taguchi, Rie; Kawahara, Chie

2011-12-01

Isolated death of elderly is recognized as a severe social problem in public health and it is an urgent requirement that a supportive community network be organized so that its occurrence is minimized. The purpose of this research was to analyze actual issues of a supportive community network for elderly within the community and to obtain clues for useful actions to prevent isolated death of elderly individuals in the future. The subjects were 14 representatives of a supportive community network for elderly in A City, B Ward and C District (as a junior high school segment). The research was conducted with a qualitative inductively approach using the Focus Group Interview (FGI). Interviews were focused on difficulties and perspectives within their daily support activities in the community, and were held three times during October 2009 to March 2010. The FGI records were then analyzed with meaningful minimal words and sentences, categorized codes, and then those codes were classified into subcategories or categories. Three categories, Individual, Neighborhood and Community network for elderly resulted from the analysis. Regarding difficulties, "Refusing supports or indifference", "Isolation or Tojikomori in the youth generation", "Lack of family support", "Relationships among their residents weakening gradually", "Unfamiliar newcomers and residents", "Residence feels burden on association with neighborhood", "Limitation of support activities under personal security", "Lack of resources for persons and places of gathering" were identified. On the other hand, perspectives in the community network for elderly were "Building relationships personally", "Invitation to community meetings as companions", "Development of safety confirmation", "Helping each other in the neighborhood", "Stimulate enforcement of bonding in daily life", "Making arrangements for regional administration and residents for supportive activites", "Fostering the trust and connection of residence". To further promotion and effective activities for community network for elderly by community residents, it is necessary that information be exchanged among resident organizations regarding their activities in achievement of social cooperation.

Reducing breast biopsies by ultrasonographic analysis and a modified self-organizing map

NASA Astrophysics Data System (ADS)

Zheng, Yi; Greenleaf, James F.; Gisvold, John J.

1997-05-01

Recent studies suggest that visual evaluation of ultrasound images could decrease negative biopsies of breast cancer diagnosis. However, visual evaluation requires highly experienced breast sonographers. The objective of this study is to develop computerized radiologist assistant to reduce breast biopsies needed for evaluating suspected breast cancer. The approach of this study utilizes a neural network and tissue features extracted from digital sonographic breast images. The features include texture parameters of breast images: characteristics of echoes within and around breast lesions, and geometrical information of breast tumors. Clusters containing only benign lesions in the feature space are then identified by a modified self- organizing map. This newly developed neural network objectively segments population distributions of lesions and accurately establishes benign and equivocal regions.t eh method was applied to high quality breast sonograms of a large number of patients collected with a controlled procedure at Mayo Clinic. The study showed that the number of biopsies in this group of women could be decreased by 40 percent to 59 percent with high confidence and that no malignancies would have been included in the nonbiopsied group. The advantages of this approach are that it is robust, simple, and effective and does not require highly experienced sonographers.

ATM encryption testing

NASA Astrophysics Data System (ADS)

Capell, Joyce; Deeth, David

1996-01-01

This paper describes why encryption was selected by Lockheed Martin Missiles & Space as the means for securing ATM networks. The ATM encryption testing program is part of an ATM network trial provided by Pacific Bell under the California Research Education Network (CalREN). The problem being addressed is the threat to data security which results when changing from a packet switched network infrastructure to a circuit switched ATM network backbone. As organizations move to high speed cell-based networks, there is a break down in the traditional security model which is designed to protect packet switched data networks from external attacks. This is due to the fact that most data security firewalls filter IP packets, restricting inbound and outbound protocols, e.g. ftp. ATM networks, based on cell-switching over virtual circuits, does not support this method for restricting access since the protocol information is not carried by each cell. ATM switches set up multiple virtual connections, thus there is no longer a single point of entry into the internal network. The problem is further complicated by the fact that ATM networks support high speed multi-media applications, including real time video and video teleconferencing which are incompatible with packet switched networks. The ability to restrict access to Lockheed Martin networks in support of both unclassified and classified communications is required before ATM network technology can be fully deployed. The Lockheed Martin CalREN ATM testbed provides the opportunity to test ATM encryption prototypes with actual applications to assess the viability of ATM encryption methodologies prior to installing large scale ATM networks. Two prototype ATM encryptors are being tested: (1) `MILKBUSH' a prototype encryptor developed by NSA for transmission of government classified data over ATM networks, and (2) a prototype ATM encryptor developed by Sandia National Labs in New Mexico, for the encryption of proprietary data.

