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Sample records for setting network eaprasnet

  1. European Academy of Paediatrics Research in Ambulatory Setting network (EAPRASnet): a multi-national general paediatric research network for better child health.

    PubMed

    del Torso, S; van Esso, D; Gerber, A; Drabik, A; Hadjipanayis, A; Nicholson, A; Grossman, Z

    2010-05-01

    In 2008, the European Academy of Paediatrics launched a paediatric-based research network - EAPRASnet (European Academy of Paediatrics Research in Ambulatory Setting network). The network has recruited primary care and general paediatricians from European and Mediterranean countries. Every paediatrician joining the network has been asked to complete a recruitment survey. The aims of the survey were to characterize paediatrician's demographics, practice arrangements and patient's demographics, to define main incentives for research, and to learn what paediatricians view as unsolved issues that need to be studied. A total of 156 paediatricians from 19 countries were recruited with 144 completing the questionnaire (92%). Majority of respondents (89%) were general paediatricians for more than half of their time. Practice arrangement of 47% of paediatricians was solo practice, with 40% in group practice. Electronic medical records were being used by 72% of respondents. Over 70% of the paediatricians had more than 1000 patients under their clinical care, and patients younger than 6 years old contributed nearly half of the patient population. Areas of most interest for research were: quality of care indicators, communication with parents, obesity, attention deficit hyperactivity disorder and effective well child care. Main incentives for participation in a research project were interest in the topic (81%) and effort to improve quality of care (71%). Lack of time was the leading reported obstacle for research activity (72%). EAPRASnet is growing, and the network's structure, operation and funding are described. Methods for joining the network and the process of study development are presented. A core group of EAP general paediatricians are committed to research in their practices. The information gathered will serve for future planning of research projects in the EAPRASnet to harmonize and optimize the care given to children in the primary care setting in Europe.

  2. Autocatalytic sets in a partitioned biochemical network.

    PubMed

    Smith, Joshua I; Steel, Mike; Hordijk, Wim

    2014-01-01

    In previous work, RAF theory has been developed as a tool for making theoretical progress on the origin of life question, providing insight into the structure and occurrence of self-sustaining and collectively autocatalytic sets within catalytic polymer networks. We present here an extension in which there are two "independent" polymer sets, where catalysis occurs within and between the sets, but there are no reactions combining polymers from both sets. Such an extension reflects the interaction between nucleic acids and peptides observed in modern cells and proposed forms of early life. We present theoretical work and simulations which suggest that the occurrence of autocatalytic sets is robust to the partitioned structure of the network. We also show that autocatalytic sets remain likely even when the molecules in the system are not polymers, and a low level of inhibition is present. Finally, we present a kinetic extension which assigns a rate to each reaction in the system, and show that identifying autocatalytic sets within such a system is an NP-complete problem. Recent experimental work has challenged the necessity of an RNA world by suggesting that peptide-nucleic acid interactions occurred early in chemical evolution. The present work indicates that such a peptide-RNA world could support the spontaneous development of autocatalytic sets and is thus a feasible alternative worthy of investigation.

  3. Best Friend Networks of Children across Settings.

    ERIC Educational Resources Information Center

    Ray, Glen E.; And Others

    1995-01-01

    Investigated children's classroom sociometry and size of their best-friend networks. For both classroom and playground settings, popular children had the most reciprocal best friends, while rejected children had the fewest, but had more on the playground than in the classroom. Results suggest that constraints and opportunities of different…

  4. Local Area Networks in the School Setting.

    ERIC Educational Resources Information Center

    Bluhm, Harry P.

    1986-01-01

    Defines local area networks (LANs); describes basic components and configurations of LANs; and discusses LANs benefits (reduced costs, better management of computer resources, enhanced communications) and pitfalls (hidden costs, time delays, network maintenance, lack of standardization, network security breaches, lack of network compatible…

  5. Securing Mobile Networks in an Operational Setting

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Stewart, David H.; Bell, Terry L.; Paulsen, Phillip E.; Shell, Dan

    2004-01-01

    This paper describes a network demonstration and three month field trial of mobile networking using mobile-IPv4. The network was implemented as part of the US Coast Guard operational network which is a ".mil" network and requires stringent levels of security. The initial demonstrations took place in November 2002 and a three month field trial took place from July through September of 2003. The mobile network utilized encryptors capable of NSA-approved Type 1 algorithms, mobile router from Cisco Systems and 802.11 and satellite wireless links. This paper also describes a conceptual architecture for wide-scale deployment of secure mobile networking in operational environments where both private and public infrastructure is used. Additional issues presented include link costs, placement of encryptors and running routing protocols over layer-3 encryption devices.

  6. Interneurons set the tune of developing networks.

    PubMed

    Ben-Ari, Yehezkel; Khalilov, Ilgam; Represa, Alfonso; Gozlan, Henri

    2004-07-01

    Despite a rather long migratory journey, interneurons are functional before vertically migrating pyramidal neurons and they constitute the source and target of the first functional synapses in the developing hippocampus. Interneuron-driven network patterns are already present in utero while principal cells are mostly quiescent. At that early stage, GABAergic synapses--which are formed before glutamatergic ones--are excitatory, suggesting that GABA is a pioneer, much like the neurons from which it is released. This review discusses this sequence of events, its functional significance and the role that interneurons might play in the construction of cortical networks.

  7. Edge union of networks on the same vertex set

    NASA Astrophysics Data System (ADS)

    Loe, Chuan Wen; Jeldtoft Jensen, Henrik

    2013-06-01

    Random network generators such as Erdős-Rényi, Watts-Strogatz and Barabási-Albert models are used as models to study real-world networks. Let G1(V, E1) and G2(V, E2) be two such networks on the same vertex set V. This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V, E1∪E2).

  8. Exploring empowerment in settings: mapping distributions of network power.

    PubMed

    Neal, Jennifer Watling

    2014-06-01

    This paper brings together two trends in the empowerment literature-understanding empowerment in settings and understanding empowerment as relational-by examining what makes settings empowering from a social network perspective. Specifically, extending Neal and Neal's (Am J Community Psychol 48(3/4):157-167, 2011) conception of network power, an empowering setting is defined as one in which (1) actors have existing relationships that allow for the exchange of resources and (2) the distribution of network power among actors in the setting is roughly equal. The paper includes a description of how researchers can examine distributions of network power in settings. Next, this process is illustrated in both an abstract example and using empirical data on early adolescents' peer relationships in urban classrooms. Finally, implications for theory, methods, and intervention related to understanding empowering settings are explored.

  9. Utilizing Maximal Independent Sets as Dominating Sets in Scale-Free Networks

    NASA Astrophysics Data System (ADS)

    Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.

    Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.

  10. Education through telemedicine networks: setting quality standards.

    PubMed

    Shershneva, Marianna B; Olson, Curtis A

    2005-01-01

    Quality standards for educational programming have received limited attention in telemedicine. We selected five sets of standards from the distance education literature established by: (1) the American Council on Education; (2) the American Distance Education Consortium; (3) the Council of Regional Accrediting Commissions; (4) the Distance Education and Training Council; (5) the Innovations in Distance Education Project. The standards were reviewed to determine the purposes they were intended to serve and the process by which they were established. The content of the five sets of standards were summarized around the 'four commonplaces' of education: learner, teacher, curriculum and context. Four major findings emerged. First, none of the sets of standards addresses all of the issues that are potentially relevant to telemedicine education; all emphasize certain topics while neglecting others. Second, there are some important aspects of telemedicine that are not addressed at all, such as patient confidentiality. Third, the standards generally provide a framework for defining high quality in distance education, leaving to those at the local level the task of deciding how a standard applies in their setting. Finally, the standards reviewed have many elements that could potentially apply to telemedicine education. Setting quality standards for education through telemedicine requires a systematic approach and a means for continuous improvement of those standards.

  11. ConfidenceSets for Network Structure

    DTIC Science & Technology

    2011-05-01

    TASK NUMBER 5f. WORK UNIT NUMBER 5c. PROGRAM ELEMENT NUMBER 5b. GRANT NUMBER 5a. CONTRACT NUMBER W911NF-11-1-0036 611103 Form Approved OMB NO...analysis. Acknowledgments Work supported in part by the National Science Foundation, National Institutes of Health, Army Research Office, and Office of...F. Lorrain and H. C. White. Structural equivalence of individuals in social networks. J. Math. Sociol ., 1:49–80, Mar. 1971. C.P. Massen and J.P.K

  12. Modelling gene and protein regulatory networks with answer set programming.

    PubMed

    Fayruzov, Timur; Janssen, Jeroen; Vermeir, Dirk; Cornelis, Chris; De Cock, Martine

    2011-01-01

    Recently, many approaches to model regulatory networks have been proposed in the systems biology domain. However, the task is far from being solved. In this paper, we propose an Answer Set Programming (ASP)-based approach to model interaction networks. We build a general ASP framework that describes the network semantics and allows modelling specific networks with little effort. ASP provides a rich and flexible toolbox that allows expanding the framework with desired features. In this paper, we tune our framework to mimic Boolean network behaviour and apply it to model the Budding Yeast and Fission Yeast cell cycle networks. The obtained steady states of these networks correspond to those of the Boolean networks.

  13. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    ERIC Educational Resources Information Center

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  14. Dominating sets and ego-centered decompositions in social networks

    NASA Astrophysics Data System (ADS)

    Boudourides, M. A.; Lenis, S. T.

    2016-09-01

    Our aim here is to address the problem of decomposing a whole network into a minimal number of ego-centered subnetworks. For this purpose, the network egos are picked out as the members of a minimum dominating set of the network. However, to find such an efficient dominating ego-centered construction, we need to be able to detect all the minimum dominating sets and to compare all the corresponding dominating ego-centered decompositions of the network. To find all the minimum dominating sets of the network, we are developing a computational heuristic, which is based on the partition of the set of nodes of a graph into three subsets, the always dominant vertices, the possible dominant vertices and the never dominant vertices, when the domination number of the network is known. To compare the ensuing dominating ego-centered decompositions of the network, we are introducing a number of structural measures that count the number of nodes and links inside and across the ego-centered subnetworks. Furthermore, we are applying the techniques of graph domination and ego-centered decomposition for six empirical social networks.

  15. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    ERIC Educational Resources Information Center

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  16. A mesoscopic network model for permanent set in crosslinked elastomers

    SciTech Connect

    Weisgraber, T H; Gee, R H; Maiti, A; Clague, D S; Chinn, S; Maxwell, R S

    2009-01-29

    A mesoscopic computational model for polymer networks and composites is developed as a coarse-grained representation of the composite microstructure. Unlike more complex molecular dynamics simulations, the model only considers the effects of crosslinks on mechanical behavior. The elastic modulus, which depends only on the crosslink density and parameters in the bond potential, is consistent with rubber elasticity theory, and the network response satisfies the independent network hypothesis of Tobolsky. The model, when applied to a commercial filled silicone elastomer, quantitatively reproduces the experimental permanent set and stress-strain response due to changes in the crosslinked network from irradiation.

  17. Weight prediction in complex networks based on neighbor set

    NASA Astrophysics Data System (ADS)

    Zhu, Boyao; Xia, Yongxiang; Zhang, Xue-Jun

    2016-12-01

    Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.

  18. Weight prediction in complex networks based on neighbor set

    PubMed Central

    Zhu, Boyao; Xia, Yongxiang; Zhang, Xue-Jun

    2016-01-01

    Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases. PMID:27905497

  19. Minimum Dominating Sets in Scale-Free Network Ensembles

    PubMed Central

    Molnár, F.; Sreenivasan, S.; Szymanski, B. K.; Korniss, G.

    2013-01-01

    We study the scaling behavior of the size of minimum dominating set (MDS) in scale-free networks, with respect to network size N and power-law exponent γ, while keeping the average degree fixed. We study ensembles generated by three different network construction methods, and we use a greedy algorithm to approximate the MDS. With a structural cutoff imposed on the maximal degree we find linear scaling of the MDS size with respect to N in all three network classes. Without any cutoff (kmax = N – 1) two of the network classes display a transition at γ ≈ 1.9, with linear scaling above, and vanishingly weak dependence below, but in the third network class we find linear scaling irrespective of γ. We find that the partial MDS, which dominates a given z < 1 fraction of nodes, displays essentially the same scaling behavior as the MDS.

  20. Designing application software in wide area network settings

    NASA Technical Reports Server (NTRS)

    Makpangou, Mesaac; Birman, Ken

    1990-01-01

    Progress in methodologies for developing robust local area network software has not been matched by similar results for wide area settings. The design of application software spanning multiple local area environments is examined. For important classes of applications, simple design techniques are presented that yield fault tolerant wide area programs. An implementation of these techniques as a set of tools for use within the ISIS system is described.

  1. Analysis of Gene Sets Based on the Underlying Regulatory Network

    PubMed Central

    Michailidis, George

    2009-01-01

    Abstract Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast. PMID:19254181

  2. Evaluation of social network user sentiments based on fuzzy sets

    NASA Astrophysics Data System (ADS)

    Luneva, E. E.; Banokin, P. I.; Yefremov, A. A.

    2015-10-01

    The article introduces social network user sentiment evaluation with proposed technique based on fuzzy sets. The advantage of proposed technique consists in ability to take into account user's influence as well as the fact that a user could be an author of several messages. Results presented in this paper can be used in mechanical engineering to analyze reviews on products as well as in robotics for developing user communication interface. The paper contains experimental data and shows the steps of sentiment value calculation of resulting messages on a certain topic. Application of proposed technique is demonstrated on experimental data from Twitter social network.

  3. Identifying a set of influential spreaders in complex networks

    PubMed Central

    Zhang, Jian-Xiong; Chen, Duan-Bing; Dong, Qiang; Zhao, Zhi-Dan

    2016-01-01

    Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency. PMID:27296252

  4. Identifying a set of influential spreaders in complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Jian-Xiong; Chen, Duan-Bing; Dong, Qiang; Zhao, Zhi-Dan

    2016-06-01

    Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

  5. On correlated reaction sets and coupled reaction sets in metabolic networks.

    PubMed

    Marashi, Sayed-Amir; Hosseini, Zhaleh

    2015-08-01

    Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.

  6. Minimum dominating set-based methods for analyzing biological networks.

    PubMed

    Nacher, Jose C; Akutsu, Tatsuya

    2016-06-01

    The fast increase of 'multi-omics' data does not only pose a computational challenge for its analysis but also requires novel algorithmic methodologies to identify complex biological patterns and decipher the ultimate roots of human disorders. To that end, the massive integration of omics data with disease phenotypes is offering a new window into the cell functionality. The minimum dominating set (MDS) approach has rapidly emerged as a promising algorithmic method to analyze complex biological networks integrated with human disorders, which can be composed of a variety of omics data, from proteomics and transcriptomics to metabolomics. Here we review the main theoretical foundations of the methodology and the key algorithms, and examine the recent applications in which biological systems are analyzed by using the MDS approach. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Products pipeline network plans set out for North Yemen

    SciTech Connect

    Venus, C.

    1984-02-13

    The growth of oil-products demand in future years is leading the Yemen Arab Republic (Y.A.R.) to improve the distribution network for the products by constructing a pipeline system. Petroleum products are currently distributed by road tankers only between the receiving terminals and the main cities, which represent the most important consumption centers, together with new industrial plants such as cement factories, power plants, etc. The technical design and economic and financial feasibility study of the project was entrusted to Omnium Technique des Transports par Pipelines (OTP). The scope covers the setting up in the Y.A.R. of the basic equipment for the supply, storage, and land transportation of petroleum products with a view to: Meeting the national demand for the next 25 years. Providing an adequate strategic reserve of petroleum products with a total storage capacity amounting to 3 months of consumption. The only exception in the transportation of the petroleum products will involve heavy fuel oil which will continue to be transported by road tankers. This article describes the basic facilities which have to be installed before the start-up of the projected network. The project includes a marine terminal in Salif and a pipeline to Sana'a with the related storage, truck loading, and pumping facilities for white products and gas oil which will be transported by pipeline.

  8. Multi-edge gene set networks reveal novel insights into global relationships between biological themes.

    PubMed

    Parikh, Jignesh R; Xia, Yu; Marto, Jarrod A

    2012-01-01

    Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.

  9. Efficient computation of minimal perturbation sets in gene regulatory networks

    PubMed Central

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; Degueurce, Gwendoline; Ibberson, Mark; Dorier, Julien; Xenarios, Ioannis

    2013-01-01

    In the last few decades, technological and experimental advancements have enabled a more precise understanding of the mode of action of drugs with respect to human cell signaling pathways and have positively influenced the design of new drug compounds. However, as the design of compounds has become increasingly target-specific, the overall effects of a drug on adjacent cellular signaling pathways remain difficult to predict because of the complexity of the interactions involved. Off-target effects of drugs are known to influence their efficacy and safety. Similarly, drugs which are more target-specific also suffer from lack of efficacy because their scope might be too limited in the context of cellular signaling. Even in situations where the signaling pathways targeted by a drug are known, the presence of point mutations in some of the components of the pathways can render a therapy ineffective in a considerable target subpopulation. Some of these issues can be addressed by predicting Minimal Intervention Sets (MIS) of elements of the signaling pathways that when perturbed give rise to a pre-defined cellular phenotype. These minimal gene perturbation sets can then be further used to screen a library of drug compounds in order to discover effective drug therapies. This manuscript describes algorithms that can be used to discover MIS in a gene regulatory network that can lead to a defined cellular phenotype. Algorithms are implemented in our Boolean modeling toolbox, GenYsis. The software binaries of GenYsis are available for download from http://www.vital-it.ch/software/genYsis/. PMID:24391592

  10. Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks.

    PubMed

    Yi, Zhang; Zhang, Lei; Yu, Jiali; Tan, Kok Kiong

    2009-06-01

    The concepts of permitted and forbidden sets enable a new perspective of the memory in neural networks. Such concepts exhibit interesting dynamics in recurrent neural networks. This paper studies the basic theories of permitted and forbidden sets of the linear threshold discrete-time recurrent neural networks. The linear threshold transfer function has been regarded as an adequate transfer function for recurrent neural networks. Networks with this transfer function form a class of hybrid analog and digital networks which are especially useful for perceptual computations. Networks in discrete time can directly provide algorithms for efficient implementation in digital hardware. The main contribution of this paper is to establish foundations of permitted and forbidden sets. Necessary and sufficient conditions for the linear threshold discrete-time recurrent neural networks are obtained for complete convergence, existence of permitted and forbidden sets, as well as conditionally multiattractivity, respectively. Simulation studies explore some possible interesting practical applications.

  11. A method for developing regulatory gene set networks to characterize complex biological systems.

    PubMed

    Suphavilai, Chayaporn; Zhu, Liugen; Chen, Jake Y

    2015-01-01

    Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

  12. Building Damage-Resilient Dominating Sets in Complex Networks against Random and Targeted Attacks

    PubMed Central

    Molnár, F.; Derzsy, N.; Szymanski, B. K.; Korniss, G.

    2015-01-01

    We study the vulnerability of dominating sets against random and targeted node removals in complex networks. While small, cost-efficient dominating sets play a significant role in controllability and observability of these networks, a fixed and intact network structure is always implicitly assumed. We find that cost-efficiency of dominating sets optimized for small size alone comes at a price of being vulnerable to damage; domination in the remaining network can be severely disrupted, even if a small fraction of dominator nodes are lost. We develop two new methods for finding flexible dominating sets, allowing either adjustable overall resilience, or dominating set size, while maximizing the dominated fraction of the remaining network after the attack. We analyze the efficiency of each method on synthetic scale-free networks, as well as real complex networks. PMID:25662371

  13. Selective Modulation of Orbitofrontal Network Activity during Negative Occasion Setting.

    PubMed

    Shobe, Justin L; Bakhurin, Konstantin I; Claar, Leslie D; Masmanidis, Sotiris C

    2017-09-27

    Discrete cues can gain powerful control over behavior to help an animal anticipate and cope with upcoming events. This is important in conditions where understanding the relationship between complex stimuli provides a means to resolving situational ambiguity. However, it is unclear how cortical circuits generate and maintain these signals that conditionally regulate behavior. To address this, we established a Pavlovian serial feature-negative conditioning paradigm, where male mice are trained on a trial in which a conditioned stimulus (CS) is presented alone and followed by reward, or a feature-negative trial in which the CS is preceded by a feature cue indicating there is no reward. Mice learn to respond with anticipatory licking to a solitary CS, but significantly suppress their responding to the same cue during feature-negative trials. We show that the feature cue forms a selective association with its paired CS, because the ability of the feature to transfer its suppressive properties to a separately rewarded cue is limited. Next, to examine the underlying neural dynamics, we conduct recordings in the orbitofrontal cortex (OFC). We find that the feature cue significantly and selectively inhibits CS-evoked activity. Finally, we find that the feature triggers a distinct OFC network state during the delay period between the feature and CS, establishing a potential link between the feature and future events. Together, our findings suggest that OFC dynamics are modulated by the feature cue and its associated conditioned stimulus in a manner consistent with an occasion setting model.SIGNIFICANCE STATEMENT The ability of patterned cues to form an inhibitory relationship with ambiguously rewarded outcomes has been appreciated since early studies on learning and memory. However, it was often assumed that these cues, despite their hierarchical nature, still made direct associative links with neural rewarding events. This model was significantly challenged, largely by the

  14. Setting Up a Public Use Local Area Network.

    ERIC Educational Resources Information Center

    Flower, Eric; Thulstrup, Lisa

    1988-01-01

    Describes a public use microcomputer cluster at the University of Maine, Orono. Various network topologies, hardware and software options, installation problems, system management, and performance are discussed. (MES)

  15. Mesoscopic structures reveal the network between the layers of multiplex data sets

    NASA Astrophysics Data System (ADS)

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  16. Mesoscopic structures reveal the network between the layers of multiplex data sets.

    PubMed

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  17. Network motifs come in sets: correlations in the randomization process.

    PubMed

    Ginoza, Reid; Mugler, Andrew

    2010-07-01

    The identification of motifs--subgraphs that appear significantly more often in a particular network than in an ensemble of randomized networks--has become a ubiquitous method for uncovering potentially important subunits within networks drawn from a wide variety of fields. We find that the most common algorithms used to generate the ensemble from the real network change subgraph counts in a highly correlated manner, such that one subgraph's status as a motif may not be independent from the statuses of the other subgraphs. We demonstrate this effect for the problem of three- and four-node motif identification in the transcriptional regulatory networks of E. coli and S. cerevisiae in which randomized networks are generated via an edge-swapping algorithm. We find strong correlations among subgraph counts; for three-node subgraphs these correlations are easily interpreted, and we present an information-theoretic tool that may be used to identify correlations among subgraphs of any size. Our results suggest that single-feature statistics such as Z scores that implicitly assume independence among subgraph counts constitute an insufficient summary of the network.

  18. Integrating proteomics profiling data sets: a network perspective.

    PubMed

    Bhat, Akshay; Dakna, Mohammed; Mischak, Harald

    2015-01-01

    Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell's intricate wiring network, which can ultimately lead to decipher the disease phenotype. Here, we describe what biological pathways are, how they function in normal and abnormal cellular systems, limitations faced by databases for integrating data, and highlight how network models are emerging as a powerful integrative framework to understand and interpret the roles of proteins and peptides in diseases.

  19. Network motifs come in sets: Correlations in the randomization process

    NASA Astrophysics Data System (ADS)

    Ginoza, Reid; Mugler, Andrew

    2010-07-01

    The identification of motifs—subgraphs that appear significantly more often in a particular network than in an ensemble of randomized networks—has become a ubiquitous method for uncovering potentially important subunits within networks drawn from a wide variety of fields. We find that the most common algorithms used to generate the ensemble from the real network change subgraph counts in a highly correlated manner, such that one subgraph’s status as a motif may not be independent from the statuses of the other subgraphs. We demonstrate this effect for the problem of three- and four-node motif identification in the transcriptional regulatory networks of E. coli and S. cerevisiae in which randomized networks are generated via an edge-swapping algorithm. We find strong correlations among subgraph counts; for three-node subgraphs these correlations are easily interpreted, and we present an information-theoretic tool that may be used to identify correlations among subgraphs of any size. Our results suggest that single-feature statistics such as Z scores that implicitly assume independence among subgraph counts constitute an insufficient summary of the network.

  20. Cut set-based risk and reliability analysis for arbitrarily interconnected networks

    DOEpatents

    Wyss, Gregory D.

    2000-01-01

    Method for computing all-terminal reliability for arbitrarily interconnected networks such as the United States public switched telephone network. The method includes an efficient search algorithm to generate minimal cut sets for nonhierarchical networks directly from the network connectivity diagram. Efficiency of the search algorithm stems in part from its basis on only link failures. The method also includes a novel quantification scheme that likewise reduces computational effort associated with assessing network reliability based on traditional risk importance measures. Vast reductions in computational effort are realized since combinatorial expansion and subsequent Boolean reduction steps are eliminated through analysis of network segmentations using a technique of assuming node failures to occur on only one side of a break in the network, and repeating the technique for all minimal cut sets generated with the search algorithm. The method functions equally well for planar and non-planar networks.

  1. Social Networking Tools in a University Setting: A Student's Perspective

    ERIC Educational Resources Information Center

    Haytko, Diana L.; Parker, R. Stephen

    2012-01-01

    As Professors, we are challenged to reach ever-changing cohorts of college students as they flow through our classes and our lives. Technological advancements happen daily and we need to decide which, if any, to incorporate into our classrooms. Our students constantly check Facebook, Twitter, MySpace and other online social networks. Should we be…

  2. Issue Obtrusiveness and the Agenda-Setting Effects of National Network News.

    ERIC Educational Resources Information Center

    Demers, David Pearce; And Others

    1989-01-01

    Examines effects of issue obtrusiveness on network news agenda-setting. Tests two competing models: (1) obtrusive contingency (agenda-setting effects decrease as personal experience with issues increase); and (2) cognitive-priming contingency (agenda-setting effects increase as obtrusiveness increases). Finds no support for obtrusive contingency…

  3. The Impact of Educational Networking in the Educational Setting

    ERIC Educational Resources Information Center

    Lozano, Melissa Nicole

    2011-01-01

    Society's reliance on technology today has created a demand for more effective ways of communication unlike any before. The need to communicate has made its way from the home to the business world and into the educational setting. This article discusses the historical journey that has landed communication in the classroom. Scholars such as Piaget…

  4. Measuring Social Networks for Medical Research in Lower-Income Settings

    PubMed Central

    Kelly, Laura; Patel, Shivani A.; Narayan, K. M. Venkat; Prabhakaran, Dorairaj; Cunningham, Solveig A.

    2014-01-01

    Social networks are believed to affect health-related behaviors and health. Data to examine the links between social relationships and health in low- and middle-income country settings are limited. We provide guidance for introducing an instrument to collect social network data as part of epidemiological surveys, drawing on experience in urban India. We describe development and fielding of an instrument to collect social network information relevant to health behaviors among adults participating in a large, population-based study of non-communicable diseases in Delhi, India. We discuss basic characteristics of social networks relevant to health including network size, health behaviors of network partners (i.e., network exposures), network homogeneity, network diversity, strength of ties, and multiplexity. Data on these characteristics can be collected using a short instrument of 11 items asked about up to 5 network members and 3 items about the network generally, administered in approximately 20 minutes. We found high willingness to respond to questions about social networks (97% response). Respondents identified an average of 3.8 network members, most often relatives (80% of network ties), particularly blood relationships. Ninety-one percent of respondents reported that their primary contacts for discussing health concerns were relatives. Among all listed ties, 91% of most frequent snack partners and 64% of exercise partners in the last two weeks were relatives. These results demonstrate that family relationships are the crux of social networks in some settings, including among adults in urban India. Collecting basic information about social networks can be feasibly and effectively done within ongoing epidemiological studies. PMID:25153127

  5. Strengthening an Interagency Network for Geoscience Data Sets

    NASA Astrophysics Data System (ADS)

    Ma, Xiaogang; Fox, Peter; Mayernik, Matthew S.

    2014-11-01

    More than 85 invited participants from government, academia, and the private sector attended the GeoData 2014 Workshop. The GeoData in the title of this workshop represents data sets collected and curated by the broad "Geo" community supported by numerous U.S. federal agencies: the Department of Energy, the Environmental Protection Agency, NASA, the National Oceanic and Atmospheric Administration, the National Science Foundation (NSF), the Department of Agriculture, and the U.S. Geological Survey, among others.

  6. A New Method for Setting Calculation Sequence of Directional Relay Protection in Multi-Loop Networks

    NASA Astrophysics Data System (ADS)

    Haijun, Xiong; Qi, Zhang

    2016-08-01

    Workload of relay protection setting calculation in multi-loop networks may be reduced effectively by optimization setting calculation sequences. A new method of setting calculation sequences of directional distance relay protection in multi-loop networks based on minimum broken nodes cost vector (MBNCV) was proposed to solve the problem experienced in current methods. Existing methods based on minimum breakpoint set (MBPS) lead to more break edges when untying the loops in dependent relationships of relays leading to possibly more iterative calculation workloads in setting calculations. A model driven approach based on behavior trees (BT) was presented to improve adaptability of similar problems. After extending the BT model by adding real-time system characters, timed BT was derived and the dependency relationship in multi-loop networks was then modeled. The model was translated into communication sequence process (CSP) models and an optimization setting calculation sequence in multi-loop networks was finally calculated by tools. A 5-nodes multi-loop network was applied as an example to demonstrate effectiveness of the modeling and calculation method. Several examples were then calculated with results indicating the method effectively reduces the number of forced broken edges for protection setting calculation in multi-loop networks.

  7. An efficient graph theory based method to identify every minimal reaction set in a metabolic network

    PubMed Central

    2014-01-01

    Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal

  8. Identifying generalities in data sets using periodic Hopfield networks : initial status report.

    SciTech Connect

    Link, Hamilton E.; Backer, Alejandro

    2004-12-01

    We present a novel class of dynamic neural networks that is capable of learning, in an unsupervised manner, attractors that correspond to generalities in a data set. Upon presentation of a test stimulus, the networks follow a sequence of attractors that correspond to subsets of increasing size or generality in the original data set. The networks, inspired by those of the insect antennal lobe, build upon a modified Hopfield network in which nodes are periodically suppressed, global inhibition is gradually strengthened, and the weight of input neurons is gradually decreased relative to recurrent connections. This allows the networks to converge on a Hopfield network's equilibrium within each suppression cycle, and to switch between attractors in between cycles. The fast mutually reinforcing excitatory connections that dominate dynamics within cycles ensures the robust error-tolerant behavior that characterizes Hopfield networks. The cyclic inhibition releases the network from what would otherwise be stable equilibriums or attractors. Increasing global inhibition and decreasing dependence on the input leads successive attractors to differ, and to display increasing generality. As the network is faced with stronger inhibition, only neurons connected with stronger mutually excitatory connections will remain on; successive attractors will consist of sets of neurons that are more strongly correlated, and will tend to select increasingly generic characteristics of the data. Using artificial data, we were able to identify configurations of the network that appeared to produce a sequence of increasingly general results. The next logical steps are to apply these networks to suitable real-world data that can be characterized by a hierarchy of increasing generality and observe the network's performance. This report describes the work, data, and results, the current understanding of the results, and how the work could be continued. The code, data, and preliminary results are

  9. What Does Love Mean? Exploring Network Culture in Two Network Settings

    ERIC Educational Resources Information Center

    Yeung, King-To

    2005-01-01

    Meaning matters in the way people form social ties. Adopting an unconventional analytic technique--the Galois lattice analysis--I show how network researchers can uncover relational meanings using conventional research techniques (i.e., closed-ended network surveys). Galois lattice analysis also inspires new ways of conceptualizing relational…

  10. Expanding Delivery System Research in Public Health Settings: Lessons from Practice-Based Research Networks

    PubMed Central

    Mays, Glen P.; Hogg, Rachel A.

    2014-01-01

    BACKGROUND Delivery system research to identify how best to organize, finance, and implement health improvement strategies has focused heavily on clinical practice settings, with relatively little attention paid to public health settings – where research is made more difficult by wide heterogeneity in settings and limited sources of existing data and measures. This study examines the approaches used by Public Health Practice-Based Research Networks (PBRNs) to expand delivery system research and evidence-based practice in public health settings. METHODS PBRN research networks employ quasi-experimental research designs, natural experiments, and mixed-method analytic techniques to evaluate how community partnerships, economic shocks, and policy changes impact delivery processes in public health settings. Additionally, network analysis methods are used to assess patterns of interaction between practitioners and researchers within PBRNs to produce and apply research findings. RESULTS Findings from individual PBRN studies elucidate the roles of information exchange, community resources, and leadership and decision-making structures in shaping implementation outcomes in public health delivery. Network analysis of PBRNs reveals broad engagement of both practitioners and researchers in scientific inquiry, with practitioners in the periphery of these networks reporting particularly large benefits from research participation. CONCLUSIONS Public Health PBRNs provide effective mechanisms for implementing delivery system research, engaging practitioners in the process, and accelerating the translation and application of research findings into public health settings. PMID:23023272

  11. Set7 mediated interactions regulate transcriptional networks in embryonic stem cells.

    PubMed

    Tuano, Natasha K; Okabe, Jun; Ziemann, Mark; Cooper, Mark E; El-Osta, Assam

    2016-11-02

    Histone methylation by lysine methyltransferase enzymes regulate the expression of genes implicated in lineage specificity and cellular differentiation. While it is known that Set7 catalyzes mono-methylation of histone and non-histone proteins, the functional importance of this enzyme in stem cell differentiation remains poorly understood. We show Set7 expression is increased during mouse embryonic stem cell (mESC) differentiation and is regulated by the pluripotency factors, Oct4 and Sox2. Transcriptional network analyses reveal smooth muscle (SM) associated genes are subject to Set7-mediated regulation. Furthermore, pharmacological inhibition of Set7 activity confirms this regulation. We observe Set7-mediated modification of serum response factor (SRF) and mono-methylation of histone H4 lysine 4 (H3K4me1) regulate gene expression. We conclude the broad substrate specificity of Set7 serves to control key transcriptional networks in embryonic stem cells.

  12. Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions

    PubMed Central

    2014-01-01

    Background An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network. Results We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes. Conclusions The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure. PMID:25189194

  13. Standardized network order sets in rural Ontario: a follow-up report on successes and sustainability.

    PubMed

    Rawn, Andrea; Wilson, Katrina

    2011-01-01

    Unifying, implementing and sustaining a large order set project requires strategic placement of key organizational professionals to provide ongoing user education, communication and support. This article will outline the successful strategies implemented by the Grey Bruce Health Network, Evidence-Based Care Program to reduce length of stay, increase patient satisfaction and increase the use of best practices resulting in quality outcomes, safer practice and better allocation of resources by using standardized Order Sets within a network of 11 hospital sites. Audits conducted in 2007 and again in 2008 revealed a reduced length of stay of 0.96 in-patient days when order sets were used on admission and readmission for the same or a related diagnosis within one month decreased from 5.5% without order sets to 3.5% with order sets.

  14. Fine Registration of Kilo-Station Networks - a Modern Procedure for Terrestrial Laser Scanning Data Sets

    NASA Astrophysics Data System (ADS)

    Hullo, J.-F.

    2016-06-01

    We propose a complete methodology for the fine registration and referencing of kilo-station networks of terrestrial laser scanner data currently used for many valuable purposes such as 3D as-built reconstruction of Building Information Models (BIM) or industrial asbuilt mock-ups. This comprehensive target-based process aims to achieve the global tolerance below a few centimetres across a 3D network including more than 1,000 laser stations spread over 10 floors. This procedure is particularly valuable for 3D networks of indoor congested environments. In situ, the use of terrestrial laser scanners, the layout of the targets and the set-up of a topographic control network should comply with the expert methods specific to surveyors. Using parametric and reduced Gauss-Helmert models, the network is expressed as a set of functional constraints with a related stochastic model. During the post-processing phase inspired by geodesy methods, a robust cost function is minimised. At the scale of such a data set, the complexity of the 3D network is beyond comprehension. The surveyor, even an expert, must be supported, in his analysis, by digital and visual indicators. In addition to the standard indicators used for the adjustment methods, including Baarda's reliability, we introduce spectral analysis tools of graph theory for identifying different types of errors or a lack of robustness of the system as well as in fine documenting the quality of the registration.

  15. Moving Large Data Sets Over High-Performance Long Distance Networks

    SciTech Connect

    Hodson, Stephen W; Poole, Stephen W; Ruwart, Thomas; Settlemyer, Bradley W

    2011-04-01

    In this project we look at the performance characteristics of three tools used to move large data sets over dedicated long distance networking infrastructure. Although performance studies of wide area networks have been a frequent topic of interest, performance analyses have tended to focus on network latency characteristics and peak throughput using network traffic generators. In this study we instead perform an end-to-end long distance networking analysis that includes reading large data sets from a source file system and committing large data sets to a destination file system. An evaluation of end-to-end data movement is also an evaluation of the system configurations employed and the tools used to move the data. For this paper, we have built several storage platforms and connected them with a high performance long distance network configuration. We use these systems to analyze the capabilities of three data movement tools: BBcp, GridFTP, and XDD. Our studies demonstrate that existing data movement tools do not provide efficient performance levels or exercise the storage devices in their highest performance modes. We describe the device information required to achieve high levels of I/O performance and discuss how this data is applicable in use cases beyond data movement performance.

  16. A managed clinical network for cardiac services: set-up, operation and impact on patient care

    PubMed Central

    E StC Hamilton, Karen; M Sullivan, Frank; T Donnan, Peter; Taylor, Rex; Ikenwilo, Divine; Scott, Anthony; Baker, Chris; Wyke, Sally

    2005-01-01

    Abstract Purpose To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. Methods This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Results Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and ‘bed down’. Its primary “modus operand” was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not

  17. A fractal model of HIV transmission on complex sociogeographic networks: towards analysis of large data sets.

    PubMed

    Wallace, R

    1993-01-01

    "A paradigm of HIV (human immunodeficiency virus) transmission along very large 'sociogeographic' networks--spatially focused nets of social interaction--is extended to include fractal (dilationally self-similar) structures upon which a metric of 'sociogeographic' distance can be defined.... Techniques are sketched for determining the sociogeographic structure of a large, geographically centered social network, providing a possible empirical basis for predicting forms and rates of spread of the initial, rapid stages of an HIV outbreak for networks not yet infected, and perhaps greatly expanding the utility of routinely collected small-area administrative data sets in the design of mutually reinforcing, multifactorial disease-control strategies."

  18. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

    PubMed Central

    Blatti, Charles; Sinha, Saurabh

    2016-01-01

    Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as

  19. Fuzzy rough sets, and a granular neural network for unsupervised feature selection.

    PubMed

    Ganivada, Avatharam; Ray, Shubhra Sankar; Pal, Sankar K

    2013-12-01

    A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed. The formation of the network mainly involves an input vector, initial connection weights and a target value. Each feature of the data is normalized between 0 and 1 and used to develop granulation structures by a user defined α-value. The input vector and the target value of the network are defined using granulation structures, based on the concept of fuzzy sets. The same granulation structures are also presented to a decision system. The decision system helps in extracting the domain knowledge about data in the form of dependency factors, using the notion of new fuzzy rough set. These dependency factors are assigned as the initial connection weights of the proposed network. It is then trained using minimization of a novel feature evaluation index in an unsupervised manner. The effectiveness of the proposed network, in evaluating selected features, is demonstrated on several real-life datasets. The results of FRGNN are found to be statistically more significant than related methods in 28 instances of 40 instances, i.e., 70% of instances, using the paired t-test.

  20. Balance between Noise and Information Flow Maximizes Set Complexity of Network Dynamics

    PubMed Central

    Mäki-Marttunen, Tuomo; Kesseli, Juha; Nykter, Matti

    2013-01-01

    Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content. PMID:23516395

  1. Risk Assessment of Distribution Network Based on Random set Theory and Sensitivity Analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Sh; Bai, C. X.; Liang, J.; Jiao, L.; Hou, Z.; Liu, B. Zh

    2017-05-01

    Considering the complexity and uncertainty of operating information in distribution network, this paper introduces the use of random set for risk assessment. The proposed method is based on the operating conditions defined in the random set framework to obtain the upper and lower cumulative probability functions of risk indices. Moreover, the sensitivity of risk indices can effectually reflect information about system reliability and operating conditions, and by use of these information the bottlenecks that suppress system reliability can be found. The analysis about a typical radial distribution network shows that the proposed method is reasonable and effective.

  2. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    NASA Technical Reports Server (NTRS)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  3. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    NASA Technical Reports Server (NTRS)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  4. Stability of Dominating Sets in Complex Networks against Random and Targeted Attacks

    NASA Astrophysics Data System (ADS)

    Molnar, F.; Derzsy, N.; Szymanski, B. K.; Korniss, G.

    2014-03-01

    Minimum dominating sets (MDS) are involved in efficiently controlling and monitoring many social and technological networks. However, MDS influence over the entire network may be significantly reduced when some MDS nodes are disabled due to random breakdowns or targeted attacks against nodes in the network. We investigate the stability of domination in scale-free networks in such scenarios. We define stability as the fraction of nodes in the network that are still dominated after some nodes have been removed, either randomly, or by targeting the highest-degree nodes. We find that although the MDS is the most cost-efficient solution (requiring the least number of nodes) for reaching every node in an undamaged network, it is also very sensitive to damage. Further, we investigate alternative methods for finding dominating sets that are less efficient (more costly) than MDS but provide better stability. Finally we construct an algorithm based on greedy node selection that allows us to precisely control the balance between domination stability and cost, to achieve any desired stability at minimum cost, or the best possible stability at any given cost. Analysis of our method shows moderate improvement of domination cost efficiency against random breakdowns, but substantial improvements against targeted attacks. Supported by DARPA, DTRA, ARL NS-CTA, ARO, and ONR.

  5. Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks

    NASA Astrophysics Data System (ADS)

    Hordijk, Wim

    2016-06-01

    Several computational models of chemical reaction networks have been presented in the literature in the past, showing the appearance and (potential) evolution of autocatalytic sets. However, the notion of autocatalytic sets has been defined differently in different modeling contexts, each one having some shortcoming or limitation. Here, we review four such models and definitions, and then formally describe and analyze them in the context of a mathematical framework for studying autocatalytic sets known as RAF theory. The main results are that: (1) RAF theory can capture the various previous definitions of autocatalytic sets and is therefore more complete and general, (2) the formal framework can be used to efficiently detect and analyze autocatalytic sets in all of these different computational models, (3) autocatalytic (RAF) sets are indeed likely to appear and evolve in such models, and (4) this could have important implications for a possible metabolism-first scenario for the origin of life.

  6. Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks.

    PubMed

    Hordijk, Wim

    2016-06-01

    Several computational models of chemical reaction networks have been presented in the literature in the past, showing the appearance and (potential) evolution of autocatalytic sets. However, the notion of autocatalytic sets has been defined differently in different modeling contexts, each one having some shortcoming or limitation. Here, we review four such models and definitions, and then formally describe and analyze them in the context of a mathematical framework for studying autocatalytic sets known as RAF theory. The main results are that: (1) RAF theory can capture the various previous definitions of autocatalytic sets and is therefore more complete and general, (2) the formal framework can be used to efficiently detect and analyze autocatalytic sets in all of these different computational models, (3) autocatalytic (RAF) sets are indeed likely to appear and evolve in such models, and (4) this could have important implications for a possible metabolism-first scenario for the origin of life.

  7. Peer Network Composition of Acculturated and Ethnoculturally-Affiliated Adolescents in a Multicultural Setting.

    ERIC Educational Resources Information Center

    Maharaj, Sherry I.; Connolly, Jennifer A.

    1994-01-01

    A study investigated the ethnocultural composition of the peer networks of 896 suburban high school students. Results indicated that gender mix, setting mix, and frequency of contact significantly differed across homogenous, integrated, and heterogeneous peer structures, demonstrating the mitigating impact of environmental factors on the interplay…

  8. How Do Social Networks Influence Learning Outcomes? A Case Study in an Industrial Setting

    ERIC Educational Resources Information Center

    Maglajlic, Seid; Helic, Denis

    2012-01-01

    and Purpose: The purpose of this research is to shed light on the impact of implicit social networks to the learning outcome of e-learning participants in an industrial setting. Design/methodology/approach: The paper presents a theoretical framework that allows the authors to measure correlation coefficients between the different affiliations that…

  9. How Do Social Networks Influence Learning Outcomes? A Case Study in an Industrial Setting

    ERIC Educational Resources Information Center

    Maglajlic, Seid; Helic, Denis

    2012-01-01

    and Purpose: The purpose of this research is to shed light on the impact of implicit social networks to the learning outcome of e-learning participants in an industrial setting. Design/methodology/approach: The paper presents a theoretical framework that allows the authors to measure correlation coefficients between the different affiliations that…

  10. Node Deployment Algorithm for Underwater Sensor Networks Based on Connected Dominating Set

    PubMed Central

    Jiang, Peng; Liu, Jun; Wu, Feng; Wang, Jianzhong; Xue, Anke

    2016-01-01

    Existing node deployment algorithms for underwater sensor networks are nearly unable to improve the network coverage rate under the premise of ensuring the full network connectivity and do not optimize the communication and move energy consumption during the deployment. Hence, a node deployment algorithm based on connected dominating set (CDS) is proposed. After randomly sowing the nodes in 3D monitoring underwater space, disconnected nodes move to the sink node until the network achieves full connectivity. The sink node then performs centralized optimization to determine the CDS and adjusts the locations of dominated nodes. Simulation results show that the proposed algorithm can achieve a high coverage rate while ensuring full connectivity and decreases the communication and movement energy consumption during deployment. PMID:26999147

  11. Detecting communities in clustered networks based on group action on set

    NASA Astrophysics Data System (ADS)

    Zhang, Zhanli; Jiang, Xin; Ma, Lili; Tang, Shaoting; Zheng, Zhiming

    2011-03-01

    In this paper, we propose a well targeted algorithm (GAS algorithm) for detecting communities in high clustered networks by presenting group action technology on community division. During the processing of this algorithm, the underlying community structure of a clustered network emerges simultaneously as the corresponding partition of orbits by the permutation groups acting on the node set are achieved. As the derivation of the orbit partition, an algebraic structure r-cycle can be considered as the origin of the community. To be a priori estimation for the community structure of the algorithm, the community separability is introduced to indicate whether a network has distinct community structure. By executing the algorithm on several typical networks and the LFR benchmark, it shows that this GAS algorithm can detect communities accurately and effectively in high clustered networks. Furthermore, we compare the GAS algorithm and the clique percolation algorithm on the LFR benchmark. It is shown that the GAS algorithm is more accurate at detecting non-overlapping communities in clustered networks. It is suggested that algebraic techniques can uncover fresh light on detecting communities in complex networks.

  12. Set processing in a network environment. [data bases and magnetic disks and tapes

    NASA Technical Reports Server (NTRS)

    Hardgrave, W. T.

    1975-01-01

    A combination of a local network, a mass storage system, and an autonomous set processor serving as a data/storage management machine is described. Its characteristics include: content-accessible data bases usable from all connected devices; efficient storage/access of large data bases; simple and direct programming with data manipulation and storage management handled by the set processor; simple data base design and entry from source representation to set processor representation with no predefinition necessary; capability available for user sort/order specification; significant reduction in tape/disk pack storage and mounts; flexible environment that allows upgrading hardware/software configuration without causing major interruptions in service; minimal traffic on data communications network; and improved central memory usage on large processors.

  13. Climate change education in informal settings: Using boundary objects to frame network dissemination

    NASA Astrophysics Data System (ADS)

    Steiner, Mary Ann

    This study of climate change education dissemination takes place in the context of a larger project where institutions in four cities worked together to develop a linked set of informal learning experiences about climate change. Each city developed an organizational network to explore new ways to connect urban audiences with climate change education. The four city-specific networks shared tools, resources, and knowledge with each other. The networks were related in mission and goals, but were structured and functioned differently depending on the city context. This study illustrates how the tools, resources, and knowledge developed in one network were shared with networks in two additional cities. Boundary crossing theory frames the study to describe the role of objects and processes in sharing between networks. Findings suggest that the goals, capacity and composition of networks resulted in a different emphasis in dissemination efforts, in one case to push the approach out to partners for their own work and in the other to pull partners into a more collaborative stance. Learning experiences developed in each city as a result of the dissemination reflected these differences in the city-specific emphasis with the push city diving into messy examples of the approach to make their own examples, and the pull city offering polished experiences to partners in order to build confidence in the climate change messaging. The networks themselves underwent different kinds of growth and change as a result of dissemination. The emphasis on push and use of messy examples resulted in active use of the principles of the approach and the pull emphasis with polished examples resulted in the cultivation of partnerships with the hub and the potential to engage in the educational approach. These findings have implications for boundary object theory as a useful grounding for dissemination designs in the context of networks of informal learning organizations to support a shift in

  14. Gene regulatory network inference using fused LASSO on multiple data sets.

    PubMed

    Omranian, Nooshin; Eloundou-Mbebi, Jeanne M O; Mueller-Roeber, Bernd; Nikoloski, Zoran

    2016-02-11

    Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.

  15. Network Conduciveness with Application to the Graph-Coloring and Independent-Set Optimization Transitions

    PubMed Central

    Barbosa, Valmir C.

    2010-01-01

    Background Given an undirected graph, we consider the two problems of combinatorial optimization, which ask that its chromatic and independence numbers be found. Although both problems are NP-hard, when either one is solved on the incrementally denser graphs of a random sequence, at certain critical values of the number of edges, it happens that the transition to the next value causes optimal solutions to be obtainable substantially more easily than right before it. Methodology/Principal Findings We introduce the notion of a network's conduciveness, a probabilistically interpretable measure of how the network's structure allows it to be conducive to roaming agents, in certain conditions, from one portion of the network to another. We demonstrate that the performance jumps of graph coloring and independent sets at the critical-value transitions in the number of edges can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non-optimal solutions and the optimal ones. Right past each transition, this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before it, while at the same time becoming less conducive in the opposite direction. Conclusions/Significance Network conduciveness provides a useful conceptual framework for explaining the performance jumps associated with graph coloring and independent sets. We believe it may also become instrumental in helping clarify further issues related to NP-hardness that remain poorly understood. Additionally, it may become useful also in other areas in which network theory has a role to play. PMID:20628597

  16. Network conduciveness with application to the graph-coloring and independent-set optimization transitions.

    PubMed

    Barbosa, Valmir C

    2010-07-08

    Given an undirected graph, we consider the two problems of combinatorial optimization, which ask that its chromatic and independence numbers be found. Although both problems are NP-hard, when either one is solved on the incrementally denser graphs of a random sequence, at certain critical values of the number of edges, it happens that the transition to the next value causes optimal solutions to be obtainable substantially more easily than right before it. We introduce the notion of a network's conduciveness, a probabilistically interpretable measure of how the network's structure allows it to be conducive to roaming agents, in certain conditions, from one portion of the network to another. We demonstrate that the performance jumps of graph coloring and independent sets at the critical-value transitions in the number of edges can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non-optimal solutions and the optimal ones. Right past each transition, this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before it, while at the same time becoming less conducive in the opposite direction. Network conduciveness provides a useful conceptual framework for explaining the performance jumps associated with graph coloring and independent sets. We believe it may also become instrumental in helping clarify further issues related to NP-hardness that remain poorly understood. Additionally, it may become useful also in other areas in which network theory has a role to play.

  17. Deficits in task-set maintenance and execution networks in Parkinson's disease.

    PubMed

    Tinaz, Sule; Lauro, Peter; Hallett, Mark; Horovitz, Silvina G

    2016-04-01

    Patients with Parkinson's disease have difficulties with self-initiating a task and maintaining a steady task performance. We hypothesized that these difficulties relate to reorganization in the sensorimotor execution, cingulo-opercular task-set maintenance, and frontoparietal adaptive control networks. We tested this hypothesis using graph theory-based network analysis of a composite network including a total of 86 nodes, derived from the three networks of interest. Resting-state functional magnetic resonance images were collected from 30 patients with Parkinson's disease (age 42-75 years, 11 females; Hoehn and Yahr score 2-3, average 2.4 ± 0.4) in their off-medication state and 30 matched control subjects (age 44-75 years, 10 females). For each node, we calculated strength as a general measure of connectivity, global efficiency and betweenness centrality as measures of functional integration, and clustering coefficient and local efficiency as measures of functional segregation. We found reduced node strength, clustering, and local efficiency in sensorimotor and posterior temporal nodes. There was also reduced node strength and betweenness centrality in the dorsal anterior insula and temporoparietal junction nodes of the cingulo-opercular network. These nodes are involved in integrating multimodal information, specifically related to self-awareness, sense of agency, and ultimately to intact perception of self-in-action. Moreover, we observed significant correlations between global disease severity and averaged graph metrics of the whole network. In addition to the well-known task-related frontostriatal mechanisms, we propose that the resting-state reorganization in the composite network can contribute to problems with self-initiation and task-set maintenance in Parkinson's disease.

  18. Design and characterization of chemical space networks for different compound data sets.

    PubMed

    Zwierzyna, Magdalena; Vogt, Martin; Maggiora, Gerald M; Bajorath, Jürgen

    2015-02-01

    Chemical Space Networks (CSNs) are generated for different compound data sets on the basis of pairwise similarity relationships. Such networks are thought to complement and further extend traditional coordinate-based views of chemical space. Our proof-of-concept study focuses on CSNs based upon fingerprint similarity relationships calculated using the conventional Tanimoto similarity metric. The resulting CSNs are characterized with statistical measures from network science and compared in different ways. We show that the homophily principle, which is widely considered in the context of social networks, is a major determinant of the topology of CSNs of bioactive compounds, designed as threshold networks, typically giving rise to community structures. Many properties of CSNs are influenced by numerical features of the conventional Tanimoto similarity metric and largely dominated by the edge density of the networks, which depends on chosen similarity threshold values. However, properties of different CSNs with constant edge density can be directly compared, revealing systematic differences between CSNs generated from randomly collected or bioactive compounds.

  19. Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

    NASA Astrophysics Data System (ADS)

    Yager, Ronald R.

    that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

  20. SiBIC: a web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining.

    PubMed

    Takahashi, Kei-ichiro; Takigawa, Ichigaku; Mamitsuka, Hiroshi

    2013-01-01

    Detecting biclusters from expression data is useful, since biclusters are coexpressed genes under only part of all given experimental conditions. We present a software called SiBIC, which from a given expression dataset, first exhaustively enumerates biclusters, which are then merged into rather independent biclusters, which finally are used to generate gene set networks, in which a gene set assigned to one node has coexpressed genes. We evaluated each step of this procedure: 1) significance of the generated biclusters biologically and statistically, 2) biological quality of merged biclusters, and 3) biological significance of gene set networks. We emphasize that gene set networks, in which nodes are not genes but gene sets, can be more compact than usual gene networks, meaning that gene set networks are more comprehensible. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/miami/faces/index.jsp.

  1. Learning contextual gene set interaction networks of cancer with condition specificity

    PubMed Central

    2013-01-01

    Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further

  2. A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

    NASA Astrophysics Data System (ADS)

    Chen, Jianrui; Wang, Hua; Wang, Lina; Liu, Weiwei

    2016-04-01

    Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

  3. Computing minimal nutrient sets from metabolic networks via linear constraint solving

    PubMed Central

    2013-01-01

    Background As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism. Results We present a method for computationally determining alternative growth media for an organism based on its metabolic network and transporter complement. Our method predicted 787 alternative anaerobic minimal nutrient sets for Escherichia coli K–12 MG1655 from the EcoCyc database. The program automatically partitioned the nutrients within these sets into 21 equivalence classes, most of which correspond to compounds serving as sources of carbon, nitrogen, phosphorous, and sulfur, or combinations of these essential elements. The nutrient sets were predicted with 72.5% accuracy as evaluated by comparison with 91 growth experiments. Novel aspects of our approach include (a) exhaustive consideration of all combinations of nutrients rather than assuming that all element sources can substitute for one another(an assumption that can be invalid in general) (b) leveraging the notion of a machinery-duplicating constraint, namely, that all intermediate metabolites used in active reactions must be produced in increasing concentrations to prevent successive dilution from cell division, (c) the use of Satisfiability Modulo Theory solvers rather than Linear Programming solvers, because our approach cannot be formulated as linear programming, (d) the use of Binary Decision Diagrams to produce an efficient implementation. Conclusions Our method for generating minimal nutrient sets from the metabolic network and transporters of an organism combines linear constraint solving with binary decision diagrams to efficiently produce solution sets to provided growth problems. PMID:23537498

  4. A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks

    NASA Astrophysics Data System (ADS)

    De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio

    2016-05-01

    This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.

  5. Experience in Strategic Networking to Promote Palliative Care in a Clinical Academic Setting in India

    PubMed Central

    Nair, Shoba; Tarey, SD; Barathi, B; Mary, Thiophin Regina; Mathew, Lovely; Daniel, Sudha Pauline

    2016-01-01

    Background: Palliative care in low and middle-income countries is a new discipline, responding to a greater patient need, than in high-income countries. By its very nature, palliative as a specialty has to network with other specialties to provide quality care to patients. For any medical discipline to grow as a specialty, it should be well established in the teaching medical institutions of that country. Data show that palliative care is more likely to establish and grow in an academic health care institution. It is a necessity that multiple networking strategies are adopted to reach this goal. Objectives: (1) To describe a strategic approach to palliative care service development and integration into clinical academic setting. (2) To present the change in metrics to evaluate progress. Design and Setting: This is a descriptive study wherein, the different strategies that are adopted by the Department of Palliative Medicine for networking in an academic health care institution and outside the institution are scrutinized. Measurement: The impact of this networking was assessed, one, at the level of academics and the other, at the level of service. The number of people who attended various training programs conducted by the department and the number of patients who availed palliative care service over the years were assessed. Results: Ten different strategies were identified that helped with networking of palliative care in the institution. During this time, the referrals to the department increased both for malignant diseases (52–395) and nonmalignant diseases (5–353) from 2000 to 2013. The academic sessions conducted by the department for undergraduates also saw an increase in the number of hours from 6 to 12, apart from the increase in a number of courses conducted by the department for doctors and nurses. Conclusion: Networking is an essential strategy for the establishment of a relatively new medical discipline like palliative care in a developing and

  6. Chip-set for quality of service support in passive optical networks

    NASA Astrophysics Data System (ADS)

    Ringoot, Edwin; Hoebeke, Rudy; Slabbinck, B. Hans; Verhaert, Michel

    1998-10-01

    In this paper the design of a chip-set for QoS provisioning in ATM-based Passive Optical Networks is discussed. The implementation of a general-purpose switch chip on the Optical Network Unit is presented, with focus on the design of the cell scheduling and buffer management logic. The cell scheduling logic supports `colored' grants, priority jumping and weighted round-robin scheduling. The switch chip offers powerful buffer management capabilities enabling the efficient support of GFR and UBR services. Multicast forwarding is also supported. In addition, the architecture of a MAC controller chip developed for a SuperPON access network is introduced. In particular, the permit scheduling logic and its implementation on the Optical Line Termination will be discussed. The chip-set enables the efficient support of services with different service requirements on the SuperPON. The permit scheduling logic built into the MAC controller chip in combination with the cell scheduling and buffer management capabilities of the switch chip can be used by network operators to offer guaranteed service performance to delay sensitive services, and to efficiently and fairly distribute any spare capacity to delay insensitive services.

  7. Informative conditions for the data set in an MIMO networked control system with delays, packet dropout and transmission scheduling

    NASA Astrophysics Data System (ADS)

    Zhang, Cong; Xiong, Zhihua; Ye, Hao

    2014-07-01

    In system identification, a data set needs to be informative to guarantee that the identification criterion has a unique global minimum asymptotically and the parameter estimation is consistent. In this paper, we study the informativity of the data set in a multiple-input and multiple-output (MIMO) networked control system (NCS), which contains possible network-induced delays, packet dropout, transmission scheduling, or a combination of these factors in network transmission. Moreover, to guarantee the data set of this MIMO NCS to be informative, a group of conditions for network transmission and controller's proportional term are developed. Finally, simulation studies are given to illustrate the result.

  8. Inferring pathway crosstalk networks using gene set co-expression signatures.

    PubMed

    Wang, Ting; Gu, Jin; Yuan, Jun; Tao, Ran; Li, Yanda; Li, Shao

    2013-07-01

    Constructing molecular interaction networks in cells is important for understanding the underlying mechanisms of biological processes. Except for single gene analysis, several gene set-based methods have been proposed to infer pathway crosstalk by analyzing large-scale gene expression data. But most of them take all pathway genes as a whole to infer the crosstalk. Biological evidence suggests that the pathway crosstalk usually occurs between some subsets rather than the whole sets of pathway genes. In this study, we propose a novel method, sGSCA (signature-based gene set co-expression analysis) which can use the co-expression correlations between subsets of pathway genes to infer the pathway crosstalk networks. The method applies sparse canonical correlation analysis (sCCA) to measure the pathway level co-expression and simultaneously obtain the subsets or signature genes that contribute to the co-expression of pathways. On simulated datasets, sGSCA can efficiently detect pathway crosstalk and the corresponding highly correlated signature genes. We applied sGSCA to two cancer gene expression datasets (one for hepatocellular cancer and the other for lung cancer). In the inferred networks, we found several important pathway crosstalks related to the cancers. The identified signature genes also show high enrichment for the cancer related genes. sGSCA can infer pathway crosstalk networks using large-scale gene expression data, and should be a useful tool for systematically studying the molecular mechanisms of complex diseases on both pathway and gene levels at the same time.

  9. Determining minimum set of driver nodes in protein-protein interaction networks.

    PubMed

    Zhang, Xiao-Fei; Ou-Yang, Le; Zhu, Yuan; Wu, Meng-Yun; Dai, Dao-Qing

    2015-05-07

    Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins. To address this problem, we develop a centrality-corrected minimum dominating set (CC-MDS) model which includes heterogeneity in degree and betweenness centralities of proteins. Both the MDS model and the CC-MDS model are applied on three human PPI networks. Unlike the MDS model, the CC-MDS model generates almost the same sets of driver proteins when we implement it using different optimization algorithms. The CC-MDS model targets more high-degree and high-betweenness proteins than the uncorrected counterpart. The more central position allows CC-MDS proteins to be more important in maintaining the overall network connectivity than MDS proteins. To indicate the functional significance, we find that CC-MDS proteins are involved in, on average, more protein complexes and GO annotations than MDS proteins. We also find that more essential genes, aging genes, disease-associated genes and virus-targeted genes appear in CC-MDS proteins than in MDS proteins. As for the involvement in regulatory functions, the sets of CC-MDS proteins show much stronger enrichment of transcription factors and protein kinases. The results about topological and functional significance demonstrate that the CC-MDS model can capture more driver proteins than the MDS model. Based on the results obtained, the CC-MDS model presents to be a powerful tool for the determination of driver proteins that can control the underlying PPI networks. The software

  10. Experience in Strategic Networking to Promote Palliative Care in a Clinical Academic Setting in India.

    PubMed

    Nair, Shoba; Tarey, S D; Barathi, B; Mary, Thiophin Regina; Mathew, Lovely; Daniel, Sudha Pauline

    2016-01-01

    Palliative care in low and middle-income countries is a new discipline, responding to a greater patient need, than in high-income countries. By its very nature, palliative as a specialty has to network with other specialties to provide quality care to patients. For any medical discipline to grow as a specialty, it should be well established in the teaching medical institutions of that country. Data show that palliative care is more likely to establish and grow in an academic health care institution. It is a necessity that multiple networking strategies are adopted to reach this goal. (1) To describe a strategic approach to palliative care service development and integration into clinical academic setting. (2) To present the change in metrics to evaluate progress. This is a descriptive study wherein, the different strategies that are adopted by the Department of Palliative Medicine for networking in an academic health care institution and outside the institution are scrutinized. The impact of this networking was assessed, one, at the level of academics and the other, at the level of service. The number of people who attended various training programs conducted by the department and the number of patients who availed palliative care service over the years were assessed. Ten different strategies were identified that helped with networking of palliative care in the institution. During this time, the referrals to the department increased both for malignant diseases (52-395) and nonmalignant diseases (5-353) from 2000 to 2013. The academic sessions conducted by the department for undergraduates also saw an increase in the number of hours from 6 to 12, apart from the increase in a number of courses conducted by the department for doctors and nurses. Networking is an essential strategy for the establishment of a relatively new medical discipline like palliative care in a developing and populous country like India, where the service is disproportionate to the demands.

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

    PubMed

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

    2013-01-01

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

  12. Linking Plant Specialization to Dependence in Interactions for Seed Set in Pollination Networks

    PubMed Central

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

    2013-01-01

    Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent. PMID:24205187

  13. Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks.

    PubMed

    Pratapa, Aditya; Balachandran, Shankar; Raman, Karthik

    2015-10-15

    Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals. We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets. The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors

    NASA Astrophysics Data System (ADS)

    Li, Huajiao; Fang, Wei; An, Haizhong; Gao, Xiangyun; Yan, Lili

    2016-05-01

    Economic networks in the real world are not homogeneous; therefore, it is important to study economic networks with heterogeneous nodes and edges to simulate a real network more precisely. In this paper, we present an empirical study of the one-mode derivative holding-based network constructed by the two-mode affiliation network of two sets of actors using the data of worldwide listed energy companies and their shareholders. First, we identify the primitive relationship in the two-mode affiliation network of the two sets of actors. Then, we present the method used to construct the derivative network based on the shareholding relationship between two sets of actors and the affiliation relationship between actors and events. After constructing the derivative network, we analyze different topological features on the node level, edge level and entire network level and explain the meanings of the different values of the topological features combining the empirical data. This study is helpful for expanding the usage of complex networks to heterogeneous economic networks. For empirical research on the worldwide listed energy stock market, this study is useful for discovering the inner relationships between the nations and regions from a new perspective.

  15. Online social networking sites-a novel setting for health promotion?

    PubMed

    Loss, Julika; Lindacher, Verena; Curbach, Janina

    2014-03-01

    Among adolescents, online social networking sites (SNS) such as Facebook are popular platforms for social interaction and may therefore be considered as 'novel settings' that could be exploited for health promotion. In this article, we examine the relevant definitions in health promotion and literature in order to analyze whether key characteristics of 'settings for health promotion' and the socio-ecological settings approach can be transferred to SNS. As many of our daily activities have shifted to cyberspace, we argue that online social interaction may gain more importance than geographic closeness for defining a 'setting'. While exposition to positive references to risk behavior by peers may render the SNS environment detrimental to health, SNS may allow people to create their own content and therefore foster participation. However, those health promotion projects delivered on SNS up until today solely relied on health education directed at end users. It remains unclear how health promotion on SNS can meet other requirements of the settings approach (e.g. building partnerships, changing the environment). As yet, one should be cautious in terming SNS a 'setting'.

  16. Neural network and rough set hybrid scheme for prediction of missing associations.

    PubMed

    Anitha, A; Acharjya, D P

    2015-01-01

    Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.

  17. A restricted branch and bound approach for setting the left turn phase sequences in signalized networks

    SciTech Connect

    Pillai, R.S.; Rathi, A.K.; Cohen, S.

    1994-07-01

    The main objective of synchronized signal timing is to keep traffic moving along arterial in platoons throughout the signal system by proper setting of left turn phase sequence at signals along the arterials/networks. The synchronization of traffic signals located along the urban/suburban arterials in metropolitan areas is perhaps one of the most cost-effective method for improving traffic flow along these streets. The popular technique for solving this problem formulates it as a mixed integer linear program and used Land and Powell branch and bound search to arrive at the optimal solution. The computation time tends to be excessive for realistic multiarterial network problems due to the exhaustive nature of the branch and bound search technique. Furthermore, the Land and Powell branch and bound code is known to be numerically unstable, which results in suboptimal solutions for network problems with a range on the cycle time variable. This paper presents the development of a fast and numerically stable heuristic, developed using MINOS linear programming solver. The new heuristic can generate optimal/near-optimal solutions in a fraction of the time needed to compute the optimal solution by Land and Powell code. The solution technique is based on restricted search using branch and bound technique. The efficiency of the heuristic approach is demonstrated by numerical results for a set of test problems.

  18. Multi-link faults localization and restoration based on fuzzy fault set for dynamic optical networks.

    PubMed

    Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo

    2013-01-28

    Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.

  19. Multidimensional mutual ordering of patterns using a set of pre-trained artificial neural networks

    NASA Astrophysics Data System (ADS)

    Kulagin, V. P.; Ivanov, A. I.; Kuznetsov, Yu M.; Chulkova, G. M.

    2017-01-01

    The article shows that large artificial neural networks can be used for mutual ordering of a set of multi-dimensional patterns of the same nature (handwritten text, voice, smells, taste). Each neural network must be pre-trained to recognize one of the patterns. As a measure of ordering one can use the entropy of patterns “Strangers” that are input to a neural network trained to recognize only examples of the pattern “familiar”. The neural network after training reduces the entropy of the examples of the pattern “Familiar” and increases the entropy of examples of pattern “Stranger.” It is shown that the entropy measure of the ordering always has two global minima. The first minimum corresponds to the pattern “Familiar”, the second to the inversion of the pattern “Familiar”. It is also shown that the Hamming distance between the patterns belonging to two different groups (groups of the two global minima) is always as large as possible.

  20. Knowledge mining from clinical datasets using rough sets and backpropagation neural network.

    PubMed

    Nahato, Kindie Biredagn; Harichandran, Khanna Nehemiah; Arputharaj, Kannan

    2015-01-01

    The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

  1. Reconstructing Protein Structures by Neural Network Pairwise Interaction Fields and Iterative Decoy Set Construction

    PubMed Central

    Mirabello, Claudio; Adelfio, Alessandro; Pollastri, Gianluca

    2014-01-01

    Predicting the fold of a protein from its amino acid sequence is one of the grand problems in computational biology. While there has been progress towards a solution, especially when a protein can be modelled based on one or more known structures (templates), in the absence of templates, even the best predictions are generally much less reliable. In this paper, we present an approach for predicting the three-dimensional structure of a protein from the sequence alone, when templates of known structure are not available. This approach relies on a simple reconstruction procedure guided by a novel knowledge-based evaluation function implemented as a class of artificial neural networks that we have designed: Neural Network Pairwise Interaction Fields (NNPIF). This evaluation function takes into account the contextual information for each residue and is trained to identify native-like conformations from non-native-like ones by using large sets of decoys as a training set. The training set is generated and then iteratively expanded during successive folding simulations. As NNPIF are fast at evaluating conformations, thousands of models can be processed in a short amount of time, and clustering techniques can be adopted for model selection. Although the results we present here are very preliminary, we consider them to be promising, with predictions being generated at state-of-the-art levels in some of the cases. PMID:24970210

  2. Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

    PubMed Central

    Nahato, Kindie Biredagn; Harichandran, Khanna Nehemiah; Arputharaj, Kannan

    2015-01-01

    The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets. PMID:25821508

  3. A broadcast-based key agreement scheme using set reconciliation for wireless body area networks.

    PubMed

    Ali, Aftab; Khan, Farrukh Aslam

    2014-05-01

    Information and communication technologies have thrived over the last few years. Healthcare systems have also benefited from this progression. A wireless body area network (WBAN) consists of small, low-power sensors used to monitor human physiological values remotely, which enables physicians to remotely monitor the health of patients. Communication security in WBANs is essential because it involves human physiological data. Key agreement and authentication are the primary issues in the security of WBANs. To agree upon a common key, the nodes exchange information with each other using wireless communication. This information exchange process must be secure enough or the information exchange should be minimized to a certain level so that if information leak occurs, it does not affect the overall system. Most of the existing solutions for this problem exchange too much information for the sake of key agreement; getting this information is sufficient for an attacker to reproduce the key. Set reconciliation is a technique used to reconcile two similar sets held by two different hosts with minimal communication complexity. This paper presents a broadcast-based key agreement scheme using set reconciliation for secure communication in WBANs. The proposed scheme allows the neighboring nodes to agree upon a common key with the personal server (PS), generated from the electrocardiogram (EKG) feature set of the host body. Minimal information is exchanged in a broadcast manner, and even if every node is missing a different subset, by reconciling these feature sets, the whole network will still agree upon a single common key. Because of the limited information exchange, if an attacker gets the information in any way, he/she will not be able to reproduce the key. The proposed scheme mitigates replay, selective forwarding, and denial of service attacks using a challenge-response authentication mechanism. The simulation results show that the proposed scheme has a great deal of

  4. Agenda setting for maternal survival: the power of global health networks and norms

    PubMed Central

    Smith, Stephanie L; Rodriguez, Mariela A

    2016-01-01

    Nearly 300 000 women—almost all poor women in low-income countries—died from pregnancy-related complications in 2010. This represents a decline since the 1980s, when an estimated half million women died each year, but is still far higher than the aims set in the United Nations Millennium Development Goals (MDGs) at the turn of the century. The 1970s, 1980s and 1990s witnessed a shift from near complete neglect of the issue to emergence of a network of individuals and organizations with a shared concern for reducing maternal deaths and growth in the number of organizations and governments with maternal health strategies and programmes. Maternal health experienced a marked change in agenda status in the 2000s, attracting significantly higher level attention (e.g. from world leaders) and greater resource commitments (e.g. as one issue addressed by US$40 billion in pledges to the 2010 Global Strategy for Women’s and Children’s Health) than ever before. Several differences between network and actor features, issue characteristics and the policy environment pre- and post-2000 help to explain the change in agenda status for global maternal mortality reduction. Significantly, a strong poverty reduction norm emerged at the turn of the century; represented by the United Nations MDGs framework, the norm set unusually strong expectations for international development actors to advance included issues. As the norm grew, it drew policy attention to the maternal health goal (MDG 5). Seeking to advance the goals agenda, world leaders launched initiatives addressing maternal and child health. New network governance and framing strategies that closely linked maternal, newborn and child health shaped the initiatives. Diverse network composition—expanding beyond a relatively narrowly focused and technically oriented group to encompass allies and leaders that brought additional resources to bear on the problem—was crucial to maternal health’s rise on the agenda in the

  5. Agenda setting for maternal survival: the power of global health networks and norms.

    PubMed

    Smith, Stephanie L; Rodriguez, Mariela A

    2016-04-01

    Nearly 300,000 women--almost all poor women in low-income countries--died from pregnancy-related complications in 2010. This represents a decline since the 1980s, when an estimated half million women died each year, but is still far higher than the aims set in the United Nations Millennium Development Goals (MDGs) at the turn of the century. The 1970s, 1980s and 1990 s witnessed a shift from near complete neglect of the issue to emergence of a network of individuals and organizations with a shared concern for reducing maternal deaths and growth in the number of organizations and governments with maternal health strategies and programmes. Maternal health experienced a marked change in agenda status in the 2000s, attracting significantly higher level attention (e.g. from world leaders) and greater resource commitments (e.g. as one issue addressed by US$40 billion in pledges to the 2010 Global Strategy for Women's and Children's Health) than ever before. Several differences between network and actor features, issue characteristics and the policy environment pre- and post-2000 help to explain the change in agenda status for global maternal mortality reduction. Significantly, a strong poverty reduction norm emerged at the turn of the century; represented by the United Nations MDGs framework, the norm set unusually strong expectations for international development actors to advance included issues. As the norm grew, it drew policy attention to the maternal health goal (MDG 5). Seeking to advance the goals agenda, world leaders launched initiatives addressing maternal and child health. New network governance and framing strategies that closely linked maternal, newborn and child health shaped the initiatives. Diverse network composition--expanding beyond a relatively narrowly focused and technically oriented group to encompass allies and leaders that brought additional resources to bear on the problem--was crucial to maternal health's rise on the agenda in the 2000s.

  6. Structural Damage Identification Based on Rough Sets and Artificial Neural Network

    PubMed Central

    Liu, Chengyin; Wu, Xiang; Wu, Ning; Liu, Chunyu

    2014-01-01

    This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties. PMID:25013847

  7. Structural damage identification based on rough sets and artificial neural network.

    PubMed

    Liu, Chengyin; Wu, Xiang; Wu, Ning; Liu, Chunyu

    2014-01-01

    This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

  8. Breast mass segmentation in digital mammography based on pulse coupled neural network and level set method

    NASA Astrophysics Data System (ADS)

    Xie, Weiying; Ma, Yide; Li, Yunsong

    2015-05-01

    A novel approach to mammographic image segmentation, termed as PCNN-based level set algorithm, is presented in this paper. Just as its name implies, a method based on pulse coupled neural network (PCNN) in conjunction with the variational level set method for medical image segmentation. To date, little work has been done on detecting the initial zero level set contours based on PCNN algorithm for latterly level set evolution. When all the pixels of the input image are fired by PCNN, the small pixel value will be a much more refined segmentation. In mammographic image, the breast tumor presents big pixel value. Additionally, the mammographic image with predominantly dark region, so that we firstly obtain the negative of mammographic image with predominantly dark region except the breast tumor before all the pixels of an input image are fired by PCNN. Therefore, in here, PCNN algorithm is employed to achieve mammary-specific, initial mass contour detection. After that, the initial contours are all extracted. We define the extracted contours as the initial zero level set contours for automatic mass segmentation by variational level set in mammographic image analysis. What's more, a new proposed algorithm improves external energy of variational level set method in terms of mammographic images in low contrast. In accordance with the gray scale of mass region in mammographic image is higher than the region surrounded, so the Laplace operator is used to modify external energy, which could make the bright spot becoming much brighter than the surrounded pixels in the image. A preliminary evaluation of the proposed method performs on a known public database namely MIAS, rather than synthetic images. The experimental results demonstrate that our proposed approach can potentially obtain better masses detection results in terms of sensitivity and specificity. Ultimately, this algorithm could lead to increase both sensitivity and specificity of the physicians' interpretation of

  9. LS-44: An improved deep space network station location set for Viking navigation

    NASA Technical Reports Server (NTRS)

    Koble, H. M.; Pease, G. E.; Yip, K. W.

    1976-01-01

    Improved estimates for the spin axis and longitude components of the Deep Space Network station locations were obtained from post-flight processing of radio metric data received from various Mariner planetary missions. The use of an upgraded set of ionospheric calibrations and the incorporation of near-Venus and near-Mercury radio metric data from the Mariner 10 spacecraft are the principal contributing effects to the improvement. These new estimates, designated Location Set (LS) 44, have supported Viking navigation activities in the vicinity of Mars. As such, the station locations were determined relative to the planetary positions inherent in JPL Development Ephemeris (DE) 84, which was used throughout the Viking mission. The article also presents and discusses a version of LS 44 based upon the latest planetary ephemeris, DE 96.

  10. LS-44: An improved deep space network station location set for Viking navigation

    NASA Technical Reports Server (NTRS)

    Koble, H. M.; Pease, G. E.; Yip, K. W.

    1976-01-01

    Improved estimates for the spin axis and longitude components of the Deep Space Network station locations were obtained from post-flight processing of radio metric data received from various Mariner planetary missions. The use of an upgraded set of ionospheric calibrations and the incorporation of near-Venus and near-Mercury radio metric data from the Mariner 10 spacecraft are the principal contributing effects to the improvement. These new estimates, designated Location Set (LS) 44, have supported Viking navigation activities in the vicinity of Mars. As such, the station locations were determined relative to the planetary positions inherent in JPL Development Ephemeris (DE) 84, which was used throughout the Viking mission. The article also presents and discusses a version of LS 44 based upon the latest planetary ephemeris, DE 96.

  11. Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries

    PubMed Central

    Sobolevsky, Stanislav; Szell, Michael; Campari, Riccardo; Couronné, Thomas; Smoreda, Zbigniew; Ratti, Carlo

    2013-01-01

    Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions. PMID:24367490

  12. Delineating geographical regions with networks of human interactions in an extensive set of countries.

    PubMed

    Sobolevsky, Stanislav; Szell, Michael; Campari, Riccardo; Couronné, Thomas; Smoreda, Zbigniew; Ratti, Carlo

    2013-01-01

    Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.

  13. GuMNet - Guadarrama Monitoring Network. Installation and set up of a high altitude monitoring network, north of Madrid. Spain

    NASA Astrophysics Data System (ADS)

    Santolaria-Canales, Edmundo

    2015-04-01

    An observational monitoring network in the Guadarrama Mountains is due to be operational at the end of 2015. This network integrates atmospheric measurements as well as subsurface observations in a high mountain climate, located up to 2.400 m.a.s.l. The data provided by GuMNet will help to improve the characterization of microclimate in high mountain areas, as well as land-atmosphere interations. The network information aims at meeting the needs of accuracy to be used for biological, agricultural, hydrological, meteorological and climatic investigations in this area. This initiative is supported and developed by research groups integrating the GuMNet Consortiuma from the Complutense and Polytechnical Universities of Madrid (UCM and UPM), the Energetic Environmental and Technological Research Centre (CIEMAT), the Spanish National Meteorological Agency (AEMET), and the National Park Sierra de Guadarrama (PNSG). The starting setup includes seven meteorological stations compatible with WMO standards, to be installed in the central area of the massif. Including a four-component net radiation sensor, an ultrasonic snow height, a pluviometer specialized for snow capture, air temperature and humidity devices and wind speed/direction sensor. Along with these atmospheric measurements, each station will include a set of subsurface measurements of temperature in shallow boreholes ( 20 m depth ) and temperature and humidity in trenches up to 1 m depth. These compatible WMO stations will be complemented by a station specialized in eddy covariance measuremets with CO2 fluxes at low altitude pastureland near Madrid. Another portable station is available to develop ad hoc comparison studies. This setup is embedded in a broader network of meteorological stations run partly by AEMET and partly by the PNSG. Most of the AEMET stations are distributed over lower altitudes, and will provide a very reliable boundary information for the atmosphere state around the Sierra. In the same way

  14. Diabetes research in children network:availability of protocol data sets.

    PubMed

    Ruedy, Katrina J; Beck, Roy W; Xing, Dongyuan; Kollman, Craig

    2007-09-01

    The Diabetes Research in Children Network (DirecNet) was established in 2001 by the National Institute of Child Health and Human Development and the National Institute of Diabetes and Digestive and Kidney Diseases through special congressional funding for type 1 diabetes research. The network consists of five clinical centers, a coordinating center, and a central laboratory. Since its inception, DirecNet has conducted nine protocols, resulting in 28 published manuscripts with an additional 2 under review and 5 in development. The protocols have involved evaluation of technology available for the treatment of type 1 diabetes, including home glucose meters (OneTouch Ultra, FreeStyle, and BD Logic), continuous glucose monitoring systems (GW2B, CGMS, FreeStyle Navigator, and Guardian RT), and hemoglobin A1c (HbA1c) devices (DCA 2000 and A1cNow). In addition, the group has conducted several studies evaluating factors affecting hypoglycemia, including exercise and bedtime snack composition. The data sets that have resulted from these studies include data from the devices being evaluated, central laboratory glucose, HbA1c and hormone data, clinical center glucose and HbA1c data, accelerometer data, and pump data depending on the procedures involved with each protocol. These data sets are, or will be, available at no charge on the study group's public Web site. Several psychosocial questionnaires developed by DirecNet are also available.

  15. Inferring disease and gene set associations with rank coherence in networks.

    PubMed

    Hwang, TaeHyun; Zhang, Wei; Xie, Maoqiang; Liu, Jinfeng; Kuang, Rui

    2011-10-01

    To validate the candidate disease genes identified from high-throughput genomic studies, a necessary step is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease-causative genes are incomplete. This article introduces a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. A learning framework is proposed to maximize the coherence between the predicted phenotype-gene set relations and the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation and two variants are designed to find the optimal solution to the objective functions of the learning framework. We evaluated the rcNet algorithms with leave-one-out cross-validation on Online Mendelian Inheritance in Man (OMIM) data and an independent test set of recently discovered disease-gene associations. In the experiments, the rcNet algorithms achieved best overall rankings compared with the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of Genome-Wide Association Study (GWAS), DNA copy number variation analysis and gene expression profiling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies. http://compbio.cs.umn.edu/dgsa_rcNet kuang@cs.umn.edu.

  16. Generating a comprehensive set of standard operating procedures for a biorepository network-The CTRNet experience.

    PubMed

    Barnes, Rebecca; Albert, Monique; Damaraju, Sambasivarao; de Sousa-Hitzler, Jean; Kodeeswaran, Sugy; Mes-Masson, Anne-Marie; Watson, Peter; Schacter, Brent

    2013-12-01

    Despite the integral role of biorepositories in fueling translational research and the advancement of medicine, there are significant gaps in harmonization of biobanking practices, resulting in variable biospecimen collection, storage, and processing. This significantly impacts accurate downstream analysis and, in particular, creates a problem for biorepository networks or consortia. The Canadian Tumour Repository Network (CTRNet; www.ctrnet.ca ) is a consortium of Canadian tumor biorepositories that aims to enhance biobanking capacity and quality through standardization. To minimize the issue of variable biobanking practices throughout its network, CTRNet has developed and maintained a comprehensive set of 45 standard operating procedures (SOPs). There were four key elements to the CTRNet SOP development process: 1) an SOP development team was formed from members across CTRNet to co-produce each SOP; 2) a principal author was appointed with responsibility for overall coordination of the SOP development process; 3) the CTRNet Management Committee (composed of principal investigators for each member biorepository) reviewed/revised each SOP completed by the development team; and 4) external expert reviewers provided feedback and recommendations on each SOP. Once final Management Committee approval was obtained, the ratified SOP was published on the CTRNet website for public access. Since the SOPs were first published on the CTRNet website (June 2008), there have been approximately 15,000 downloads of one or more CTRNet SOPs/Policies by users from over 60 countries. In accordance with biobanking best practices, CTRNet performs an exhaustive review of its SOPs at set intervals, to coincide with each granting cycle. The last revision was completed in May 2012.

  17. Exploratory Analysis in Time-Varying Data Sets: a Healthcare Network Application.

    PubMed

    Manukyan, Narine; Eppstein, Margaret J; Horbar, Jeffrey D; Leahy, Kathleen A; Kenny, Michael J; Mukherjee, Shreya; Rizzo, Donna M

    2013-07-01

    We introduce a new method for exploratory analysis of large data sets with time-varying features, where the aim is to automatically discover novel relationships between features (over some time period) that are predictive of any of a number of time-varying outcomes (over some other time period). Using a genetic algorithm, we co-evolve (i) a subset of predictive features, (ii) which attribute will be predicted (iii) the time period over which to assess the predictive features, and (iv) the time period over which to assess the predicted attribute. After validating the method on 15 synthetic test problems, we used the approach for exploratory analysis of a large healthcare network data set. We discovered a strong association, with 100% sensitivity, between hospital participation in multi-institutional quality improvement collaboratives during or before 2002, and changes in the risk-adjusted rates of mortality and morbidity observed after a 1-2 year lag. The proposed approach is a potentially powerful and general tool for exploratory analysis of a wide range of time-series data sets.

  18. Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets

    NASA Astrophysics Data System (ADS)

    Cha, Kenny H.; Hadjiiski, Lubomir M.; Samala, Ravi K.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.

    2016-03-01

    We are developing a CAD system for detection of bladder cancer in CTU. In this study we investigated the application of deep-learning convolutional neural network (DL-CNN) to the segmentation of the bladder, which is a challenging problem because of the strong boundary between the non-contrast and contrast-filled regions in the bladder. We trained a DL-CNN to estimate the likelihood of a pixel being inside the bladder using neighborhood information. The segmented bladder was obtained from thresholding and hole-filling of the likelihood map. We compared the segmentation performance of the DL-CNN alone and with additional cascaded 3D and 2D level sets to refine the segmentation using 3D hand-segmented contours as reference standard. The segmentation accuracy was evaluated by five performance measures: average volume intersection %, average % volume error, average absolute % error, average minimum distance, and average Jaccard index for a data set of 81 training and 92 test cases. For the training set, DLCNN with level sets achieved performance measures of 87.2+/-6.1%, 6.0+/-9.1%, 8.7+/-6.1%, 3.0+/-1.2 mm, and 81.9+/-7.6%, respectively, while the DL-CNN alone obtained the values of 73.6+/-8.5%, 23.0+/-8.5%, 23.0+/-8.5%, 5.1+/-1.5 mm, and 71.5+/-9.2%, respectively. For the test set, the DL-CNN with level sets achieved performance measures of 81.9+/-12.1%, 10.2+/-16.2%, 14.0+/-13.0%, 3.6+/-2.0 mm, and 76.2+/-11.8%, respectively, while DL-CNN alone obtained 68.7+/-12.0%, 27.2+/-13.7%, 27.4+/-13.6%, 5.7+/-2.2 mm, and 66.2+/-11.8%, respectively. DL-CNN alone is effective in segmenting bladders but may not follow the details of the bladder wall. The combination of DL-CNN with level sets provides highly accurate bladder segmentation.