Wildlife, Snow, Coffee, and Video: The IPY Activities of the University of Alaska Young Researchers' Network

NASA Astrophysics Data System (ADS)

Pringle, D.; Alvarez-Aviles, L.; Carlson, D.; Harbeck, J.; Druckenmiller, M.; Newman, K.; Mueller, D.; Petrich, C.; Roberts, A.; Wang, Y.

2007-12-01

The University of Alaska International Polar Year (IPY) Young Researchers' Network is a group of graduate students and postdoctoral fellows. Our interdisciplinary group operates as a volunteer network to promote the International Polar Year through education and outreach aimed at the general public and Alaskan students of all ages. The Young Researchers' Network sponsors and organizes science talks or Science Cafés by guest speakers in public venues such as coffee shops and bookstores. We actively engage high school students in IPY research concerning the ionic concentrations and isotopic ratios of precipitation through Project Snowball. Our network provides hands-on science activities to encourage environmental awareness and initiate community wildlife monitoring programs such as Wildlife Day by Day. We mentor individual high school students pursuing their own research projects related to IPY through the Alaska High School Science Symposium. Our group also interacts with the general public at community events and festivals to share the excitement of IPY for example at the World Ice Art Championship and Alaska State Fair. The UA IPY Young Researchers' Network continues to explore new partnerships with educators and students in an effort to enhance science and education related to Alaska and the polar regions in general. For more information please visit: http://ipy-youth.uaf.edu or e-mail: ipy-youth@alaska.edu

The Future of Centrally-Organized Wholesale Electricity Markets

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

Glazer, Craig; Morrison, Jay; Breakman, Paul

The electricity grid in the United States is organized around a network of large, centralized power plants and high voltage transmission lines that transport electricity, sometimes over large distances, before it is delivered to the customer through a local distribution grid. This network of centralized generation and high voltage transmission lines is called the “bulk power system.” Costs relating to bulk power generation typically account for more than half of a customer’s electric bill.1 For this reason, the structure and functioning of wholesale electricity markets have major impacts on costs and economic value for consumers, as well as energy securitymore » and national security. Diverse arrangements for bulk power wholesale markets have evolved over the last several decades. The Southeast and Western United States outside of California have a “bilateral-based” bulk power system where market participants enter into long-term bilateral agreements — using competitive procurements through power marketers, direct arrangements among utilities or with other generation owners, and auctions and exchanges.« less

Guidance of vascular development: lessons from the nervous system.

PubMed

Larrivée, Bruno; Freitas, Catarina; Suchting, Steven; Brunet, Isabelle; Eichmann, Anne

2009-02-27

The vascular system of vertebrates consists of an organized, branched network of arteries, veins, and capillaries that penetrates all the tissues of the body. One of the most striking features of the vascular system is that its branching pattern is highly stereotyped, with major and secondary branches forming at specific sites and developing highly conserved organ-specific vascular patterns. The factors controlling vascular patterning are not yet completely understood. Recent studies have highlighted the anatomic and structural similarities between blood vessels and nerves. The 2 networks are often aligned, with nerve fibers and blood vessels following parallel routes. Furthermore, both systems require precise control over their guidance and growth. Several molecules with attractive and repulsive properties have been found to modulate the proper guidance of both nerves and blood vessels. These include the Semaphorins, the Slits, and the Netrins and their receptors. In this review, we describe the molecular mechanisms by which blood vessels and axons achieve proper path finding and the molecular cues that are involved in their guidance.

3D superstructures with an orthorhombic lattice assembled by colloidal PbS quantum dots.

PubMed

Ushakova, Elena V; Cherevkov, Sergei A; Litvin, Aleksandr P; Parfenov, Peter S; Kasatkin, Igor A; Fedorov, Anatoly V; Gun'ko, Yurii K; Baranov, Alexander V

2018-05-03

We report a new type of metamaterial comprising a highly ordered 3D network of 3-7 nm lead sulfide quantum dots self-assembled in an organic matrix formed by amphiphilic ligands (oleic acid molecules). The obtained 3D superstructures possess an orthorhombic lattice with the distance between the nanocrystals as large as 10-40 nm. Analysis of self-assembly and destruction of the superstructures in time performed by a SAXS technique shows that their morphology depends on the quantity of amphiphilic ligands and width of the quantum dot size and its distribution. Formation of the superstructures is discussed in terms of a model describing the lyotropic crystal formation by micelles from three-phase mixtures. The results show that the organic molecules possessing surfactant properties and capable of forming micelles with nanoparticles as a micelle core can be utilized as building blocks for the creation of novel metamaterials based on a highly ordered 3D network of semiconductors, metals or magnetic nanoparticles.

Carbon nanotubes/fluorinated polymers nanocomposite thin films for electrical contacts lubrication

NASA Astrophysics Data System (ADS)

Benedetto, A.; Viel, P.; Noël, S.; Izard, N.; Chenevier, P.; Palacin, S.

2007-09-01

The need to operate in extreme environmental conditions (ultra high vacuum, high temperatures, aerospatial environment, …) and the miniaturization toward micro electromechanical systems is demanding new materials in the field of low-level electrical contacts lubrication. Dry and chemically immobilized lubrication is expected to be an alternative to the traditional wet lubricants oils. With the goal to conciliate electrical conductivity and lubricant properties we designed nanocomposite thin films composed of a 2D carbon nanotubes network embedded in an organic matrix. The nanotubes networks were deposited on gold surfaces modified by electrochemical cathodic grafting of poly(acrylonitrile). The same substrate served for covalently bonding the low-friction organic matrix. Three different matrixes were tested: a perfluorinated oligomer chemically grafted and two different polyfluorinated acrylates electrochemically grafted. The nanocomposite thin films have been characterized by ATR FT-IR, XPS and Raman spectroscopy. We measured the effects of the different matrixes and the nanotubes addition on the tribological properties and on the contact resistances of the films.

Membrane Properties and the Balance between Excitation and Inhibition Control Gamma-Frequency Oscillations Arising from Feedback Inhibition

PubMed Central

Economo, Michael N.; White, John A.

2012-01-01

Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30–80 Hz gamma rhythm in which network oscillations arise through ‘stochastic synchrony’ capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs. PMID:22275859

Exploring the networking behaviors of hospital organizations.

PubMed

Di Vincenzo, Fausto

2018-05-08

Despite an extensive body of knowledge exists on network outcomes and on how hospital network structures may contribute to the creation of outcomes at different levels of analysis, less attention has been paid to understanding how and why hospital organizational networks evolve and change. The aim of this paper is to study the dynamics of networking behaviors of hospital organizations. Stochastic actor-based model for network dynamics was used to quantitatively examine data covering six-years of patient transfer relations among 35 hospital organizations. Specifically, the study investigated about determinants of patient transfer evolution modeling partner selection choice as a combination of multiple organizational attributes and endogenous network-based processes. The results indicate that having overlapping specialties and treating patients with the same case-mix decrease the likelihood of observing network ties between hospitals. Also, results revealed as geographical proximity and membership of the same LHA have a positive impact on the networking behavior of hospitals organizations, there is a propensity in the network to choose larger hospitals as partners, and to transfer patients between hospitals facing similar levels of operational uncertainty. Organizational attributes (overlapping specialties and case-mix), institutional factors (LHA), and geographical proximity matter in the formation and shaping of hospital networks over time. Managers can benefit from the use of these findings by clearly identifying the role and strategic positioning of their hospital with respect to the entire network. Social network analysis can yield novel information and also aid policy makers in the formation of interventions, encouraging alliances among providers as well as planning health system restructuring.