  19. Determining the Most Vital Arcs Within a Multi-Mode Communication Network Using Set-Based Measures

    DTIC Science & Technology

    2015-03-26

    Order ( ATO ), All Pairs Short- est Path (APSP), Minimum Cost Flow (MCF), Set-Based Efficiency, Set-Based Cost Efficiency, Network, Most Vital...8 2.6 ATO Dissemination...Tasking Order ( ATO ) can be seen in Figure 1. Traditionally the air tasking cycle is a 72-hour iterative process. The ATO production team is always working

  20. Implementing evidence-based policy in a network setting: road safety policy in the Netherlands.

    PubMed

    Bax, Charlotte; de Jong, Martin; Koppenjan, Joop

    2010-01-01

    In the early 1990s, in order to improve road safety in The Netherlands, the Institute for Road Safety Research (SWOV) developed an evidence-based "Sustainable Safety" concept. Based on this concept, Dutch road safety policy, was seen as successful and as a best practice in Europe. In The Netherlands, the policy context has now changed from a sectoral policy setting towards a fragmented network in which safety is a facet of other transport-related policies. In this contribution, it is argued that the implementation strategy underlying Sustainable Safety should be aligned with the changed context. In order to explore the adjustments needed, two perspectives of policy implementation are discussed: (1) national evidence-based policies with sectoral implementation; and (2) decentralized negotiation on transport policy in which road safety is but one aspect. We argue that the latter approach matches the characteristics of the newly evolved policy context best, and conclude with recommendations for reformulating the implementation strategy.

  1. Hydro-Climatic Data Network (HCDN) Streamflow Data Set, 1874-1988

    USGS Publications Warehouse

    Slack, James Richard; Lumb, Alan M.; Landwehr, Jurate Maciunas

    1993-01-01

    The potential consequences of climate change to continental water resources are of great concern in the management of those resources. Critically important to society is what effect fluctuations in the prevailing climate may have on hydrologic conditions, such as the occurrence and magnitude of floods or droughts and the seasonal distribution of water supplies within a region. Records of streamflow that are unaffected by artificial diversions, storage, or other works of man in or on the natural stream channels or in the watershed can provide an account of hydrologic responses to fluctuations in climate. By examining such records given known past meteorologic conditions, we can better understand hydrologic responses to those conditions and anticipate the effects of postulated changes in current climate regimes. Furthermore, patterns in streamflow records can indicate when a change in the prevailing climate regime may have occurred in the past, even in the absence of concurrent meteorologic records. A streamflow data set, which is specifically suitable for the study of surface-water conditions throughout the United States under fluctuations in the prevailing climatic conditions, has been developed. This data set, called the Hydro-Climatic Data Network, or HCDN, consists of streamflow records for 1,659 sites throughout United States and its Territories. Records cumulatively span the period 1874 through 1988, inclusive, and represent a total of 73,231 water years of information. Development of the HCDN Data Set: Records for the HCDN were obtained through a comprehensive search of the extensive surface- water data holdings of the U.S. Geological Survey (USGS), which are contained in the USGS National Water Storage and Retrieval System (WATSTORE). All streamflow discharge records in WATSTORE through September 30, 1988, were examined for inclusion in the HCDN in accordance with strictly defined criteria of measurement accuracy and natural conditions. No reconstructed

  2. The Global Network Neonatal Cause of Death algorithm for low-resource settings.

    PubMed

    Garces, Ana L; McClure, Elizabeth M; Pérez, Wilton; Hambidge, K Michael; Krebs, Nancy F; Figueroa, Lester; Bose, Carl L; Carlo, Waldemar A; Tenge, Constance; Esamai, Fabian; Goudar, Shivaprasad S; Saleem, Sarah; Patel, Archana B; Chiwila, Melody; Chomba, Elwyn; Tshefu, Antoinette; Derman, Richard J; Hibberd, Patricia L; Bucher, Sherri; Liechty, Edward A; Bauserman, Melissa; Moore, Janet L; Koso-Thomas, Marion; Miodovnik, Menachem; Goldenberg, Robert L

    2017-06-01

    This study estimated the causes of neonatal death using an algorithm for low-resource areas, where 98% of the world's neonatal deaths occur. We enrolled women in India, Pakistan, Guatemala, the Democratic Republic of Congo, Kenya and Zambia from 2014 to 2016 and tracked their delivery and newborn outcomes for up to 28 days. Antenatal care and delivery symptoms were collected using a structured questionnaire, clinical observation and/or a physical examination. The Global Network Cause of Death algorithm was used to assign the cause of neonatal death, analysed by country and day of death. One-third (33.1%) of the 3068 neonatal deaths were due to suspected infection, 30.8% to prematurity, 21.2% to asphyxia, 9.5% to congenital anomalies and 5.4% did not have a cause of death assigned. Prematurity and asphyxia-related deaths were more common on the first day of life (46.7% and 52.9%, respectively), while most deaths due to infection occurred after the first day of life (86.9%). The distribution of causes was similar to global data reported by other major studies. The Global Network algorithm provided a reliable cause of neonatal death in low-resource settings and can be used to inform public health strategies to reduce mortality. ©2017 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  3. Experiences in set-up and usage of a geodetic real-time differential correction network

    NASA Astrophysics Data System (ADS)

    Martin, Sven; Jahn, Cord-Hinrich

    2000-10-01

    Global Navigation Satellite Systems (GNSS) are commonly used for geodetic and land surveying applications. The stand alone accuracy provided by these GNSS are insufficient for the majority of these operations (GPS, 1995), therefore some form of differential correction method is required. Accordingly, the state survey offices of Germany have installed a differential correction service for geodetic applications. Code- and phase-corrections are broadcast in the VHF-band using the RTCM V2.1 format (RTCM, 1994). One major problem is that the accuracy depends on the distance to a reference station (length of baseline) because of residual orbit and atmospheric biases. To achieve a more precise solution, a number of reference stations are connected together to form a network. Within this network these influences are computed and a set of "area correction parameters" are also transmitted in RTCM message Type 59. Field trials and measurements have confirmed the high accuracy of this service. This paper describes the system itself, investigations of communication methods as well as site planning. In addition measurements from field trials will be presented to demonstrate the high accuracy in a real-time environment.

  4. Analysis of the Effect of Degree Correlation on the Size of Minimum Dominating Sets in Complex Networks

    PubMed Central

    2016-01-01

    Network controllability is an important topic in wide-ranging research fields. However, the relationship between controllability and network structure is poorly understood, although degree heterogeneity is known to determine the controllability. We focus on the size of a minimum dominating set (MDS), a measure of network controllability, and investigate the effect of degree-degree correlation, which is universally observed in real-world networks, on the size of an MDS. We show that disassortativity or negative degree-degree correlation reduces the size of an MDS using analytical treatments and numerical simulation, whereas positive correlations hardly affect the size of an MDS. This result suggests that disassortativity enhances network controllability. Furthermore, apart from the controllability issue, the developed techniques provide new ways of analyzing complex networks with degree-degree correlations. PMID:27327273

  5. Study on the potential for delay tolerant networks by health workers in low resource settings.

    PubMed

    Syed-Abdul, Shabbir; Scholl, Jeremiah; Lee, Peisan; Jian, Wen-Shan; Liou, Der-Ming; Li, Yu-Chuan

    2012-09-01

    Medical Informatics Systems (MIS) have been suggested as having great potential to improve health care delivery in low resource settings. One of the major barriers for adopting MIS in this context is a lack of adequate network/communication infrastructure. Delay Tolerant Networking (DTN) is an approach for establishing network connectivity in situations where it is possible to support physical transport of the digital information. To date most DTN research has been technically oriented, and very few services have been implemented to support healthcare systems using the technology. It is thus unclear about the potential that DTN may have for supporting MIS systems in low resource settings. The goals of the paper are twofold, first, to gain an initial estimate of interest in different services that can be supported by DTN. Second, to find out the necessary frequency associated with each service for supporting health work in low resource settings. Fifty questionnaires were distributed to attendants at the International Conference on Global Health that had acknowledged having health work experience in a poor connectivity context. The respondents were using a 5-point Likert scale regarding if 9 different potential DTN services "would be useful". They also were asked how often data delivery would be necessary for these services to be useful. The Chi square was calculated to measure acceptance. 37 responses were received, aggregating the response rate of 74%. The respondents represented having work experience from 8 months to 15 years from 35 resource poor countries. The Chi square test showed very high statistical significance for "strongly agree and agree" for the potential usefulness of the proposed DTN services, with a p-value less than 0.001. The frequency of data delivery that would be necessary for services to be useful varied considerably. This study provides evidence of potential for DTN to support useful services that support health work in low resource settings

  6. RIDDLE: reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network

    PubMed Central

    2012-01-01

    The growing availability of large-scale functional networks has promoted the development of many successful techniques for predicting functions of genes. Here we extend these network-based principles and techniques to functionally characterize whole sets of genes. We present RIDDLE (Reflective Diffusion and Local Extension), which uses well developed guilt-by-association principles upon a human gene network to identify associations of gene sets. RIDDLE is particularly adept at characterizing sets with no annotations, a major challenge where most traditional set analyses fail. Notably, RIDDLE found microRNA-450a to be strongly implicated in ocular diseases and development. A web application is available at http://www.functionalnet.org/RIDDLE. PMID:23268829

  7. Adaptation and implementation of standardized order sets in a network of multi-hospital corporations in rural Ontario.

    PubMed

    Meleskie, Jessica; Eby, Don

    2009-01-01

    Standardized, preprinted or computer-generated physician orders are an attractive project for organizations that wish to improve the quality of patient care. The successful development and maintenance of order sets is a major undertaking. This article recounts the collaborative experience of the Grey Bruce Health Network in adapting and implementing an existing set of physician orders for use in its three hospital corporations. An Order Set Committee composed of primarily front-line staff was given authority over the order set development, approval and implementation processes. This arrangement bypassed the traditional approval process and facilitated the rapid implementation of a large number of order sets in a short time period.

  8. [Case finding in early prevention networks - a heuristic for ambulatory care settings].

    PubMed

    Barth, Michael; Belzer, Florian

    2016-06-01

    One goal of early prevention is the support of families with small children up to three years who are exposed to psychosocial risks. The identification of these cases is often complex and not well-directed, especially in the ambulatory care setting. Development of a model of a feasible and empirical based strategy for case finding in ambulatory care. Based on the risk factors of postpartal depression, lack of maternal responsiveness, parental stress with regulation disorders and poverty a lexicographic and non-compensatory heuristic model with simple decision rules, will be constructed and empirically tested. Therefore the original data set from an evaluation of the pediatric documentary form on psychosocial issues of families with small children in well-child visits will be used and reanalyzed. The first diagnostic step in the non-compensatory and hierarchical classification process is the assessment of postpartal depression followed by maternal responsiveness, parental stress and poverty. The classification model identifies 89.0 % cases from the original study. Compared to the original study the decision process becomes clearer and more concise. The evidence-based and data-driven model exemplifies a strategy for the assessment of psychosocial risk factors in ambulatory care settings. It is based on four evidence-based risk factors and offers a quick and reliable classification. A further advantage of this model is that after a risk factor is identified the diagnostic procedure will be stopped and the counselling process can commence. For further validation of the model studies, in well suited early prevention networks are needed.

  9. Association between hospital volume and network membership and an analgesia, sedation and delirium order set quality score: a cohort study.

    PubMed

    Dale, Christopher R; Hayden, Shailaja J; Treggiari, Miriam M; Curtis, J Randall; Seymour, Christopher W; Yanez, N David; Fan, Vincent S

    2012-06-18

    Protocols for the delivery of analgesia, sedation and delirium care of the critically ill, mechanically ventilated patient have been shown to improve outcomes but are not uniformly used. The extent to which elements of analgesia, sedation and delirium guidelines are incorporated into order sets at hospitals across a geographic area is not known. We hypothesized that both greater hospital volume and membership in a hospital network are associated with greater adherence of order sets to sedation guidelines. Sedation order sets from all nonfederal hospitals without pediatric designation in Washington State that provided ongoing care to mechanically ventilated patients were collected and their content systematically abstracted. Hospital data were collected from Washington State sources and interviews with ICU leadership in each hospital. An expert-validated score of order set quality was created based on the 2002 four-society guidelines. Clustered multivariable linear regression was used to assess the relationship between hospital characteristics and the order set quality score. Fifty-one Washington State hospitals met the inclusion criteria and all provided order sets. Based on expert consensus, 21 elements were included in the analgesia, sedation and delirium order set quality score. Each element was equally weighted and contributed one point to the score. Hospital order set quality scores ranged from 0 to 19 (median = 8, interquartile range 6 to 14). In multivariable analysis, a greater number of acute care days (P = 0.01) and membership in a larger hospital network (P = 0.01) were independently associated with a greater quality score. Hospital volume and membership in a larger hospital network were independently associated with a higher quality score for ICU analgesia, sedation and delirium order sets. Further research is needed to determine whether greater order-set quality is associated with improved outcomes in the critically ill. The development of critical

  10. GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

    PubMed Central

    2012-01-01

    Background Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. PMID:22876834

  11. The roles of reward, default, and executive control networks in set-shifting impairments in schizophrenia.

    PubMed

    Waltz, James A; Kasanova, Zuzana; Ross, Thomas J; Salmeron, Betty J; McMahon, Robert P; Gold, James M; Stein, Elliot A

    2013-01-01

    Patients with schizophrenia (SZ) show deficits on tasks of rapid reinforcement learning, like probabilistic reversal learning (PRL), but the neural bases for those impairments are not known. Recent evidence of relatively intact sensitivity to negative outcomes in the ventral striatum (VS) in many SZ patients suggests that PRL deficits may be largely attributable to processes downstream from feedback processing, involving both the activation of executive control task regions and deactivation of default mode network (DMN) components. We analyzed data from 29 chronic SZ patients and 21 matched normal controls (NCs) performing a PRL task in an MRI scanner. Subjects were presented with eight pairs of fractal stimuli, for 50 trials each. For each pair, subjects learned to choose the more frequently-rewarded (better) stimulus. Each time a criterion was reached, the better stimulus became the worse one, and the worse became the better. Responses to feedback events were assessed through whole-brain and regions-of-interest (ROI) analyses in DMN. We also assessed correlations between BOLD signal contrasts and clinical measures in SZs. Relative to NCs, SZ patients showed comparable deactivation of VS in response to negative feedback, but reduced deactivation of DMN components including medial prefrontal cortex (mPFC). The magnitudes of patients' punishment-evoked deactivations in VS and ventromedial PFC correlated significantly with clinical ratings for avolition/anhedonia. These findings suggest that schizophrenia is associated with a reduced ability to deactivate components of default mode networks, following the presentation of informative feedback and that motivational deficits in SZ relate closely to feedback-evoked activity in reward circuit components. These results also confirm a role for ventrolateral and dorsomedial PFC in the execution of response-set shifts.

  12. Identification of regulatory networks and hub genes controlling soybean seed set and size using RNA sequencing analysis.

    PubMed

    Du, Juan; Wang, Shoudong; He, Cunman; Zhou, Bin; Ruan, Yong-Ling; Shou, Huixia

    2017-04-01

    To understand the gene expression networks controlling soybean seed set and size, transcriptome analyses were performed in three early seed developmental stages, using two genotypes with contrasting seed size. The two-dimensional data set provides a comprehensive and systems-level view on dynamic gene expression networks underpinning soybean seed set and subsequent development. Using pairwise comparisons and weighted gene coexpression network analyses, we identified modules of coexpressed genes and hub genes for each module. Of particular importance are the discoveries of specific modules for the large seed size variety and for seed developmental stages. A large number of candidate regulators for seed size, including those involved in hormonal signaling pathways and transcription factors, were transiently and specifically induced in the early developmental stages. The soybean homologs of a brassinosteroid signaling receptor kinase, a brassinosteroid-signaling kinase, were identified as hub genes operating in the seed coat network in the early seed maturation stage. Overexpression of a candidate seed size regulatory gene, GmCYP78A5, in transgenic soybean resulted in increased seed size and seed weight. Together, these analyses identified a large number of potential key regulators controlling soybean seed set, seed size, and, consequently, yield potential, thereby providing new insights into the molecular networks underlying soybean seed development. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  13. WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network.

    PubMed

    Yanshuo, Chu; Zhenxing, Wang; Rongjie, Wang; Ningyi, Zhang; Jie, Li; Yang, Hu; Mingxiang, Teng; Yadong, Wang

    2017-09-04

    Structural controllability is the generalization of traditional controllability for dynamical systems. During the last decade, interesting biological discoveries have been inferred by applied structural controllability analysis to biological networks. However, false positive/negative information (i.e. nodes and edges) widely exists in biological networks that documented in public data sources, which can hinder accurate analysis of structural controllability. In this study, we propose WDNfinder, a comprehensive analysis package that provides structural controllability with consideration of node connection strength in biological networks. When applied to the human cancer signaling network and p53-mediate DNA damage response network, WDNfinder shows high accuracy on essential nodes prediction in these networks. Compared to existing methods, WDNfinder can significantly narrow down the set of minimum driver node set (MDS) under the restriction of domain knowledge. When using p53-mediate DNA damage response network as illustration, we find more meaningful MDSs by WDNfinder. The source code is implemented in python and publicly available together with relevant data on GitHub: https://github.com/dustincys/WDNfinder .

  14. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set.

    PubMed

    Savchenko, A V

    2013-10-01

    The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN.

  15. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

    PubMed Central

    Guziolowski, Carito; Videla, Santiago; Eduati, Federica; Thiele, Sven; Cokelaer, Thomas; Siegel, Anne; Saez-Rodriguez, Julio

    2013-01-01

    Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. Results: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input–output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary information: Supplementary materials are available at Bioinformatics online. Contact: santiago.videla@irisa.fr PMID:23853063

  16. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

    PubMed

    Guziolowski, Carito; Videla, Santiago; Eduati, Federica; Thiele, Sven; Cokelaer, Thomas; Siegel, Anne; Saez-Rodriguez, Julio

    2013-09-15

    Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary materials are available at Bioinformatics online. santiago.videla@irisa.fr.

  17. Determinants of Low Cloud Properties - An Artificial Neural Network Approach Using Observation Data Sets

    NASA Astrophysics Data System (ADS)

    Andersen, Hendrik; Cermak, Jan

    2015-04-01

    This contribution studies the determinants of low cloud properties based on the application of various global observation data sets in machine learning algorithms. Clouds play a crucial role in the climate system as their radiative properties and precipitation patterns significantly impact the Earth's energy balance. Cloud properties are determined by environmental conditions, as cloud formation requires the availability of water vapour ("precipitable water") and condensation nuclei in sufficiently saturated conditions. A main challenge in the research of aerosol-cloud interactions is the separation of aerosol effects from meteorological influence. To gain understanding of the processes that govern low cloud properties in order to increase accuracy of climate models and predictions of future changes in the climate system is thus of great importance. In this study, artificial neural networks are used to relate a selection of predictors (meteorological parameters, aerosol loading) to a set of predictands (cloud microphysical and optical properties). As meteorological parameters, wind direction and velocity, sea level pressure, static stability of the lower troposphere, atmospheric water vapour and temperature at the surface are used (re-analysis data by the European Centre for Medium-Range Weather Forecasts). In addition to meteorological conditions, aerosol loading is used as a predictor of cloud properties (MODIS collection 6 aerosol optical depth). The statistical model reveals significant relationships between predictors and predictands and is able to represent the aerosol-cloud-meteorology system better than frequently used bivariate relationships. The most important predictors can be identified by the additional error when excluding one predictor at a time. The sensitivity of each predictand to each of the predictors is analyzed.

  18. Deconstructing myths, building alliances: a networking model to enhance tobacco control in hospital mental health settings.

    PubMed

    Ballbè, Montse; Gual, Antoni; Nieva, Gemma; Saltó, Esteve; Fernández, Esteve

    2016-01-01

    Life expectancy for people with severe mental disorders is up to 25 years less in comparison to the general population, mainly due to diseases caused or worsened by smoking. However, smoking is usually a neglected issue in mental healthcare settings. The aim of this article is to describe a strategy to improve tobacco control in the hospital mental healthcare services of Catalonia (Spain). To bridge this gap, the Catalan Network of Smoke-free Hospitals launched a nationwide bottom-up strategy in Catalonia in 2007. The strategy relied on the creation of a working group of key professionals from various hospitals -the early adopters- based on Rogers' theory of the Diffusion of Innovations. In 2016, the working group is composed of professionals from 17 hospitals (70.8% of all hospitals in the region with mental health inpatient units). Since 2007, tobacco control has improved in different areas such as increasing mental health professionals' awareness of smoking, training professionals on smoking cessation interventions and achieving good compliance with the national smoking ban. The working group has produced and disseminated various materials, including clinical practice and best practice guidelines, implemented smoking cessation programmes and organised seminars and training sessions on smoking cessation measures in patients with mental illnesses. The next challenge is to ensure effective follow-up for smoking cessation after discharge. While some areas of tobacco control within these services still require significant improvement, the aforementioned initiative promotes successful tobacco control in these settings. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  19. Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding.

    PubMed

    Dai, Wenrui; Xiong, Hongkai; Wang, Jia; Zheng, Yuan F

    2014-02-01

    Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.

  20. A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification.

    PubMed

    Zhao, Jing; Lin, Lo-Yi; Lin, Chih-Min

    2016-01-01

    The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN). To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs) and intuitionistic fuzzy cross-entropy (IFCE) with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories.

  1. A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification

    PubMed Central

    Zhao, Jing; Lin, Lo-Yi

    2016-01-01

    The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN). To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs) and intuitionistic fuzzy cross-entropy (IFCE) with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories. PMID:27298619

  2. Blood pressure long term regulation: A neural network model of the set point development

    PubMed Central

    2011-01-01

    Background The notion of the nucleus tractus solitarius (NTS) as a comparator evaluating the error signal between its rostral neural structures (RNS) and the cardiovascular receptor afferents into it has been recently presented. From this perspective, stress can cause hypertension via set point changes, so offering an answer to an old question. Even though the local blood flow to tissues is influenced by circulating vasoactive hormones and also by local factors, there is yet significant sympathetic control. It is well established that the state of maturation of sympathetic innervation of blood vessels at birth varies across animal species and it takes place mostly during the postnatal period. During ontogeny, chemoreceptors are functional; they discharge when the partial pressures of oxygen and carbon dioxide in the arterial blood are not normal. Methods The model is a simple biological plausible adaptative neural network to simulate the development of the sympathetic nervous control. It is hypothesized that during ontogeny, from the RNS afferents to the NTS, the optimal level of each sympathetic efferent discharge is learned through the chemoreceptors' feedback. Its mean discharge leads to normal oxygen and carbon dioxide levels in each tissue. Thus, the sympathetic efferent discharge sets at the optimal level if, despite maximal drift, the local blood flow is compensated for by autoregulation. Such optimal level produces minimum chemoreceptor output, which must be maintained by the nervous system. Since blood flow is controlled by arterial blood pressure, the long-term mean level is stabilized to regulate oxygen and carbon dioxide levels. After development, the cardiopulmonary reflexes play an important role in controlling efferent sympathetic nerve activity to the kidneys and modulating sodium and water excretion. Results Starting from fixed RNS afferents to the NTS and random synaptic weight values, the sympathetic efferents converged to the optimal values

  3. Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression.

    PubMed

    Zhang, Shao-Wu; Shao, Dong-Dong; Zhang, Song-Yao; Wang, Yi-Bin

    2014-06-01

    The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for

  4. The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

    PubMed

    Djurfeldt, Mikael

    2012-07-01

    The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

  5. Computers and Communication Networks in Educational Settings in the Twenty-First Century: Preparation for Educators' New Roles.

    ERIC Educational Resources Information Center

    Kook, Joong-Kak

    1997-01-01

    Discussion of changes in classrooms as a result of communication networks focuses on teachers' roles in future educational settings. Topics include teachers as information consultants, as team collaborators, as facilitators, as course developers, and as academic advisors; and the computer and communication skills needed by teachers. (LRW)

  6. Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

    PubMed

    Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

    2009-06-03

    Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

  7. Setting Access Permission through Transitive Relationship in Web-based Social Networks

    NASA Astrophysics Data System (ADS)

    Hong, Dan; Shen, Vincent Y.

    The rising popularity of various social networking websites has created a huge problem on Internet privacy. Although it is easy to post photos, comments, opinions on some events, etc. on the Web, some of these data (such as a person’s location at a particular time, criticisms of a politician, etc.) are private and should not be accessed by unauthorized users. Although social networks facilitate sharing, the fear of sending sensitive data to a third party without knowledge or permission of the data owners discourages people from taking full advantage of some social networking applications. We exploit the existing relationships on social networks and build a ‘‘trust network’’ with transitive relationship to allow controlled data sharing so that the privacy and preferences of data owners are respected. The trust network linking private data owners, private data requesters, and intermediary users is a directed weighted graph. The permission value for each private data requester can be automatically assigned in this network based on the transitive relationship. Experiments were conducted to confirm the feasibility of constructing the trust network from existing social networks, and to assess the validity of permission value assignments in the query process. Since the data owners only need to define the access rights of their closest contacts once, this privacy scheme can make private data sharing easily manageable by social network participants.

  8. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation.

    PubMed

    Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

    2011-01-01

    Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Analysis based on the experience of three telemedicine networks (in operation for 7-10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of their own networks

  9. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

    PubMed Central

    Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

    2011-01-01

    Background Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. Objective To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Methods Analysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Findings Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Conclusion Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit

  10. Primary health care service delivery networks for the prevention and management of type 2 diabetes: using social network methods to describe interorganisational collaboration in a rural setting.

    PubMed

    McDonald, Julie; Jayasuriya, Rohan; Harris, Mark Fort

    2011-01-01

    Adults with type 2 diabetes or with behavioural risk factors require comprehensive and well coordinated responses from a range of health care providers who often work in different organisational settings. This study examines three types of collaborative links between organisations involved in a rural setting. Social network methods were employed using survey data on three types of links, and data was collected from a purposive sample of 17 organisations representing the major provider types. The analysis included a mix of unconfirmed and confirmed links, and network measures. General practices were the most influential provider group in initiating referrals, and they referred to the broadest range of organisations in the network. Team care arrangements formed a small part of the general practice referral network. They were used more for access to private sector allied health care providers and less for sharing care with public sector health services. Involvement in joint programs/activities was limited to public and non-government sector services, with no participation from the private sector. The patterns of interactions suggest that informal referral networks provide access to services and coordination of care for individual patients with diabetes. Two population subgroups would benefit from more proactive approaches to ensure equitable access to services and coordination of care across organisational boundaries: people with more complex health care needs and people at risk of developing diabetes.

  11. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    PubMed

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

  12. Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis.

    PubMed

    Lee, Won Jun; Kim, Sang Cheol; Yoon, Jung-Ho; Yoon, Sang Jun; Lim, Johan; Kim, You-Sun; Kwon, Sung Won; Park, Jeong Hill

    2016-01-01

    Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological

  13. Proactive Approach for Safe Use of Antimicrobial Coatings in Healthcare Settings: Opinion of the COST Action Network AMiCI

    PubMed Central

    Ahonen, Merja; Kahru, Anne; Ivask, Angela; Kasemets, Kaja; Kõljalg, Siiri; Mantecca, Paride; Vinković Vrček, Ivana; Keinänen-Toivola, Minna M.; Crijns, Francy

    2017-01-01

    Infections and infectious diseases are considered a major challenge to human health in healthcare units worldwide. This opinion paper was initiated by EU COST Action network AMiCI (AntiMicrobial Coating Innovations) and focuses on scientific information essential for weighing the risks and benefits of antimicrobial surfaces in healthcare settings. Particular attention is drawn on nanomaterial-based antimicrobial surfaces in frequently-touched areas in healthcare settings and the potential of these nano-enabled coatings to induce (eco)toxicological hazard and antimicrobial resistance. Possibilities to minimize those risks e.g., at the level of safe-by-design are demonstrated. PMID:28362344

  14. Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

    NASA Astrophysics Data System (ADS)

    Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

    2016-03-01

    Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate

  15. 78 FR 79649 - Energy Conservation Program: Proposed Determination of Set-Top Boxes and Network Equipment as a...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-12-31

    ...The U.S. Department of Energy (DOE) withdraws a proposed determination published June 15, 2011 that set-top boxes (STBs) and network equipment qualify as a covered product under Part A of Title III of the Energy Policy and Conservation Act (EPCA), as amended. DOE is taking this action in light of a consensus agreement entered by a broadly representative group that DOE believes has the potential to achieve significant energy savings in STBs.

  16. Effects of Relative Mean Sea Level Variations on Tidal Networks Generated on Experimental Setting

    NASA Astrophysics Data System (ADS)

    Stefanon, L.; Carniello, L.; D'Alpaos, A.; Rinaldo, A.

    2012-12-01

    We present the results of laboratory experiments carried out in a large experimental apparatus aimed at reproducing a typical lagoonal environment subject to tidal forcings. The experimental apparatus consists of two adjoining basins reproducing the sea and the lagoon. The tide is generated at the sea by a vertical steel sharp-edge weir, oscillating vertically. The weir is driven by an ad hoc developed software which continuously corrects the weir motion on the basis of water levels measured at the sea, allowing us to generate a sinusoidal tide of fixed amplitude and period, oscillating around mean water level. The bottom of the lagoon is covered by a layer of cohesionless plastic grains, with a density of 1041 kg/m3. The cohesionless plastic grains are characterized by a nearly uniform grain size distribution, with a median grain size of 0.8 mm. The lack of external sediment supply, the absence of vegetation, and the prevalence of bedload transport prevent any deposition processes and lateral surface accretion, attributing a purely erosive character to the experimental lagoon. As a consequence, in this experimental lagoon the main morphodynamic process responsible for tidal network initiation and development is the differential erosion between the channels and the adjacent surface. The experiments were designed in order to analyze the effects of mean sea level variations on channel network dynamics, focusing on the changes of the relevant geomorphic characteristics of the experimental networks, such as e.g. drainage density, based on the probability distribution of unchanneled lengths, and flowing tidal prism. Our results suggest that a decrease in the tidal prism leads to network retreat and contraction of channel cross sections. Conversely, an increase in the tidal prism promotes network re-incision and re-expansion of channel cross sections. In general, contractions and expansions tend to occur within the same planar blueprint and the network re-expands cutting

  17. Mining for bioactive scaffolds with scaffold networks: improved compound set enrichment from primary screening data.

    PubMed

    Varin, Thibault; Schuffenhauer, Ansgar; Ertl, Peter; Renner, Steffen

    2011-07-25

    Identification of meaningful chemical patterns in the increasing amounts of high-throughput-generated bioactivity data available today is an increasingly important challenge for successful drug discovery. Herein, we present the scaffold network as a novel approach for mapping and navigation of chemical and biological space. A scaffold network represents the chemical space of a library of molecules consisting of all molecular scaffolds and smaller "parent" scaffolds generated therefrom by the pruning of rings, effectively leading to a network of common scaffold substructure relationships. This algorithm provides an extension of the scaffold tree algorithm that, instead of a network, generates a tree relationship between a heuristically rule-based selected subset of parent scaffolds. The approach was evaluated for the identification of statistically significantly active scaffolds from primary screening data for which the scaffold tree approach has already been shown to be successful. Because of the exhaustive enumeration of smaller scaffolds and the full enumeration of relationships between them, about twice as many statistically significantly active scaffolds were identified compared to the scaffold-tree-based approach. We suggest visualizing scaffold networks as islands of active scaffolds.

  18. Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks

    NASA Astrophysics Data System (ADS)

    Kunimoto, Ryo; Bajorath, Jürgen

    2017-09-01

    Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

  19. Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks.

    PubMed

    Kunimoto, Ryo; Bajorath, Jürgen

    2017-09-04

    Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

  20. Secure Your Wireless Network: Going Wireless Comes with Its Own Special Set of Security Concerns

    ERIC Educational Resources Information Center

    Bloomquist, Jane; Musa, Atif

    2004-01-01

    Imagine a completely wireless school, an open network in which all students and staff can roam around using laptops or handheld computers to browse the Internet, access files and applications on the school server, and communicate with each other and the world via e-mail. It's a great picture--and at some schools the future is already here. But…

  1. Secure Your Wireless Network: Going Wireless Comes with Its Own Special Set of Security Concerns

    ERIC Educational Resources Information Center

    Bloomquist, Jane; Musa, Atif

    2004-01-01

    Imagine a completely wireless school, an open network in which all students and staff can roam around using laptops or handheld computers to browse the Internet, access files and applications on the school server, and communicate with each other and the world via e-mail. It's a great picture--and at some schools the future is already here. But…

  2. Networks and landscapes: a framework for setting goals and evaluating performance at the large landscape scale

    Treesearch

    R Patrick Bixler; Shawn Johnson; Kirk Emerson; Tina Nabatchi; Melly Reuling; Charles Curtin; Michele Romolini; Morgan Grove

    2016-01-01

    The objective of large landscape conser vation is to mitigate complex ecological problems through interventions at multiple and overlapping scales. Implementation requires coordination among a diverse network of individuals and organizations to integrate local-scale conservation activities with broad-scale goals. This requires an understanding of the governance options...

  3. 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…

  4. Before the year 2000: Artificial neural networks may set the standard

    SciTech Connect

    Michal, R.A.

    1994-07-01

    The use of artifical neural networks (ANNs) for monitoring of equipment and components in nuclear power plants could be commonplace before the turn of the century. Within five years, the relative inexpensiveness of neural networks could usher in a technology that will be used to detect incipient faults in machinery and increase effectiveness of maintenance scheduling. Working since November 1992 with the Electric Power Research Institute on research and development of the technology, SynEx and another Virginia-based company, A&T, Inc., will later this year demonstrate prototype ANN systems at Consolidated Edison Company and New York State Electric Gas fossil fuel power plants. (Fossil fuel plants were chosen for the project because of easier access, as opposed to the security measures in place at nuclear facilities.) The demonstration will utilize sensors and the neural network systems to detect abnormal equipment behavior, sending signals back to centralized monitoring boards located in each plant`s control room. The cost of the project, including research and development, will reach $1 million. However, the cost of installing a neural network at a nuclear plant within the next five years, according to Birdsall, could be as low as $10,000 to $15,000, with hopes of reducing the expenditure to just $5000.

  5. Algorithms for Data Sharing, Coordination, and Communication in Dynamic Network Settings

    DTIC Science & Technology

    2007-12-03

    Chakraborty, and Nancy Lynch. Clock Synchronization for Wireless Networks. In Teruo Higashino , editor, Principles of Distributed Systems: OPODIS 2004: 8th...Teruo Higashino , editor, Principles of Distributed Systems: OPODIS 2004: 8th International Conference on Principles of Distributed 4 Systems, Grenoble

  6. Summary information and data sets for the HBCU Solar Measurements Network

    SciTech Connect

    Marion, W

    1994-08-01

    Since 1985, the National Renewable Energy Laboratory (NREL), formerly the Solar Energy Research Institute (SERI), has operated a solar radiation measurement network of six stations located at Historically Black Colleges and Universities (HBCUs) in the southeastern United States. NREL initiated this network to provide better regional coverage and to comply with President Reagan`s Executive Order 12320, dated September 15, 1981, directing all federal agencies to implement programs to strengthen the nation`s HBCUs. Funding for the HBCU network has been provided by the Department of Energy`s (DOE`s) Resource Assessment Program, Photovoltaic Program, and Solar Thermal Program, and it is currently funded by the Solar Radiation Resource Assessment Project. The objectives of the HBCU network are (1) To significantly improve the assessment of solar radiation resources in the southeastern United States; (2) To enlist the help of the HBCUs in collecting high-quality solar radiation data; (3) To encourage the distribution of solar radiation resource information and the development of solar energy applications in the Southeast; (4) To encourage the development of academic and research programs in solar energy at HBCUs.

  7. 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…

  8. Recent developments in the setting up of the Malta Seismic Network

    NASA Astrophysics Data System (ADS)

    Agius, Matthew; Galea, Pauline; D'Amico, Sebastiano

    2015-04-01

    Weak to moderate earthquakes in the Sicily Channel have until now been either poorly located or left undetected. The number of seismic stations operated by various networks: Italy (INGV), Tunisia (TT), and Libya (LNSN) have now improved considerably, however most of the seismicity occurs offshore, in the central part of the Channel, away from the mainland stations. Seismic data availability from island stations across the Channel has been limited or had intermittent transmission hindering proper real-time earthquake monitoring and hypocentre relocation. In order to strengthen the seismic monitoring of the Sicily Channel, in particular the central parts of the Channel, the Seismic Monitoring and Research Unit (SMRU), University of Malta, has, in the last year, been installing a permanent seismic network across the Maltese archipelago: the Malta Seismic Network (ML). Furthermore the SMRU has upgraded its IT facilities to run a virtual regional seismic network composed of the stations on Pantelleria and Lampedusa, together with all the currently publicly available stations in the region. Selected distant seismic stations found elsewhere in the Mediterranean and across the globe have also been incorporated in the system in order to enhance the overall performance of the monitoring and to detect potentially damaging regional earthquakes. Data acquisition and processing of the seismic networks are run by SeisComP. The new installations are part of the project SIMIT (B1-2.19/11) funded by the Italia-Malta Operational Programme 2007-2013. The new system allows the SMRU to rapidly perform more accurate hypocentre locations in the region, and issue automatic SMS alert for potentially felt events in the Sicily Channel detected by the network and for strong earthquakes elsewhere. Within the SIMIT project, the alert system will include civil protection departments in Malta and Sicily. We present the recent developments of the real and virtual seismic network, and discuss the

  9. LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

    PubMed Central

    Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

    2016-01-01

    Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

  10. MAXBAND Version 3.1: Heuristic and optimal approach for setting the left turn phase sequences in signalized networks

    SciTech Connect

    Pillai, R.S.; Rathi, A.K.

    1995-02-01

    The main objective of synchronized signal timing is to keep traffic moving along arterials in platoons throughout the signal system by proper setting of left turn phase sequence at signals along the arterials/networks. The synchronization of traffic signals located along the urban/suburban arterials in metropolitan areas is perhaps one of the most cost-effective methods for improving traffic flow along these streets. MAXBAND Version 2.1 (formerly known as MAXBAND-86), a progression-based optimization model, is used for generating signal timing plan for urban networks. This model formulates the problem as a mixed integer linear program and uses Land and Powell branch and bound search to arrive at the optimal solution. The computation time of MAXBAND Version 2.1 tends to be excessive for realistic multiarterial network problems due to the exhaustive nature of the branch and bound search technique. Furthermore, the Land and Powell branch and bound code is known to be numerically unstable, which results in suboptimal solutions for network problems with a range on the cycle time variable. This report presents the development of a new version of MAXBAND called MAXBAND Version 3.1. This new version has a fast heuristic algorithm and a fast optimal algorithm for generating signal timing plan for arterials and networks. MAXBAND 3.1 can generate optimal/near-optimal solutions in fraction of the time needed to compute the optimal solution by Version 2.1. The heuristic algorithm in the new model is based on restricted search using branch and bound technique. The algorithm for generating the optimal solution is faster and more efficient than version 2.1 algorithm. Furthermore, the new version is numerically stable. The efficiency of the new model is demonstrated by numerical results for a set of test problems.

  11. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    PubMed

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online.

  12. A Validated Set of MIDAS V5 Task Network Model Scenarios to Evaluate Nextgen Closely Spaced Parallel Operations Concepts

    NASA Technical Reports Server (NTRS)

    Gore, Brian Francis; Hooey, Becky Lee; Haan, Nancy; Socash, Connie; Mahlstedt, Eric; Foyle, David C.

    2013-01-01

    The Closely Spaced Parallel Operations (CSPO) scenario is a complex, human performance model scenario that tested alternate operator roles and responsibilities to a series of off-nominal operations on approach and landing (see Gore, Hooey, Mahlstedt, Foyle, 2013). The model links together the procedures, equipment, crewstation, and external environment to produce predictions of operator performance in response to Next Generation system designs, like those expected in the National Airspaces NextGen concepts. The task analysis that is contained in the present report comes from the task analysis window in the MIDAS software. These tasks link definitions and states for equipment components, environmental features as well as operational contexts. The current task analysis culminated in 3300 tasks that included over 1000 Subject Matter Expert (SME)-vetted, re-usable procedural sets for three critical phases of flight; the Descent, Approach, and Land procedural sets (see Gore et al., 2011 for a description of the development of the tasks included in the model; Gore, Hooey, Mahlstedt, Foyle, 2013 for a description of the model, and its results; Hooey, Gore, Mahlstedt, Foyle, 2013 for a description of the guidelines that were generated from the models results; Gore, Hooey, Foyle, 2012 for a description of the models implementation and its settings). The rollout, after landing checks, taxi to gate and arrive at gate illustrated in Figure 1 were not used in the approach and divert scenarios exercised. The other networks in Figure 1 set up appropriate context settings for the flight deck.The current report presents the models task decomposition from the tophighest level and decomposes it to finer-grained levels. The first task that is completed by the model is to set all of the initial settings for the scenario runs included in the model (network 75 in Figure 1). This initialization process also resets the CAD graphic files contained with MIDAS, as well as the embedded

  13. Quantum state reconstruction of an oscillator network in an optomechanical setting

    NASA Astrophysics Data System (ADS)

    Moore, Darren W.; Tufarelli, Tommaso; Paternostro, Mauro; Ferraro, Alessandro

    2016-11-01

    We introduce a scheme to reconstruct an arbitrary quantum state of a mechanical oscillator network. We assume that a single element of the network is coupled to a cavity field via a linearized optomechanical interaction, the time dependence of which is controlled by a classical driving field. By designing a suitable interaction profile, we show how the statistics of an arbitrary mechanical quadrature can be encoded in the cavity field, which can then be measured. We discuss the important special case of Gaussian state reconstruction and study numerically the effectiveness of our scheme for a finite number of measurements. Finally, we speculate on possible routes to extend our ideas to the regime of single-photon optomechanics.

  14. A Novel Integer-Coded Memetic Algorithm for the Set k-Cover Problem in Wireless Sensor Networks.

    PubMed

    Liao, Chien-Chih; Ting, Chuan-Kang

    2017-08-21

    The Set k-Cover problem aims to partition a set of nodes for the maximal number of covers. This problem is crucial for extending the lifetime of wireless sensor networks (WSNs) under the constraint of covering all targets. More specifically, the Set k-Cover problem enables partitioning the set of sensors into several covers over all targets and activating the covers by turns to effectively extend the WSN lifetime. To resolve this problem, we propose a novel memetic algorithm (MA) based on integer-coded genetic algorithm and local search. This paper adapts the crossover and mutation operators to integer representation and, furthermore, designs a new fitness function that considers both the number of covers and the contribution of each sensor to covers. A local improvement method, called the recycling operator, is developed to enhance the performance on the Set k-Cover problem. Experimental results show that the proposed MA significantly outperforms five evolutionary algorithms in terms of the number of covers obtained, hit rate (HR), and running time. In particular, the new MA increases 38.1% HR and saves 78.7% running time of state-of-the-art MA on average. The preferable results validate the effectiveness and efficiency of the proposed MA for the Set k-Cover problem.

  15. Acoustic Metadata Management and Transparent Access to Networked Oceanographic Data Sets

    DTIC Science & Technology

    2014-09-30

    needs of the PAM community in general. Networking capabilities provide the ability to share data and export summary data to OBIS- SEAMAP . In...Observing System – Spatial Ecological Analysis of Megavertebrate Populations (OBIS- SEAMAP ). 6. Two manuscripts were submitted, and a third was published... SEAMAP means that summaries of work undertaken by individual laboratories can now be shared with the broader community. We demonstrated this with the

  16. SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets.

    PubMed

    Becker, Michael G; Walker, Philip L; Pulgar-Vidal, Nadège C; Belmonte, Mark F

    2017-01-01

    Transcription factors and their associated DNA binding sites are key regulatory elements of cellular differentiation, development, and environmental response. New tools that predict transcriptional regulation of biological processes are valuable to researchers studying both model and emerging-model plant systems. SeqEnrich predicts transcription factor networks from co-expressed Arabidopsis or Brassica napus gene sets. The networks produced by SeqEnrich are supported by existing literature and predicted transcription factor-DNA interactions that can be functionally validated at the laboratory bench. The program functions with gene sets of varying sizes and derived from diverse tissues and environmental treatments. SeqEnrich presents as a powerful predictive framework for the analysis of Arabidopsis and Brassica napus co-expression data, and is designed so that researchers at all levels can easily access and interpret predicted transcriptional circuits. The program outperformed its ancestral program ChipEnrich, and produced detailed transcription factor networks from Arabidopsis and Brassica napus gene expression data. The SeqEnrich program is ideal for generating new hypotheses and distilling biological information from large-scale expression data.

  17. Physical rehabilitation in post-conflict settings: analysis of public policy and stakeholder networks.

    PubMed

    Blanchet, Karl; Girois, Susan; Urseau, Isabelle; Smerdon, Christine; Drouet, Yann; Jama, Ali

    2014-01-01

    Physical rehabilitation plays a determinant role in post-conflict contexts to restore disabled citizens' mobility and independence. While the main objectives of any physical rehabilitation programme are to ensure that the services provided are accessible and of good quality to meet existing needs, it is intended that the services need to be supported over the long term by public health and social welfare authorities. This article presents the results of a study conducted in three post-conflict countries on the relationships between the level of commitment of national governments to rehabilitation services and the influence of social networks on national policy related to physical rehabilitation. From a policy and resource standpoint, the environment in Nepal is the most favourable for creating leverage at the national level to influence the commitment of ministries in the rehabilitation sector, compared with Cambodia and Somaliland. Stakeholder network analysis in Nepal, furthermore, reveals a dominant civil society and private sector supporting rehabilitation services, including intense involvement of local organisations and user groups. Implications for Rehabilitation Physical rehabilitation is not on the top of the agenda of governments in fragile states. The commitment and involvement of national authorities in the rehabilitation sector is positively influenced by civil society and international organisations. The denser the social network of the rehabilitation sector is, the more influence the actors can exert influence over national authorities.

  18. Treatment challenges in and outside a network setting: Soft tissue sarcomas.

    PubMed

    Pasquali, Sandro; Bonvalot, Sylvie; Tzanis, Dimitri; Casali, Paolo G; Trama, Annalisa; Gronchi, Alessandro

    2017-09-19

    Patients with soft tissue sarcoma (STS) experienced better outcomes when treated according to existing clinical practice guidelines either at reference institution or dedicated treatment networks. Despite increasing evidence supporting referral to sarcoma specialised units, up to half of patients are not managed according to guidelines, particularly those in the early stage of their disease requiring surgery. Also, criteria to certify expertise of institutions, such as the treatment volume, are debated and health authorities have only recently started identification of these centres and creation of treatment networks in Europe as well as in several countries. This process have important implications for both patient outcomes and innovation of existing treatment strategies through clinical research, making improvement of clinical pathways a priority for health care authorities. This article will discuss issues with management of patients with STS, such as pathological diagnosis and adherence to guidelines, and the definition of referral centres and networks will be illustrated along with existing experiences and population-based data. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

  19. Mitochondrial superoxide in osteocytes perturbs canalicular networks in the setting of age-related osteoporosis

    PubMed Central

    Kobayashi, Keiji; Nojiri, Hidetoshi; Saita, Yoshitomo; Morikawa, Daichi; Ozawa, Yusuke; Watanabe, Kenji; Koike, Masato; Asou, Yoshinori; Shirasawa, Takuji; Yokote, Koutaro; Kaneko, Kazuo; Shimizu, Takahiko

    2015-01-01

    Osteocytes are major bone cells that play a crucial role in maintaining the quality of and healing damage to bone tissue. The number of living osteocytes and canalicular networks declines in an age-dependent manner. However, the pathological effects of mitochondrial redox imbalances on osteocytes and bone metabolism have not been fully elucidated. We generated mice lacking mitochondrial superoxide dismutase 2 (Sod2) in osteocytes. Like an aged bone, Sod2 depletion in the osteocytes positively enhanced the production of cellular superoxide in vivo. A bone morphological analysis demonstrated that the Sod2-deficient femurs showed remarkable bone loss in an age-dependent manner. Interestingly, Sod2 loss induced markedly disorganized osteocytic canalicular networks and decreased the number of live osteocytes. Furthermore, Sod2 deficiency significantly suppressed bone formation and increased bone resorption concomitant with the upregulation of sclerostin and receptor activator of NF-κB ligand (RANKL). In vitro experiments also revealed that treatment with paraquat, a superoxide inducer in mitochondria, promoted the RANKL expression via, in part, ERK phosphorylation. These findings demonstrate that the mitochondrial superoxide induced in osteocytes by Sod2 ablation causes age-related bone loss due to the impairment of canalicular networks and bone metabolism via the deregulation of the sclerostin and RANKL expression. PMID:25779629

  20. Mitochondrial superoxide in osteocytes perturbs canalicular networks in the setting of age-related osteoporosis.

    PubMed

    Kobayashi, Keiji; Nojiri, Hidetoshi; Saita, Yoshitomo; Morikawa, Daichi; Ozawa, Yusuke; Watanabe, Kenji; Koike, Masato; Asou, Yoshinori; Shirasawa, Takuji; Yokote, Koutaro; Kaneko, Kazuo; Shimizu, Takahiko

    2015-03-16

    Osteocytes are major bone cells that play a crucial role in maintaining the quality of and healing damage to bone tissue. The number of living osteocytes and canalicular networks declines in an age-dependent manner. However, the pathological effects of mitochondrial redox imbalances on osteocytes and bone metabolism have not been fully elucidated. We generated mice lacking mitochondrial superoxide dismutase 2 (Sod2) in osteocytes. Like an aged bone, Sod2 depletion in the osteocytes positively enhanced the production of cellular superoxide in vivo. A bone morphological analysis demonstrated that the Sod2-deficient femurs showed remarkable bone loss in an age-dependent manner. Interestingly, Sod2 loss induced markedly disorganized osteocytic canalicular networks and decreased the number of live osteocytes. Furthermore, Sod2 deficiency significantly suppressed bone formation and increased bone resorption concomitant with the upregulation of sclerostin and receptor activator of NF-κB ligand (RANKL). In vitro experiments also revealed that treatment with paraquat, a superoxide inducer in mitochondria, promoted the RANKL expression via, in part, ERK phosphorylation. These findings demonstrate that the mitochondrial superoxide induced in osteocytes by Sod2 ablation causes age-related bone loss due to the impairment of canalicular networks and bone metabolism via the deregulation of the sclerostin and RANKL expression.

  1. A replica exchange transition interface sampling method with multiple interface sets for investigating networks of rare events

    NASA Astrophysics Data System (ADS)

    Swenson, David W. H.; Bolhuis, Peter G.

    2014-07-01

    The multiple state transition interface sampling (TIS) framework in principle allows the simulation of a large network of complex rare event transitions, but in practice suffers from convergence problems. To improve convergence, we combine multiple state TIS [J. Rogal and P. G. Bolhuis, J. Chem. Phys. 129, 224107 (2008)] with replica exchange TIS [T. S. van Erp, Phys. Rev. Lett. 98, 268301 (2007)]. In addition, we introduce multiple interface sets, which allow more than one order parameter to be defined for each state. We illustrate the methodology on a model system of multiple independent dimers, each with two states. For reaction networks with up to 64 microstates, we determine the kinetics in the microcanonical ensemble, and discuss the convergence properties of the sampling scheme. For this model, we find that the kinetics depend on the instantaneous composition of the system. We explain this dependence in terms of the system's potential and kinetic energy.

  2. Searching for optimal setting conditions in technological processes using parametric estimation models and neural network mapping approach: a tutorial.

    PubMed

    Fjodorova, Natalja; Novič, Marjana

    2015-09-03

    Engineering optimization is an actual goal in manufacturing and service industries. In the tutorial we represented the concept of traditional parametric estimation models (Factorial Design (FD) and Central Composite Design (CCD)) for searching optimal setting parameters of technological processes. Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network (FFBN NN)) was described in comparison with traditional methods. The FFBN NN mapping technique enables visualization of all optimal solutions in considered processes due to the projection of input as well as output parameters in the same coordinates of 2D map. This phenomenon supports the more efficient way of improving the performance of existing systems. Comparison of two methods was performed on the bases of optimization of solder paste printing processes as well as optimization of properties of cheese. Application of both methods enables the double check. This increases the reliability of selected optima or specification limits.

  3. A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network

    PubMed Central

    Ye, Yuan-Nong; Ma, Bin-Guang; Dong, Chuan; Zhang, Hong; Chen, Ling-Ling; Guo, Feng-Biao

    2016-01-01

    A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry. PMID:27713529

  4. Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

    PubMed Central

    Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

    2014-01-01

    This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

  5. Functional network construction in Arabidopsis using rule-based machine learning on large-scale data sets.

    PubMed

    Bassel, George W; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J; Bacardit, Jaume

    2011-09-01

    The meta-analysis of large-scale postgenomics data sets within public databases promises to provide important novel biological knowledge. Statistical approaches including correlation analyses in coexpression studies of gene expression have emerged as tools to elucidate gene function using these data sets. Here, we present a powerful and novel alternative methodology to computationally identify functional relationships between genes from microarray data sets using rule-based machine learning. This approach, termed "coprediction," is based on the collective ability of groups of genes co-occurring within rules to accurately predict the developmental outcome of a biological system. We demonstrate the utility of coprediction as a powerful analytical tool using publicly available microarray data generated exclusively from Arabidopsis thaliana seeds to compute a functional gene interaction network, termed Seed Co-Prediction Network (SCoPNet). SCoPNet predicts functional associations between genes acting in the same developmental and signal transduction pathways irrespective of the similarity in their respective gene expression patterns. Using SCoPNet, we identified four novel regulators of seed germination (ALTERED SEED GERMINATION5, 6, 7, and 8), and predicted interactions at the level of transcript abundance between these novel and previously described factors influencing Arabidopsis seed germination. An online Web tool to query SCoPNet has been developed as a community resource to dissect seed biology and is available at http://www.vseed.nottingham.ac.uk/.

  6. Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer.

    PubMed

    Fang, Xiaocong; Netzer, Michael; Baumgartner, Christian; Bai, Chunxue; Wang, Xiangdong

    2013-02-01

    Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear. The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis. Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis. Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways. New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Paediatric Obesity Research in Early Childhood and the Primary Care Setting: The TARGet Kids! Research Network

    PubMed Central

    Morinis, Julia; Maguire, Jonathon; Khovratovich, Marina; McCrindle, Brian W.; Parkin, Patricia C.; Birken, Catherine S.

    2012-01-01

    Primary paediatric health care is the foundation for preventative child health. In light of the recent obesity epidemic, paediatricians find themselves at the frontline of identification and management of childhood obesity. However, it is well recognized that evidence based approaches to obesity prevention and subsequent translation of this evidence into practice are critically needed. This paper explores the role of primary care in obesity prevention and introduces a novel application and development of a primary care research network in Canada—TARGet Kids!—to develop and translate an evidence-base on effective screening and prevention of childhood obesity. PMID:22690197

  8. Paediatric obesity research in early childhood and the primary care setting: the TARGet Kids! research network.

    PubMed

    Morinis, Julia; Maguire, Jonathon; Khovratovich, Marina; McCrindle, Brian W; Parkin, Patricia C; Birken, Catherine S

    2012-04-01

    Primary paediatric health care is the foundation for preventative child health. In light of the recent obesity epidemic, paediatricians find themselves at the frontline of identification and management of childhood obesity. However, it is well recognized that evidence based approaches to obesity prevention and subsequent translation of this evidence into practice are critically needed. This paper explores the role of primary care in obesity prevention and introduces a novel application and development of a primary care research network in Canada--TARGet Kids!--to develop and translate an evidence-base on effective screening and prevention of childhood obesity.

  9. Editor's Highlight: Evaluation of a Microelectrode Array-Based Assay for Neural Network Ontogeny Using Training Set Chemicals.

    PubMed

    Brown, Jasmine P; Hall, Diana; Frank, Christopher L; Wallace, Kathleen; Mundy, William R; Shafer, Timothy J

    2016-11-01

    Thousands of compounds in the environment have not been characterized for developmental neurotoxicity (DNT) hazard. To address this issue, methods to screen compounds rapidly for DNT hazard evaluation are necessary and are being developed for key neurodevelopmental processes. In order to develop an assay for network formation, this study evaluated effects of a training set of chemicals on network ontogeny by measuring spontaneous electrical activity in neural networks grown on microelectrode arrays (MEAs). Rat (0-24 h old) primary cortical cells were plated in 48 well-MEA plates and exposed to 6 compounds: acetaminophen, bisindolylmaleimide-1 (Bis-1), domoic acid, mevastatin, sodium orthovanadate, and loperamide for a period of 12 days. Spontaneous network activity was recorded on days 2, 5, 7, 9, and 12 and viability was assessed using the Cell Titer Blue assay on day 12. Network activity (e.g. mean firing rate [MFR], burst rate [BR], etc), increased between days 5 and 12. Random Forest analysis indicated that across all compounds and times, temporal correlation of firing patterns (r), MFR, BR, number of active electrodes and % of spikes in a burst were the most influential parameters in separating control from treated wells. All compounds except acetaminophen (≤ 30 µM) caused concentration-related effects on one or more of these parameters. Domoic acid and sodium orthovanadate altered several of these parameters in the absence of cytotoxicity. Although cytotoxicity was observed with Bis1, mevastatin, and loperamide, some parameters were affected by these compounds at concentrations below those resulting in cytotoxicity. These results demonstrate that this assay may be suitable for screening of compounds for DNT hazard identification.

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

    PubMed

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

    2009-12-15

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

  11. The analysis of a cardiological network in a regulated setting: a spatial interaction approach.

    PubMed

    Lippi Bruni, Matteo; Nobilio, Lucia; Ugolini, Cristina

    2008-02-01

    We analyse referral patterns for patients undergoing percutaneous transluminal coronary angioplasty (PTCA) in the Emilia Romagna region of Italy, a procedure for which the assumption of a negative association between volume and adverse outcomes is used to justify its territorial concentration. Nevertheless, recent clinical evidence shows PTCA superiority for immediate treatment of acute myocardial infarction, which advises an increase in the number of points of delivery. Our paper aims to develop analytical tools designed to provide support to policy makers when they are asked to evaluate the spatial distribution of catheterisation laboratories that perform PTCA. Information is drawn from the regional administrative hospital discharge data (SDO) for the year 2002. We first use entropy indexes to investigate the spatial accessibility of the cardiological network. Secondly, by means of a gravity model estimated using Bayesian techniques we identify the determinants of patient flows in terms of demand and supply factors. Our results suggest that information on destinations is processed hierarchically and that agglomeration-like forces are dominant. Furthermore, although self-sufficiency of provision at the provincial level has been achieved to a large extent, there is still scope to improve the organisational efficiency of the network.

  12. Patients' experience of chronic illness care in a network of teaching settings.

    PubMed

    Houle, Janie; Beaulieu, Marie-Dominique; Lussier, Marie-Thérèse; Del Grande, Claudio; Pellerin, Jean-Pierre; Authier, Marie; Duplain, Réjean; Tran, Tri Minh; Allison, François

    2012-12-01

    To evaluate chronic illness care delivery from the patient's perspective and to examine its main correlates. Cross-sectional, descriptive study using questionnaires and medical chart review. Nine teaching family practices in Quebec. A total of 364 patients with diabetes, hypertension, or chronic obstructive pulmonary disease. Score on the Patient Assessment of Chronic Illness Care (PACIC) questionnaire, which evaluates the patient's perspective on the care received based on the chronic care model (CCM); patients characteristics (sex, level of education, number of chronic illnesses); patient-physician relationship (relational continuity, interpersonal communication assessed from the patient's perspective); and interdisciplinary care and technical quality of care abstracted from patients' medical charts. The mean PACIC score obtained (2.8 out of 5) indicates that, on average, CCM-concordant care "generally did not occur" or occurred only "sometimes" in this network of teaching practices. However, with a mean technical quality-of-care score of nearly 80%, physicians in this network showed a high degree of adherence to clinical guidelines for the chronic illnesses under study. Patient education level lower than high school was negatively associated with PACIC scores, while positive associations were found with male sex, number of chronic illnesses, relational continuity, interpersonal communication, interdisciplinary care, and technical quality of care. Patients with less education reported receiving less CCM-concordant care. The patient-physician relationship was the strongest correlate of PACIC scores, while interdisciplinary care and technical quality of care had modest contributions.

  13. Heuristic method for searches on large data-sets organised using network models

    NASA Astrophysics Data System (ADS)

    Ruiz-Fernández, D.; Quintana-Pacheco, Y.

    2016-05-01

    Searches on large data-sets have become an important issue in recent years. An alternative, which has achieved good results, is the use of methods relying on data mining techniques, such as cluster-based retrieval. This paper proposes a heuristic search that is based on an organisational model that reflects similarity relationships among data elements. The search is guided by using quality estimators of model nodes, which are obtained by the progressive evaluation of the given target function for the elements associated with each node. The results of the experiments confirm the effectiveness of the proposed algorithm. High-quality solutions are obtained evaluating a relatively small percentage of elements in the data-sets.

  14. Orthopaedic trauma research priority-setting exercise and development of a research network.

    PubMed

    Willett, K M; Gray, B; Moran, C G; Giannoudis, P V; Pallister, I

    2010-07-01

    Clinical practice should be informed by high quality evidence, of which randomised controlled trials (RCTs) are considered the gold standard. Surgical trials are inherently difficult with potential problems around clinical equipoise and participant acceptability. This is often most true with trial designs comparing operative and non-operative treatments. It is hoped that research activity can be maximised by collaborating in (a) the identification of research questions and (b) involvement in clinical trials. Development of the national research networks can be utilised to provide support for research endeavours within the orthopaedic trauma community. To identify and prioritise the research questions felt to be of most importance by the orthopaedic trauma community. Research studies will be considered for questions given the highest priority. A Delphi approach was used to determine consensus between the faculty members of the AOUK. A two round process was used to elicit the research questions and then to rank them in order of priority. 217 members of the AOUK Long Bone Faculty were asked to submit research questions, predominantly consultant orthopaedic surgeons. A 22% response rate generated 147 questions. These were collated and the most frequent 24 sent back out for ranking by mean scores. A 55% response to this second round identified 10 top questions. Literature searches for these 10 looked at current knowledge of the subject, completed and ongoing research projects. We also looked at the advantages and disadvantages of undertaking a study and the most appropriate methodology. The response rates demonstrated a clear interest in developing a collaborative research strategy. This can be enhanced by utilising the support of the National Institute of Health Research Clinical Research Networks (NIHR CRN). 2010 Elsevier Ltd. All rights reserved.

  15. Spatial Fingerprints of Community Structure in Human Interaction Network for an Extensive Set of Large-Scale Regions

    PubMed Central

    Kallus, Zsófia; Barankai, Norbert; Szüle, János; Vattay, Gábor

    2015-01-01

    Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization. PMID:25993329

  16. SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets

    PubMed Central

    Petsalaki, Evangelia; Helbig, Andreas O.; Gopal, Anjali; Pasculescu, Adrian; Roth, Frederick P.; Pawson, Tony

    2015-01-01

    While phospho-proteomics studies have shed light on the dynamics of cellular signaling, they mainly describe global effects and rarely explore mechanistic details, such as kinase/substrate relationships. Tools and databases, such as NetworKIN and PhosphoSitePlus, provide valuable regulatory details on signaling networks but rely on prior knowledge. They therefore provide limited information on less studied kinases and fewer unexpected relationships given that better studied signaling events can mask condition- or cell-specific ‘network wiring’. SELPHI is a web-based tool providing in-depth analysis of phospho-proteomics data that is intuitive and accessible to non-bioinformatics experts. It uses correlation analysis of phospho-sites to extract kinase/phosphatase and phospho-peptide associations, and highlights the potential flow of signaling in the system under study. We illustrate SELPHI via analysis of phospho-proteomics data acquired in the presence of erlotinib—a tyrosine kinase inhibitor (TKI)—in cancer cells expressing TKI-resistant and -sensitive variants of the Epidermal Growth Factor Receptor. In this data set, SELPHI revealed information overlooked by the reporting study, including the known role of MET and EPHA2 kinases in conferring resistance to erlotinib in TKI sensitive strains. SELPHI can significantly enhance the analysis of phospho-proteomics data contributing to improved understanding of sample-specific signaling networks. SELPHI is freely available via http://llama.mshri.on.ca/SELPHI. PMID:25948583

  17. Partnerships in mental healthcare service delivery in low-resource settings: developing an innovative network in rural Nepal.

    PubMed

    Acharya, Bibhav; Maru, Duncan; Schwarz, Ryan; Citrin, David; Tenpa, Jasmine; Hirachan, Soniya; Basnet, Madhur; Thapa, Poshan; Swar, Sikhar; Halliday, Scott; Kohrt, Brandon; Luitel, Nagendra P; Hung, Erick; Gauchan, Bikash; Pokharel, Rajeev; Ekstrand, Maria

    2017-01-13

    Mental illnesses are the largest contributors to the global burden of non-communicable diseases. However, there is extremely limited access to high quality, culturally-sensitive, and contextually-appropriate mental healthcare services. This situation persists despite the availability of interventions with proven efficacy to improve patient outcomes. A partnerships network is necessary for successful program adaptation and implementation. We describe our partnerships network as a case example that addresses challenges in delivering mental healthcare and which can serve as a model for similar settings. Our perspectives are informed from integrating mental healthcare services within a rural public hospital in Nepal. Our approach includes training and supervising generalist health workers by off-site psychiatrists. This is made possible by complementing the strengths and weaknesses of the various groups involved: the public sector, a non-profit organization that provides general healthcare services and one that specializes in mental health, a community advisory board, academic centers in high- and low-income countries, and bicultural professionals from the diaspora community. We propose a partnerships model to assist implementation of promising programs to expand access to mental healthcare in low- resource settings. We describe the success and limitations of our current partners in a mental health program in rural Nepal.

  18. Setting up a Wireless Local Area Network (WLAN) for a healthcare system.

    PubMed

    Wang, Jin; Du, Hongwei

    2005-01-01

    WLAN can help the medical professionals to improve their working efficiency and reduce medical errors. In this paper, the important issues of deploying WLAN in hospitals are discussed. It gives a comprehensive overview of how to set up the mobility, Quality of Service (QoS) and security of the WLAN for a healthcare system. IEEE 802.11e standard and the Health Insurance Portability and Accountability ACT (HIPAA) regulations are discussed and some suggestions are given to meet the specific requirements of a healthcare environment.

  19. Spiking neural networks with different reinforcement learning (RL) schemes in a multiagent setting.

    PubMed

    Christodoulou, Chris; Cleanthous, Aristodemos

    2010-12-31

    This paper investigates the effectiveness of spiking agents when trained with reinforcement learning (RL) in a challenging multiagent task. In particular, it explores learning through reward-modulated spike-timing dependent plasticity (STDP) and compares it to reinforcement of stochastic synaptic transmission in the general-sum game of the Iterated Prisoner's Dilemma (IPD). More specifically, a computational model is developed where we implement two spiking neural networks as two "selfish" agents learning simultaneously but independently, competing in the IPD game. The purpose of our system (or collective) is to maximise its accumulated reward in the presence of reward-driven competing agents within the collective. This can only be achieved when the agents engage in a behaviour of mutual cooperation during the IPD. Previously, we successfully applied reinforcement of stochastic synaptic transmission to the IPD game. The current study utilises reward-modulated STDP with eligibility trace and results show that the system managed to exhibit the desired behaviour by establishing mutual cooperation between the agents. It is noted that the cooperative outcome was attained after a relatively short learning period which enhanced the accumulation of reward by the system. As in our previous implementation, the successful application of the learning algorithm to the IPD becomes possible only after we extended it with additional global reinforcement signals in order to enhance competition at the neuronal level. Moreover it is also shown that learning is enhanced (as indicated by an increased IPD cooperative outcome) through: (i) strong memory for each agent (regulated by a high eligibility trace time constant) and (ii) firing irregularity produced by equipping the agents' LIF neurons with a partial somatic reset mechanism.

  20. The Setting-up of Multi-Site School Collaboratives: The Benefits of This Organizational Reform in Terms of Networking Opportunities and Their Effects

    ERIC Educational Resources Information Center

    Mifsud, Denise

    2015-01-01

    This article, which is set within the Maltese education scenario of unfolding decentralization through the setting-up of multi-site school collaboratives (legally termed "colleges") via a policy mandate, explores a particular aspect of this reform--that of "networking". This is examined in terms of the potential for…

  1. Universal set of dynamically protected gates for bipartite qubit networks: Soft pulse implementation of the [[5,1,3

    NASA Astrophysics Data System (ADS)

    De, Amrit; Pryadko, Leonid P.

    2016-04-01

    We model repetitive quantum error correction (QEC) with the single-error-correcting five-qubit code on a network of individually controlled qubits with always-on Ising couplings. We use our previously designed universal set of quantum gates based on sequences of shaped decoupling pulses. In addition to being accurate quantum gates, the sequences also provide dynamical decoupling (DD) of low-frequency phase noise. The simulation involves integrating the unitary dynamics of six qubits over the duration of tens of thousands of control pulses, using classical stochastic phase noise as a source of decoherence. The combined DD and QEC protocol dramatically improves the coherence, with the QEC alone being responsible for more than an order of magnitude infidelity reduction.

  2. Energy-efficient key distribution using electrocardiograph biometric set for secure communications in wireless body healthcare networks.

    PubMed

    Shi, Jinyang; Lam, Kwok-Yan; Gu, Ming; Li, Mingze; Chung, Siu-Leung

    2011-10-01

    Wireless body sensor network (WBSN) has gained significant interests as an important infrastructure for real-time biomedical healthcare systems, while the security of the sensitive health information becomes one of the main challenges. Due to the constraints of limited power, traditional cryptographic key distribution schemes are not suitable for WBSN. This paper proposes a novel energy-efficient approach, BodyKey, which can distribute the keys using the electrocardiograph biometrics. BodyKey represents the biometric features as ordered set, and deals with the biometric variations using set reconciliation. In this way, only limited necessary information needs to be communicated for key agreement, and the total energy consumption for key distribution can thus be reduced. Experiments on the PhysioBank Database show that BodyKey can perform an energy consumption rate of 0.01 mJ/bit with an equal accuracy rate of 97.28%, allowing the system to be used as an energy-efficient key distribution scheme for secure communications in WBSN.

  3. Distributed Event-Based Set-Membership Filtering for a Class of Nonlinear Systems With Sensor Saturations Over Sensor Networks.

    PubMed

    Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos

    2016-07-07

    In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

  4. Practice-Based Research Networks, Part II: A Descriptive Analysis of the Athletic Training Practice-Based Research Network in the Secondary School Setting

    PubMed Central

    McLeod, Tamara C. Valovich; Lam, Kenneth C.; Bay, R. Curtis; Sauers, Eric L.; Valier, Alison R. Snyder

    2012-01-01

    Context Analysis of health care service models requires the collection and evaluation of basic practice characterization data. Practice-based research networks (PBRNs) provide a framework for gathering data useful in characterizing clinical practice. Objective To describe preliminary secondary school setting practice data from the Athletic Training Practice-Based Research Network (AT-PBRN). Design Descriptive study. Setting Secondary school athletic training facilities within the AT-PBRN. Patients or Other Participants Clinicians (n = 22) and their patients (n = 2523) from the AT-PBRN. Main Outcome Measure(s) A Web-based survey was used to obtain data on clinical practice site and clinician characteristics. Patient and practice characteristics were obtained via deidentified electronic medical record data collected between September 1, 2009, and April 1, 2011. Descriptive data regarding the clinician and CPS practice characteristics are reported as percentages and frequencies. Descriptive analysis of patient encounters and practice characteristic data was performed, with the percentages and frequencies of the type of injuries recorded at initial evaluation, type of treatment received at initial evaluation, daily treatment, and daily sign-in procedures. Results The AT-PBRN had secondary school sites in 7 states, and most athletic trainers at those sites (78.2%) had less than 5 years of experience. The secondary school sites within the AT-PBRN documented 2523 patients treated across 3140 encounters. Patients most frequently sought care for a current injury (61.3%), followed by preventive services (24.0%), and new injuries (14.7%). The most common diagnoses were ankle sprain/strain (17.9%), hip sprain/strain (12.5%), concussion (12.0%), and knee pain (2.5%). The most frequent procedures were athletic trainer evaluation (53.9%), hot- or cold-pack application (26.0%), strapping (10.3%), and therapeutic exercise (5.7%). The median number of treatments per injury was 3

  5. Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

    NASA Astrophysics Data System (ADS)

    Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

    2013-03-01

    Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

  6. Practice-based research networks, part II: a descriptive analysis of the athletic training practice-based research network in the secondary school setting.

    PubMed

    Valovich McLeod, Tamara C; Lam, Kenneth C; Bay, R Curtis; Sauers, Eric L; Snyder Valier, Alison R

    2012-01-01

    Analysis of health care service models requires the collection and evaluation of basic practice characterization data. Practice-based research networks (PBRNs) provide a framework for gathering data useful in characterizing clinical practice. To describe preliminary secondary school setting practice data from the Athletic Training Practice-Based Research Network (AT-PBRN). Descriptive study. Secondary school athletic training facilities within the AT-PBRN. Clinicians (n = 22) and their patients (n = 2523) from the AT-PBRN. A Web-based survey was used to obtain data on clinical practice site and clinician characteristics. Patient and practice characteristics were obtained via deidentified electronic medical record data collected between September 1, 2009, and April 1, 2011. Descriptive data regarding the clinician and CPS practice characteristics are reported as percentages and frequencies. Descriptive analysis of patient encounters and practice characteristic data was performed, with the percentages and frequencies of the type of injuries recorded at initial evaluation, type of treatment received at initial evaluation, daily treatment, and daily sign-in procedures. The AT-PBRN had secondary school sites in 7 states, and most athletic trainers at those sites (78.2%) had less than 5 years of experience. The secondary school sites within the AT-PBRN documented 2523 patients treated across 3140 encounters. Patients most frequently sought care for a current injury (61.3%), followed by preventive services (24.0%), and new injuries (14.7%). The most common diagnoses were ankle sprain/strain (17.9%), hip sprain/strain (12.5%), concussion (12.0%), and knee pain (2.5%). The most frequent procedures were athletic trainer evaluation (53.9%), hot- or cold-pack application (26.0%), strapping (10.3%), and therapeutic exercise (5.7%). The median number of treatments per injury was 3 (interquartile range = 2, 4; range = 2-19). These preliminary data describe services provided by

  7. Naturally-Emerging Technology-Based Leadership Roles in Three Independent Schools: A Social Network-Based Case Study Using Fuzzy Set Qualitative Comparative Analysis

    ERIC Educational Resources Information Center

    Velastegui, Pamela J.

    2013-01-01

    This hypothesis-generating case study investigates the naturally emerging roles of technology brokers and technology leaders in three independent schools in New York involving 92 school educators. A multiple and mixed method design utilizing Social Network Analysis (SNA) and fuzzy set Qualitative Comparative Analysis (FSQCA) involved gathering…

  8. Naturally-Emerging Technology-Based Leadership Roles in Three Independent Schools: A Social Network-Based Case Study Using Fuzzy Set Qualitative Comparative Analysis

    ERIC Educational Resources Information Center

    Velastegui, Pamela J.

    2013-01-01

    This hypothesis-generating case study investigates the naturally emerging roles of technology brokers and technology leaders in three independent schools in New York involving 92 school educators. A multiple and mixed method design utilizing Social Network Analysis (SNA) and fuzzy set Qualitative Comparative Analysis (FSQCA) involved gathering…

  9. A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

    PubMed Central

    Lu, Le; Seff, Ari; Cherry, Kevin M.; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M.

    2015-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards ~100% sensitivity at the cost of high FP levels (~40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work. PMID:25333158

  10. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    PubMed

    Roth, Holger R; Lu, Le; Seff, Ari; Cherry, Kevin M; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M

    2014-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.

  11. A nonlinear training set superposition filter derived by neural network training methods for implementation in a shift-invariant optical correlator

    NASA Astrophysics Data System (ADS)

    Kypraios, Ioannis; Young, Rupert C. D.; Birch, Philip M.; Chatwin, Christopher R.

    2003-08-01

    The various types of synthetic discriminant function (sdf) filter result in a weighted linear superposition of the training set images. Neural network training procedures result in a non-linear superposition of the training set images or, effectively, a feature extraction process, which leads to better interpolation properties than achievable with the sdf filter. However, generally, shift invariance is lost since a data dependant non-linear weighting function is incorporated in the input data window. As a compromise, we train a non-linear superposition filter via neural network methods with the constraint of a linear input to allow for shift invariance. The filter can then be used in a frequency domain based optical correlator. Simulation results are presented that demonstrate the improved training set interpolation achieved by the non-linear filter as compared to a linear superposition filter.

  12. Instructor Experiences with a Social Networking Site in a Higher Education Setting: Expectations, Frustrations, Appropriation, and Compartmentalization

    ERIC Educational Resources Information Center

    Veletsianos, George; Kimmons, Royce; French, Karen D.

    2013-01-01

    Researchers and practitioners have suggested that the use of social networking sites in formal education may be a worthwhile endeavor. Toward this goal, emerging learning platforms have included social networking features. Nevertheless, empirical literature examining user experiences, and more specifically instructor experiences, with these tools…

  13. From global agenda-setting to domestic implementation: successes and challenges of the global health network on tobacco control.

    PubMed

    Gneiting, Uwe

    2016-04-01

    Global policy attention to tobacco control has increased significantly since the 1990 s and culminated in the first international treaty negotiated under the auspices of the World Health Organization--the Framework Convention on Tobacco Control (FCTC). Although the political process that led to the creation of the FCTC has been extensively researched, the FCTC's progression from an aspirational treaty towards a global health governance framework with tangible policy effects within FCTC member countries has not been well-understood to date. This article analyses the role of the global health network of tobacco control advocates and scientists, which formed during the FCTC negotiations during the late 1990 s, in translating countries' commitment to the FCTC into domestic policy change. By comparing the network's influence around two central tobacco control interventions (smoke-free environments and taxation), the study identifies several scope conditions, which have shaped the network's effectiveness around the FCTC's implementation: the complexity of the policy issue and the relative importance of non-health expertise, the required scope of domestic political buy-in, the role of the general public as network allies, and the strength of policy opposition. These political factors had a greater influence on the network's success than the evidence base for the effectiveness of tobacco control interventions. The network's variable success points to a trade-off faced by global health networks between their need to maintain internal cohesion and their ability to form alliances with actors in their social environment.

  14. Outreach in natural settings: the use of peer leaders for HIV prevention among injecting drug users' networks.

    PubMed Central

    Latkin, C A

    1998-01-01

    OBJECTIVE: Guided by a social influence and empowerment framework, peer leaders in the injecting drug user (IDU) community were trained to promote human immunodeficiency virus (HIV) prevention among their contacts within and beyond their sex and drug networks. METHODS: From 1994 to 1995 in Baltimore, Maryland, 36 peer leaders who participated in the 10-session training program were administered pretest and posttest surveys. Evaluation included leaders' self-reported HIV-related behaviors and outreach activities. Survey data also were collected from 78 of the leaders' risk network members. RESULTS: Peer leaders reported a significant increase in condom use and in cleaning used needles with bleach. The leaders' risk network members, compared with controls, were significantly more likely to report greater needle hygiene. In an assessment of diffusion of information, the majority of risk network members who were current injectors reported receiving needle-cleaning materials from the leaders, and the majority of risk network members were able to correctly identify the HIV prevention slogans that had been taught to the leaders. The leaders documented 2165 HIV prevention interactions, of which 84% were with active drug users. CONCLUSIONS: The results from this study suggest that, in the IDU community, training peer leaders as HIV educators may promote HIV prevention among the leaders' risk network members and others at risk of acquiring and transmitting HIV. This training also may provide the leaders with effective prosocial roles. PMID:9722820

  15. Networks.

    ERIC Educational Resources Information Center

    Maughan, George R.; Petitto, Karen R.; McLaughlin, Don

    2001-01-01

    Describes the connectivity features and options of modern campus communication and information system networks, including signal transmission (wire-based and wireless), signal switching, convergence of networks, and network assessment variables, to enable campus leaders to make sound future-oriented decisions. (EV)

  16. Time series analysis of benzo[A]pyrene-induced transcriptome changes suggests that a network of transcription factors regulates the effects on functional gene sets.

    PubMed

    van Delft, Joost H M; Mathijs, Karen; Staal, Yvonne C M; van Herwijnen, Marcel H M; Brauers, Karen J J; Boorsma, André; Kleinjans, Jos C S

    2010-10-01

    Chemical carcinogens may cause a multitude of effects inside cells, thereby affecting transcript levels of genes by direct activation of transcription factors (TF) or indirectly through the formation of DNA damage. As the temporal profiles of these responses may be profoundly different, examining time-dependent changes may provide new insights in TF networks related to cellular responses to chemical carcinogens. Therefore, we investigated in human hepatoma cells gene expression changes caused by benzo[a]pyrene at 12 time points after exposure, in relation to DNA adduct and cell cycle. Temporal profiles for functional gene sets demonstrate both early and late effects in up- and downregulation of relevant gene sets involved in cell cycle, apoptosis, DNA repair, and metabolism of amino acids and lipids. Many significant transcription regulation networks appeared to be around TF that are proto-oncogenes or tumor suppressor genes. The time series analysis tool Short Time-series Expression Miner (STEM) was used to identify time-dependent correlation of pathways, gene sets, TF networks, and biological parameters. Most correlations are with DNA adduct levels, which is an early response, and less with the later responses on G1 and S phase cells. The majority of the modulated genes in the Reactome pathways can be regulated by several of these TF, e.g., 73% by nuclear factor-kappa B and 34-42% by c-MYC, SRF, AP1, and E2F1. All these TF can also regulate one or more of the others. Our data indicate that a complex network of a few TF is responsible for the majority of the transcriptional changes induced by BaP. This network hardly changes over time, despite that the transcriptional profiles clearly alter, suggesting that also other regulatory mechanisms are involved.

  17. Social-Professional Networks in Long-Term Care Settings With People With Dementia: An Approach to Better Care? A Systematic Review.

    PubMed

    Mitchell, Janet I; Long, Janet C; Braithwaite, Jeffrey; Brodaty, Henry

    2016-02-01

    Dementia is a syndrome associated with stigma and social isolation. Forty-two percent of people with dementia in the United States and almost 40% in the United Kingdom live in assisted living and residential care facilities. Up to 90% of residents with dementia experience behavioral and psychological symptoms of dementia (BPSD). Currently psychotropic drugs are often used to manage BPSD, despite the drugs' limited efficacy and adverse effects. Even though psychosocial approaches are as effective as medical ones without side effects, their uptake has been slow. Social networks that investigate the structure of relationships among residents and staff may represent an important resource to increase the uptake of psychosocial approaches and facilitate improvements in care. To conduct a systematic review of social network studies set in long-term care (LTC), including residents with dementia, and identify network factors influencing the care available to residents. Peer-reviewed articles across CINAHL, EMBASE, IBSS, Medline, PsychInfo, Scopus, and Web of Science were searched from January 1994 to December 2014 inclusive, using PRISMA guidelines. Studies included those examining social networks of residents or staff in LTC. Nine articles from studies in the United States, Europe, Asia, and Australia met search criteria. Resident networks had few social connections. One study proposed that residents with high centrality be encouraged to welcome new residents and disseminate information. The high density in 2 staff network studies was associated with the cooperation needed to provide care to residents with dementia. Staff's boundary-spanning led to higher-status nurses becoming more involved in decision-making and problem-solving in one study. In another, the outcome was staff treating residents with more respect and actively caring for them. These studies suggest interventions using a network approach may improve care services in LTC. Copyright © 2016 AMDA – The

  18. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

    PubMed Central

    2006-01-01

    We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified. PMID:16686963

  19. Patient visits to a national practice-based research network: comparing pediatric research in office settings with the National Ambulatory Medical Care Survey.

    PubMed

    Slora, Eric J; Thoma, Kathleen A; Wasserman, Richard C; Pedlow, Steven E; Bocian, Alison B

    2006-08-01

    Our objective with this study was to assess the extent to which patients who are seen by practitioners in Pediatric Research in Office Settings, a national primary care practice-based research network, are representative of those who are seen in ambulatory office-based pediatric primary care in the United States. Pediatric Research in Office Settings patient data were collected from the offices of 57 randomly selected network practitioners as part of an Agency for Healthcare Research and Quality-funded effort to describe primary care visits and replicate the National Ambulatory Medical Care Survey in primary care practice-based research networks. These data were from 1706 randomly selected pediatric patient visits that occurred between March and June 2002. National comparison data were 948 randomly selected pediatric patient visits that occurred between March and June 2000 in the offices of the 33 primary care pediatric practitioners who had participated in the National Ambulatory Medical Care Survey. The groups were compared on patient demographics (age, gender, race, ethnicity, and socioeconomic status, as represented by Medicaid status), visit characteristics (percentages of patients referred, practitioner designation of visit as acute versus nonacute, and continuity of care), the top patient/parent-articulated reasons for visit, and the top practitioner diagnoses. Comparisons revealed substantial similarities between Pediatric Research in Office Settings and national data, including gender, ethnicity, socioeconomic status, and visit characteristics. Differences were noted for age and race, with Pediatric Research in Office Settings children approximately 1 year older and comprising a significantly lower proportion of black patients than their National Ambulatory Medical Care Survey counterparts. Although the top 6 reasons that were articulated by parents for outpatient visits in the 2 groups were remarkably similar in rank order and proportions, there were

  20. Attitudes toward Using Social Networking Sites in Educational Settings with Underperforming Latino Youth: A Mixed Methods Study

    ERIC Educational Resources Information Center

    Howard, Keith E.; Curwen, Margie Sauceda; Howard, Nicol R.; Colón-Muñiz, Anaida

    2015-01-01

    The researchers examined the online social networking attitudes of underperforming Latino high school students in an alternative education program that uses technology as the prime venue for learning. A sequential explanatory mixed methods study was used to cross-check multiple sources of data explaining students' levels of comfort with utilizing…

  1. The Use of an Educational Social Networking Site for English Language Learning beyond the Classroom in a Japanese University Setting

    ERIC Educational Resources Information Center

    Okumura, Shinji

    2016-01-01

    This study describes an attempt of using an educational social networking platform, which is called Edmodo, for English language learning outside classrooms at tertiary level. Considering the notion of communicative competence, the instructor incorporated Edmodo into his English classes as a project which is a formal assignment. In the project,…

  2. Attitudes toward Using Social Networking Sites in Educational Settings with Underperforming Latino Youth: A Mixed Methods Study

    ERIC Educational Resources Information Center

    Howard, Keith E.; Curwen, Margie Sauceda; Howard, Nicol R.; Colón-Muñiz, Anaida

    2015-01-01

    The researchers examined the online social networking attitudes of underperforming Latino high school students in an alternative education program that uses technology as the prime venue for learning. A sequential explanatory mixed methods study was used to cross-check multiple sources of data explaining students' levels of comfort with utilizing…

  3. Neural Network Function Classifier

    DTIC Science & Technology

    2003-02-07

    neural network sets. Each of the neural networks in a particular set is trained to recognize a particular data set type. The best function representation of the data set is determined from the neural network output. The system comprises sets of trained neural networks having neural networks trained to identify different types of data. The number of neural networks within each neural network set will depend on the number of function types that are represented. The system further comprises

  4. Two-dimensional paper network format that enables simple multistep assays for use in low-resource settings in the context of malaria antigen detection.

    PubMed

    Fu, Elain; Liang, Tinny; Spicar-Mihalic, Paolo; Houghtaling, Jared; Ramachandran, Sujatha; Yager, Paul

    2012-05-15

    The lateral flow test has become the standard bioassay format in low-resource settings because it is rapid, easy to use, and low in cost, uses reagents stored in dry form, and is equipment-free. However, lateral flow tests are often limited to a single chemical delivery step and not capable of the multistep processing characteristic of high performance laboratory-based assays. To address this limitation, we are developing a paper network platform that extends the conventional lateral flow test to two dimensions; this allows incorporation of multistep chemical processing, while still retaining the advantages of conventional lateral flow tests. Here, we demonstrate this format for an easy-to-use, signal-amplified sandwich format immunoassay for the malaria protein PfHRP2. The card contains reagents stored in dry form such that the user need only add sample and water. The multiple flows in the device are activated in a single user step of folding the card closed; the configuration of the paper network automatically delivers the appropriate volumes of (i) sample plus antibody conjugated to a gold particle label, (ii) a rinse buffer, and (iii) a signal amplification reagent to the capture region. These results highlight the potential of the paper network platform to enhance access to high-quality diagnostic capabilities in low-resource settings in the developed and developing worlds.