A game-theoretic approach to optimize ad hoc networks inspired by small-world network topology

NASA Astrophysics Data System (ADS)

Tan, Mian; Yang, Tinghong; Chen, Xing; Yang, Gang; Zhu, Guoqing; Holme, Petter; Zhao, Jing

2018-03-01

Nodes in ad hoc networks are connected in a self-organized manner. Limited communication radius makes information transmit in multi-hop mode, and each forwarding needs to consume the energy of nodes. Insufficient communication radius or exhaustion of energy may cause the absence of some relay nodes and links, further breaking network connectivity. On the other hand, nodes in the network may refuse to cooperate due to objective faulty or personal selfish, hindering regular communication in the network. This paper proposes a model called Repeated Game in Small World Networks (RGSWN). In this model, we first construct ad hoc networks with small-world feature by forming "communication shortcuts" between multiple-radio nodes. Small characteristic path length reduces average forwarding times in networks; meanwhile high clustering coefficient enhances network robustness. Such networks still maintain relative low global power consumption, which is beneficial to extend the network survival time. Then we use MTTFT strategy (Mend-Tolerance Tit-for-Tat) for repeated game as a rule for the interactions between neighbors in the small-world networks. Compared with other five strategies of repeated game, this strategy not only punishes the nodes' selfishness more reasonably, but also has the best tolerance to the network failure. This work is insightful for designing an efficient and robust ad hoc network.

Vertical Transmission of Social Roles Drives Resilience to Poaching in Elephant Networks.

PubMed

Goldenberg, Shifra Z; Douglas-Hamilton, Iain; Wittemyer, George

2016-01-11

Network resilience to perturbation is fundamental to functionality in systems ranging from synthetic communication networks to evolved social organization [1]. While theoretical work offers insight into causes of network robustness, examination of natural networks can identify evolved mechanisms of resilience and how they are related to the selective pressures driving structure. Female African elephants (Loxodonta africana) exhibit complex social networks with node heterogeneity in which older individuals serve as connectivity hubs [2, 3]. Recent ivory poaching targeting older elephants in a well-studied population has mirrored the targeted removal of highly connected nodes in the theoretical literature that leads to structural collapse [4, 5]. Here we tested the response of this natural network to selective knockouts. We find that the hierarchical network topology characteristic of elephant societies was highly conserved across the 16-year study despite ∼70% turnover in individual composition of the population. At a population level, the oldest available individuals persisted to fill socially central positions in the network. For analyses using known mother-daughter pairs, social positions of daughters during the disrupted period were predicted by those of their mothers in years prior, were unrelated to individual histories of family mortality, and were actively built. As such, daughters replicated the social network roles of their mothers, driving the observed network resilience. Our study provides a rare bridge between network theory and an evolved system, demonstrating social redundancy to be the mechanism by which resilience to perturbation occurred in this socially advanced species. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Interaction between Heterotrophic Bacteria and Coliform, Fecal Coliform, Fecal Streptococci Bacteria in the Water Supply Networks.