  5. Networking.

    ERIC Educational Resources Information Center

    Duvall, Betty

    Networking is an information giving and receiving system, a support system, and a means whereby women can get ahead in careers--either in new jobs or in current positions. Networking information can create many opportunities: women can talk about how other women handle situations and tasks, and previously established contacts can be used in…

  6. Social Perception of Public Water Supply Network and Groundwater Quality in an Urban Setting Facing Saltwater Intrusion and Water Shortages.

    PubMed

    Alameddine, Ibrahim; Jawhari, Gheeda; El-Fadel, Mutasem

    2017-04-01

    Perceptions developed by consumers regarding the quality of water reaching their household can affect the ultimate use of the water. This study identified key factors influencing consumers' perception of water quality in a highly urbanized coastal city, experiencing chronic water shortages, overexploitation of groundwater, and accelerated saltwater intrusion. Household surveys were administered to residents to capture views and perceptions of consumed water. Concomitantly, groundwater and tap water samples were collected and analyzed at each residence for comparison with perceptions. People's rating of groundwater quality was found to correlate to the measured water quality both in the dry and wet seasons. In contrast, perceptions regarding the water quality of the public water supply network did not show any correlation with the measured tap water quality indicators. Logistic regression models developed to predict perception based on salient variables indicated that age, apartment ownership, and levels of total dissolved solids play a significant role in shaping perceptions regarding groundwater quality. Perceptions concerning the water quality of the public water supply network appeared to be independent of the measured total dissolved solids levels at the tap but correlated to those measured in the wells. The study highlights misconceptions that can arise as a result of uncontrolled cross-connections of groundwater to the public supply network water and the development of misaligned perceptions based on prior consumption patterns, water shortages, and a rapidly salinizing groundwater aquifer.

  7. Social Perception of Public Water Supply Network and Groundwater Quality in an Urban Setting Facing Saltwater Intrusion and Water Shortages

    NASA Astrophysics Data System (ADS)

    Alameddine, Ibrahim; Jawhari, Gheeda; El-Fadel, Mutasem

    2017-04-01

    Perceptions developed by consumers regarding the quality of water reaching their household can affect the ultimate use of the water. This study identified key factors influencing consumers' perception of water quality in a highly urbanized coastal city, experiencing chronic water shortages, overexploitation of groundwater, and accelerated saltwater intrusion. Household surveys were administered to residents to capture views and perceptions of consumed water. Concomitantly, groundwater and tap water samples were collected and analyzed at each residence for comparison with perceptions. People's rating of groundwater quality was found to correlate to the measured water quality both in the dry and wet seasons. In contrast, perceptions regarding the water quality of the public water supply network did not show any correlation with the measured tap water quality indicators. Logistic regression models developed to predict perception based on salient variables indicated that age, apartment ownership, and levels of total dissolved solids play a significant role in shaping perceptions regarding groundwater quality. Perceptions concerning the water quality of the public water supply network appeared to be independent of the measured total dissolved solids levels at the tap but correlated to those measured in the wells. The study highlights misconceptions that can arise as a result of uncontrolled cross-connections of groundwater to the public supply network water and the development of misaligned perceptions based on prior consumption patterns, water shortages, and a rapidly salinizing groundwater aquifer.

  8. Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets[C][W][OA

    PubMed Central

    Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Bacardit, Jaume

    2011-01-01

    The meta-analysis of large-scale postgenomics data sets within public databases promises to provide important novel biological knowledge. Statistical approaches including correlation analyses in coexpression studies of gene expression have emerged as tools to elucidate gene function using these data sets. Here, we present a powerful and novel alternative methodology to computationally identify functional relationships between genes from microarray data sets using rule-based machine learning. This approach, termed “coprediction,” is based on the collective ability of groups of genes co-occurring within rules to accurately predict the developmental outcome of a biological system. We demonstrate the utility of coprediction as a powerful analytical tool using publicly available microarray data generated exclusively from Arabidopsis thaliana seeds to compute a functional gene interaction network, termed Seed Co-Prediction Network (SCoPNet). SCoPNet predicts functional associations between genes acting in the same developmental and signal transduction pathways irrespective of the similarity in their respective gene expression patterns. Using SCoPNet, we identified four novel regulators of seed germination (ALTERED SEED GERMINATION5, 6, 7, and 8), and predicted interactions at the level of transcript abundance between these novel and previously described factors influencing Arabidopsis seed germination. An online Web tool to query SCoPNet has been developed as a community resource to dissect seed biology and is available at http://www.vseed.nottingham.ac.uk/. PMID:21896882

  9. From global agenda-setting to domestic implementation: successes and challenges of the global health network on tobacco control

    PubMed Central

    Gneiting, Uwe

    2016-01-01

    Global policy attention to tobacco control has increased significantly since the 1990s and culminated in the first international treaty negotiated under the auspices of the World Health Organization—the Framework Convention on Tobacco Control (FCTC). Although the political process that led to the creation of the FCTC has been extensively researched, the FCTC’s progression from an aspirational treaty towards a global health governance framework with tangible policy effects within FCTC member countries has not been well-understood to date. This article analyses the role of the global health network of tobacco control advocates and scientists, which formed during the FCTC negotiations during the late 1990s, in translating countries’ commitment to the FCTC into domestic policy change. By comparing the network’s influence around two central tobacco control interventions (smoke-free environments and taxation), the study identifies several scope conditions, which have shaped the network’s effectiveness around the FCTC’s implementation: the complexity of the policy issue and the relative importance of non-health expertise, the required scope of domestic political buy-in, the role of the general public as network allies, and the strength of policy opposition. These political factors had a greater influence on the network’s success than the evidence base for the effectiveness of tobacco control interventions. The network’s variable success points to a trade-off faced by global health networks between their need to maintain internal cohesion and their ability to form alliances with actors in their social environment. PMID:26253698

  10. Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling.

    PubMed

    Hemmateenejad, Bahram; Miri, Ramin; Safarpour, Mohammad A; Mehdipour, Ahmad R

    2006-08-01

    A genetic algorithm-based artificial neural network model has been developed for the accurate prediction of the blood-brain barrier partitioning (in logBB scale) of chemicals. A data set of 123 logBB (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO-3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, logP as a measure of hydrophobicity and different topological indices were also calculated. A three-layered artificial neural network with backpropagation of an error-learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and logBB data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the logBB data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMS(V) and RMS(CV) equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the logP and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the logBB of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the logBB data. Copyright 2006 Wiley Periodicals, Inc.

  11. moGrams: A Network-Based Methodology for Visualizing the Set of Nondominated Solutions in Multiobjective Optimization.

    PubMed

    Trawinski, Krzysztof; Chica, Manuel; Pancho, David P; Damas, Sergio; Cordon, Oscar

    2017-01-16

    An appropriate visualization of multiobjective nondominated solutions is a valuable asset for decision making. Although there are methods for visualizing the solutions in the design space, they do not provide any information about their relationship. In this paper, we propose a novel methodology that allows the visualization of the nondominated solutions in the design space and their relationships by means of a network. The nodes represent the solutions in the objective space while the edges show the relationships among the solutions in the design space. Our proposal (called moGrams) thus provides a joint visualization of both objective and design spaces. It aims at helping the decision maker to get more understanding of the problem so that (s)he can choose the most appropriate and flexible final solution. moGrams can be applied to any multicriteria problem in which the solutions are related by a similarity metric. Besides, the decision maker interaction is facilitated by modifying the network based on the current preferences to obtain a clearer view. An exhaustive experimental study is performed using four multiobjective problems with a variable number of objectives to show both usefulness and versatility of moGrams. The results exhibit interesting characteristics of our methodology for visualizing and analyzing solutions of multiobjective problems.

  12. On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting

    PubMed Central

    van Maanen, B.; Coco, G.; Bryan, K. R.

    2015-01-01

    An ecomorphodynamic model was developed to study how Avicennia marina mangroves influence channel network evolution in sandy tidal embayments. The model accounts for the effects of mangrove trees on tidal flow patterns and sediment dynamics. Mangrove growth is in turn controlled by hydrodynamic conditions. The presence of mangroves was found to enhance the initiation and branching of tidal channels, partly because the extra flow resistance in mangrove forests favours flow concentration, and thus sediment erosion in between vegetated areas. The enhanced branching of channels is also the result of a vegetation-induced increase in erosion threshold. On the other hand, this reduction in bed erodibility, together with the soil expansion driven by organic matter production, reduces the landward expansion of channels. The ongoing accretion in mangrove forests ultimately drives a reduction in tidal prism and an overall retreat of the channel network. During sea-level rise, mangroves can potentially enhance the ability of the soil surface to maintain an elevation within the upper portion of the intertidal zone, while hindering both the branching and headward erosion of the landward expanding channels. The modelling results presented here indicate the critical control exerted by ecogeomorphological interactions in driving landscape evolution. PMID:26339195

  13. On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting.

    PubMed

    van Maanen, B; Coco, G; Bryan, K R

    2015-08-08

    An ecomorphodynamic model was developed to study how Avicennia marina mangroves influence channel network evolution in sandy tidal embayments. The model accounts for the effects of mangrove trees on tidal flow patterns and sediment dynamics. Mangrove growth is in turn controlled by hydrodynamic conditions. The presence of mangroves was found to enhance the initiation and branching of tidal channels, partly because the extra flow resistance in mangrove forests favours flow concentration, and thus sediment erosion in between vegetated areas. The enhanced branching of channels is also the result of a vegetation-induced increase in erosion threshold. On the other hand, this reduction in bed erodibility, together with the soil expansion driven by organic matter production, reduces the landward expansion of channels. The ongoing accretion in mangrove forests ultimately drives a reduction in tidal prism and an overall retreat of the channel network. During sea-level rise, mangroves can potentially enhance the ability of the soil surface to maintain an elevation within the upper portion of the intertidal zone, while hindering both the branching and headward erosion of the landward expanding channels. The modelling results presented here indicate the critical control exerted by ecogeomorphological interactions in driving landscape evolution.

  14. Linking the input to the output: new sets of neurons complement the polarization vision network in the locust central complex.

    PubMed

    Heinze, Stanley; Homberg, Uwe

    2009-04-15

    Polarized light is a key feature of the blue sky, used by many animals as a sensory cue for compass navigation. Like other insects, locusts perceive the E-vector orientation of polarized light with a specialized region of their compound eye, the dorsal rim area. Neurons in the brain relay this information through several processing stages to the central complex. The central complex has a modular neuroarchitecture, composed of vertical columns and horizontal layers. Several types of central-complex neurons respond to dorsally presented, rotating E-vectors with tonic modulation of their firing frequency. These neurons were found at the input stage of the central complex, as well as near the proposed output stage, where neurons are tuned to form a compass-like representation of E-vector orientations underlying the columnar organization of the central complex. To identify neurons suited to link input and output elements, we recorded intracellularly from 45 neurons of the central complex. We report several novel types of polarization-sensitive neurons. One of these is suited to fill the gap between input and output stages of the central-complex polarization vision network. Three types of neurons were sensitive to polarized light in only 50% of experiments suggesting that they are recruited to the network depending on behavioral context. Finally, we identified two types of neurons suited to transfer information toward thoracic motor circuits. The data underscore the key role of two subunits of the central complex, the lower division of the central body and the protocerebral bridge, in sky compass orientation.

  15. Engaging communities to strengthen research ethics in low-income settings: selection and perceptions of members of a network of representatives in coastal Kenya.

    PubMed

    Kamuya, Dorcas M; Marsh, Vicki; Kombe, Francis K; Geissler, P Wenzel; Molyneux, Sassy C

    2013-04-01

    There is wide agreement that community engagement is important for many research types and settings, often including interaction with 'representatives' of communities. There is relatively little published experience of community engagement in international research settings, with available information focusing on Community Advisory Boards or Groups (CAB/CAGs), or variants of these, where CAB/G members often advise researchers on behalf of the communities they represent. In this paper we describe a network of community members ('KEMRI Community Representatives', or 'KCRs') linked to a large multi-disciplinary research programme on the Kenyan Coast. Unlike many CAB/Gs, the intention with the KCR network has evolved to be for members to represent the geographical areas in which a diverse range of health studies are conducted through being typical of those communities. We draw on routine reports, self-administered questionnaires and interviews to: 1) document how typical KCR members are of the local communities in terms of basic characteristics, and 2) explore KCR's perceptions of their roles, and of the benefits and challenges of undertaking these roles. We conclude that this evolving network is a potentially valuable way of strengthening interactions between a research institution and a local geographic community, through contributing to meeting intrinsic ethical values such as showing respect, and instrumental values such as improving consent processes. However, there are numerous challenges involved. Other ways of interacting with members of local communities, including community leaders, and the most vulnerable groups least likely to be vocal in representative groups, have always been, and remain, essential. © 2013 Blackwell Publishing Ltd.

  16. miR-124, -128, and -137 Orchestrate Neural Differentiation by Acting on Overlapping Gene Sets Containing a Highly Connected Transcription Factor Network.

    PubMed

    Santos, Márcia C T; Tegge, Allison N; Correa, Bruna R; Mahesula, Swetha; Kohnke, Luana Q; Qiao, Mei; Ferreira, Marco A R; Kokovay, Erzsebet; Penalva, Luiz O F

    2016-01-01

    The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect.

  17. ENGAGING COMMUNITIES TO STRENGTHEN RESEARCH ETHICS IN LOW-INCOME SETTINGS: SELECTION AND PERCEPTIONS OF MEMBERS OF A NETWORK OF REPRESENTATIVES IN COASTAL KENYA

    PubMed Central

    Kamuya, Dorcas M; Marsh, Vicki; Kombe, Francis K; Geissler, P Wenzel; Molyneux, Sassy C

    2013-01-01

    There is wide agreement that community engagement is important for many research types and settings, often including interaction with ‘representatives’ of communities. There is relatively little published experience of community engagement in international research settings, with available information focusing on Community Advisory Boards or Groups (CAB/CAGs), or variants of these, where CAB/G members often advise researchers on behalf of the communities they represent. In this paper we describe a network of community members (‘KEMRI Community Representatives’, or ‘KCRs’) linked to a large multi-disciplinary research programme on the Kenyan Coast. Unlike many CAB/Gs, the intention with the KCR network has evolved to be for members to represent the geographical areas in which a diverse range of health studies are conducted through being typical of those communities. We draw on routine reports, self-administered questionnaires and interviews to: 1) document how typical KCR members are of the local communities in terms of basic characteristics, and 2) explore KCR's perceptions of their roles, and of the benefits and challenges of undertaking these roles. We conclude that this evolving network is a potentially valuable way of strengthening interactions between a research institution and a local geographic community, through contributing to meeting intrinsic ethical values such as showing respect, and instrumental values such as improving consent processes. However, there are numerous challenges involved. Other ways of interacting with members of local communities, including community leaders, and the most vulnerable groups least likely to be vocal in representative groups, have always been, and remain, essential. PMID:23433404

  18. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    PubMed

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  19. Toxoplasmosis in Iran: A guide for general physicians working in the Iranian health network setting: A systematic review.

    PubMed

    Alavi, Seyed Mohammad; Alavi, Leila

    2016-01-01

    Human toxoplasmosis is an important zoonotic infection worldwide which is caused by the intracellular parasite Toxoplasma gondii (T.gondii). The aim of this study was to review briefly the general aspects of toxoplasma infection in in Iranian health system network. We searched published toxoplasmosis related articles in English databases including Science Direct, Pub Med, Scopus, Google Scholar, Magiran, Iran Medex, Iran Doc and Scientific Information Database (SID) for toxoplasmosis. Out of 1267 articles from the English and Persian databases search, 40 articles were suitable with our research objectives and so were selected for the study. It is estimated that at least a third of the world human population is infected with T.gondii, suggesting it as one of the most common parasitic infections through the world. Maternal infection during pregnancy may affect dangerous outcome for the fetus, or even cause intrauterine death. Reactivation of a previous infection in immunocompromised patient such as drug induced, AIDS and organ transplantation can cause life-threating central nervous system infection. Ocular toxoplasmosis is one of the most important causes of blindness, especially in individuals with a deficient immune system. According to the increasing burden of toxoplasmosis on human health, the findings of this study highlight the appropriate preventive measures, diagnosis, and management of this disease.

  20. Toxoplasmosis in Iran: A guide for general physicians working in the Iranian health network setting: A systematic review

    PubMed Central

    Alavi, Seyed Mohammad; Alavi, Leila

    2016-01-01

    Background: Human toxoplasmosis is an important zoonotic infection worldwide which is caused by the intracellular parasite Toxoplasma gondii (T.gondii). The aim of this study was to review briefly the general aspects of toxoplasma infection in in Iranian health system network. Methods: We searched published toxoplasmosis related articles in English databases including Science Direct, Pub Med, Scopus, Google Scholar, Magiran, Iran Medex, Iran Doc and Scientific Information Database (SID) for toxoplasmosis. Results: Out of 1267 articles from the English and Persian databases search, 40 articles were suitable with our research objectives and so were selected for the study. It is estimated that at least a third of the world human population is infected with T.gondii, suggesting it as one of the most common parasitic infections through the world. Maternal infection during pregnancy may affect dangerous outcome for the fetus, or even cause intrauterine death. Reactivation of a previous infection in immunocompromised patient such as drug induced, AIDS and organ transplantation can cause life-threating central nervous system infection. Ocular toxoplasmosis is one of the most important causes of blindness, especially in individuals with a deficient immune system. Conclusion: According to the increasing burden of toxoplasmosis on human health, the findings of this study highlight the appropriate preventive measures, diagnosis, and management of this disease. PMID:27999640

  1. Management of Type 2 Diabetes in the Primary Care Setting: A Practice-Based Research Network Study

    PubMed Central

    Spann, Stephen J.; Nutting, Paul A.; Galliher, James M.; Peterson, Kevin A.; Pavlik, Valory N.; Dickinson, L. Miriam; Volk, Robert J.

    2006-01-01

    PURPOSE We wanted to describe how primary care clinicians care for patients with type 2 diabetes. METHODS We undertook a cross-sectional study of 95 primary care clinicians and 822 of their established patients with type 2 diabetes from 4 practice-based, primary care research networks in the United States. Clinicians were surveyed about their training and practice. Patients completed a self-administered questionnaire about their care, and medical records were reviewed for complications, treatment, and diabetes-control indicators. RESULTS Participating clinicians (average age, 45.7 years) saw an average of 32.6 adult patients with diabetes per month. Patients (average age, 59.7 years) reported a mean duration of diabetes of 9.1 years, with 34.3% having had the disease more than 10 years. Nearly one half (47.5%) of the patients had at least 1 diabetes-related complication, and 60.8% reported a body mass index greater than 30. Mean glycosylated hemoglobin (HbA1c) level was 7.6% (SD 1.73), and 40.5% of patients had values <7%. Only 35.3% of patients had adequate blood pressure control (<130/85 mm Hg), and only 43.7% had low-density lipoprotein cholesterol (LDL-C) levels <100 mg/dL. Only 7.0% of patients met all 3 control targets. Multilevel models showed that patient ethnicity, practice type, involvement of midlevel clinicians, and treatment were associated with HbA1c level; patient age, education level, and practice type were associated with blood pressure control; and patient ethnicity was associated with LDL-C control. CONCLUSIONS Only modest numbers of patients achieve established targets of diabetes control. Reengineering primary care practice may be necessary to substantially improve care. PMID:16449393

  2. A Systematic Review and Network Meta-Analysis of Biologic Agents in the First Line Setting for Advanced Colorectal Cancer

    PubMed Central

    Kumachev, Alexander; Yan, Marie; Berry, Scott; Ko, Yoo-Joung; Martinez, Maria C. R.; Shah, Keya; Chan, Kelvin K. W.

    2015-01-01

    Background Epithelial growth factor receptor inhibitors (EGFRis) and bevacizumab (BEV) are used in combination with chemotherapy for the treatment of metastatic colorectal cancer (mCRC). However, few randomized controlled trials (RCTs) have directly compared their relative efficacy on progression-free survival (PFS) and overall survival (OS). Methods We conducted a systematic review of first-line RCTs comparing (1) EGFRis vs. BEV, with chemotherapy in both arms (2) EGFRis + chemotherapy vs. chemotherapy alone, or (3) BEV + chemotherapy vs. chemotherapy alone, using Cochrane methodology. Data on and PFS and OS were extracted using the Parmar method. Pairwise meta-analyses and Bayesian network meta-analyses (NMA) were conducted to estimate the direct, indirect and combined PFS and OS hazard ratios (HRs) comparing EGFRis to BEV. Results Seventeen RCTs contained extractable data for quantitative analysis. Combining direct and indirect data using an NMA did not show a statistical difference between EGFRis versus BEV (PFS HR = 1.11 (95% CR: 0.92–1.36) and OS HR = 0.91 (95% CR: 0.75–1.09)). Direct meta-analysis (3 RCTs), indirect (14 RCTs) and combined (17 RCTs) NMA of PFS HRs were concordant and did not show a difference between EGFRis and BEV. Meta-analysis of OS using direct evidence, largely influenced by one trial, showed an improvement with EGFRis therapy (HR = 0.79 (95% CR: 0.65–0.98)), while indirect and combined NMA of OS did not show a difference between EGFRis and BEV Successive inclusions of trials over time in the combined NMA did not show superiority of EGFRis over BEV. Conclusions Our findings did not support OS or PFS benefits of EGFRis over BEV in first-line mCRC. PMID:26474403

  3. Monitoring soil moisture from middle to high elevation in Switzerland: set-up and first results from the SOMOMOUNT network

    NASA Astrophysics Data System (ADS)

    Pellet, Cécile; Hauck, Christian

    2017-06-01

    Besides its important role in the energy and water balance at the soil-atmosphere interface, soil moisture can be a particular important factor in mountain environments since it influences the amount of freezing and thawing in the subsurface and can affect the stability of slopes. In spite of its importance, the technical challenges and its strong spatial variability usually prevents soil moisture from being measured operationally at high and/or middle altitudes. This study describes the new Swiss soil moisture monitoring network SOMOMOUNT (soil moisture in mountainous terrain) launched in 2013. It consists of six entirely automated soil moisture stations distributed along an altitudinal gradient between the Jura Mountains and the Swiss Alps, ranging from 1205 to 3410 m a.s.l. in elevation. In addition to the standard instrumentation comprising frequency domain sensor and time domain reflectometry (TDR) sensors along vertical profiles, soil probes and meteorological data are available at each station. In this contribution we present a detailed description of the SOMOMOUNT instrumentation and calibration procedures. Additionally, the liquid soil moisture (LSM) data collected during the first 3 years of the project are discussed with regard to their soil type and climate dependency as well as their altitudinal distribution. The observed elevation dependency of LSM is found to be non-linear, with an increase of the mean annual values up to ˜ 2000 m a.s.l. followed by a decreasing trend towards higher elevations. This altitude threshold marks the change between precipitation-/evaporation-controlled and frost-affected LSM regimes. The former is characterized by high LSM throughout the year and minimum values in summer, whereas the latter typically exhibits long-lasting winter minimum LSM values and high variability during the summer.

  4. Event-based distributed set-membership filtering for a class of time-varying non-linear systems over sensor networks with saturation effects

    NASA Astrophysics Data System (ADS)

    Wei, Guoliang; Liu, Shuai; Wang, Licheng; Wang, Yongxiong

    2016-07-01

    In this paper, based on the event-triggered mechanism, the problem of distributed set-membership filtering is concerned for a class of time-varying non-linear systems over sensor networks subject to saturation effects. Different from the traditional periodic sample-data approach, the filter is updated only when the predefined event is satisfied, which the event is defined according to the measurement output. For each node, the proposed novel event-triggered mechanism can reduce the unnecessary information transmission between sensors and filters. The purpose of the addressed problem is to design a series of distributed set-membership filters, for all the admissible unknown but bounded noises, non-linearities and sensor saturation, such that the set of all possible states can be determined. The desired filter parameters are obtained by solving a recursive linear matrix inequality that can be computed recursively using the available MATLAB toolbox. Finally, a simulation example is exploited to show the effectiveness of the proposed design approach in this paper.

  5. Pebble and bedrock abrasion during fluvial transport in active orogenic setting : experimental study and application to natural hydrographic networks.

    NASA Astrophysics Data System (ADS)

    Attal, M.; Lavé, J.

    2003-04-01

    At mountain range scale, rivers play an important role in shaping the landscape : in response to active uplift, they incise into bedrock and ensure base level lowering for hillslopes erosion. At the same time, they ensure evacuation of erosion products out of the range as suspended- or bedload. Incision rates are commonly equated with a stream power law, assuming that river incision depends only on hydrodynamic variables. However, this simplification is not mechanically satisfying : in many settings, river bedload fluxes exert an important control on incision rates, by limiting bedrock exposure or by providing an efficient tool for river mechanical abrasion. It is therefore important to better quantify the abrasion processes during bedload transport both to deduce pebble size reduction that controls carrying capacity and bedrock exposure, and to derive bedrock incision laws. Such characterization can be constrained through experimental studies or field measurements. Experimental studies on pebble and bedrock abrasion have been conducted for a long time [e.g. Daubree, 1879]. They generally provide incision rates around two orders of magnitude below natural downstream fining rates. Previous authors have suggested that this discrepancy could be explained by the fact that experimental device doesn’t reproduce really the abrasion phenomena effective in natural rivers, like saltation and following impacts. In this way, we have built an experimental device in order to reproduce these abrasion phenomena. It consists of a circular flume of 30 cm width and of 60 cm curvature radius. Water is injected tangentially on four points ; it generates a flow that produce sediment motion. Velocity vertical profile is roughly similar to what could be observed in natural rivers. The bottom and the sides of the device are interchangeable, in order to measure distinctly pebble abrasion or the interactions between sediment load and substratum. The aim of this experimental study is to

  6. European Network of Forensic Science Institutes (ENFSI): Evaluation of new commercial STR multiplexes that include the European Standard Set (ESS) of markers.

    PubMed

    Welch, L A; Gill, P; Phillips, C; Ansell, R; Morling, N; Parson, W; Palo, J U; Bastisch, I

    2012-12-01

    To support and to underpin the European initiative to increase the European set of standard markers (ESS), by the addition of five new loci, a collaborative project was organised by the European Network of Forensic Science Institutes (ENFSI) DNA working group in order to assess the new multiplex kits available. We have prepared allele frequency databases from 26 EU populations. Concordance studies were carried out to verify that genotyping results were consistent between kits. Population genetics studies were conducted and it was estimated that F(ST)<0.001. The results showed that the kits were comparable to each other in terms of performance and major discrepancy issues were highlighted. We provide details of allele frequencies for each of the populations analysed per laboratory.

  7. Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

    NASA Astrophysics Data System (ADS)

    Zaborowicz, M.; Przybył, J.; Koszela, K.; Boniecki, P.; Mueller, W.; Raba, B.; Lewicki, A.; Przybył, K.

    2014-04-01

    The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

  8. Network Kits.

    ERIC Educational Resources Information Center

    Falk, Howard

    1999-01-01

    Describes interconnection methods, speed, and comparative equipment costs of networking starter kits. These kits supply network-connection devices that plug into or connect to each computer that is part of a network; they may also provide interconnection cables and installation software needed to set up a network. Reviews 20 kits that use a…

  9. Hydro-climatic data network (HCDN); a U.S. Geological Survey streamflow data set for the United States for the study of climate variations, 1874-1988

    USGS Publications Warehouse

    Slack, J.R.; Landwehr, Jurate Maciunas

    1992-01-01

    Records of streamflow can provide an account of climatic variation over a hydrologic basin. The ability to do so is conditioned on the absence of confounding factors that diminish the climate signal. A national data set of streamflow records that are relatively free of confounding anthropogenic influences has been developed for the purpose of studying the variation in surface-water conditions throughout the United States. Records in the U.S. Geological Survey (USGS) National Water Storage and Retrieval System (WATSTORE) data base for active and discontinued streamflow gaging stations through water year 1988 (that is, through September 30, 1988) were reviewed jointly with data specialists in each USGS District office. The resulting collection of stations, each with its respective period of record satisfying the qualifying criteria, is called the Hydro-Climatic Data Network, or HCDN. The HCDN consists of 1,659 sites throughout the United States and its territories, totaling 73,231 water years of daily mean discharge values. For each station in the HCDN, information necessary for its identification, along with any qualifying comments about the available record and a set of descriptive watershed characteristics are provided in tabular format in this report, both on paper and on computer disk (enclosed). For each station in the HCDN, the appropriate daily mean discharge values were compiled, and statistical characteristics, including monthly mean discharges and annual mean, minimum and maximum discharges, were derived. The discharge data values are provided in a companion report.

  10. Network Attack Reference Data Set

    DTIC Science & Technology

    2004-12-01

    change without human intervention, while anomaly detection techniques often result in a high degree of false positives and can sometimes be re-trained...change without human intervention and therefore can introduce false negatives in its reports. Anomaly detection techniques, through systems that detect...attacks) to the best of our knowledge. Us- ing TCPUtils, the IP addresses of this traffic were translated to reflect the chosen class B address ranges

  11. Comparison of Urinary Tract Infection Rates Associated with Transurethral Catheterization, Suprapubic Tube and Clean Intermittent Catheterization in the Postoperative Setting: A Network Meta-Analysis.

    PubMed

    Han, Christopher S; Kim, Sinae; Radadia, Kushan D; Zhao, Philip T; Elsamra, Sammy E; Olweny, Ephrem O; Weiss, Robert E

    2017-07-20

    We performed a network meta-analysis of available randomized, controlled trials to elucidate the risks of urinary tract infection associated with transurethral catheterization, suprapubic tubes and intermittent catheterization in the postoperative setting. PubMed®, EMBASE® and Google Scholar™ searches were performed for eligible randomized, controlled trials from January 1980 to July 2015 that included patients who underwent transurethral catheterization, suprapubic tube placement or intermittent catheterization at the time of surgery and catheterization lasting up to postoperative day 30. The primary outcome of comparison was the urinary tract infection rate via a network meta-analysis with random effects model using the netmeta package in R 3.2 (www.r-project.org/). Included in analysis were 14 randomized, controlled trials in a total of 1,391 patients. Intermittent catheterization and suprapubic tubes showed no evidence of decreased urinary tract infection rates compared to transurethral catheterization. Suprapubic tubes and intermittent catheterization had comparable urinary tract infection rates (OR 0.903, 95% CI 0.479-2.555). On subgroup analysis of 10 randomized, controlled trials with available mean catheterization duration data in a total of 928 patients intermittent catheterization and suprapubic tube were associated with significantly decreased risk of urinary tract infection compared to transurethral catheterization when catheterization duration was greater than 5 days (OR 0.173, 95% CI 0.073-0.412 and OR 0.142, 95% CI 0.073-0.276, respectively). Transurethral catheterization is not associated with an increased urinary tract infection risk compared to suprapubic tubes and intermittent catheterization if catheterization duration is 5 days or less. However, a suprapubic tube or intermittent catheterization is associated with a lower rate of urinary tract infection if longer term catheterization is expected in the postoperative period. Copyright

  12. Action Research Monographs. Complete Set. Pennsylvania Action Research Network, 1998-99. A Section 353 Project of the Pennsylvania Department of Education, Bureau of Adult Basic and Literacy Education. A Learning from Practice Project.

    ERIC Educational Resources Information Center

    Pennsylvania State Univ., McKeesport.

    This publication consists of the complete set of 23 monographs developed by the Pennsylvania Action Research Network to supplement the 67 monographs produced over the past 3 years. The specific audience are literacy, General Educational Development (GED), and English-as-a Second Language (ESL) practitioners. The titles are: "Use of…

  13. Identification of miR-34 regulatory networks in settings of disease and antimiR-therapy: Implications for treating cardiac pathology and other diseases.

    PubMed

    Ooi, Jenny Y Y; Bernardo, Bianca C; Singla, Saloni; Patterson, Natalie L; Lin, Ruby C Y; McMullen, Julie R

    2016-04-28

    Expression of the miR-34 family (miR-34a, -34b, -34c) is elevated in settings of heart disease, and inhibition with antimiR-34a/antimiR-34 has emerged as a promising therapeutic strategy. Under chronic cardiac disease settings, targeting the entire miR-34 family is more effective than targeting miR-34a alone. The identification of transcription factor (TF)-miRNA regulatory networks has added complexity to understanding the therapeutic potential of miRNA-based therapies. Here, we sought to determine whether antimiR-34 targets secondary miRNAs via TFs which could contribute to antimiR-34-mediated protection. Using miRNA-Seq we identified differentially regulated miRNAs in hearts from mice with cardiac pathology due to transverse aortic constriction (TAC), and focused on miRNAs which were also regulated by antimiR-34. Two clusters of stress-responsive miRNAs were classified as "pathological" and "cardioprotective," respectively. Using ChIPBase we identified 45 TF binding sites on the promoters of "pathological" and "cardioprotective" miRNAs, and 5 represented direct targets of miR-34, with the capacity to regulate other miRNAs. Knockdown studies in a cardiomyoblast cell line demonstrated that expression of 2 "pathological" miRNAs (let-7e, miR-31) was regulated by one of the identified TFs. Furthermore, by qPCR we confirmed that expression of let-7e and miR-31 was lower in hearts from antimiR-34 treated TAC mice; this may explain why targeting the entire miR-34 family is more effective than targeting miR-34a alone. Finally, we showed that Acsl4 (a common target of miR-34, let-7e and miR-31) was increased in hearts from TAC antimiR-34 treated mice. In summary, antimiR-34 regulates the expression of other miRNAs and this has implications for drug development.

  14. Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework

    PubMed Central

    Wen, Tzai-Hung; Jiang, Joe-Air; Sun, Chih-Hong; Juang, Jehn-Yih; Lin, Tzu-Shiang

    2013-01-01

    Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management. PMID:24287859

  15. Monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (WSN) framework.

    PubMed

    Wen, Tzai-Hung; Jiang, Joe-Air; Sun, Chih-Hong; Juang, Jehn-Yih; Lin, Tzu-Shiang

    2013-11-27

    Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

  16. Status of the Prevention of Multidrug-Resistant Organisms in International Settings: A Survey of the Society for Healthcare Epidemiology of America Research Network.

    PubMed

    Safdar, Nasia; Sengupta, Sharmila; Musuuza, Jackson S; Juthani-Mehta, Manisha; Drees, Marci; Abbo, Lilian M; Milstone, Aaron M; Furuno, Jon P; Varman, Meera; Anderson, Deverick J; Morgan, Daniel J; Miller, Loren G; Snyder, Graham M

    2017-01-01

    OBJECTIVE To examine self-reported practices and policies to reduce infection and transmission of multidrug-resistant organisms (MDRO) in healthcare settings outside the United States. DESIGN Cross-sectional survey. PARTICIPANTS International members of the Society for Healthcare Epidemiology of America (SHEA) Research Network. METHODS Electronic survey of infection control and prevention practices, capabilities, and barriers outside the United States and Canada. Participants were stratified according to their country's economic development status as defined by the World Bank as low-income, lower-middle-income, upper-middle-income, and high-income. RESULTS A total of 76 respondents (33%) of 229 SHEA members outside the United States and Canada completed the survey questionnaire, representing 30 countries. Forty (53%) were high-, 33 (43%) were middle-, and 1 (1%) was a low-income country. Country data were missing for 2 respondents (3%). Of the 76 respondents, 64 (84%) reported having a formal or informal antibiotic stewardship program at their institution. High-income countries were more likely than middle-income countries to have existing MDRO policies (39/64 [61%] vs 25/64 [39%], P=.003) and to place patients with MDRO in contact precautions (40/72 [56%] vs 31/72 [44%], P=.05). Major barriers to preventing MDRO transmission included constrained resources (infrastructure, supplies, and trained staff) and challenges in changing provider behavior. CONCLUSIONS In this survey, a substantial proportion of institutions reported encountering barriers to implementing key MDRO prevention strategies. Interventions to address capacity building internationally are urgently needed. Data on the infection prevention practices of low income countries are needed. Infect Control Hosp Epidemiol. 2016:1-8.

  17. Using rough sets, neural networks, and logistic regression to predict compliance with cholesterol guidelines goals in patients with coronary artery disease.

    PubMed

    Dubey, Anil K

    2003-01-01

    Coronary artery disease is a leading cause of death and disability in the United States and throughout the developed world. Results from large randomized, blinded, placebo-controlled trials have demonstrated clearly the benefit of lowering LDL cholesterol in lowering the risk for coronary artery disease. Unfortunately, despite the quantity of evidence, and the availability of medications that can efficiently lower LDL cholesterol with few side effects, not everyone who could benefit from cholesterol lowering interventions actually receives them. Despite the dissemination of national care guidelines for the evaluation and treatment of cholesterol levels (NCEP - National Cholesterol Education Program), compliance with such guidelines is suboptimal. There clearly is room for improvement in narrowing the gap between evidence based guidelines and actual clinical practice. The ability to classify those patients who are or will likely to be noncompliant on the basis of patient data routinely collected during patient care could be potentially useful by enabling the focusing of limited health care resources to those who are or will be at high risk of being under treated. In order to explore this possibility further, we attempted to create such classifiers of cholesterol guideline compliance. To do this, we obtained data from an ambulatory electronic medical record system at use at the MGH adult primary care practices for over 20 years. We obtained the data from this hierarchically-structured EMR using its own native query language, called MQL (Medical Query Language). Next, we applied to the collected data the machine learning techniques of rough set theory, neural networks (feed forward backpropagation nets), and logistic regression. We did this by using commonly available software that for the most part is freely available via the internet. We then compared the accuracy of the classifier models using the receiver operating characteristic (ROC) area and C-index summary

  18. Automated International Information Networks; Systems Design Concepts, Goal-Setting and Priorities. FID/TM Panel at the ASIS Meeting in San Francisco, 2 October, 1969.

    ERIC Educational Resources Information Center

    Samuelson, Kjell

    An invitation to participate in this panel discussion was sent to official representatives of organizations which had an expressed interest in information networks. Since some of the represented international bodies had started preliminary planning for network communication, the discussion was centered around systems design concepts. However, as…

  19. Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network.

    PubMed

    Sperber, Nina R; Carpenter, Janet S; Cavallari, Larisa H; J Damschroder, Laura; Cooper-DeHoff, Rhonda M; Denny, Joshua C; Ginsburg, Geoffrey S; Guan, Yue; Horowitz, Carol R; Levy, Kenneth D; Levy, Mia A; Madden, Ebony B; Matheny, Michael E; Pollin, Toni I; Pratt, Victoria M; Rosenman, Marc; Voils, Corrine I; W Weitzel, Kristen; Wilke, Russell A; Ryanne Wu, R; Orlando, Lori A

    2017-05-22

    To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges. We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes. Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians' knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions. This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic

  20. The Use of Alternative Social Networking Sites in Higher Educational Settings: A Case Study of the E-Learning Benefits of Ning in Education

    ERIC Educational Resources Information Center

    Brady, Kevin P.; Holcomb, Lori B.; Smith, Bethany V.

    2010-01-01

    Distance education as a primary means of instruction is expanding significantly at the college and university level. Simultaneously, the growth of social networking sites (SNS) including Facebook, LinkedIn, and MySpace is also rising among today's college students. An increasing number of higher education instructors are beginning to combine…

  1. Neurobiology: Setting the Set Point for Neural Homeostasis.

    PubMed

    Truszkowski, Torrey L S; Aizenman, Carlos D

    2015-12-07

    Neural homeostasis allows neural networks to maintain a dynamic range around a given set point. How this set point is determined remains unknown. New evidence shows that alterations of activity during a critical developmental period can alter the homeostatic set point, resulting in epilepsy-like activity. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Ready ... set ... grow.

    PubMed

    Hudson, T

    1998-01-05

    Three years after going public, FPA Medical Management rose from obscurity to become the nation's second largest practice manager. Its network of primary care doctors ballooned from only 95 to nearly 6,700. Now, as executives mull over $2 billion in acquisitions, the company is set to bulk up even more.

  3. Do networking activities outside of the classroom protect students against being bullied? A field study with students in secondary school settings in Germany.

    PubMed

    Blickle, Gerhard; Meurs, James A; Schoepe, Christine

    2013-01-01

    Research has shown that having close relationships with fellow classmates can provide a buffer for students against bullying and the negative outcomes associated with it. But, research has not explicitly examined the potential benefits of social networking behaviors outside of the classroom for those who could be bullied. This study addresses this gap and finds that, although a bullying climate in the classroom increases overall bullying, students high on external networking activities did not experience an increase in the bullying they received when in a classroom with a high bullying climate. However, the same group of students reported the largest degree of received bulling under conditions of a low bullying climate. We discuss the implications of our results and provide directions for future research.

  4. Automatic sets and Delone sets

    NASA Astrophysics Data System (ADS)

    Barbé, A.; von Haeseler, F.

    2004-04-01

    Automatic sets D\\subset{\\bb Z}^m are characterized by having a finite number of decimations. They are equivalently generated by fixed points of certain substitution systems, or by certain finite automata. As examples, two-dimensional versions of the Thue-Morse, Baum-Sweet, Rudin-Shapiro and paperfolding sequences are presented. We give a necessary and sufficient condition for an automatic set D\\subset{\\bb Z}^m to be a Delone set in {\\bb R}^m . The result is then extended to automatic sets that are defined as fixed points of certain substitutions. The morphology of automatic sets is discussed by means of examples.

  5. Recent developments and applications of a real-time tool to detect magma migration in different volcanic settings and network optimization.

    NASA Astrophysics Data System (ADS)

    Taisne, B.; Aoki, Y.; Caudron, C.

    2014-12-01

    Triggering mechanism of a seismic swarm has to be identified with great confidence in real time. Crisis response will not be the same whether magma is involved or not. The recent developments of a method based on the Seismic Amplitude Ratio Analysis enable a rapid and unambiguous diagnosis to detect migrating micro-seismicity. The beauty of this method lies in the fact that the ratio of seismic energy, recorded at different stations, is independent of the seismic energy radiated at the source and depends only on the location of the source and attenuation of the medium. Since drastic changes in attenuation are unlikely to occur at the time scale of magma intrusion, temporal evolutions in the measured ratio have to be explained by a change in the source location. Based on simple assumptions, this technique can be used to assess the potential of existing monitoring seismic network to detect migrating events in real-time. Of much importance, it can also be used to design monitoring seismic network based on the available number of sensors, as well as from field constraints. The method will be implemented in MSNoise software (http://www.msnoise.org/). This allows us to mine existing datasets, to compare the different noise-based techniques, but also to use the method for monitoring purposes. We will present how the key question: "Migration or No Migration" could be answered in real time without need of complex calculation nor full knowledge of the site effect and attenuation of the medium.

  6. Text Sets.

    ERIC Educational Resources Information Center

    Giorgis, Cyndi; Johnson, Nancy J.

    2002-01-01

    Presents annotations of approximately 30 titles grouped in text sets. Defines a text set as five to ten books on a particular topic or theme. Discusses books on the following topics: living creatures; pirates; physical appearance; natural disasters; and the Irish potato famine. (SG)

  7. Text Sets.

    ERIC Educational Resources Information Center

    Giorgis, Cyndi; Johnson, Nancy J.

    2002-01-01

    Presents annotations of approximately 30 titles grouped in text sets. Defines a text set as five to ten books on a particular topic or theme. Discusses books on the following topics: living creatures; pirates; physical appearance; natural disasters; and the Irish potato famine. (SG)

  8. Networking computers.

    PubMed

    McBride, D C

    1997-03-01

    This decade the role of the personal computer has shifted dramatically from a desktop device designed to increase individual productivity and efficiency to an instrument of communication linking people and machines in different places with one another. A computer in one city can communicate with another that may be thousands of miles away. Networking is how this is accomplished. Just like the voice network used by the telephone, computer networks transmit data and other information via modems over these same telephone lines. A network can be created over both short and long distances. Networks can be established within a hospital or medical building or over many hospitals or buildings covering many geographic areas. Those confined to one location are called LANs, local area networks. Those that link computers in one building to those at other locations are known as WANs, or wide area networks. The ultimate wide area network is the one we've all been hearing so much about these days--the Internet, and its World Wide Web. Setting up a network is a process that requires careful planning and commitment. To avoid potential pitfalls and to make certain the network you establish meets your needs today and several years down the road, several steps need to be followed. This article reviews the initial steps involved in getting ready to network.

  9. Sequencing-based gene network analysis provides a core set of gene resource for understanding thermal adaptation in Zhikong scallop Chlamys farreri.

    PubMed

    Fu, X; Sun, Y; Wang, J; Xing, Q; Zou, J; Li, R; Wang, Z; Wang, S; Hu, X; Zhang, L; Bao, Z

    2014-01-01

    Marine organisms are commonly exposed to variable environmental conditions, and many of them are under threat from increased sea temperatures caused by global climate change. Generating transcriptomic resources under different stress conditions are crucial for understanding molecular mechanisms underlying thermal adaptation. In this study, we conducted transcriptome-wide gene expression profiling of the scallop Chlamys farreri challenged by acute and chronic heat stress. Of the 13 953 unique tags, more than 850 were significantly differentially expressed at each time point after acute heat stress, which was more than the number of tags differentially expressed (320-350) under chronic heat stress. To obtain a systemic view of gene expression alterations during thermal stress, a weighted gene coexpression network was constructed. Six modules were identified as acute heat stress-responsive modules. Among them, four modules involved in apoptosis regulation, mRNA binding, mitochondrial envelope formation and oxidation reduction were downregulated. The remaining two modules were upregulated. One was enriched with chaperone and the other with microsatellite sequences, whose coexpression may originate from a transcription factor binding site. These results indicated that C. farreri triggered several cellular processes to acclimate to elevated temperature. No modules responded to chronic heat stress, suggesting that the scallops might have acclimated to elevated temperature within 3 days. This study represents the first sequencing-based gene network analysis in a nonmodel aquatic species and provides valuable gene resources for the study of thermal adaptation, which should assist in the development of heat-tolerant scallop lines for aquaculture.

  10. Youths navigating social networks and social support systems in settings of chronic crisis: the case of youth-headed households in Rwanda.

    PubMed

    Lee, Laura May

    2012-10-01

    Youth-headed households in Rwanda live in a context of chronic crisis, where poverty, disease and uncertainty are not exceptional but characterise people's daily lived reality. Struggling under the pressures of economic deprivation, social isolation, abuse and exploitation, these youths experience social suffering and feel the impact of social forces on their everyday lives. Yet, amid constraints in the environment, youths demonstrate resilience by actively navigating their social networks and working to create opportunities for the future. The article describes qualitative research carried out in three communities in Rwanda, between 2006 and 2010, examining the support systems of Rwandan youths affected by the HIV epidemic and socio-political conflict and exploring how youth heads of households navigate social networks in order to buffer the suffering in their lives. It is argued that social support is vital for these youths as they struggle to survive, seek to gain a degree of control over their lives, and strive to have a hopeful future. Examples show the remarkable ability of such youths to confront adversity by mobilising resources and exhibiting agency, although they may continue to experience suffering when support is lacking. The article concludes that to improve wellbeing and reduce suffering for youth-headed households, it is critical to recognise the social relations that may limit or enhance these youths' ability to navigate their social environment. Youths' agency needs to be recognised as a means to reduce the detrimental impacts of their actions and instead build on positive strategies, enabling them as they navigate their life course towards future possibilities. Finally, the dual role of youth heads of households - as individuals in adult roles and as youths - should be recognised, with initiatives to build them up designed around economic strengthening and mentorship.

  11. Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares.

    PubMed

    Le Floch, Edith; Guillemot, Vincent; Frouin, Vincent; Pinel, Philippe; Lalanne, Christophe; Trinchera, Laura; Tenenhaus, Arthur; Moreno, Antonio; Zilbovicius, Monica; Bourgeron, Thomas; Dehaene, Stanislas; Thirion, Bertrand; Poline, Jean-Baptiste; Duchesnay, Edouard

    2012-10-15

    Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach

  12. TELECOM 1 multiservices network

    NASA Astrophysics Data System (ADS)

    Lombard, D.; Ramat, P.; Rancy, F.

    The main objectives of the TELECOM 1 French domestic satellite project are to set up a business communication network which is to carry a wide range of digital services including data, voice, and pictures between a number of small earth stations located on the subscribers' premises. The parallel development of terrestrial specialized services networks has enabled the fitting of the TELECOM 1 network with high interworking capabilities with these networks. It has also allowed TELECOM 1 to be designed as the basis of the Future Integrated Services Digital Network. The TELECOM 1 network consists of the terrestrial network, the satellite network, and the maintenance network. Various elements which include the terrestrial network; the satellite network, and its modulation, TDMA frame and terminals; the System Management Center; the signalling system; and the demand assignment operation which are involved in the operation of the multiservices network are presented. The TELECOM 1 network evolution until 1990 through the rapid development of the ISDN is discussed.

  13. What's on YOUR Facebook profile? Evaluation of an educational intervention to promote appropriate use of privacy settings by medical students on social networking sites.

    PubMed

    Walton, Jennifer M; White, Jonathan; Ross, Shelley

    2015-01-01

    Background The rise of social media has led to growing concerns about the potential implications of 'unprofessional' postings by physicians and medical students on individuals, institutions, and the medical profession. Relevant and effective guidelines have been difficult to develop and enforce, and there is a need for students and physicians to consider how their online activities may be perceived in the context of their professional roles. The purpose of this project was to examine the Internet presence of a graduating Canadian medical school class by scanning students' public profiles on the social media site Facebook, incorporate this information into an educational activity addressing professionalism and social media, and evaluate the impact of this activity on student behavior. Methods A systematic search for public Facebook profiles of each member of the class was conducted, and data were collected on the types of publicly visible material. These were presented as part of an educational session on social media and professionalism. One month later, the Facebook search was repeated. Results Of 152 students in the class, profiles were found for 121 (79.8%). The majority of students used appropriately restrictive privacy settings; however, a significant minority had publicly visible information, including comments, photographs, location, and status as a medical student. The educational innovation was well received with more than 90% of students agreeing that this topic was important and well addressed. A follow-up search found that many students had altered their privacy settings to make less information publicly available. Conclusions A small but significant proportion of students share potentially unprofessional content on social media. An interactive educational intervention, which includes specific disclosure of how participants appear to others on social media, resulted in a significant change in student behavior.

  14. What's on YOUR Facebook profile? Evaluation of an educational intervention to promote appropriate use of privacy settings by medical students on social networking sites

    PubMed Central

    Walton, Jennifer M.; White, Jonathan; Ross, Shelley

    2015-01-01

    Background The rise of social media has led to growing concerns about the potential implications of ‘unprofessional’ postings by physicians and medical students on individuals, institutions, and the medical profession. Relevant and effective guidelines have been difficult to develop and enforce, and there is a need for students and physicians to consider how their online activities may be perceived in the context of their professional roles. The purpose of this project was to examine the Internet presence of a graduating Canadian medical school class by scanning students’ public profiles on the social media site Facebook, incorporate this information into an educational activity addressing professionalism and social media, and evaluate the impact of this activity on student behavior. Methods A systematic search for public Facebook profiles of each member of the class was conducted, and data were collected on the types of publicly visible material. These were presented as part of an educational session on social media and professionalism. One month later, the Facebook search was repeated. Results Of 152 students in the class, profiles were found for 121 (79.8%). The majority of students used appropriately restrictive privacy settings; however, a significant minority had publicly visible information, including comments, photographs, location, and status as a medical student. The educational innovation was well received with more than 90% of students agreeing that this topic was important and well addressed. A follow-up search found that many students had altered their privacy settings to make less information publicly available. Conclusions A small but significant proportion of students share potentially unprofessional content on social media. An interactive educational intervention, which includes specific disclosure of how participants appear to others on social media, resulted in a significant change in student behavior. PMID:26198434

  15. What's on YOUR Facebook profile? Evaluation of an educational intervention to promote appropriate use of privacy settings by medical students on social networking sites.

    PubMed

    Walton, Jennifer M; White, Jonathan; Ross, Shelley

    2015-01-01

    The rise of social media has led to growing concerns about the potential implications of 'unprofessional' postings by physicians and medical students on individuals, institutions, and the medical profession. Relevant and effective guidelines have been difficult to develop and enforce, and there is a need for students and physicians to consider how their online activities may be perceived in the context of their professional roles. The purpose of this project was to examine the Internet presence of a graduating Canadian medical school class by scanning students' public profiles on the social media site Facebook, incorporate this information into an educational activity addressing professionalism and social media, and evaluate the impact of this activity on student behavior. A systematic search for public Facebook profiles of each member of the class was conducted, and data were collected on the types of publicly visible material. These were presented as part of an educational session on social media and professionalism. One month later, the Facebook search was repeated. Of 152 students in the class, profiles were found for 121 (79.8%). The majority of students used appropriately restrictive privacy settings; however, a significant minority had publicly visible information, including comments, photographs, location, and status as a medical student. The educational innovation was well received with more than 90% of students agreeing that this topic was important and well addressed. A follow-up search found that many students had altered their privacy settings to make less information publicly available. A small but significant proportion of students share potentially unprofessional content on social media. An interactive educational intervention, which includes specific disclosure of how participants appear to others on social media, resulted in a significant change in student behavior.

  16. Rough sets and near sets in medical imaging: a review.

    PubMed

    Hassanien, Aboul Ella; Abraham, Ajith; Peters, James F; Schaefer, Gerald; Henry, Christopher

    2009-11-01

    This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

  17. Network dismantling

    PubMed Central

    Braunstein, Alfredo; Dall’Asta, Luca; Semerjian, Guilhem; Zdeborová, Lenka

    2016-01-01

    We study the network dismantling problem, which consists of determining a minimal set of vertices in which removal leaves the network broken into connected components of subextensive size. For a large class of random graphs, this problem is tightly connected to the decycling problem (the removal of vertices, leaving the graph acyclic). Exploiting this connection and recent works on epidemic spreading, we present precise predictions for the minimal size of a dismantling set in a large random graph with a prescribed (light-tailed) degree distribution. Building on the statistical mechanics perspective, we propose a three-stage Min-Sum algorithm for efficiently dismantling networks, including heavy-tailed ones for which the dismantling and decycling problems are not equivalent. We also provide additional insights into the dismantling problem, concluding that it is an intrinsically collective problem and that optimal dismantling sets cannot be viewed as a collection of individually well-performing nodes. PMID:27791075

  18. Dynamic interactions of proteins in complex networks: identifying the complete set of interacting E2s for functional investigation of E3-dependent protein ubiquitination.

    PubMed

    Christensen, Devin E; Klevit, Rachel E

    2009-10-01

    A ubiquitin ligase (E3) functions at the crossroad between ubiquitin activation and the attachment of ubiquitin to protein substrates. During this process, the E3 interacts with both a substrate and a ubiquitin-conjugating enzyme (E2). Although a major goal when investigating an E3 is to identify its substrates, recent evidence indicates that the E2 dictates the type of ubiquitin modification that will occur on the substrate. There are approximately 30 E2s identified in the human genome, many of which remain to be characterized. We found that the RING E3 BRCA1/BARD1 can interact with 10 different E2s. The ability of BRCA1 to interact with multiple E2s is likely to be a common feature among other RING and U-box E3s. We and others have also found that certain E2s show a preference for attaching either the first ubiquitin to a substrate lysine or ubiquitin to itself (chain building), suggesting that E2s may play a role in dictating product formation. Therefore, when investigating the functions of an E3 it is advisable to identify all E2s that interact with the E3 so that these can be used in E3-dependent substrate-ubiquitination assays. We describe a method used to identify all the E2s that interact with BRCA1. Defining the set of E2s that interact with other RING and U-box E3s will open the door for predictive models and lead to a better understand of substrate ubiquitination.

  19. Multifractal nature of network induced time delay in networked control systems

    NASA Astrophysics Data System (ADS)

    Tian, Yu-Chu; Yu, Zu-Guo; Fidge, Colin

    2007-01-01

    When modelling and simulating networked control systems (NCSs) over TCP/IP network protocols, we obtained network traffic data sets with irregular behaviour. Analysing the data sets revealed multifractal network traffic. Typical data sets are given in this Letter together with our preliminary analysis. The network architecture and traffic specifications that generated the multifractal traffic are also described in detail.

  20. Social Networking for the K-12 Set

    ERIC Educational Resources Information Center

    Klein, Jim

    2008-01-01

    Education technology leaders are ever seeking new ways to eliminate the traditional social and geographic boundaries that hinder communication and collaboration for both K-12 students and educators. Larger districts with geographically dispersed schools often find that innovative ideas for technology use and integration are balkanized into…

  1. Introduction to Fuzzy Set Theory

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  2. Robustness of Interdependent Networks

    NASA Astrophysics Data System (ADS)

    Havlin, Shlomo

    2011-03-01

    In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of many interdependent networks. We will present a framework for understanding the robustness of interacting networks subject to such cascading failures and provide a basic analytic approach that may be useful in future studies. We present exact analytical solutions for the critical fraction of nodes that upon removal will lead to a failure cascade and to a complete fragmentation of two interdependent networks in a first order transition. Surprisingly, analyzing complex systems as a set of interdependent networks may alter a basic assumption that network theory has relied on: while for a single network a broader degree distribution of the network nodes results in the network being more robust to random failures, for interdependent networks, the broader the distribution is, the more vulnerable the networks become to random failure. We also show that reducing the coupling between the networks leads to a change from a first order percolation phase transition to a second order percolation transition at a critical point. These findings pose a significant challenge to the future design of robust networks that need to consider the unique properties of interdependent networks.

  3. Networked Innovation in Innovation Networks: A Home Appliances Case Study

    NASA Astrophysics Data System (ADS)

    Berasategi, Luis; Arana, Joseba; Castellano, Eduardo

    Amongst different types of Collaborative Networked Organizations it is possible to highlight those created to develop and market product, process or business model innovation. In this type of innovation network, which has special characteristics, the challenge is to introduce effective networked innovation in the very same innovation network. This paper presents the main features of TALAI-SAREA © methodology that includes a reference model, a set of analysis tools and a method for implementing networked innovation in innovation networks.

  4. Finding overlapping communities using seed set

    NASA Astrophysics Data System (ADS)

    Yang, Jin-Xuan; Zhang, Xiao-Dong

    2017-02-01

    The local optimization algorithm using seed set to find overlapping communities has become more and more a significant method, but it is a great challenge how to choose a good seed set. In this paper, a new method is proposed to achieve the choice of candidate seed sets, and yields a new algorithm to find overlapping communities in complex networks. By testing in real world networks and synthetic networks, this method can successfully detect overlapping communities and outperform other state-of-the-art overlapping community detection methods.

  5. Network connectivity value.

    PubMed

    Dragicevic, Arnaud; Boulanger, Vincent; Bruciamacchie, Max; Chauchard, Sandrine; Dupouey, Jean-Luc; Stenger, Anne

    2017-02-23

    In order to unveil the value of network connectivity, we formalize the construction of ecological networks in forest environments as an optimal control dynamic graph-theoretic problem. The network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological network is fully connected, and a case of incomplete graph, where the ecological network is partially connected. The results show that the network equilibrium depends on the size of the reception zone, while the network connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing connectivity in partially connected networks is more expensive than in fully connected networks, but should be undertaken when the opportunity costs are significant.

  6. News Competition: Physics Olympiad hits Thailand Report: Institute carries out survey into maths in physics at university Event: A day for everyone teaching physics Conference: Welsh conference celebrates birthday Schools: Researchers in Residence scheme set to close Teachers: A day for new physics teachers Social: Network combines fun and physics Forthcoming events

    NASA Astrophysics Data System (ADS)

    2011-09-01

    Competition: Physics Olympiad hits Thailand Report: Institute carries out survey into maths in physics at university Event: A day for everyone teaching physics Conference: Welsh conference celebrates birthday Schools: Researchers in Residence scheme set to close Teachers: A day for new physics teachers Social: Network combines fun and physics Forthcoming events

  7. SET OF CUT SETS AND OPTIMUM FLOW,

    DTIC Science & Technology

    maintain the same terminal flow. The method presented stems from the work of Ford and Fulkerson which relates maximum terminal flow to the cut set...separating the terminals. A new set of cut sets called a ’set of M- cut sets’ is introduced from which it is possible to improve edge flows while maintaining maximum terminal flow.

  8. Generalized classifier neural network.

    PubMed

    Ozyildirim, Buse Melis; Avci, Mutlu

    2013-03-01

    In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.

  9. Spectral gene set enrichment (SGSE).

    PubMed

    Frost, H Robert; Li, Zhigang; Moore, Jason H

    2015-03-03

    Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracy-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Unsupervised gene set testing can provide important information about the biological signal held in high-dimensional genomic data sets. Because it uses the association between gene sets and samples PCs to generate a measure of unsupervised enrichment, the SGSE method is independent of cluster or network creation algorithms and, most importantly, is able to utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely to represent noise.

  10. Extracting information from multiplex networks

    NASA Astrophysics Data System (ADS)

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ ˜ S for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

  11. Extracting information from multiplex networks.

    PubMed

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

  12. Scalable Virtual Network Mapping Algorithm for Internet-Scale Networks

    NASA Astrophysics Data System (ADS)

    Yang, Qiang; Wu, Chunming; Zhang, Min

    The proper allocation of network resources from a common physical substrate to a set of virtual networks (VNs) is one of the key technical challenges of network virtualization. While a variety of state-of-the-art algorithms have been proposed in an attempt to address this issue from different facets, the challenge still remains in the context of large-scale networks as the existing solutions mainly perform in a centralized manner which requires maintaining the overall and up-to-date information of the underlying substrate network. This implies the restricted scalability and computational efficiency when the network scale becomes large. This paper tackles the virtual network mapping problem and proposes a novel hierarchical algorithm in conjunction with a substrate network decomposition approach. By appropriately transforming the underlying substrate network into a collection of sub-networks, the hierarchical virtual network mapping algorithm can be carried out through a global virtual network mapping algorithm (GVNMA) and a local virtual network mapping algorithm (LVNMA) operated in the network central server and within individual sub-networks respectively with their cooperation and coordination as necessary. The proposed algorithm is assessed against the centralized approaches through a set of numerical simulation experiments for a range of network scenarios. The results show that the proposed hierarchical approach can be about 5-20 times faster for VN mapping tasks than conventional centralized approaches with acceptable communication overhead between GVNCA and LVNCA for all examined networks, whilst performs almost as well as the centralized solutions.

  13. Neural Networks

    SciTech Connect

    Smith, Patrick I.

    2003-09-23

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  14. A network of networks.

    PubMed

    Iedema, Rick; Verma, Raj; Wutzke, Sonia; Lyons, Nigel; McCaughan, Brian

    2017-04-10

    Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author's participation in network activities and discussions with the agency's staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency's implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of "centralised decentralisation", co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid - the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a "network of networks" through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture "strategic hybrids", and enhance the impact of network activities on health system reform. Social implications Network governance requires more

  15. Quantifying randomness in real networks

    NASA Astrophysics Data System (ADS)

    Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-10-01

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

  16. Quantifying randomness in real networks.

    PubMed

    Orsini, Chiara; Dankulov, Marija M; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-10-20

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

  17. Celestial data routing network

    NASA Astrophysics Data System (ADS)

    Bordetsky, Alex

    2000-11-01

    Imagine that information processing human-machine network is threatened in a particular part of the world. Suppose that an anticipated threat of physical attacks could lead to disruption of telecommunications network management infrastructure and access capabilities for small geographically distributed groups engaged in collaborative operations. Suppose that small group of astronauts are exploring the solar planet and need to quickly configure orbital information network to support their collaborative work and local communications. The critical need in both scenarios would be a set of low-cost means of small team celestial networking. To the geographically distributed mobile collaborating groups such means would allow to maintain collaborative multipoint work, set up orbital local area network, and provide orbital intranet communications. This would be accomplished by dynamically assembling the network enabling infrastructure of the small satellite based router, satellite based Codec, and set of satellite based intelligent management agents. Cooperating single function pico satellites, acting as agents and personal switching devices together would represent self-organizing intelligent orbital network of cooperating mobile management nodes. Cooperative behavior of the pico satellite based agents would be achieved by comprising a small orbital artificial neural network capable of learning and restructing the networking resources in response to the anticipated threat.

  18. Network generation enhances interpretation of proteomics data sets by a combination of two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry.

    PubMed

    Wang, Xijun; Zhang, Aihua; Sun, Hui; Wu, Gelin; Sun, Wenjun; Yan, Guangli

    2012-10-21

    Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in diseases. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively interpret network models from large scale interactome data. In this study, we carried out comparative proteomics to construct and identify the proteins networks associated with hepatic injury (HI) which are largely unknown, as a case study. All proteins expressed were separated and identified by two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of-flight-time-of-flight mass spectrometry (MALDI-TOF/TOF MS). Protein-interacting networks and pathways were mapped using STRING analysis program. We have performed for the first time a comprehensive profiling of changes in protein expression of HI rats, to uncover the networks altered by treated with CCl(4). Identification of fifteen spots (seven over-expressed and eight under-expressed) were established by MALDI-TOF/TOF MS. These proteins were subjected to functional pathway analysis using STRING software for better understanding of the biological context of the identified proteins. It suggested that modulation of multiple vital physiological pathways including DNA repair process, cell apoptosis, oxidation reduction, signal transduction, metabolic process, intracellular signaling cascade, regulation of biological processes, cell communication, regulation of cellular process, and molecular transport. In summary, the present study provides the first protein-interacting network maps and novel insights into the biological responses and potential pathways of HI. The generation of protein interaction networks clearly enhances the interpretation of proteomic data, particularly in respect of understanding molecular mechanisms of panel protein biomarkers.

  19. UpSet: Visualization of Intersecting Sets

    PubMed Central

    Lex, Alexander; Gehlenborg, Nils; Strobelt, Hendrik; Vuillemot, Romain; Pfister, Hanspeter

    2016-01-01

    Understanding relationships between sets is an important analysis task that has received widespread attention in the visualization community. The major challenge in this context is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. In this paper we introduce UpSet, a novel visualization technique for the quantitative analysis of sets, their intersections, and aggregates of intersections. UpSet is focused on creating task-driven aggregates, communicating the size and properties of aggregates and intersections, and a duality between the visualization of the elements in a dataset and their set membership. UpSet visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes. Sorting according to various measures enables a task-driven analysis of relevant intersections and aggregates. The elements represented in the sets and their associated attributes are visualized in a separate view. Queries based on containment in specific intersections, aggregates or driven by attribute filters are propagated between both views. We also introduce several advanced visual encodings and interaction methods to overcome the problems of varying scales and to address scalability. UpSet is web-based and open source. We demonstrate its general utility in multiple use cases from various domains. PMID:26356912

  20. Network Management of the SPLICE Computer Network.

    DTIC Science & Technology

    1982-12-01

    user. We now submit what we feel is a responsible and complete lefinition of network management. Our definition includes: collecti:n of measurements...it’s limited ability to detect the stimulus for the set of signals it is monitDring. 2. SqtSL phd12 Although various definitions exist, a software...the network possess a greater than normal degree sf intelligence, ilen-. ion and ma’ntenance tend to oe nore : osty than centralized mon itoring. 5

  1. Competing edge networks

    NASA Astrophysics Data System (ADS)

    Parsons, Mark; Grindrod, Peter

    2012-06-01

    We introduce a model for a pair of nonlinear evolving networks, defined over a common set of vertices, subject to edgewise competition. Each network may grow new edges spontaneously or through triad closure. Both networks inhibit the other's growth and encourage the other's demise. These nonlinear stochastic competition equations yield to a mean field analysis resulting in a nonlinear deterministic system. There may be multiple equilibria; and bifurcations of different types are shown to occur within a reduced parameter space. This situation models competitive communication networks such as BlackBerry Messenger displacing SMS; or instant messaging displacing emails.

  2. Quantifying randomness in real networks

    PubMed Central

    Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-01-01

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs. PMID:26482121

  3. The INGV tectonomagnetic network

    NASA Astrophysics Data System (ADS)

    Masci, F.; Palangio, P.; di Persio, M.

    2008-01-01

    The Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV) tectonomagnetic network was installed in Central Italy since the middle of 1989 to investigate possible magnetic anomalies related to earthquakes. The network is part of the INGV L'Aquila Geomagnetic Observatory and is located in an area extending approximately in latitude range [41.6°-42.8°] N and longitude range [13.0°-14.3°] E. Actually the network consists of four stations where the total magnetic field intensity data are collected using proton precession magnetometers. New stations will be added to the network starting from the end of 2007. Here we are reporting the whole data set of the network's stations for the period 2004-2006. No significant anomaly in the local geomagnetic field correlated to the seismic activity has been found. Some considerations about misleading structures present in the data sets are reported.

  4. Emergent Complex Network Geometry

    PubMed Central

    Wu, Zhihao; Menichetti, Giulia; Rahmede, Christoph; Bianconi, Ginestra

    2015-01-01

    Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we show that a single two parameter network model, the growing geometrical network, can generate complex network geometries with non-trivial distribution of curvatures, combining exponential growth and small-world properties with finite spectral dimensionality. In one limit, the non-equilibrium dynamical rules of these networks can generate scale-free networks with clustering and communities, in another limit planar random geometries with non-trivial modularity. Finally we find that these properties of the geometrical growing networks are present in a large set of real networks describing biological, social and technological systems. PMID:25985280

  5. Autocatalytic Sets and RNA Secondary Structure

    NASA Astrophysics Data System (ADS)

    Hordijk, Wim

    2017-04-01

    The dominant paradigm in origin of life research is that of an RNA world. However, despite experimental progress towards the spontaneous formation of RNA, the RNA world hypothesis still has its problems. Here, we introduce a novel computational model of chemical reaction networks based on RNA secondary structure and analyze the existence of autocatalytic sub-networks in random instances of this model, by combining two well-established computational tools. Our main results are that (i) autocatalytic sets are highly likely to exist, even for very small reaction networks and short RNA sequences, and (ii) sequence diversity seems to be a more important factor in the formation of autocatalytic sets than sequence length. These findings could shed new light on the probability of the spontaneous emergence of an RNA world as a network of mutually collaborative ribozymes.

  6. Autocatalytic Sets and RNA Secondary Structure.

    PubMed

    Hordijk, Wim

    2017-04-04

    The dominant paradigm in origin of life research is that of an RNA world. However, despite experimental progress towards the spontaneous formation of RNA, the RNA world hypothesis still has its problems. Here, we introduce a novel computational model of chemical reaction networks based on RNA secondary structure and analyze the existence of autocatalytic sub-networks in random instances of this model, by combining two well-established computational tools. Our main results are that (i) autocatalytic sets are highly likely to exist, even for very small reaction networks and short RNA sequences, and (ii) sequence diversity seems to be a more important factor in the formation of autocatalytic sets than sequence length. These findings could shed new light on the probability of the spontaneous emergence of an RNA world as a network of mutually collaborative ribozymes.

  7. Statistical Mechanics of the Minimum Dominating Set Problem

    NASA Astrophysics Data System (ADS)

    Zhao, Jin-Hua; Habibulla, Yusupjan; Zhou, Hai-Jun

    2015-06-01

    The minimum dominating set (MDS) problem has wide applications in network science and related fields. It aims at constructing a node set of smallest size such that any node of the network is either in this set or is adjacent to at least one node of this set. Although this optimization problem is generally very difficult, we show it can be exactly solved by a generalized leaf-removal (GLR) process if the network contains no core. We present a percolation theory to describe the GLR process on random networks, and solve a spin glass model by mean field method to estimate the MDS size. We also implement a message-passing algorithm and a local heuristic algorithm that combines GLR with greedy node-removal to obtain near-optimal solutions for single random networks. Our algorithms also perform well on real-world network instances.

  8. Cross-cultural and site-based influences on demographic, well-being, and social network predictors of risk perception in hazard and disaster settings in Ecuador and Mexico: predictors of risk perception in hazard and disaster settings in Ecuador and Mexico.

    PubMed

    Jones, Eric C; Faas, Albert J; Murphy, Arthur D; Tobin, Graham A; Whiteford, Linda M; McCarty, Christopher

    2013-03-01

    Although virtually all comparative research about risk perception focuses on which hazards are of concern to people in different culture groups, much can be gained by focusing on predictors of levels of risk perception in various countries and places. In this case, we examine standard and novel predictors of risk perception in seven sites among communities affected by a flood in Mexico (one site) and volcanic eruptions in Mexico (one site) and Ecuador (five sites). We conducted more than 450 interviews with questions about how people feel at the time (after the disaster) regarding what happened in the past, their current concerns, and their expectations for the future. We explore how aspects of the context in which people live have an effect on how strongly people perceive natural hazards in relationship with demographic, well-being, and social network factors. Generally, our research indicates that levels of risk perception for past, present, and future aspects of a specific hazard are similar across these two countries and seven sites. However, these contexts produced different predictors of risk perception-in other words, there was little overlap between sites in the variables that predicted the past, present, or future aspects of risk perception in each site. Generally, current stress was related to perception of past danger of an event in the Mexican sites, but not in Ecuador; network variables were mainly important for perception of past danger (rather than future or present danger), although specific network correlates varied from site to site across the countries.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  10. Improved Autoassociative Neural Networks

    NASA Technical Reports Server (NTRS)

    Hand, Charles

    2003-01-01

    Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

  11. Transcriptome profiling based on protein-protein interaction networks provides a core set of genes for understanding blood immune response mechanisms against Edwardsiella tarda infection in Japanese flounder (Paralichthys olivaceus).

    PubMed

    Li, Zan; Liu, Xiumei; Liu, Jinxiang; Zhang, Kai; Yu, Haiyang; He, Yan; Wang, Xubo; Qi, Jie; Wang, Zhigang; Zhang, Quanqi

    2017-09-18

    Marine organisms are commonly under threat from various pathogens. Edwardsiella tarda is one of the fish pathogens that can infect both cultured and wild fish species. E. tarda can also infect other vertebrates, including amphibians, reptiles, and mammals. Bacteremia caused by E. tarda can be a fatal disease in humans. Blood acts as a pipeline for the fish immune system. Generating blood transcriptomic resources from fish challenged by E. tarda is crucial for understanding molecular mechanisms underlying blood immune response process. In this study, we performed transcriptome-wide gene expression profiling of Japanese flounder (Paralichthys olivaceus) challenged by 8 and 48 h E. tarda stress. An average of 37 million clean reads per library was obtained, and approximately 85.6% of these reads were successfully mapped to the reference genome. In addition, 808 and 1265 differential expression genes (DEGs) were found at 8 and 48 h post-injection, respectively. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to search immune-related DEGs. A protein-protein interaction network was constructed to obtain the interaction relationship of immune genes during pathogens stress. Based on KEGG and protein association networks analysis, 30 hub genes were discovered and validated by quantitative RT-PCR. This study represents the first transcriptome analysis based on protein-protein interaction networks in fish and provides us with valuable gene resources for the research of fish blood immunity, which can significantly assist us to further understand the molecular mechanisms of humans and other vertebrates against E. tarda. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Analysis on relationship between extreme pathways and correlated reaction sets

    PubMed Central

    Xi, Yanping; Chen, Yi-Ping Phoebe; Cao, Ming; Wang, Weirong; Wang, Fei

    2009-01-01

    Background Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets. Results Correlated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an 'all or none' manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the 'all or none' manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set. Conclusion Both extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an

  13. Data in support of a central role of plasminogen activator inhibitor-2 polymorphism in recurrent cardiovascular disease risk in the setting of high HDL cholesterol and C-reactive protein using Bayesian network modeling.

    PubMed

    Corsetti, James P; Salzman, Peter; Ryan, Dan; Moss, Arthur J; Zareba, Wojciech; Sparks, Charles E

    2016-09-01

    Data is presented that was utilized as the basis for Bayesian network modeling of influence pathways focusing on the central role of a polymorphism of plasminogen activator inhibitor-2 (PAI-2) on recurrent cardiovascular disease risk in patients with high levels of HDL cholesterol and C-reactive protein (CRP) as a marker of inflammation, "Influences on Plasminogen Activator Inhibitor-2 Polymorphism-Associated Recurrent Cardiovascular Disease Risk in Patients with High HDL Cholesterol and Inflammation" (Corsetti et al., 2016; [1]). The data consist of occurrence of recurrent coronary events in 166 post myocardial infarction patients along with 1. clinical data on gender, race, age, and body mass index; 2. blood level data on 17 biomarkers; and 3. genotype data on 53 presumptive CVD-related single nucleotide polymorphisms. Additionally, a flow diagram of the Bayesian modeling procedure is presented along with Bayesian network subgraphs (root nodes to outcome events) utilized as the data from which PAI-2 associated influence pathways were derived (Corsetti et al., 2016; [1]).

  14. Enabling network-aware applications

    SciTech Connect

    Tierney, Brian L.; Gunter, Dan; Lee, Jason; Stouffer, Martin

    2001-08-01

    Many high performance distributed applications use only a small fraction of their available bandwidth. A common cause of this problem is not a flaw in the application design, but rather improperly tuned network settings. Proper tuning techniques, such as setting the correct TCP buffers and using parallel streams, are well known in the networking community, but outside the networking community they are infrequently applied. In this paper, we describe a service that makes the task of network tuning trivial for application developers and users. Widespread use of this service should virtually eliminate a common stumbling block for high performance distributed applications.

  15. A security architecture for health information networks.

    PubMed

    Kailar, Rajashekar; Muralidhar, Vinod

    2007-10-11

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.

  16. Introduction: optimization in networks.

    PubMed

    Motter, Adilson E; Toroczkai, Zoltan

    2007-06-01

    The recent surge in the network modeling of complex systems has set the stage for a new era in the study of fundamental and applied aspects of optimization in collective behavior. This Focus Issue presents an extended view of the state of the art in this field and includes articles from a large variety of domains in which optimization manifests itself, including physical, biological, social, and technological networked systems.

  17. Network planning under uncertainties

    NASA Astrophysics Data System (ADS)

    Ho, Kwok Shing; Cheung, Kwok Wai

    2008-11-01

    generic framework for solving the network planning problem under uncertainties. In addition to reviewing the various network planning problems involving uncertainties, we also propose that a unified framework based on robust optimization can be used to solve a rather large segment of network planning problem under uncertainties. Robust optimization is first introduced in the operations research literature and is a framework that incorporates information about the uncertainty sets for the parameters in the optimization model. Even though robust optimization is originated from tackling the uncertainty in the optimization process, it can serve as a comprehensive and suitable framework for tackling generic network planning problems under uncertainties. In this paper, we begin by explaining the main ideas behind the robust optimization approach. Then we demonstrate the capabilities of the proposed framework by giving out some examples of how the robust optimization framework can be applied to the current common network planning problems under uncertain environments. Next, we list some practical considerations for solving the network planning problem under uncertainties with the proposed framework. Finally, we conclude this article with some thoughts on the future directions for applying this framework to solve other network planning problems.

  18. Community structure in networks

    NASA Astrophysics Data System (ADS)

    Newman, Mark

    2004-03-01

    Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

  19. Self Evolving Modular Network

    NASA Astrophysics Data System (ADS)

    Tokunaga, Kazuhiro; Kawabata, Nobuyuki; Furukawa, Tetsuo

    We propose a novel modular network called the Self-Evolving Modular Network (SEEM). The SEEM has a modular network architecture with a graph structure and these following advantages: (1) new modules are added incrementally to allow the network to adapt in a self-organizing manner, and (2) graph's paths are formed based on the relationships between the models represented by modules. The SEEM is expected to be applicable to evolving functions of an autonomous robot in a self-organizing manner through interaction with the robot's environment and categorizing large-scale information. This paper presents the architecture and an algorithm for the SEEM. Moreover, performance characteristic and effectiveness of the network are shown by simulations using cubic functions and a set of 3D-objects.

  20. Statistical mechanics of maximal independent sets

    NASA Astrophysics Data System (ADS)

    Dall'Asta, Luca; Pin, Paolo; Ramezanpour, Abolfazl

    2009-12-01

    The graph theoretic concept of maximal independent set arises in several practical problems in computer science as well as in game theory. A maximal independent set is defined by the set of occupied nodes that satisfy some packing and covering constraints. It is known that finding minimum and maximum-density maximal independent sets are hard optimization problems. In this paper, we use cavity method of statistical physics and Monte Carlo simulations to study the corresponding constraint satisfaction problem on random graphs. We obtain the entropy of maximal independent sets within the replica symmetric and one-step replica symmetry breaking frameworks, shedding light on the metric structure of the landscape of solutions and suggesting a class of possible algorithms. This is of particular relevance for the application to the study of strategic interactions in social and economic networks, where maximal independent sets correspond to pure Nash equilibria of a graphical game of public goods allocation.

  1. Jacobi Set Computation

    SciTech Connect

    Bhatia, Harsh

    2016-07-28

    Jacobi Set Computation is a software to compute the Jacobi set of 2 piecewise linear scalar functions defined on a triangular mesh. This functionality is useful for analyzing multiple scalar fields simultaneously.

  2. Network Consistent Data Association.

    PubMed

    Chakraborty, Anirban; Das, Abir; Roy-Chowdhury, Amit K

    2016-09-01

    Existing data association techniques mostly focus on matching pairs of data-point sets and then repeating this process along space-time to achieve long term correspondences. However, in many problems such as person re-identification, a set of data-points may be observed at multiple spatio-temporal locations and/or by multiple agents in a network and simply combining the local pairwise association results between sets of data-points often leads to inconsistencies over the global space-time horizons. In this paper, we propose a Novel Network Consistent Data Association (NCDA) framework formulated as an optimization problem that not only maintains consistency in association results across the network, but also improves the pairwise data association accuracies. The proposed NCDA can be solved as a binary integer program leading to a globally optimal solution and is capable of handling the challenging data-association scenario where the number of data-points varies across different sets of instances in the network. We also present an online implementation of NCDA method that can dynamically associate new observations to already observed data-points in an iterative fashion, while maintaining network consistency. We have tested both the batch and the online NCDA in two application areas-person re-identification and spatio-temporal cell tracking and observed consistent and highly accurate data association results in all the cases.

  3. Sets, Planets, and Comets

    ERIC Educational Resources Information Center

    Baker, Mark; Beltran, Jane; Buell, Jason; Conrey, Brian; Davis, Tom; Donaldson, Brianna; Detorre-Ozeki, Jeanne; Dibble, Leila; Freeman, Tom; Hammie, Robert; Montgomery, Julie; Pickford, Avery; Wong, Justine

    2013-01-01

    Sets in the game "Set" are lines in a certain four-dimensional space. Here we introduce planes into the game, leading to interesting mathematical questions, some of which we solve, and to a wonderful variation on the game "Set," in which every tableau of nine cards must contain at least one configuration for a player to pick up.

  4. Noncomputable Spectral Sets

    NASA Astrophysics Data System (ADS)

    Teutsch, Jason

    2007-01-01

    It is possible to enumerate all computer programs. In particular, for every partial computable function, there is a shortest program which computes that function. f-MIN is the set of indices for shortest programs. In 1972, Meyer showed that f-MIN is Turing equivalent to 0'', the halting set with halting set oracle. This paper generalizes the notion of shortest programs, and we use various measures from computability theory to describe the complexity of the resulting "spectral sets." We show that under certain Godel numberings, the spectral sets are exactly the canonical sets 0', 0'', 0''', ... up to Turing equivalence. This is probably not true in general, however we show that spectral sets always contain some useful information. We show that immunity, or "thinness" is a useful characteristic for distinguishing between spectral sets. In the final chapter, we construct a set which neither contains nor is disjoint from any infinite arithmetic set, yet it is 0-majorized and contains a natural spectral set. Thus a pathological set becomes a bit more friendly. Finally, a number of interesting open problems are left for the inspired reader.

  5. Sets, Planets, and Comets

    ERIC Educational Resources Information Center

    Baker, Mark; Beltran, Jane; Buell, Jason; Conrey, Brian; Davis, Tom; Donaldson, Brianna; Detorre-Ozeki, Jeanne; Dibble, Leila; Freeman, Tom; Hammie, Robert; Montgomery, Julie; Pickford, Avery; Wong, Justine

    2013-01-01

    Sets in the game "Set" are lines in a certain four-dimensional space. Here we introduce planes into the game, leading to interesting mathematical questions, some of which we solve, and to a wonderful variation on the game "Set," in which every tableau of nine cards must contain at least one configuration for a player to pick up.

  6. Network susceptibilities: Theory and applications.

    PubMed

    Manik, Debsankha; Rohden, Martin; Ronellenfitsch, Henrik; Zhang, Xiaozhu; Hallerberg, Sarah; Witthaut, Dirk; Timme, Marc

    2017-01-01

    We introduce the concept of network susceptibilities quantifying the response of the collective dynamics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase-oscillator models, power grid models, and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibilities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.

  7. Exact controllability of complex networks

    PubMed Central

    Yuan, Zhengzhong; Zhao, Chen; Di, Zengru; Wang, Wen-Xu; Lai, Ying-Cheng

    2013-01-01

    Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems. PMID:24025746

  8. Network susceptibilities: Theory and applications

    NASA Astrophysics Data System (ADS)

    Manik, Debsankha; Rohden, Martin; Ronellenfitsch, Henrik; Zhang, Xiaozhu; Hallerberg, Sarah; Witthaut, Dirk; Timme, Marc

    2017-01-01

    We introduce the concept of network susceptibilities quantifying the response of the collective dynamics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase-oscillator models, power grid models, and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibilities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.

  9. Structural complexity of quantum networks

    SciTech Connect

    Siomau, Michael

    2016-06-10

    Quantum network is a set of nodes connected with channels, through which the nodes communicate photons and classical information. Classical structural complexity of a quantum network may be defined through its physical structure, i.e. mutual position of nodes and channels connecting them. We show here that the classical structural complexity of a quantum network does not restrict the structural complexity of entanglement graphs, which may be created in the quantum network with local operations and classical communication. We show, in particular, that 1D quantum network can simulate both simple entanglement graphs such as lattices and random graphs and complex small-world graphs.

  10. Network cosmology.

    PubMed

    Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

    2012-01-01

    Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

  11. Network Cosmology

    PubMed Central

    Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

    2012-01-01

    Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

  12. PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

    PubMed Central

    2014-01-01

    Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in

  13. Weighted network analysis of earthquake seismic data

    NASA Astrophysics Data System (ADS)

    Chakraborty, Abhijit; Mukherjee, G.; Manna, S. S.

    2015-09-01

    Three different earthquake seismic data sets are used to construct the earthquake networks following the prescriptions of Abe and Suzuki (2004). It has been observed that different links of this network appear with highly different strengths. This prompted us to extend the study of earthquake networks by considering it as the weighted network. Different properties of such weighted network have been found to be quite different from those of their un-weighted counterparts.

  14. Network Solutions.

    ERIC Educational Resources Information Center

    Vietzke, Robert; And Others

    1996-01-01

    This special section explains the latest developments in networking technologies, profiles school districts benefiting from successful implementations, and reviews new products for building networks. Highlights include ATM (asynchronous transfer mode), cable modems, networking switches, Internet screening software, file servers, network management…

  15. OPTIMAL NETWORK TOPOLOGY DESIGN

    NASA Technical Reports Server (NTRS)

    Yuen, J. H.

    1994-01-01

    This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.

  16. Acronical Risings and Settings

    NASA Astrophysics Data System (ADS)

    Hockey, Thomas A.