PubMed

Amanidaz, Nazak; Zafarzadeh, Ali; Mahvi, Amir Hossein

2015-12-01

This study investigated the interaction between heterotrophic bacteria and coliform, fecal coliforms, fecal streptococci bacteria in water supply networks. This study was conducted during 2013 on water supply distribution network in Aq Qala City, Golestan Province, Northern Iran and standard methods were applied for microbiological analysis. The surface method was applied to test the heterotrophic bacteria and MPN method was used for coliform, fecal coliform and fecal streptococci bacteria measurements. In 114 samples, heterotrophic bacteria count were over 500 CFU/ml, which the amount of fecal coliform, coliform, and fecal streptococci were 8, 32, and 20 CFU/100 ml, respectively. However, in the other 242 samples, with heterotrophic bacteria count being less than 500 CFU/ml, the amount of fecal coliform, coliform, and fecal streptococci was 7, 23, and 11 CFU/100ml, respectively. The relationship between heterotrophic bacteria, coliforms and fecal streptococci was highly significant (P<0.05). We observed the concentration of coliforms, fecal streptococci bacteria being high, whenever the concentration of heterotrophic bacteria in the water network systems was high. Interaction between heterotrophic bacteria and coliform, fecal coliforms, fecal streptococci bacteria in the Aq Qala City water supply networks was not notable. It can be due to high concentrations of organic carbon, bio-films and nutrients, which are necessary for growth, and survival of all microorganisms.

The Interaction between Heterotrophic Bacteria and Coliform, Fecal Coliform, Fecal Streptococci Bacteria in the Water Supply Networks

PubMed Central

AMANIDAZ, Nazak; ZAFARZADEH, Ali; MAHVI, Amir Hossein

2015-01-01

Background: This study investigated the interaction between heterotrophic bacteria and coliform, fecal coliforms, fecal streptococci bacteria in water supply networks. Methods: This study was conducted during 2013 on water supply distribution network in Aq Qala City, Golestan Province, Northern Iran and standard methods were applied for microbiological analysis. The surface method was applied to test the heterotrophic bacteria and MPN method was used for coliform, fecal coliform and fecal streptococci bacteria measurements. Results: In 114 samples, heterotrophic bacteria count were over 500 CFU/ml, which the amount of fecal coliform, coliform, and fecal streptococci were 8, 32, and 20 CFU/100 ml, respectively. However, in the other 242 samples, with heterotrophic bacteria count being less than 500 CFU/ml, the amount of fecal coliform, coliform, and fecal streptococci was 7, 23, and 11 CFU/100ml, respectively. The relationship between heterotrophic bacteria, coliforms and fecal streptococci was highly significant (P<0.05). We observed the concentration of coliforms, fecal streptococci bacteria being high, whenever the concentration of heterotrophic bacteria in the water network systems was high. Conclusion: Interaction between heterotrophic bacteria and coliform, fecal coliforms, fecal streptococci bacteria in the Aq Qala City water supply networks was not notable. It can be due to high concentrations of organic carbon, bio-films and nutrients, which are necessary for growth, and survival of all microorganisms. PMID:26811820

Regular Topologies for Gigabit Wide-Area Networks. Volume 1

NASA Technical Reports Server (NTRS)

Shacham, Nachum; Denny, Barbara A.; Lee, Diane S.; Khan, Irfan H.; Lee, Danny Y. C.; McKenney, Paul

1994-01-01

In general terms, this project aimed at the analysis and design of techniques for very high-speed networking. The formal objectives of the project were to: (1) Identify switch and network technologies for wide-area networks that interconnect a large number of users and can provide individual data paths at gigabit/s rates; (2) Quantitatively evaluate and compare existing and proposed architectures and protocols, identify their strength and growth potentials, and ascertain the compatibility of competing technologies; and (3) Propose new approaches to existing architectures and protocols, and identify opportunities for research to overcome deficiencies and enhance performance. The project was organized into two parts: 1. The design, analysis, and specification of techniques and protocols for very-high-speed network environments. In this part, SRI has focused on several key high-speed networking areas, including Forward Error Control (FEC) for high-speed networks in which data distortion is the result of packet loss, and the distribution of broadband, real-time traffic in multiple user sessions. 2. Congestion Avoidance Testbed Experiment (CATE). This part of the project was done within the framework of the DARTnet experimental T1 national network. The aim of the work was to advance the state of the art in benchmarking DARTnet's performance and traffic control by developing support tools for network experimentation, by designing benchmarks that allow various algorithms to be meaningfully compared, and by investigating new queueing techniques that better satisfy the needs of best-effort and reserved-resource traffic. This document is the final technical report describing the results obtained by SRI under this project. The report consists of three volumes: Volume 1 contains a technical description of the network techniques developed by SRI in the areas of FEC and multicast of real-time traffic. Volume 2 describes the work performed under CATE. Volume 3 contains the source code of all software developed under CATE.

Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance.

PubMed

Kyeong, Sunghyon; Kim, Eunjoo; Park, Hae-Jeong; Hwang, Dong-Uk

2014-08-05

Novelty seeking (NS) and harm avoidance (HA) are two major dimensions of temperament in Cloninger׳s neurobiological model of personality. Previous neurofunctional and biological studies on temperament dimensions of HA and NS suggested that the temperamental traits have significant correlations with cortical and subcortical brain regions. However, no study to date has investigated the functional network modular organization as a function of the temperament dimension. The temperament dimensions were originally proposed to be independent of one another. However, a meta-analysis based on 16 published articles found a significant negative correlation between HA and NS (Miettunen et al., 2008). Based on this negative correlation, the current study revealed the whole-brain connectivity modular architecture for two contrasting temperament groups. The k-means clustering algorithm, with the temperamental traits of HA and NS as an input, was applied to divide the 40 subjects into two temperament groups: 'high HA and low NS' versus 'low HA and high NS'. Using the graph theoretical framework, we found a functional segregation of whole brain network architectures derived from resting-state functional MRI. In the 'high HA and low NS' group, the regulatory brain regions, such as the prefrontal cortex (PFC), are clustered together with the limbic system. In the 'low HA and high NS' group, however, brain regions lying on the dopaminergic pathways, such as the PFC and basal ganglia, are partitioned together. These findings suggest that the neural basis of inhibited, passive, and inactive behaviors in the 'high HA and low NS' group was derived from the increased network associations between the PFC and limbic clusters. In addition, supporting evidence of topological differences between the two temperament groups was found by analyzing the functional connectivity density and gray matter volume, and by computing the relationships between the morphometry and function of the brain. Copyright © 2014 Elsevier B.V. All rights reserved.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-06-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed Central

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-01-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. PMID:21666777

Controlled recovery of phylogenetic communities from an evolutionary model using a network approach

NASA Astrophysics Data System (ADS)

Sousa, Arthur M. Y. R.; Vieira, André P.; Prado, Carmen P. C.; Andrade, Roberto F. S.

2016-04-01

This works reports the use of a complex network approach to produce a phylogenetic classification tree of a simple evolutionary model. This approach has already been used to treat proteomic data of actual extant organisms, but an investigation of its reliability to retrieve a traceable evolutionary history is missing. The used evolutionary model includes key ingredients for the emergence of groups of related organisms by differentiation through random mutations and population growth, but purposefully omits other realistic ingredients that are not strictly necessary to originate an evolutionary history. This choice causes the model to depend only on a small set of parameters, controlling the mutation probability and the population of different species. Our results indicate that for a set of parameter values, the phylogenetic classification produced by the used framework reproduces the actual evolutionary history with a very high average degree of accuracy. This includes parameter values where the species originated by the evolutionary dynamics have modular structures. In the more general context of community identification in complex networks, our model offers a simple setting for evaluating the effects, on the efficiency of community formation and identification, of the underlying dynamics generating the network itself.

Adding the ‘heart’ to hanging drop networks for microphysiological multi-tissue experiments†

PubMed Central

Yazdi, Saeed Rismani; Shadmani, Amir; Bürgel, Sebastian C.; Misun, Patrick M.; Hierlemann, Andreas; Frey, Olivier

2017-01-01

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid–air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip. PMID:26401602

Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment.

PubMed

Pandey, Mayank; Pandey, Ashutosh Kumar; Mishra, Ashutosh; Tripathi, B D

2015-09-01

Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation study in river sediments revealed that exchangeable, reducible and oxidizable fractions were dominant in all the studied metals (Cr, Ni, Cu, Zn, Cd, Pb) except Mn and Fe. High pollution load index (1.64-3.89) recommends urgent need of mitigation measures. Self-organizing Map-Artificial Neural Network (SOM-ANN) was applied to the data set for the prediction of major point sources of pollution in the river Ganga. Copyright © 2015 Elsevier Ltd. All rights reserved.