    2012-01-01

    A concept found in historical primary sources, and useful in contemporary historiography, is the acronical rising and setting of stars (or planets). Topocentric terms, they provide information about a star's relationship to the Sun and thus its visibility in the sky. Yet there remains ambiguity as to what these two phrases actually mean. "Acronical” is said to have come from the Greek akros ("point,” "summit,” or "extremity") and nux ("night"). While all sources agree that the word is originally Greek, there are alternate etymologies for it. A more serious difficulty with acronical rising and setting is that there are two competing definitions. One I call the Poetical Definition. Acronical rising (or setting) is one of the three Poetical Risings (or Settings) known to classicists. (The other two are cosmical rising/setting, discussed below, and the more familiar helical rising/setting.) The term "poetical" refers to these words use in classical poetry, e. g., that of Columella, Hesiod, Ovid, Pliny the Younger, and Virgil. The Poetical Definition of "acronical” usually is meant in this context. The Poetical Definition of "acronical” is as follows: When a star rises as the Sun sets, it rises acronically. When a star sets as the Sun sets, it sets acronically. In contrast with the Poetical Definition, there also is what I call the Astronomical Definition. The Astronomical Definition is somewhat more likely to appear in astronomical, mathematical, or navigational works. When the Astronomical Definition is recorded in dictionaries, it is often with the protasis "In astronomy, . . . ." The Astronomical Definition of "acronical” is as follows: When a star rises as the Sun sets, it rises acronically. When a star sets as the Sun rises, it sets acronically. I will attempt to sort this all out in my talk.

  17. Patterns in randomly evolving networks: Idiotypic networks

    NASA Astrophysics Data System (ADS)

    Brede, Markus; Behn, Ulrich

    2003-03-01

    We present a model for the evolution of networks of occupied sites on undirected regular graphs. At every iteration step in a parallel update, I randomly chosen empty sites are occupied and occupied sites having occupied neighbor degree outside of a given interval (tl,tu) are set empty. Depending on the influx I and the values of both lower threshold and upper threshold of the occupied neighbor degree, different kinds of behavior can be observed. In certain regimes stable long-living patterns appear. We distinguish two types of patterns: static patterns arising on graphs with low connectivity and dynamic patterns found on high connectivity graphs. Increasing I patterns become unstable and transitions between almost stable patterns, interrupted by disordered phases, occur. For still larger I the lifetime of occupied sites becomes very small and network structures are dominated by randomness. We develop methods to analyze the nature and dynamics of these network patterns, give a statistical description of defects and fluctuations around them, and elucidate the transitions between different patterns. Results and methods presented can be applied to a variety of problems in different fields and a broad class of graphs. Aiming chiefly at the modeling of functional networks of interacting antibodies and B cells of the immune system (idiotypic networks), we focus on a class of graphs constructed by bit chains. The biological relevance of the patterns and possible operational modes of idiotypic networks are discussed.

  18. Latent geometry of bipartite networks

    NASA Astrophysics Data System (ADS)

    Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

    2017-03-01

    Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

  19. Latent geometry of bipartite networks.

    PubMed

    Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

    2017-03-01

    Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

  20. Sequential state generation by model neural networks.

    PubMed Central

    Kleinfeld, D

    1986-01-01

    Sequential patterns of neural output activity form the basis of many biological processes, such as the cyclic pattern of outputs that control locomotion. I show how such sequences can be generated by a class of model neural networks that make defined sets of transitions between selected memory states. Sequence-generating networks depend upon the interplay between two sets of synaptic connections. One set acts to stabilize the network in its current memory state, while the second set, whose action is delayed in time, causes the network to make specified transitions between the memories. The dynamic properties of these networks are described in terms of motion along an energy surface. The performance of the networks, both with intact connections and with noisy or missing connections, is illustrated by numerical examples. In addition, I present a scheme for the recognition of externally generated sequences by these networks. PMID:3467316

  1. Networking standards

    NASA Technical Reports Server (NTRS)

    Davies, Mark

    1991-01-01

    The enterprise network is currently a multivendor environment consisting of many defacto and proprietary standards. During the 1990s, these networks will evolve towards networks which are based on international standards in both Local Area Network (LAN) and Wide Area Network (WAN) space. Also, you can expect to see the higher level functions and applications begin the same transition. Additional information is given in viewgraph form.

  2. The development of the International Network for Frontier Research on Earthquake Precursors (INFREP) by designing new analysing software and by setting up new recording locations of radio VLF/LF signals in Romania

    NASA Astrophysics Data System (ADS)

    Moldovan, Iren-Adelina; Petruta Constantin, Angela; Emilian Toader, Victorin; Toma-Danila, Dragos; Biagi, Pier Francesco; Maggipinto, Tommaso; Dolea, Paul; Septimiu Moldovan, Adrian

    2014-05-01

    Based on scientific evidences supporting the causality between earthquake preparatory stages, space weather and solar activity and different types of electromagnetic (EM) disturbances together with the benefit of having full access at ground and space based EM data, INFREP proposes a complex and cross correlated investigation of phenomena that occur in the coupled system Lithosphere-Atmosphere-Ionsophere in order to identify possible causes responsible for anomalous effects observed in the propagation characteristics of radio waves, especially at low (LF) and very low frequency (VLF). INFREP, a network of VLF (20-60 kHz) and LF (150-300 kHz) radio receivers, was put into operation in Europe in 2009, having as principal goal, the study of disturbances produced by the earthquakes on the propagation properties of these signals. The Romanian NIEP VLF / LF monitoring system consisting in a radio receiver -made by Elettronika S.R.L. (Italy) and provided by the Bari University- and the infrastructure that is necessary to record and transmit the collected data, is a part of the international initiative INFREP. The NIEP VLF / LF receiver installed in Romania was put into operation in February 2009 in Bucharest and relocated to the Black-Sea shore (Dobruja Seismologic Observatory) in December 2009. The first development of the Romanian EM monitoring system was needed because after changing the receiving site from Bucharest to Eforie we obtained unsatisfactory monitoring data, characterized by large fluctuations of the received signals' intensities. Trying to understand this behavior has led to the conclusion that the electric component of the electromagnetic field was possibly influenced by the local conditions. Starting from this observation we have run some tests and changed the vertical antenna with a loop-type antenna that is more appropriate in highly electric-field polluted environments. Since the amount of recorded data is huge, for streamlining the research process

  3. A Security Architecture for Health Information Networks

    PubMed Central

    Kailar, Rajashekar

    2007-01-01

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862

  4. Optical Neural Network Classifier Architectures

    DTIC Science & Technology

    1998-04-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and...function neural network based on a previously demonstrated binary-input version. The greyscale-input capability broadens the range of applications for...a reduced feature set of multiwavelet images to improve training times and discrimination capability of the neural network . The design uses a joint

  5. Transmission design in a multimode HFC network

    NASA Astrophysics Data System (ADS)

    Cambron, G. Keith

    1996-11-01

    Contemporary hybrid fiber coaxial (HFC) networks are capable of supporting a wide range of services including traditional analog video, telephony, digital video, and data services. Each service has unique performance or service requirements. This contribution examines transmission design for one such network. Pacific Bell's advanced communications network (ACN). The design methodology begins with a set of end to end service quality objectives. Network impairments, such as noise, distortion and delay, are allocated across the network elements using a set of standard network models. These models are a representative set of the actual field designs and bound the network operating parameters. Network components, headend equipment, and customer premises equipment are specified analytically or characterized empirically in relationship to the chosen impairment set. The component parameters are then included in analytical models to estimate overall network performance. In addition to the forward path transmission considerations examined by traditional coaxial network designers, other dimensions including power consumption, traffic demand, and message latency are taken into account. Analytical models are used to estimate the effects of multiple modulation schemes within the unified network. The variability introduced by on demand services such as telephony and interactive digital services changes the base computational domain from deterministic models to stochastic ones. These models are then used to set operating parameters at measurable points throughout the network for proof of performance prior to turn up, and for ongoing performance monitoring. For closure, empirical results are compared with model projections as a way of verifying and improving the predictive models.

  6. Semantic Networks and Social Networks

    ERIC Educational Resources Information Center

    Downes, Stephen

    2005-01-01

    Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

  7. Semantic Networks and Social Networks

    ERIC Educational Resources Information Center

    Downes, Stephen

    2005-01-01

    Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

  8. Scaling in Computer Network Traffic

    DTIC Science & Technology

    2007-11-02

    Laboratory for Applied Network Research). ♠ CAIDA (Cooperative Association for Internet Data Analysis). ♥ ♠ WAND (Waikato Applied Network Dynamics [DAG...permission of CAIDA , c© 2001 CAIDA /UC Regents. Mapnet Author: Bradley Huffaker. 15 Flows and Packets Flows are sets of packets associated to the same data

  9. PhyloNetworks: a package for phylogenetic networks.

    PubMed

    Solís-Lemus, Claudia; Bastide, Paul; Ané, Cécile

    2017-09-04

    PhyloNetworks is a Julia package for the inference, manipulation, visualization and use of phylogenetic networks in an interactive environment. Inference of phylogenetic networks is done with maximum pseudolikelihood from gene trees or multi-locus sequences (SNaQ), with possible bootstrap analysis. PhyloNetworks is the first software providing tools to summarize a set of networks (from a bootstrap or posterior sample) with measures of tree edge support, hybrid edge support, and hybrid node support. Networks can be used for phylogenetic comparative analysis of continuous traits, to estimate ancestral states or do a phylogenetic regression. The software is available in open source and with documentation at https://github.com/crsl4/PhyloNetworks.jl. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  10. A network approach based on cliques

    NASA Astrophysics Data System (ADS)

    Fadigas, I. S.; Pereira, H. B. B.

    2013-05-01

    The characterization of complex networks is a procedure that is currently found in several research studies. Nevertheless, few studies present a discussion on networks in which the basic element is a clique. In this paper, we propose an approach based on a network of cliques. This approach consists not only of a set of new indices to capture the properties of a network of cliques but also of a method to characterize complex networks of cliques (i.e., some of the parameters are proposed to characterize the small-world phenomenon in networks of cliques). The results obtained are consistent with results from classical methods used to characterize complex networks.

  11. Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets.

    PubMed

    Zhou, Xi; Chen, Pengcheng; Wei, Qiang; Shen, Xueling; Chen, Xin

    2013-08-15

    A molecular interaction network can be viewed as a network in which genes with related functions are connected. Therefore, at a systems level, connections between individual genes in a molecular interaction network can be used to infer the collective functional linkages between biologically meaningful gene sets. We present the human interactome resource and the gene set linkage analysis (GSLA) tool for the functional interpretation of biologically meaningful gene sets observed in experiments. GSLA determines whether an observed gene set has significant functional linkages to established biological processes. When an observed gene set is not enriched by known biological processes, traditional enrichment-based interpretation methods cannot produce functional insights, but GSLA can still evaluate whether those genes work in concert to regulate specific biological processes, thereby suggesting the functional implications of the observed gene set. The quality of human interactome resource and the utility of GSLA are illustrated with multiple assessments. http://www.cls.zju.edu.cn/hir/

  12. Communicability across evolving networks.

    PubMed

    Grindrod, Peter; Parsons, Mark C; Higham, Desmond J; Estrada, Ernesto

    2011-04-01

    Many natural and technological applications generate time-ordered sequences of networks, defined over a fixed set of nodes; for example, time-stamped information about "who phoned who" or "who came into contact with who" arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time's arrow is captured naturally through the noncommutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction.

  13. Computer Network Defense Roadmap

    DTIC Science & Technology

    2009-05-01

    networks, podcasts , and wikis, and mobile end- user devices, has brought a new set of challenges to CND. The DON will work with the JTF-GNO and other...within an overarching, full spectrum enterprise access control schema that supports the end-to-end requirements in a coalition, first responder/non

  14. Pseudo-set framing.

    PubMed

    Barasz, Kate; John, Leslie K; Keenan, Elizabeth A; Norton, Michael I

    2017-10-01

    Pseudo-set framing-arbitrarily grouping items or tasks together as part of an apparent "set"-motivates people to reach perceived completion points. Pseudo-set framing changes gambling choices (Study 1), effort (Studies 2 and 3), giving behavior (Field Data and Study 4), and purchase decisions (Study 5). These effects persist in the absence of any reward, when a cost must be incurred, and after participants are explicitly informed of the arbitrariness of the set. Drawing on Gestalt psychology, we develop a conceptual account that predicts what will-and will not-act as a pseudo-set, and defines the psychological process through which these pseudo-sets affect behavior: over and above typical reference points, pseudo-set framing alters perceptions of (in)completeness, making intermediate progress seem less complete. In turn, these feelings of incompleteness motivate people to persist until the pseudo-set has been fulfilled. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  15. Economic communication model set

    NASA Astrophysics Data System (ADS)

    Zvereva, Olga M.; Berg, Dmitry B.

    2017-06-01

    This paper details findings from the research work targeted at economic communications investigation with agent-based models usage. The agent-based model set was engineered to simulate economic communications. Money in the form of internal and external currencies was introduced into the models to support exchanges in communications. Every model, being based on the general concept, has its own peculiarities in algorithm and input data set since it was engineered to solve the specific problem. Several and different origin data sets were used in experiments: theoretic sets were estimated on the basis of static Leontief's equilibrium equation and the real set was constructed on the basis of statistical data. While simulation experiments, communication process was observed in dynamics, and system macroparameters were estimated. This research approved that combination of an agent-based and mathematical model can cause a synergetic effect.

  16. Observability of Neuronal Network Motifs

    PubMed Central

    Whalen, Andrew J.; Brennan, Sean N.; Sauer, Timothy D.; Schiff, Steven J.

    2014-01-01

    We quantify observability in small (3 node) neuronal networks as a function of 1) the connection topology and symmetry, 2) the measured nodes, and 3) the nodal dynamics (linear and nonlinear). We find that typical observability metrics for 3 neuron motifs range over several orders of magnitude, depending upon topology, and for motifs containing symmetry the network observability decreases when observing from particularly confounded nodes. Nonlinearities in the nodal equations generally decrease the average network observability and full network information becomes available only in limited regions of the system phase space. Our findings demonstrate that such networks are partially observable, and suggest their potential efficacy in reconstructing network dynamics from limited measurement data. How well such strategies can be used to reconstruct and control network dynamics in experimental settings is a subject for future experimental work. PMID:25909092

  17. Fractal and multifractal analyses of bipartite networks

    PubMed Central

    Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

    2017-01-01

    Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions. PMID:28361962

  18. Fractal and multifractal analyses of bipartite networks

    NASA Astrophysics Data System (ADS)

    Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

    2017-03-01

    Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

  19. Fractal and multifractal analyses of bipartite networks.

    PubMed

    Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

    2017-03-31

    Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

  20. Learning network representations

    NASA Astrophysics Data System (ADS)

    Moyano, Luis G.

    2017-02-01

    In this review I present several representation learning methods, and discuss the latest advancements with emphasis in applications to network science. Representation learning is a set of techniques that has the goal of efficiently mapping data structures into convenient latent spaces. Either for dimensionality reduction or for gaining semantic content, this type of feature embeddings has demonstrated to be useful, for example, for node classification or link prediction tasks, among many other relevant applications to networks. I provide a description of the state-of-the-art of network representation learning as well as a detailed account of the connections with other fields of study such as continuous word embeddings and deep learning architectures. Finally, I provide a broad view of several applications of these techniques to networks in various domains.

  1. Growing networks with superjoiners.

    PubMed

    Jabr-Hamdan, Ameerah; Sun, Jie; Ben-Avraham, Daniel

    2014-11-01

    We study the Krapivsky-Redner (KR) network growth model, but where new nodes can connect to any number of existing nodes, m, picked from a power-law distribution p(m)∼m^{-α}. Each of the m new connections is still carried out as in the KR model with probability redirection r (corresponding to degree exponent γ_{KR}=1+1/r in the original KR model). The possibility to connect to any number of nodes resembles a more realistic type of growth in several settings, such as social networks, routers networks, and networks of citations. Here we focus on the in-, out-, and total-degree distributions and on the potential tension between the degree exponent α, characterizing new connections (outgoing links), and the degree exponent γ_{KR}(r) dictated by the redirection mechanism.

  2. Growing networks with superjoiners

    NASA Astrophysics Data System (ADS)

    Jabr-Hamdan, Ameerah; Sun, Jie; ben-Avraham, Daniel

    2014-11-01

    We study the Krapivsky-Redner (KR) network growth model, but where new nodes can connect to any number of existing nodes, m , picked from a power-law distribution p (m ) ˜m-α . Each of the m new connections is still carried out as in the KR model with probability redirection r (corresponding to degree exponent γKR=1 +1 /r in the original KR model). The possibility to connect to any number of nodes resembles a more realistic type of growth in several settings, such as social networks, routers networks, and networks of citations. Here we focus on the in-, out-, and total-degree distributions and on the potential tension between the degree exponent α , characterizing new connections (outgoing links), and the degree exponent γKR(r ) dictated by the redirection mechanism.

  3. NASA Integrated Space Communications Network

    NASA Technical Reports Server (NTRS)

    Tai, Wallace; Wright, Nate; Prior, Mike; Bhasin, Kul

    2012-01-01

    The NASA Integrated Network for Space Communications and Navigation (SCaN) has been in the definition phase since 2010. It is intended to integrate NASA s three existing network elements, i.e., the Space Network, Near Earth Network, and Deep Space Network, into a single network. In addition to the technical merits, the primary purpose of the Integrated Network is to achieve a level of operating cost efficiency significantly higher than it is today. Salient features of the Integrated Network include (a) a central system element that performs service management functions and user mission interfaces for service requests; (b) a set of common service execution equipment deployed at the all stations that provides return, forward, and radiometric data processing and delivery capabilities; (c) the network monitor and control operations for the entire integrated network are conducted remotely and centrally at a prime-shift site and rotating among three sites globally (a follow-the-sun approach); (d) the common network monitor and control software deployed at all three network elements that supports the follow-the-sun operations.

  4. Aggregating Hydrometeorological Data from International Monitoring Networks Across Earth's Largest Lake System to Quantify Uncertainty in Historical Water Budget Records, Improve Regional Water Budget Projections, and Differentiate Drivers Behind a Recent Record-Setting Surge in Water Levels

    NASA Astrophysics Data System (ADS)

    Gronewold, A.; Bruxer, J.; Smith, J.; Hunter, T.; Fortin, V.; Clites, A. H.; Durnford, D.; Qian, S.; Seglenieks, F.

    2015-12-01

    Resolving and projecting the water budget of the North American Great Lakes basin (Earth's largest lake system) requires aggregation of data from a complex array of in situ monitoring and remote sensing products that cross an international border (leading to potential sources of bias and other inconsistencies), and are relatively sparse over the surfaces of the lakes themselves. Data scarcity over the surfaces of the lakes is a particularly significant problem because, unlike Earth's other large freshwater basins, the Great Lakes basin water budget is (on annual scales) comprised of relatively equal contributions from runoff, over-lake precipitation, and over-lake evaporation. Consequently, understanding drivers behind changes in regional water storage and water levels requires a data management framework that can reconcile uncertainties associated with data scarcity and bias, and propagate those uncertainties into regional water budget projections and historical records. Here, we assess the development of a historical hydrometeorological database for the entire Great Lakes basin with records dating back to the late 1800s, and describe improvements that are specifically intended to differentiate hydrological, climatological, and anthropogenic drivers behind recent extreme changes in Great Lakes water levels. Our assessment includes a detailed analysis of the extent to which extreme cold winters in central North America in 2013-2014 (caused by the anomalous meridional upper air flow - commonly referred to in the public media as the "polar vortex" phenomenon) altered the thermal and hydrologic regimes of the Great Lakes and led to a record setting surge in water levels between January 2014 and December 2015.

  5. Airport Surface Network Architecture Definition

    NASA Technical Reports Server (NTRS)

    Nguyen, Thanh C.; Eddy, Wesley M.; Bretmersky, Steven C.; Lawas-Grodek, Fran; Ellis, Brenda L.

    2006-01-01

    Currently, airport surface communications are fragmented across multiple types of systems. These communication systems for airport operations at most airports today are based dedicated and separate architectures that cannot support system-wide interoperability and information sharing. The requirements placed upon the Communications, Navigation, and Surveillance (CNS) systems in airports are rapidly growing and integration is urgently needed if the future vision of the National Airspace System (NAS) and the Next Generation Air Transportation System (NGATS) 2025 concept are to be realized. To address this and other problems such as airport surface congestion, the Space Based Technologies Project s Surface ICNS Network Architecture team at NASA Glenn Research Center has assessed airport surface communications requirements, analyzed existing and future surface applications, and defined a set of architecture functions that will help design a scalable, reliable and flexible surface network architecture to meet the current and future needs of airport operations. This paper describes the systems approach or methodology to networking that was employed to assess airport surface communications requirements, analyze applications, and to define the surface network architecture functions as the building blocks or components of the network. The systems approach used for defining these functions is relatively new to networking. It is viewing the surface network, along with its environment (everything that the surface network interacts with or impacts), as a system. Associated with this system are sets of services that are offered by the network to the rest of the system. Therefore, the surface network is considered as part of the larger system (such as the NAS), with interactions and dependencies between the surface network and its users, applications, and devices. The surface network architecture includes components such as addressing/routing, network management, network

  6. Networked Microgrids Scoping Study

    SciTech Connect

    Backhaus, Scott N.; Dobriansky, Larisa; Glover, Steve; Liu, Chen-Ching; Looney, Patrick; Mashayekh, Salman; Pratt, Annabelle; Schneider, Kevin; Stadler, Michael; Starke, Michael; Wang, Jianhui; Yue, Meng

    2016-12-05

    Much like individual microgrids, the range of opportunities and potential architectures of networked microgrids is very diverse. The goals of this scoping study are to provide an early assessment of research and development needs by examining the benefits of, risks created by, and risks to networked microgrids. At this time there are very few, if any, examples of deployed microgrid networks. In addition, there are very few tools to simulate or otherwise analyze the behavior of networked microgrids. In this setting, it is very difficult to evaluate networked microgrids systematically or quantitatively. At this early stage, this study is relying on inputs, estimations, and literature reviews by subject matter experts who are engaged in individual microgrid research and development projects, i.e., the authors of this study The initial step of the study gathered input about the potential opportunities provided by networked microgrids from these subject matter experts. These opportunities were divided between the subject matter experts for further review. Part 2 of this study is comprised of these reviews. Part 1 of this study is a summary of the benefits and risks identified in the reviews in Part 2 and synthesis of the research needs required to enable networked microgrids.

  7. Ranking Information in Networks

    NASA Astrophysics Data System (ADS)

    Eliassi-Rad, Tina; Henderson, Keith

    Given a network, we are interested in ranking sets of nodes that score highest on user-specified criteria. For instance in graphs from bibliographic data (e.g. PubMed), we would like to discover sets of authors with expertise in a wide range of disciplines. We present this ranking task as a Top-K problem; utilize fixed-memory heuristic search; and present performance of both the serial and distributed search algorithms on synthetic and real-world data sets.

  8. Convolution in Convolution for Network in Network.

    PubMed

    Pang, Yanwei; Sun, Manli; Jiang, Xiaoheng; Li, Xuelong

    2017-03-16

    Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 x 1 convolutions in spatial domain, NiN has stronger ability of feature representation and hence results in better recognition performance. However, MLP itself consists of fully connected layers that give rise to a large number of parameters. In this paper, we propose to replace dense shallow MLP with sparse shallow MLP. One or more layers of the sparse shallow MLP are sparely connected in the channel dimension or channel-spatial domain. The proposed method is implemented by applying unshared convolution across the channel dimension and applying shared convolution across the spatial dimension in some computational layers. The proposed method is called convolution in convolution (CiC). The experimental results on the CIFAR10 data set, augmented CIFAR10 data set, and CIFAR100 data set demonstrate the effectiveness of the proposed CiC method.

  9. Network neuroscience

    PubMed Central

    Bassett, Danielle S; Sporns, Olaf

    2017-01-01

    Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

  10. Network Basics.

    ERIC Educational Resources Information Center

    Tennant, Roy

    1992-01-01

    Explains how users can find and access information resources available on the Internet. Highlights include network information centers (NICs); lists, both formal and informal; computer networking protocols, including international standards; electronic mail; remote log-in; and file transfer. (LRW)

  11. Network Basics.

    ERIC Educational Resources Information Center

    Tennant, Roy

    1992-01-01

    Explains how users can find and access information resources available on the Internet. Highlights include network information centers (NICs); lists, both formal and informal; computer networking protocols, including international standards; electronic mail; remote log-in; and file transfer. (LRW)

  12. Network neuroscience.

    PubMed

    Bassett, Danielle S; Sporns, Olaf

    2017-02-23

    Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.

  13. Learning Analytics for Networked Learning Models

    ERIC Educational Resources Information Center

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

    2014-01-01

    Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

  14. Network science.

    PubMed

    Barabási, Albert-László

    2013-03-28

    Professor Barabási's talk described how the tools of network science can help understand the Web's structure, development and weaknesses. The Web is an information network, in which the nodes are documents (at the time of writing over one trillion of them), connected by links. Other well-known network structures include the Internet, a physical network where the nodes are routers and the links are physical connections, and organizations, where the nodes are people and the links represent communications.

  15. Norovirus in Healthcare Settings

    MedlinePlus

    ... about VAP Diseases and Organisms Acinetobacter Burkholderia cepacia Clostridium difficile Patients Clinicians FAQs about C. difficile for ... Facilities/Settings State Health Departments Tracking C. difficile Clostridium Sordellii Carbapenem-resistant Enterobacteriaceae (CRE) Tracking CRE Interim ...

  16. Artist Place Settings

    ERIC Educational Resources Information Center

    Pellegrino, Linda

    2009-01-01

    Art history can be a little dry at times, but the author is always trying to incorporate new ways of teaching it. In this article, she describes a project in which students were to create a place setting out of clay that had to be unified through a famous artist's style. This place setting had to consist of at least five pieces (dinner plate, cup…

  17. Artist Place Settings

    ERIC Educational Resources Information Center

    Pellegrino, Linda

    2009-01-01

    Art history can be a little dry at times, but the author is always trying to incorporate new ways of teaching it. In this article, she describes a project in which students were to create a place setting out of clay that had to be unified through a famous artist's style. This place setting had to consist of at least five pieces (dinner plate, cup…

  18. Integrated Networks.

    ERIC Educational Resources Information Center

    Robinovitz, Stewart

    1987-01-01

    A strategy for integrated data and voice networks implemented at the University of Michigan is described. These networks often use multi-technologies, multi-vendors, and multi-transmission media that will be fused into a single integrated network. Transmission media include twisted-pair wire, coaxial cable, fiber optics, and microwave. (Author/MLW)

  19. Integrated Networks.

    ERIC Educational Resources Information Center

    Robinovitz, Stewart

    1987-01-01

    A strategy for integrated data and voice networks implemented at the University of Michigan is described. These networks often use multi-technologies, multi-vendors, and multi-transmission media that will be fused into a single integrated network. Transmission media include twisted-pair wire, coaxial cable, fiber optics, and microwave. (Author/MLW)

  20. Wide Range SET Pulse Measurement

    NASA Technical Reports Server (NTRS)

    Shuler, Robert L.; Chen, Li

    2012-01-01

    small number of SETs were not significantly higher in the test over the control circuits. At higher LET the test circuit SETs are one or two orders of magnitude greater than for the control circuit. The NFET circuit produces more and slightly longer SETs as expected. But the differences do not appear to be significant enough to modify strategies now used to avoid capture of SETs in chips such as FPGAs. Complete data and graphs will be in the full paper / presentation. In the summary figure below left, NOCL is the reference circuit without any input, and number of stages triggered is plotted. Simulation at right shows the smallest pulse captured (stage 2) at about 300 ps. Our conclusion is that the method is promising, but that improvements in the merge network are desirable before applying in a deep submicron process

  1. Multicriteria identification sets method

    NASA Astrophysics Data System (ADS)

    Kamenev, G. K.

    2016-11-01

    A multicriteria identification and prediction method for mathematical models of simulation type in the case of several identification criteria (error functions) is proposed. The necessity of the multicriteria formulation arises, for example, when one needs to take into account errors of completely different origins (not reducible to a single characteristic) or when there is no information on the class of noise in the data to be analyzed. An identification sets method is described based on the approximation and visualization of the multidimensional graph of the identification error function and sets of suboptimal parameters. This method allows for additional advantages of the multicriteria approach, namely, the construction and visual analysis of the frontier and the effective identification set (frontier and the Pareto set for identification criteria), various representations of the sets of Pareto effective and subeffective parameter combinations, and the corresponding predictive trajectory tubes. The approximation is based on the deep holes method, which yields metric ɛ-coverings with nearly optimal properties, and on multiphase approximation methods for the Edgeworth-Pareto hull. The visualization relies on the approach of interactive decision maps. With the use of the multicriteria method, multiple-choice solutions of identification and prediction problems can be produced and justified by analyzing the stability of the optimal solution not only with respect to the parameters (robustness with respect to data) but also with respect to the chosen set of identification criteria (robustness with respect to the given collection of functionals).

  2. Setting conservation priorities.

    PubMed

    Wilson, Kerrie A; Carwardine, Josie; Possingham, Hugh P

    2009-04-01

    A generic framework for setting conservation priorities based on the principles of classic decision theory is provided. This framework encapsulates the key elements of any problem, including the objective, the constraints, and knowledge of the system. Within the context of this framework the broad array of approaches for setting conservation priorities are reviewed. While some approaches prioritize assets or locations for conservation investment, it is concluded here that prioritization is incomplete without consideration of the conservation actions required to conserve the assets at particular locations. The challenges associated with prioritizing investments through time in the face of threats (and also spatially and temporally heterogeneous costs) can be aided by proper problem definition. Using the authors' general framework for setting conservation priorities, multiple criteria can be rationally integrated and where, how, and when to invest conservation resources can be scheduled. Trade-offs are unavoidable in priority setting when there are multiple considerations, and budgets are almost always finite. The authors discuss how trade-offs, risks, uncertainty, feedbacks, and learning can be explicitly evaluated within their generic framework for setting conservation priorities. Finally, they suggest ways that current priority-setting approaches may be improved.

  3. Set theory and physics

    SciTech Connect

    Svozil, K.

    1995-11-01

    Inasmuch as physical theories are formalizable, set theory provides a framework for theoretical physics. Four speculations about the relevance of set theoretical modeling for physics are presented: the role of transcendental set theory (i) in chaos theory, (ii) for paradoxical decompositions of solid three-dimensional objects, (iii) in the theory of effective computability (Church-Turing thesis) related to the possible {open_quotes}solution of supertasks,{close_quotes} and (iv) for weak solutions. Several approaches to set theory and their advantages and disadvantages for physical applications are discussed: Cantorian {open_quotes}naive{close_quotes} (i.e., nonaxiomatic) set theory, contructivism, and operationalism. In the author`s opinion, an attitude, of {open_quotes}suspended attention{close_quotes} (a term borrowed from psychoanalysis) seems most promising for progress. Physical and set theoretical entities must be operationalized wherever possible. At the same time, physicists should be open to {open_quotes}bizarre{close_quotes} or {open_quotes}mindboggling{close_quotes} new formalisms, which need not be operationalizable or testable at the time of their creation, but which may successfully lead to novel fields of phenomenology and technology.

  4. Set theory and physics

    NASA Astrophysics Data System (ADS)

    Svozil, K.

    1995-11-01

    Inasmuch as physical theories are formalizable, set theory provides a framework for theoretical physics. Four speculations about the relevance of set theoretical modeling for physics are presented: the role of transcendental set theory (i) in chaos theory, (ii) for paradoxical decompositions of solid three-dimensional objects, (iii) in the theory of effective computability (Church-Turing thesis) related to the possible “solution of supertasks,” and (iv) for weak solutions. Several approaches to set theory and their advantages and disadvatages for physical applications are discussed: Canlorian “naive” (i.e., nonaxiomatic) set theory, contructivism, and operationalism. In the author's opinion, an attitude of “suspended attention” (a term borrowed from psychoanalysis) seems most promising for progress. Physical and set theoretical entities must be operationalized wherever possible. At the same time, physicists should be open to “bizarre” or “mindboggling” new formalisms, which need not be operationalizable or testable at the lime of their creation, but which may successfully lead to novel fields of phenomenology and technology.

  5. Inferring network structure from cascades

    NASA Astrophysics Data System (ADS)

    Ghonge, Sushrut; Vural, Dervis Can

    2017-07-01

    Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

  6. Controllability of the better chosen partial networks

    NASA Astrophysics Data System (ADS)

    Liu, Xueming; Pan, Linqiang

    2016-08-01

    How to control large complex networks is a great challenge. Recent studies have proved that the whole network can be sufficiently steered by injecting control signals into a minimum set of driver nodes, and the minimum numbers of driver nodes for many real networks are high, indicating that it is difficult to control them. For some large natural and technological networks, it is impossible and not feasible to control the full network. For example, in biological networks like large-scale gene regulatory networks it is impossible to control all the genes. This prompts us to explore the question how to choose partial networks that are easy for controlling and important in networked systems. In this work, we propose a method to achieve this goal. By computing the minimum driver nodes densities of the partial networks of Erdös-Rényi (ER) networks, scale-free (SF) networks and 23 real networks, we find that our method performs better than random method that chooses nodes randomly. Moreover, we find that the nodes chosen by our method tend to be the essential elements of the whole systems, via studying the nodes chosen by our method of a real human signaling network and a human protein interaction network and discovering that the chosen nodes from these networks tend to be cancer-associated genes. The implementation of our method shows some interesting connections between the structure and the controllability of networks, improving our understanding of the control principles of complex systems.

  7. Controlling centrality in complex networks

    PubMed Central

    Nicosia, V.; Criado, R.; Romance, M.; Russo, G.; Latora, V.

    2012-01-01

    Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes. PMID:22355732

  8. Attractor Metabolic Networks

    PubMed Central

    De la Fuente, Ildefonso M.; Cortes, Jesus M.; Pelta, David A.; Veguillas, Juan

    2013-01-01

    Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency

  9. Small-world networks

    NASA Astrophysics Data System (ADS)

    Strogatz, Steven

    Everyone is familiar with the small-world phenomenon: soon after meeting a stranger, we are often suprised to discover that we have a mutual friend, or that we are somehow linked by a short chain of friends. In this talk, I'll present evidence that the small-world phenomenon is more than a curiosity of social networks — it is actually a general property of large, sparse networks whose topology is neither completely regular nor completely random. To check this idea, Duncan Watts and I have analyzed three networks of scientific interest: the neural network of the nematode worm C. elegans, the electrical power grid of the western United States, and the collaboration graph of actors in feature films. All three are small worlds, in the sense that the average number of "handshakes" separating any two members is extremely small (close to the theoretical lower limit set by a random graph). Yet at the same time, all three networks exhibit much more local clustering than a random net, demonstrating that they are not random. I'll also discuss a class of model networks that interpolate between regular lattices and random graphs. Previous theoretical research on complex systems in a wide range of disciplines has focused almost exclusively on networks that are either regular or random. Real networks often lie somewhere in between. Our mathematical model shows that networks in this middle ground tend to exhibit the small-world phenomenon, thanks to the presence of a few long-range edges that link parts of the graph that would otherwise be far apart. Furthermore, we find that when various dynamical systems are coupled in a small-world fashion, they exhibit much greater propagation speed, computational power, and synchronizability than their locally connected, regular counterparts. We explore the implications of these results for simple models of disease spreading, global computation in cellular automata, and collective locking of biological oscillators.

  10. Novel gene sets improve set-level classification of prokaryotic gene expression data.

    PubMed

    Holec, Matěj; Kuželka, Ondřej; Železný, Filip

    2015-10-28

    Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

  11. Communications, Navigation, and Network Reconfigurable Test-bed Flight Hardware Compatibility Test S

    NASA Technical Reports Server (NTRS)

    2010-01-01

    Communications, Navigation, and Network Reconfigurable Test-bed Flight Hardware Compatibility Test Sets and Networks Integration Management Office Testing for the Tracking and Data Relay Satellite System

  12. Superelastic networks

    SciTech Connect

    Obukhov, S.P.; Rubinstein, M.; Colby, R.H.

    1993-12-31

    This paper discusses the elastic modulus, swelling, and deswelling behavior of networks as a function of their concentration and the preparation state. Based on these results, the authors expect that networks prepared by crosslinking long chains at low concentration, followed by removal of solvent, will have superelastic properties - the deswollen networks will have low modulus and will be capable of stretching by enormous amounts without breaking. This is because deswelling introduces only temporary entanglements. These temporary entanglements change the static configuration of the network strands. The authors discuss the non-Gaussian nature of these strands and the linear viscoelastic response of the superelastic networks.

  13. Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

    PubMed Central

    Kugler, Karl G.; Mueller, Laurin A. J.; Graber, Armin; Dehmer, Matthias

    2011-01-01

    Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure. PMID:21829532

  14. Control efficacy of complex networks

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-06-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.

  15. Control efficacy of complex networks

    PubMed Central

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks. PMID:27324438

  16. Vulnerability of network of networks

    NASA Astrophysics Data System (ADS)

    Havlin, S.; Kenett, D. Y.; Bashan, A.; Gao, J.; Stanley, H. E.

    2014-10-01

    Our dependence on networks - be they infrastructure, economic, social or others - leaves us prone to crises caused by the vulnerabilities of these networks. There is a great need to develop new methods to protect infrastructure networks and prevent cascade of failures (especially in cases of coupled networks). Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How, and at which cost can one restructure the network such that it will become more robust against malicious attacks? The gradual increase in attacks on the networks society depends on - Internet, mobile phone, transportation, air travel, banking, etc. - emphasize the need to develop new strategies to protect and defend these crucial networks of communication and infrastructure networks. One example is the threat of liquid explosives a few years ago, which completely shut down air travel for days, and has created extreme changes in regulations. Such threats and dangers warrant the need for new tools and strategies to defend critical infrastructure. In this paper we review recent advances in the theoretical understanding of the vulnerabilities of interdependent networks with and without spatial embedding, attack strategies and their affect on such networks of networks as well as recently developed strategies to optimize and repair failures caused by such attacks.

  17. Complex attentional control settings.

    PubMed

    Parrott, Stacey E; Levinthal, Brian R; Franconeri, Steven L

    2010-12-01

    The visual system prioritizes information through a variety of mechanisms, including "attentional control settings" that specify features (e.g., colour) that are relevant to current goals. Recent work shows that these control settings may be more complex than previously thought, such that participants can monitor for independent features at different locations (Adamo, Pun, Pratt, & Ferber, 2008). However, this result leaves unclear whether these control settings affect early attentional selection or later target processing. We dissociated between these possibilities in two ways. In Experiment 1, participants were asked to determine whether a target object, which was preceded by an uninformative cue, matched one of two target templates (e.g., a blue vertical object or a green horizontal object). Participants monitored for independent features in the same location, but in different objects, which should reduce the effectiveness of the control setting if it is due to early attentional selection, but not if it is due to later target processing. In Experiment 2, we removed the ability of the cue to prime the target identity, which makes the opposite prediction. Together, the results suggest that complex attentional control settings primarily affect later target identity processing, and not early attentional selection.

  18. Gene set analysis using variance component tests.

    PubMed

    Huang, Yen-Tsung; Lin, Xihong

    2013-06-28

    Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

  19. Gene set analysis using variance component tests

    PubMed Central

    2013-01-01

    Background Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. Results We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). Conclusion We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data. PMID:23806107

  20. Synthetic biological networks

    NASA Astrophysics Data System (ADS)

    Archer, Eric; Süel, Gürol M.

    2013-09-01

    Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics.

  1. Communications network analysis tool

    NASA Astrophysics Data System (ADS)

    Phillips, Wayne; Dunn, Gary

    1989-11-01

    The Communications Network Analysis Tool (CNAT) is a set of computer programs that aids in the performance evaluation of a communication system in a real-world scenario. Communication network protocols can be modeled and battle group connectivity can be analyzed in the presence of jamming and the benefit of relay platforms can be studied. The Joint Tactical Information Distribution System (JTIDS) Communication system architecture is currently being modeled; however, the computer software is modular enough to allow substitution of a new code representative of prospective communication protocols.

  2. Topological entropy of catalytic sets: Hypercycles revisited

    NASA Astrophysics Data System (ADS)

    Sardanyés, Josep; Duarte, Jorge; Januário, Cristina; Martins, Nuno

    2012-02-01

    The dynamics of catalytic networks have been widely studied over the last decades because of their implications in several fields like prebiotic evolution, virology, neural networks, immunology or ecology. One of the most studied mathematical bodies for catalytic networks was initially formulated in the context of prebiotic evolution, by means of the hypercycle theory. The hypercycle is a set of self-replicating species able to catalyze other replicator species within a cyclic architecture. Hypercyclic organization might arise from a quasispecies as a way to increase the informational containt surpassing the so-called error threshold. The catalytic coupling between replicators makes all the species to behave like a single and coherent evolutionary multimolecular unit. The inherent nonlinearities of catalytic interactions are responsible for the emergence of several types of dynamics, among them, chaos. In this article we begin with a brief review of the hypercycle theory focusing on its evolutionary implications as well as on different dynamics associated to different types of small catalytic networks. Then we study the properties of chaotic hypercycles with error-prone replication with symbolic dynamics theory, characterizing, by means of the theory of topological Markov chains, the topological entropy and the periods of the orbits of unimodal-like iterated maps obtained from the strange attractor. We will focus our study on some key parameters responsible for the structure of the catalytic network: mutation rates, autocatalytic and cross-catalytic interactions.

  3. Short pulse test set

    NASA Astrophysics Data System (ADS)

    1990-11-01

    This report discusses the construction and operation of the Short Pulse Test Set that has been built for the U.S. Army Missile Command for the purpose of applying short (25 to 100 nanosecond), high voltage pulses to electronic explosive devices (EEDs) in both the pin-to-pin and pins-to-case mode. The test set employs the short pulse generating techniques first described in the Franklin Institute Research Laboratories (now Franklin Research Center) Report I-C3410, 'Pins-to-Case Short Pulse Sensitivity Studies for the Atlas DC Switch', December 1974. This report, authored by Ramie H. Thompson, was prepared for Picatinny Arsenal under contract DAAA21-72C-0766. The test set described herein utilizes a computer controlled high speed digitizer to monitor the pulse voltage and current and provides software to process and display these data.

  4. Associative memory in phasing neuron networks

    SciTech Connect

    Nair, Niketh S; Bochove, Erik J.; Braiman, Yehuda

    2014-01-01

    We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

  5. Neural-Network Object-Recognition Program

    NASA Technical Reports Server (NTRS)

    Spirkovska, L.; Reid, M. B.

    1993-01-01

    HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

  6. Optimizing Nutrient Uptake in Biological Transport Networks

    NASA Astrophysics Data System (ADS)

    Ronellenfitsch, Henrik; Katifori, Eleni

    2013-03-01

    Many biological systems employ complex networks of vascular tubes to facilitate transport of solute nutrients, examples include the vascular system of plants (phloem), some fungi, and the slime-mold Physarum. It is believed that such networks are optimized through evolution for carrying out their designated task. We propose a set of hydrodynamic governing equations for solute transport in a complex network, and obtain the optimal network architecture for various classes of optimizing functionals. We finally discuss the topological properties and statistical mechanics of the resulting complex networks, and examine correspondence of the obtained networks to those found in actual biological systems.

  7. Benford's Distribution in Complex Networks.

    PubMed

    Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K

    2016-10-17

    Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford's distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford's law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford's law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford's distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford's law for scalefree networks but only for very narrow range of their parameters.

  8. Networks of strong ties

    NASA Astrophysics Data System (ADS)

    Shi, Xiaolin; Adamic, Lada A.; Strauss, Martin J.

    2007-05-01

    Social networks transmitting covert or sensitive information cannot use all ties for this purpose. Rather, they can only use a subset of ties that are strong enough to be “trusted”. This paper addresses whether it is still possible, under this restriction, for information to be transmitted widely and rapidly in social networks. We use transitivity as evidence of strong ties, requiring one or more shared contacts in order to count an edge as strong. We examine the effect of removing all non-transitive ties in two real social network data sets, imposing varying thresholds in the number of shared contacts. We observe that transitive ties occupy a large portion of the network and that removing all other ties, while causing some individuals to become disconnected, preserves the majority of the giant connected component. Furthermore, the average shortest path, important for the rapid diffusion of information, increases only slightly relative to the original network. We also evaluate the cost of forming transitive ties by modeling a random graph composed entirely of closed triads and comparing its connectivity and average shortest path with the equivalent Erdös-Renyi random graph. Both the empirical study and random model point to a robustness of strong ties with respect to the connectivity and small world property of social networks.

  9. Statistically Validated Networks in Bipartite Complex Systems

    PubMed Central

    Tumminello, Michele; Miccichè, Salvatore; Lillo, Fabrizio; Piilo, Jyrki; Mantegna, Rosario N.