Coordination polymer gels with important environmental and biological applications.

PubMed

Jung, Jong Hwa; Lee, Ji Ha; Silverman, Julian R; John, George

2013-02-07

Coordination Polymer Gels (CPGs) constitute a subset of solid-like metal ion and bridging organic ligand structures (similar to metal-organic frameworks) that form multi-dimensional networks through a trapped solvent as a result of non-covalent interactions. While physical properties of these gels are similar to conventional high molecular weight organic polymer gels, coordination polymer gel systems are often fully reversible and can be assembled and disassembled in the presence of additional energy (heat, sonication, shaking) to give a solution of solvated gelators. Compared to gels resulting from purely organic self-assembled low molecular weight gelators, metal ions incorporated into the fibrilar networks spanning the bulk solvent can impart CPGs with added functionalities. The solid/liquid nature of the gels allows for species to migrate through the gel system and interact with metals, ligands, and the solvent. Chemosensing, catalysis, fluorescence, and drug-delivery applications are some of the many potential uses for these dynamic systems, taking advantage of the metal ion's coordination, the organic polydentate ligand's orientation and functionality, or a combination of these properties. By fine tuning these systems through metal ion and ligand selection and by directing self-assembly with external stimuli the rational synthesis of practical systems can be envisaged.

Self-organized Anonymous Authentication in Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Freudiger, Julien; Raya, Maxim; Hubaux, Jean-Pierre

Pervasive communications bring along new privacy challenges, fueled by the capability of mobile devices to communicate with, and thus “sniff on”, each other directly. We design a new mechanism that aims at achieving location privacy in these forthcoming mobile networks, whereby mobile nodes collect the pseudonyms of the nodes they encounter to generate their own privacy cloaks. Thus, privacy emerges from the mobile network and users gain control over the disclosure of their locations. We call this new paradigm self-organized location privacy. In this work, we focus on the problem of self-organized anonymous authentication that is a necessary prerequisite for location privacy. We investigate, using graph theory, the optimality of different cloak constructions and evaluate with simulations the achievable anonymity in various network topologies. We show that peer-to-peer wireless communications and mobility help in the establishment of self-organized anonymous authentication in mobile networks.

Organization and scaling in water supply networks

NASA Astrophysics Data System (ADS)

Cheng, Likwan; Karney, Bryan W.

2017-12-01

Public water supply is one of the society's most vital resources and most costly infrastructures. Traditional concepts of these networks capture their engineering identity as isolated, deterministic hydraulic units, but overlook their physics identity as related entities in a probabilistic, geographic ensemble, characterized by size organization and property scaling. Although discoveries of allometric scaling in natural supply networks (organisms and rivers) raised the prospect for similar findings in anthropogenic supplies, so far such a finding has not been reported in public water or related civic resource supplies. Examining an empirical ensemble of large number and wide size range, we show that water supply networks possess self-organized size abundance and theory-explained allometric scaling in spatial, infrastructural, and resource- and emission-flow properties. These discoveries establish scaling physics for water supply networks and may lead to novel applications in resource- and jurisdiction-scale water governance.

Assembling the puzzle for promoting physical activity in Brazil: a social network analysis.

PubMed

Brownson, Ross C; Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Hallal, Pedro C; Hoehner, Christine; Malta, Deborah Carvalho; Reis, Rodrigo S; Ramos, Luiz Roberto; Ribeiro, Isabela C; Soares, Jesus; Pratt, Michael

2010-07-01

Physical inactivity is a significant public health problem in Brazil that may be addressed by partnerships and networks. In conjunction with Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), the aim of this study was to conduct a social network analysis of physical activity in Brazil. An online survey was completed by 28 of 35 organizations contacted from December 2008 through March 2009. Network analytic methods examined measures of collaboration, importance, leadership, and attributes of the respondent and organization. Leadership nominations for organizations studied ranged from 0 to 23. Positive predictors of collaboration included: south region, GUIA membership, years working in physical activity, and research, education, and promotion/practice areas of physical activity. The most frequently reported barrier to collaboration was bureaucracy. Social network analysis identified factors that are likely to improve collaboration among organizations in Brazil.

Cerebral energy metabolism and the brain's functional network architecture: an integrative review.

PubMed

Lord, Louis-David; Expert, Paul; Huckins, Jeremy F; Turkheimer, Federico E

2013-09-01

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's 'functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.

Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

PubMed

Shen, Xianjun; Yi, Li; Jiang, Xingpeng; He, Tingting; Yang, Jincai; Xie, Wei; Hu, Po; Hu, Xiaohua

2017-01-01

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

PubMed

Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2018-06-14

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

PubMed

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

2016-11-22

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

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

PubMed Central

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Fabricating porous materials using interpenetrating inorganic-organic composite gels

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

Seo, Dong-Kyun; Volosin, Alex

Porous materials are fabricated using interpenetrating inorganic-organic composite gels. A mixture or precursor solution including an inorganic gel precursor, an organic polymer gel precursor, and a solvent is treated to form an inorganic wet gel including the organic polymer gel precursor and the solvent. The inorganic wet gel is then treated to form a composite wet gel including an organic polymer network in the body of the inorganic wet gel, producing an interpenetrating inorganic-organic composite gel. The composite wet gel is dried to form a composite material including the organic polymer network and an inorganic network component. The composite materialmore » can be treated further to form a porous composite material, a porous polymer or polymer composite, a porous metal oxide, and other porous materials.« less

42 CFR 422.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

...— Arrangement means a written agreement between an MA organization and a provider or provider network, under which— (1) The provider or provider network agrees to furnish for a specific MA plan(s) specified... organization (PPO) that serves one or more entire regions. An MA regional plan must have a network of...

Comparative Analysis of Human Communication Networks in Selected Formal Organizations.

ERIC Educational Resources Information Center

Farace, Richard V.; Johnson, Jerome David

This paper briefly describes the organization of a "data bank" containing research on communication networks, specifies the kinds of information compiled about various network properties, discusses some specific results of the work done to date, and presents some general conclusions about the overall project and its potential advantages to…

Large-Scale, Three–Dimensional, Free–Standing, and Mesoporous Metal Oxide Networks for High–Performance Photocatalysis

PubMed Central

Bai, Hua; Li, Xinshi; Hu, Chao; Zhang, Xuan; Li, Junfang; Yan, Yan; Xi, Guangcheng

2013-01-01

Mesoporous nanostructures represent a unique class of photocatalysts with many applications, including splitting of water, degradation of organic contaminants, and reduction of carbon dioxide. In this work, we report a general Lewis acid catalytic template route for the high–yield producing single– and multi–component large–scale three–dimensional (3D) mesoporous metal oxide networks. The large-scale 3D mesoporous metal oxide networks possess large macroscopic scale (millimeter–sized) and mesoporous nanostructure with huge pore volume and large surface exposure area. This method also can be used for the synthesis of large–scale 3D macro/mesoporous hierarchical porous materials and noble metal nanoparticles loaded 3D mesoporous networks. Photocatalytic degradation of Azo dyes demonstrated that the large–scale 3D mesoporous metal oxide networks enable high photocatalytic activity. The present synthetic method can serve as the new design concept for functional 3D mesoporous nanomaterials. PMID:23857595

Fusion Energy Sciences Network Requirements

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

Dart, Eli; Tierney, Brian

2012-09-26

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy Office of Science, the single largest supporter of basic research in the physical sciences in the United States. In support of the Office of Science programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In December 2011, ESnet and the Office of Fusion Energy Sciences (FES), of the DOE Officemore » of Science (SC), organized a workshop to characterize the networking requirements of the programs funded by FES. The requirements identified at the workshop are summarized in the Findings section, and are described in more detail in the body of the report.« 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.