    2011-01-01

    Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved. PMID:21483858

  10. Network Monitor and Control of Disruption-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Torgerson, J. Leigh

    2014-01-01

    For nearly a decade, NASA and many researchers in the international community have been developing Internet-like protocols that allow for automated network operations in networks where the individual links between nodes are only sporadically connected. A family of Disruption-Tolerant Networking (DTN) protocols has been developed, and many are reaching CCSDS Blue Book status. A NASA version of DTN known as the Interplanetary Overlay Network (ION) has been flight-tested on the EPOXI spacecraft and ION is currently being tested on the International Space Station. Experience has shown that in order for a DTN service-provider to set up a large scale multi-node network, a number of network monitor and control technologies need to be fielded as well as the basic DTN protocols. The NASA DTN program is developing a standardized means of querying a DTN node to ascertain its operational status, known as the DTN Management Protocol (DTNMP), and the program has developed some prototypes of DTNMP software. While DTNMP is a necessary component, it is not sufficient to accomplish Network Monitor and Control of a DTN network. JPL is developing a suite of tools that provide for network visualization, performance monitoring and ION node control software. This suite of network monitor and control tools complements the GSFC and APL-developed DTN MP software, and the combined package can form the basis for flight operations using DTN.

  11. Network Monitor and Control of Disruption-Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Torgerson, J. Leigh

    2014-01-01

    For nearly a decade, NASA and many researchers in the international community have been developing Internet-like protocols that allow for automated network operations in networks where the individual links between nodes are only sporadically connected. A family of Disruption-Tolerant Networking (DTN) protocols has been developed, and many are reaching CCSDS Blue Book status. A NASA version of DTN known as the Interplanetary Overlay Network (ION) has been flight-tested on the EPOXI spacecraft and ION is currently being tested on the International Space Station. Experience has shown that in order for a DTN service-provider to set up a large scale multi-node network, a number of network monitor and control technologies need to be fielded as well as the basic DTN protocols. The NASA DTN program is developing a standardized means of querying a DTN node to ascertain its operational status, known as the DTN Management Protocol (DTNMP), and the program has developed some prototypes of DTNMP software. While DTNMP is a necessary component, it is not sufficient to accomplish Network Monitor and Control of a DTN network. JPL is developing a suite of tools that provide for network visualization, performance monitoring and ION node control software. This suite of network monitor and control tools complements the GSFC and APL-developed DTN MP software, and the combined package can form the basis for flight operations using DTN.

  12. The Crystal Set

    ERIC Educational Resources Information Center

    Greenslade, Thomas B., Jr.

    2014-01-01

    In past issues of this journal, the late H. R. Crane wrote a long series of articles under the running title of "How Things Work." In them, Dick dealt with many questions that physics teachers asked themselves, but did not have the time to answer. This article is my attempt to work through the physics of the crystal set, which I thought…

  13. Therapists in Oncology Settings

    ERIC Educational Resources Information Center

    Hendrick, Susan S.

    2013-01-01

    This article describes the author's experiences of working with cancer patients/survivors both individually and in support groups for many years, across several settings. It also documents current best-practice guidelines for the psychosocial treatment of cancer patients/survivors and their families. The author's view of the important qualities…

  14. Goal Setting and Hope

    ERIC Educational Resources Information Center

    Curran, Katie; Reivich, Karen

    2011-01-01

    The science behind the mechanisms and mediators that lead to successful goal accomplishment has been a focus of research since the 1970s. When an individual desires to make a change or accomplish an outcome, research shows that he or she will be more successful if he or she attends to a number of variables that are key in goal setting.…

  15. Diffusion on Delone sets

    NASA Astrophysics Data System (ADS)

    Haeseler, Sebastian; Huang, Xueping; Lenz, Daniel; Pogorzelski, Felix

    2017-06-01

    We consider graphs associated to Delone sets in Euclidean space. Such graphs arise in various ways from tilings. Here, we provide a unified framework. In this context, we study the associated Laplace operators and show Gaussian heat kernel bounds for their semigroups. These results apply to both metric and discrete graphs.

  16. Setting Environmental Standards

    ERIC Educational Resources Information Center

    Fishbein, Gershon

    1975-01-01

    Recent court decisions have pointed out the complexities involved in setting environmental standards. Environmental health is composed of multiple causative agents, most of which work over long periods of time. This makes the cause-and-effect relationship between health statistics and environmental contaminant exposures difficult to prove in…

  17. Therapists in Oncology Settings

    ERIC Educational Resources Information Center

    Hendrick, Susan S.

    2013-01-01

    This article describes the author's experiences of working with cancer patients/survivors both individually and in support groups for many years, across several settings. It also documents current best-practice guidelines for the psychosocial treatment of cancer patients/survivors and their families. The author's view of the important qualities…

  18. The Crystal Set

    ERIC Educational Resources Information Center

    Greenslade, Thomas B., Jr.

    2014-01-01

    In past issues of this journal, the late H. R. Crane wrote a long series of articles under the running title of "How Things Work." In them, Dick dealt with many questions that physics teachers asked themselves, but did not have the time to answer. This article is my attempt to work through the physics of the crystal set, which I thought…

  19. Goal Setting and Hope

    ERIC Educational Resources Information Center

    Curran, Katie; Reivich, Karen

    2011-01-01

    The science behind the mechanisms and mediators that lead to successful goal accomplishment has been a focus of research since the 1970s. When an individual desires to make a change or accomplish an outcome, research shows that he or she will be more successful if he or she attends to a number of variables that are key in goal setting.…

  20. Building Temperature Set Point

    SciTech Connect

    Meincke, Carol L.; Evans, Christopher A.

    2014-09-01

    This white paper provides information and recommendations for an actionable and enforceable corporate policy statement on temperature set points for office and related spaces at Sandia and presents a strategy that balances the need to achieve the energy goals with optimizing employee comfort and productivity.

  1. Probabilistic Open Set Recognition

    NASA Astrophysics Data System (ADS)

    Jain, Lalit Prithviraj

    Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

  2. Neural Networks

    DTIC Science & Technology

    1990-01-01

    FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT NO. NO. NO. ACCESSION NO 11 TITLE (Include Security Classification) NEURAL NETWORKS 12. PERSONAL...SUB-GROUP Neural Networks Optical Architectures Nonlinear Optics Adaptation 19. ABSTRACT (Continue on reverse if necessary and identify by block number...341i Y C-odes , lo iii/(iv blank) 1. INTRODUCTION Neural networks are a type of distributed processing system [1

  3. Characterizing Network Services through Cluster-Set Variations

    SciTech Connect

    Bartoletti, A; Tang, N

    2005-03-23

    Common Internet services can be reliably distinguished based solely upon the locations of clusters in traffic-based features (ratios of inbound to outbound packets, ratios of packets to payloads, etc.) This capability has value in revealing the nature of ''hidden'' (tunneled) services and in detecting anomalous changes to known services. We provide measures of session capture volumes sufficient to make confidence-level assertions regarding ''unknown'' services, and outline a throughput system for providing alarms for service anomalies.

  4. Setting conservation priorities for migratory networks under uncertainty.

    PubMed

    Dhanjal-Adams, Kiran L; Klaassen, Marcel; Nicol, Sam; Possingham, Hugh P; Chadès, Iadine; Fuller, Richard A

    2017-06-01

    Conserving migratory species requires protecting connected habitat along the pathways they travel. Despite recent improvements in tracking animal movements, migratory connectivity remains poorly resolved at a population level for the vast majority of species, thus conservation prioritization is hampered. To address this data limitation, we developed a novel approach to spatial prioritization based on a model of potential connectivity derived from empirical data on species abundance and distance traveled between sites during migration. We applied the approach to migratory shorebirds of the East Asian-Australasian Flyway. Conservation strategies that prioritized sites based on connectivity and abundance metrics together maintained larger populations of birds than strategies that prioritized sites based only on abundance metrics. The conservation value of a site therefore depended on both its capacity to support migratory animals and its position within the migratory pathway; the loss of crucial sites led to partial or total population collapse. We suggest that conservation approaches that prioritize sites supporting large populations of migrants should, where possible, also include data on the spatial arrangement of sites. © 2016 Society for Conservation Biology.

  5. Registration of multiple image sets with thin-plate spline

    NASA Astrophysics Data System (ADS)

    He, Liang; Houk, James C.

    1994-09-01

    A thin-plate spline method for spatial warping was used to register multiple image sets during 3D reconstruction of histological sections. In a neuroanatomical study, the same labeling method was applied to several turtle brains. Each case produced a set of microscopic sections. Spatial warping was employed to map data sets from multiple cases onto a template coordinate system. This technique enabled us to produce an anatomical reconstruction of a neural network that controls limb movement.

  6. Risk and reliability assessment for telecommunications networks

    SciTech Connect

    Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.

    1996-08-01

    Sandia National Laboratories has assembled an interdisciplinary team to explore the applicability of probabilistic logic modeling (PLM) techniques to model network reliability for a wide variety of communications network architectures. The authors have found that the reliability and failure modes of current generation network technologies can be effectively modeled using fault tree PLM techniques. They have developed a ``plug-and-play`` fault tree analysis methodology that can be used to model connectivity and the provision of network services in a wide variety of current generation network architectures. They have also developed an efficient search algorithm that can be used to determine the minimal cut sets of an arbitrarily-interconnected (non-hierarchical) network without the construction of a fault tree model. This paper provides an overview of these modeling techniques and describes how they are applied to networks that exhibit hybrid network structures (i.e., a network in which some areas are hierarchical and some areas are not hierarchical).

  7. Constrained target controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Guo, Wei-Feng; Zhang, Shao-Wu; Wei, Ze-Gang; Zeng, Tao; Liu, Fei; Zhang, Jingsong; Wu, Fang-Xiang; Chen, Luonan

    2017-06-01

    It is of great theoretical interest and practical significance to study how to control a system by applying perturbations to only a few driver nodes. Recently, a hot topic of modern network researches is how to determine driver nodes that allow the control of an entire network. However, in practice, to control a complex network, especially a biological network, one may know not only the set of nodes which need to be controlled (i.e. target nodes), but also the set of nodes to which only control signals can be applied (i.e. constrained control nodes). Compared to the general concept of controllability, we introduce the concept of constrained target controllability (CTC) of complex networks, which concerns the ability to drive any state of target nodes to their desirable state by applying control signals to the driver nodes from the set of constrained control nodes. To efficiently investigate the CTC of complex networks, we further design a novel graph-theoretic algorithm called CTCA to estimate the ability of a given network to control targets by choosing driver nodes from the set of constrained control nodes. We extensively evaluate the CTC of numerous real complex networks. The results indicate that biological networks with a higher average degree are easier to control than biological networks with a lower average degree, while electronic networks with a lower average degree are easier to control than web networks with a higher average degree. We also show that our CTCA can more efficiently produce driver nodes for target-controlling the networks than existing state-of-the-art methods. Moreover, we use our CTCA to analyze two expert-curated bio-molecular networks and compare to other state-of-the-art methods. The results illustrate that our CTCA can efficiently identify proven drug targets and new potentials, according to the constrained controllability of those biological networks.

  8. Network reliability

    NASA Technical Reports Server (NTRS)

    Johnson, Marjory J.

    1985-01-01

    Network control (or network management) functions are essential for efficient and reliable operation of a network. Some control functions are currently included as part of the Open System Interconnection model. For local area networks, it is widely recognized that there is a need for additional control functions, including fault isolation functions, monitoring functions, and configuration functions. These functions can be implemented in either a central or distributed manner. The Fiber Distributed Data Interface Medium Access Control and Station Management protocols provide an example of distributed implementation. Relative information is presented here in outline form.

  9. Innovation Networks

    NASA Astrophysics Data System (ADS)

    Pyka, Andreas; Scharnhorst, Andrea

    The idea for this book started when we organized a topical workshop entitled "Innovation Networks - New Approaches in Modeling and Analyzing" (held in Augsburg, Germany in October 2005), under the auspices of Exystence, a network of excellence funded in the European Union's Fifth Framework Program. Unlike other conferences on innovation and networks, however, this workshop brought together scientists from economics, sociology, communication science, science and technology studies, and physics. With this book we aim to build further on a bridge connecting the bodies of knowledge on networks in economics, the social sciences and, more recently, statistical physics.

  10. The correlation of metrics in complex networks with applications in functional brain networks

    NASA Astrophysics Data System (ADS)

    Li, C.; Wang, H.; de Haan, W.; Stam, C. J.; Van Mieghem, P.

    2011-11-01

    An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi-Albert graphs, Erdös-Rényi random graphs and Watts-Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös-Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.

  11. Robust distribution network reconfiguration

    SciTech Connect

    Lee, Changhyeok; Liu, Cong; Mehrotra, Sanjay; Bie, Zhaohong

    2015-03-01

    We propose a two-stage robust optimization model for the distribution network reconfiguration problem with load uncertainty. The first-stage decision is to configure the radial distribution network and the second-stage decision is to find the optimal a/c power flow of the reconfigured network for given demand realization. We solve the two-stage robust model by using a column-and-constraint generation algorithm, where the master problem and subproblem are formulated as mixed-integer second-order cone programs. Computational results for 16, 33, 70, and 94-bus test cases are reported. We find that the configuration from the robust model does not compromise much the power loss under the nominal load scenario compared to the configuration from the deterministic model, yet it provides the reliability of the distribution system for all scenarios in the uncertainty set.

  12. Leadership in School Networks: Findings from the Networked Learning Communities Programme

    ERIC Educational Resources Information Center

    Jopling, Michael; Spender, Barbara

    2006-01-01

    The Networked Learning Community (NLC) programme has been a major research and development activity of the National College of School Leadership (NCSL). One hundred and four voluntary, funded school networks were set up following a process of bidding and selection operated by a core NCSL team, the Networked Learning Group (NLG). A second tranche…

  13. Deploying temporary networks for upscaling of sparse network stations

    NASA Astrophysics Data System (ADS)

    Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

    2016-10-01

    Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

  14. Marihuana and setting.

    PubMed

    Hollister, L E; Overall, J E; Gerber, M L

    1975-06-01

    Marihuana or placebo cigarettes were smoked by 12 subjects in two environments, one "favorable" and one "neutral". The object was to determine the contribution of setting to the effects reported from the drug. Two quantifiable self-report measurements, the linear euphoriant scale and the card-sort version of the Addiction Research Center Inventory (marihuana and hallucinogen scales), were the major reporting criteria. Analyses of variance consistently demonstrated strong effects for subjects and drug but not for the environmental conditions. Reports of marihuana effects may be assumed to be highly colored by psychological differences in the mental set of subjects, or biological variations in their responses to the drug. The actual environment in which the drug is taken seems to play little, if any, role.

  15. Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

    PubMed Central

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database. PMID:25392692

  16. A flood-based information flow analysis and network minimization method for gene regulatory networks.

    PubMed

    Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

    2013-04-24

    Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

  17. Network Sampling and Classification:An Investigation of Network Model Representations

    PubMed Central

    Airoldi, Edoardo M.; Bai, Xue; Carley, Kathleen M.

    2011-01-01

    Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of network metrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed. PMID:21666773

  18. The Crystal Set

    NASA Astrophysics Data System (ADS)

    Greenslade, Thomas B.

    2014-04-01

    In past issues of this journal, the late H. R. Crane wrote a long series of articles under the running title of "How Things Work." In them, Dick dealt with many questions that physics teachers asked themselves, but did not have the time to answer. This article is my attempt to work through the physics of the crystal set, which I thought I knew, but actually did not.

  19. The Moon has set

    NASA Astrophysics Data System (ADS)

    Herschberg, I. S.; Mebius, J. E.

    1989-08-01

    The Sappho epigram mentioned in the title is shown to contain implicit astronomical information, which must have contributed to the expressiveness of Sappho's short poem to contemporary audiences. Astronomical computations are given to discover the earliest and the latest time of year for which the Pleiads set at midnight while being visible earlier in the evening, taking into account the atmospheric refraction. The time of year for which Sappho's poem is valid is concluded to run from 17 Jan. to 29 Mar.

  20. A neural computation approach to the set covering problem

    SciTech Connect

    Grossman, T.

    1995-07-01

    This paper presents a neural network algorithm which is capable of finding approximate solutions for unicost set covering problems. The network has two types of units (neurons), with different dynamics and activation functions. One type represents the objects to be covered (the rows in the matrix representation of the problem) and another represents the ``covering`` sets (the 0,1 variables). They are connected as a bipartite graph which represents the incidence relations between objects and sets (i.e the 0,1 adjacency matrix). When the parameters of the units are correctly tuned, the stable states of the system correspond to the minimal covers. I show that in its basic mode of operation, descent dynamics, when the network is set in an arbitrary initial state it converges in less than 2n steps (where n is the number of variables), to a stable state which represents a valid solution. In this mode, the network implements a greedy heuristic in which the choice function is based on the unit inputs (which are determined by the activation functions and the network state). On top of the basic network dynamics, the algorithm applies an adaptive restart procedure which helps to search more effectively for ``good`` initial states and results in better performance.

  1. Triage in military settings.

    PubMed

    Falzone, E; Pasquier, P; Hoffmann, C; Barbier, O; Boutonnet, M; Salvadori, A; Jarrassier, A; Renner, J; Malgras, B; Mérat, S

    2017-02-01

    Triage, a medical term derived from the French word "trier", is the practical process of sorting casualties to rationally allocate limited resources. In combat settings with limited medical resources and long transportation times, triage is challenging since the objectives are to avoid overcrowding medical treatment facilities while saving a maximum of soldiers and to get as many of them back into action as possible. The new face of modern warfare, asymmetric and non-conventional, has led to the integrative evolution of triage into the theatre of operations. This article defines different triage scores and algorithms currently implemented in military settings. The discrepancies associated with these military triage systems are highlighted. The assessment of combat casualty severity requires several scores and each nation adopts different systems for triage on the battlefield with the same aim of quickly identifying those combat casualties requiring lifesaving and damage control resuscitation procedures. Other areas of interest for triage in military settings are discussed, including predicting the need for massive transfusion, haemodynamic parameters and ultrasound exploration.

  2. LMSS communication network design

    NASA Technical Reports Server (NTRS)

    1982-01-01

    The architecture of the telecommunication network as the first step in the design of the LMSS system is described. A set of functional requirements including the total number of users to be served by the LMSS are hypothesized. The design parameters are then defined at length and are systematically selected such that the resultant system is capable of serving the hypothesized number of users. The design of the backhaul link is presented. The number of multiple backhaul beams required for communication to the base stations is determined. A conceptual procedure for call-routing and locating a mobile subscriber within the LMSS network is presented. The various steps in placing a call are explained, and the relationship between the two sets of UHF and S-band multiple beams is developed. A summary of the design parameters is presented.

  3. Bounds for phylogenetic network space metrics.

    PubMed

    Francis, Andrew; Huber, Katharina T; Moulton, Vincent; Wu, Taoyang

    2017-08-23

    Phylogenetic networks are a generalization of phylogenetic trees that allow for representation of reticulate evolution. Recently, a space of unrooted phylogenetic networks was introduced, where such a network is a connected graph in which every vertex has degree 1 or 3 and whose leaf-set is a fixed set X of taxa. This space, denoted [Formula: see text], is defined in terms of two operations on networks-the nearest neighbor interchange and triangle operations-which can be used to transform any network with leaf set X into any other network with that leaf set. In particular, it gives rise to a metric d on [Formula: see text] which is given by the smallest number of operations required to transform one network in [Formula: see text] into another in [Formula: see text]. The metric generalizes the well-known NNI-metric on phylogenetic trees which has been intensively studied in the literature. In this paper, we derive a bound for the metric d as well as a related metric [Formula: see text] which arises when restricting d to the subset of [Formula: see text] consisting of all networks with [Formula: see text] vertices, [Formula: see text]. We also introduce two new metrics on networks-the SPR and TBR metrics-which generalize the metrics on phylogenetic trees with the same name and give bounds for these new metrics. We expect our results to eventually have applications to the development and understanding of network search algorithms.

  4. Setting Goals for Achievement in Physical Education Settings

    ERIC Educational Resources Information Center

    Baghurst, Timothy; Tapps, Tyler; Kensinger, Weston

    2015-01-01

    Goal setting has been shown to improve student performance, motivation, and task completion in academic settings. Although goal setting is utilized by many education professionals to help students set realistic and proper goals, physical educators may not be using goal setting effectively. Without incorporating all three types of goals and…

  5. Setting Goals for Achievement in Physical Education Settings

    ERIC Educational Resources Information Center

    Baghurst, Timothy; Tapps, Tyler; Kensinger, Weston

    2015-01-01

    Goal setting has been shown to improve student performance, motivation, and task completion in academic settings. Although goal setting is utilized by many education professionals to help students set realistic and proper goals, physical educators may not be using goal setting effectively. Without incorporating all three types of goals and…

  6. A neural network prototyping package within IRAF

    NASA Technical Reports Server (NTRS)

    Bazell, D.; Bankman, I.

    1992-01-01

    We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

  7. Cognitive Networks

    DTIC Science & Technology

    2007-06-15

    networks have the potential to change this trend by adding intelligence to the network. This work introduces the concept and provides a foundation for future ...of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 7.2 Future Work...CRs) could interact within the system-level scope of a CN. Saracco [9] refers to CNs in his investigation into the future of information technology

  8. Network Flows

    DTIC Science & Technology

    1988-12-01

    Researchers have suggested other solution strategies, using ideas from nonlinear progamming for solving this general separable convex cost flow problems. Some...plane methods and branch and bound procedures of integer programming, primal-dual methods of linear and nonlinear programming, and polyhedral methods...Combinatorial Optimization: Networks and Matroids), Bazaraa and Jarvis [1978] (Linear Programming and Network Flows), Minieka [1978] (Optimization Algorithms for

  9. Temporal networks

    NASA Astrophysics Data System (ADS)

    Holme, Petter; Saramäki, Jari

    2012-10-01

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

  10. Bimodality in Network Control

    NASA Astrophysics Data System (ADS)

    Jia, Tao; Liu, Yang-Yu; Posfai, Marton; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2013-03-01

    Controlling complex systems is a fundamental challenge of network science. Recent tools enable us to identify the minimum driver nodes, from which we can control a system. They also indicate a multiplicity of minimum driver node sets (MDS's): multiple combinations of the same number of nodes can achieve control over the system. This multiplicity allows us to classify individual nodes as critical if they are involved in all control configurations, intermittent if they occasionally act as driver nodes and redundant if they do not play any role in control. We develop computational and analytical framework analyzing nodes in each category in both model and real networks. We find that networks with identical degree distribution can be in two distinct control modes, ``centralized'' or ``distributed'', with drastic change on the role of each node in maintaining the controllability and orders of magnitude difference in the number of MDS's. In analyzing both model and real networks, we find that the two modes can be inferred directly from the network's degree distribution. Finally we show that the two control modes can be switched by small structural perturbations, leading to potential applications of control theory in real systems.

  11. Wireless local area network security.

    PubMed

    Bergeron, Bryan P

    2004-01-01

    Wireless local area networks (WLANs) are increasingly popular in clinical settings because they facilitate the use of wireless PDAs, laptops, and other pervasive computing devices at the point of care. However, because of the relative immaturity of wireless network technology and evolving standards, WLANs, if improperly configured, can present significant security risks. Understanding the security limitations of the technology and available fixes can help minimize the risks of clinical data loss and maintain compliance with HIPAA guidelines.

  12. Policy issues in interconnecting networks

    NASA Technical Reports Server (NTRS)

    Leiner, Barry M.

    1989-01-01

    To support the activities of the Federal Research Coordinating Committee (FRICC) in creating an interconnected set of networks to serve the research community, two workshops were held to address the technical support of policy issues that arise when interconnecting such networks. The workshops addressed the required and feasible technologies and architectures that could be used to satisfy the desired policies for interconnection. The results of the workshop are documented.

  13. Osprey: a network visualization system.

    PubMed

    Breitkreutz, Bobby-Joe; Stark, Chris; Tyers, Mike

    2002-01-01

    We have developed a software platform called Osprey for visualization and manipulation of complex interaction networks. Osprey builds data-rich graphical representations that are color-coded for gene function and experimental interaction data. Mouse-over functions allow rapid elaboration and organization of network diagrams in a spoke model format. User-defined large-scale data sets can be readily combined with Osprey for comparison of different methods.

  14. Policies for implementing network firewalls

    SciTech Connect

    Brown, C.D.

    1994-05-01

    Corporate networks are frequently protected by {open_quotes}firewalls{close_quotes} or gateway systems that control access to/from other networks, e.g., the Internet, in order to reduce the network`s vulnerability to hackers and other unauthorized access. Firewalls typically limit access to particular network nodes and application protocols, and they often perform special authentication and authorization functions. One of the difficult issues associated with network firewalls is determining which applications should be permitted through the firewall. For example, many networks permit the exchange of electronic mail with the outside but do not permit file access to be initiated by outside users, as this might allow outside users to access sensitive data or to surreptitiously modify data or programs (e.g., to intall Trojan Horse software). However, if access through firewalls is severely restricted, legitimate network users may find it difficult or impossible to collaborate with outside users and to share data. Some of the most serious issues regarding firewalls involve setting policies for firewalls with the goal of achieving an acceptable balance between the need for greater functionality and the associated risks. Two common firewall implementation techniques, screening routers and application gateways, are discussed below, followed by some common policies implemented by network firewalls.

  15. Correlations in star networks: from Bell inequalities to network inequalities

    NASA Astrophysics Data System (ADS)

    Tavakoli, Armin; Olivier Renou, Marc; Gisin, Nicolas; Brunner, Nicolas

    2017-07-01

    The problem of characterizing classical and quantum correlations in networks is considered. Contrary to the usual Bell scenario, where distant observers share a physical system emitted by one common source, a network features several independent sources, each distributing a physical system to a subset of observers. In the quantum setting, the observers can perform joint measurements on initially independent systems, which may lead to strong correlations across the whole network. In this work, we introduce a technique to systematically map a Bell inequality to a family of Bell-type inequalities bounding classical correlations on networks in a star-configuration. Also, we show that whenever a given Bell inequality can be violated by some entangled state ρ, then all the corresponding network inequalities can be violated by considering many copies of ρ distributed in the star network. The relevance of these ideas is illustrated by applying our method to a specific multi-setting Bell inequality. We derive the corresponding network inequalities, and study their quantum violations.

  16. Modeling Unreliable Observations in Bayesian Networks by Credal Networks

    NASA Astrophysics Data System (ADS)

    Antonucci, Alessandro; Piatti, Alberto

    Bayesian networks are probabilistic graphical models widely employed in AI for the implementation of knowledge-based systems. Standard inference algorithms can update the beliefs about a variable of interest in the network after the observation of some other variables. This is usually achieved under the assumption that the observations could reveal the actual states of the variables in a fully reliable way. We propose a procedure for a more general modeling of the observations, which allows for updating beliefs in different situations, including various cases of unreliable, incomplete, uncertain and also missing observations. This is achieved by augmenting the original Bayesian network with a number of auxiliary variables corresponding to the observations. For a flexible modeling of the observational process, the quantification of the relations between these auxiliary variables and those of the original Bayesian network is done by credal sets, i.e., convex sets of probability mass functions. Without any lack of generality, we show how this can be done by simply estimating the bounds of likelihoods of the observations for the different values of the observed variables. Overall, the Bayesian network is transformed into a credal network, for which a standard updating problem has to be solved. Finally, a number of transformations that might simplify the updating of the resulting credal network is provided.

  17. Attractors: architects of network organization?

    PubMed

    Mpitsos, G J

    2000-05-01

    An attractor is defined here informally as a state of activity toward which a system settles. The settling or relaxation process dissipates the effects produced by external perturbations. In neural systems the relaxation process occurs temporally in the responses of each neuron and spatially across the network such that the activity settles into a subset of the available connections. Within limits, the set of neurons toward which the coordinated neural firing settles can be different from one time to another, and a given set of neurons can generate different types of attractor activity, depending on how the input environment activates the network. Findings such as these indicate that though information resides in the details of neuroanatomic structure, the expression of this information is in the dynamics of attractors. As such, attractors are sources of information that can be used not only in adaptive behavior, but also to effect the neural architecture that generates the attractor. The discussion here focuses on the latter possibility. A conjecture is offered to show that the relaxation dynamic of an attractor may 'guide' activity-dependent learning processes in such a way that synaptic strengths, firing thresholds, the physical connections between neurons, and the size of the network are automatically set in an optimal, interrelated fashion. This inter-relatedness among network parameters would not be expected from more classical, 'switchboard' approaches to neural integration. The ideas are discussed within the context of 'pulse-propagated networks' or equivalently as 'spike-activated networks' in which the specific order in time intervals between action potentials carries important information for cooperative activity to emerge among neurons in a network. Though the proposed ideas are forward-looking, being based on preliminary work in biological and artificial networks, they are testable in biological neural networks reconstructed from identified neurons in

  18. Binets: Fundamental Building Blocks for Phylogenetic Networks.

    PubMed

    van Iersel, Leo; Moulton, Vincent; de Swart, Eveline; Wu, Taoyang

    2017-05-01

    Phylogenetic networks are a generalization of evolutionary trees that are used by biologists to represent the evolution of organisms which have undergone reticulate evolution. Essentially, a phylogenetic network is a directed acyclic graph having a unique root in which the leaves are labelled by a given set of species. Recently, some approaches have been developed to construct phylogenetic networks from collections of networks on 2- and 3-leaved networks, which are known as binets and trinets, respectively. Here we study in more depth properties of collections of binets, one of the simplest possible types of networks into which a phylogenetic network can be decomposed. More specifically, we show that if a collection of level-1 binets is compatible with some binary network, then it is also compatible with a binary level-1 network. Our proofs are based on useful structural results concerning lowest stable ancestors in networks. In addition, we show that, although the binets do not determine the topology of the network, they do determine the number of reticulations in the network, which is one of its most important parameters. We also consider algorithmic questions concerning binets. We show that deciding whether an arbitrary set of binets is compatible with some network is at least as hard as the well-known graph isomorphism problem. However, if we restrict to level-1 binets, it is possible to decide in polynomial time whether there exists a binary network that displays all the binets. We also show that to find a network that displays a maximum number of the binets is NP-hard, but that there exists a simple polynomial-time 1/3-approximation algorithm for this problem. It is hoped that these results will eventually assist in the development of new methods for constructing phylogenetic networks from collections of smaller networks.

  19. Index Sets and Vectorization

    SciTech Connect

    Keasler, J A

    2012-03-27

    Vectorization is data parallelism (SIMD, SIMT, etc.) - extension of ISA enabling the same instruction to be performed on multiple data items simultaeously. Many/most CPUs support vectorization in some form. Vectorization is difficult to enable, but can yield large efficiency gains. Extra programmer effort is required because: (1) not all algorithms can be vectorized (regular algorithm structure and fine-grain parallelism must be used); (2) most CPUs have data alignment restrictions for load/store operations (obey or risk incorrect code); (3) special directives are often needed to enable vectorization; and (4) vector instructions are architecture-specific. Vectorization is the best way to optimize for power and performance due to reduced clock cycles. When data is organized properly, a vector load instruction (i.e. movaps) can replace 'normal' load instructions (i.e. movsd). Vector operations can potentially have a smaller footprint in the instruction cache when fewer instructions need to be executed. Hybrid index sets insulate users from architecture specific details. We have applied hybrid index sets to achieve optimal vectorization. We can extend this concept to handle other programming models.

  20. Technological Networks

    NASA Astrophysics Data System (ADS)

    Mitra, Bivas

    The study of networks in the form of mathematical graph theory is one of the fundamental pillars of discrete mathematics. However, recent years have witnessed a substantial new movement in network research. The focus of the research is shifting away from the analysis of small graphs and the properties of individual vertices or edges to consideration of statistical properties of large scale networks. This new approach has been driven largely by the availability of technological networks like the Internet [12], World Wide Web network [2], etc. that allow us to gather and analyze data on a scale far larger than previously possible. At the same time, technological networks have evolved as a socio-technological system, as the concepts of social systems that are based on self-organization theory have become unified in technological networks [13]. In today’s society, we have a simple and universal access to great amounts of information and services. These information services are based upon the infrastructure of the Internet and the World Wide Web. The Internet is the system composed of ‘computers’ connected by cables or some other form of physical connections. Over this physical network, it is possible to exchange e-mails, transfer files, etc. On the other hand, the World Wide Web (commonly shortened to the Web) is a system of interlinked hypertext documents accessed via the Internet where nodes represent web pages and links represent hyperlinks between the pages. Peer-to-peer (P2P) networks [26] also have recently become a popular medium through which huge amounts of data can be shared. P2P file sharing systems, where files are searched and downloaded among peers without the help of central servers, have emerged as a major component of Internet traffic. An important advantage in P2P networks is that all clients provide resources, including bandwidth, storage space, and computing power. In this chapter, we discuss these technological networks in detail. The review

  1. Multimedia telehomecare system using standard TV set.

    PubMed

    Guillén, S; Arredondo, M T; Traver, V; García, J M; Fernández, C

    2002-12-01

    Nowadays, there are a very large number of patients that need specific health support at home. The deployment of broadband communication networks is making feasible the provision of home care services with a proper quality of service. This paper presents a telehomecare multimedia platform that runs over integrated services digital network and internet protocol using videoconferencing standards H.320 and H.323, and standard TV set for patient interaction. This platform allows online remote monitoring: ECG, heart sound, blood pressure. Usability, affordability, and interoperability were considered for the design and development of its hardware and software components. A first evaluation of technical and usability aspects were carried forward with 52 patients of a private clinic and 10 students in the University. Results show a high rate (mean = 4.33, standard deviation--SD = 1.63 in a five-points Likert scale) in the global perception of users on the quality of images, voice, and feeling of virtual presence.

  2. Composing Music with Complex Networks

    NASA Astrophysics Data System (ADS)

    Liu, Xiaofan; Tse, Chi K.; Small, Michael

    In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

  3. Control of collective network chaos.

    PubMed

    Wagemakers, Alexandre; Barreto, Ernest; Sanjuán, Miguel A F; So, Paul

    2014-06-01

    Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of "reduced" ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

  4. Control of collective network chaos

    NASA Astrophysics Data System (ADS)

    Wagemakers, Alexandre; Barreto, Ernest; Sanjuán, Miguel A. F.; So, Paul

    2014-06-01

    Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of "reduced" ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

  5. A quantum network of clocks

    NASA Astrophysics Data System (ADS)

    Kómár, P.; Kessler, E. M.; Bishof, M.; Jiang, L.; Sørensen, A. S.; Ye, J.; Lukin, M. D.

    2014-08-01

    The development of precise atomic clocks plays an increasingly important role in modern society. Shared timing information constitutes a key resource for navigation with a direct correspondence between timing accuracy and precision in applications such as the Global Positioning System. By combining precision metrology and quantum networks, we propose a quantum, cooperative protocol for operating a network of geographically remote optical atomic clocks. Using nonlocal entangled states, we demonstrate an optimal utilization of global resources, and show that such a network can be operated near the fundamental precision limit set by quantum theory. Furthermore, the internal structure of the network, combined with quantum communication techniques, guarantees security both from internal and external threats. Realization of such a global quantum network of clocks may allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy.

  6. Control of collective network chaos

    SciTech Connect

    Wagemakers, Alexandre Sanjuán, Miguel A. F.

    2014-06-01

    Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of “reduced” ordinary differential equations can be derived that captures this mean field behavior. Here, we show that chaos control algorithms designed using the reduced equations can be successfully applied to imperfect realizations of the full network. To systematically study the effectiveness of this technique, we measure the quality of control as we relax conditions that are required for the strict accuracy of the reduced equations, and hence, the controller. Although the effects are network-dependent, we show that the method is effective for surprisingly small networks, for modest departures from global coupling, and even with mild inaccuracy in the estimate of network heterogeneity.

  7. IGS Network Coordinator Report - 2002

    NASA Technical Reports Server (NTRS)

    Moore, Angelyn

    2004-01-01

    The IGS network is a set of permanent, continuously-operating, dual-frequency GPS stations operated by over 100 worldwide agencies. The dataset is pooled at IGS Data Centers for routine use by IGS Analysis Centers in creating precise IGS products, as well as free access by other analysts around the world. The IGS Central Bureau hosts the IGS Network Coordinator, who assures adherence to standards and provides information regarding the IGS network via the Central Bureau Information System website at http://igscb.jpl.nasa.gov.

  8. Detecting communities in large networks

    NASA Astrophysics Data System (ADS)

    Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

    2005-07-01

    We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

  9. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

    PubMed Central

    Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

    2006-01-01

    A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

  10. Network Structure and City Size

    PubMed Central

    Levinson, David

    2012-01-01

    Network structure varies across cities. This variation may yield important knowledge about how the internal structure of the city affects its performance. This paper systematically compares a set of surface transportation network structure variables (connectivity, hierarchy, circuity, treeness, entropy, accessibility) across the 50 largest metropolitan areas in the United States. A set of scaling parameters are discovered to show how network size and structure vary with city size. These results suggest that larger cities are physically more inter-connected. Hypotheses are presented as to why this might obtain. This paper then consistently measures and ranks access to jobs across 50 US metropolitan areas. It uses that accessibility measure, along with network structure variables and city size to help explain journey-to-work time and auto mode share in those cities. A 1 percent increase in accessibility reduces average metropolitan commute times by about 90 seconds each way. A 1 percent increase in network connectivity reduces commute time by 0.1 percent. A 1 percent increase in accessibility results in a 0.0575 percent drop in auto mode share, while a 1 percent increase in treeness reduces auto mode share by 0.061 percent. Use of accessibility and network structure measures is important for planning and evaluating the performance of network investments and land use changes. PMID:22253764

  11. Innovation network

    PubMed Central

    Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R.

    2016-01-01

    Technological progress builds upon itself, with the expansion of invention in one domain propelling future work in linked fields. Our analysis uses 1.8 million US patents and their citation properties to map the innovation network and its strength. Past innovation network structures are calculated using citation patterns across technology classes during 1975–1994. The interaction of this preexisting network structure with patent growth in upstream technology fields has strong predictive power on future innovation after 1995. This pattern is consistent with the idea that when there is more past upstream innovation for a particular technology class to build on, then that technology class innovates more. PMID:27681628

  12. Innovation network.

    PubMed

    Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R

    2016-10-11

    Technological progress builds upon itself, with the expansion of invention in one domain propelling future work in linked fields. Our analysis uses 1.8 million US patents and their citation properties to map the innovation network and its strength. Past innovation network structures are calculated using citation patterns across technology classes during 1975-1994. The interaction of this preexisting network structure with patent growth in upstream technology fields has strong predictive power on future innovation after 1995. This pattern is consistent with the idea that when there is more past upstream innovation for a particular technology class to build on, then that technology class innovates more.

  13. Tool setting device

    DOEpatents

    Brown, Raymond J.

    1977-01-01

    The present invention relates to a tool setting device for use with numerically controlled machine tools, such as lathes and milling machines. A reference position of the machine tool relative to the workpiece along both the X and Y axes is utilized by the control circuit for driving the tool through its program. This reference position is determined for both axes by displacing a single linear variable displacement transducer (LVDT) with the machine tool through a T-shaped pivotal bar. The use of the T-shaped bar allows the cutting tool to be moved sequentially in the X or Y direction for indicating the actual position of the machine tool relative to the predetermined desired position in the numerical control circuit by using a single LVDT.

  14. Modularity and community structure in networks

    PubMed Central

    Newman, M. E. J.

    2006-01-01

    Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets. PMID:16723398

  15. Camera network video summarization

    NASA Astrophysics Data System (ADS)

    Panda, Rameswar; Roy-Chowdhury, Amit K.

    2017-05-01

    Networks of vision sensors are deployed in many settings, ranging from security needs to disaster response to environmental monitoring. Many of these setups have hundreds of cameras and tens of thousands of hours of video. The difficulty of analyzing such a massive volume of video data is apparent whenever there is an incident that requires foraging through vast video archives to identify events of interest. As a result, video summarization, that automatically extract a brief yet informative summary of these videos, has attracted intense attention in the recent years. Much progress has been made in developing a variety of ways to summarize a single video in form of a key sequence or video skim. However, generating a summary from a set of videos captured in a multi-camera network still remains as a novel and largely under-addressed problem. In this paper, with the aim of summarizing videos in a camera network, we introduce a novel representative selection approach via joint embedding and capped l21-norm minimization. The objective function is two-fold. The first is to capture the structural relationships of data points in a camera network via an embedding, which helps in characterizing the outliers and also in extracting a diverse set of representatives. The second is to use a capped l21-norm to model the sparsity and to suppress the influence of data outliers in representative selection. We propose to jointly optimize both of the objectives, such that embedding can not only characterize the structure, but also indicate the requirements of sparse representative selection. Extensive experiments on standard multi-camera datasets well demonstrate the efficacy of our method over state-of-the-art methods.

  16. Studying the complex expression dependences between sets of coexpressed genes.

    PubMed

    Huerta, Mario; Casanova, Oriol; Barchino, Roberto; Flores, Jose; Querol, Enrique; Cedano, Juan

    2014-01-01

    Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

  17. Sentinel Network

    Cancer.gov

    The Sentinel Network is an integrated, electronic, national medical product safety initiative that compiles information about the safe and effective use of medical products accessible to patients and healthcare practitioners.

  18. Exchange Network

    EPA Pesticide Factsheets

    The Environmental Information Exchange Network (EIEN) is an Internet-based system used by state, tribal and territorial partners to securely share environmental and health information with one another and EPA.

  19. Emulation of the Active Immune Response in a Computer Network

    DTIC Science & Technology

    2009-01-15

    Dynamics of the Estimation Process 53 15 Dual Network Interface for concurrent execution of testbed experiments and lab management 57 16 Hardware Testbed...Two Physical Nodes 62 20 Network Security Testbed Management Software Stack 63 21 Virtual Network Topology for Worm Propagation Experiment Generated...system could be reformulated in terms of the characteristics of computer networks and interpreted as a set of instructions to a network manager . This

  20. Developer Network

    SciTech Connect

    2012-08-21

    NREL's Developer Network, developer.nrel.gov, provides data that users can access to provide data to their own analyses, mobile and web applications. Developers can retrieve the data through a Web services API (application programming interface). The Developer Network handles overhead of serving up web services such as key management, authentication, analytics, reporting, documentation standards, and throttling in a common architecture, while allowing web services and APIs to be maintained and managed independently.

  1. An efficient quantum scheme for Private Set Intersection

    NASA Astrophysics Data System (ADS)

    Shi, Run-hua; Mu, Yi; Zhong, Hong; Cui, Jie; Zhang, Shun

    2016-01-01

    Private Set Intersection allows a client to privately compute set intersection with the collaboration of the server, which is one of the most fundamental and key problems within the multiparty collaborative computation of protecting the privacy of the parties. In this paper, we first present a cheat-sensitive quantum scheme for Private Set Intersection. Compared with classical schemes, our scheme has lower communication complexity, which is independent of the size of the server's set. Therefore, it is very suitable for big data services in Cloud or large-scale client-server networks.

  2. Sentient networks

    SciTech Connect

    Chapline, G.

    1998-03-01

    The engineering problems of constructing autonomous networks of sensors and data processors that can provide alerts for dangerous situations provide a new context for debating the question whether man-made systems can emulate the cognitive capabilities of the mammalian brain. In this paper we consider the question whether a distributed network of sensors and data processors can form ``perceptions`` based on sensory data. Because sensory data can have exponentially many explanations, the use of a central data processor to analyze the outputs from a large ensemble of sensors will in general introduce unacceptable latencies for responding to dangerous situations. A better idea is to use a distributed ``Helmholtz machine`` architecture in which the sensors are connected to a network of simple processors, and the collective state of the network as a whole provides an explanation for the sensory data. In general communication within such a network will require time division multiplexing, which opens the door to the possibility that with certain refinements to the Helmholtz machine architecture it may be possible to build sensor networks that exhibit a form of artificial consciousness.

  3. Telemedicine in clinical setting

    PubMed Central

    Zhang, Xiao-Ying; Zhang, Peiying

    2016-01-01

    The telemedicine department of a hospital is an emerging branch in upcoming hospitals and may become an essential component of every hospital. It basically utilizes the information technologies along with telecommunication systems in order to provide clinical care and assistance. Furthermore, the branch of telemedicine offers significant opportunities for the process of developmental freedom from illness, early death, and preventable diseases. It advances development by providing relevant drugs and the necessary care aimed to alleviate patient suffering. It is also beneficial for patients in rural remote areas who usually do not have adequate access to advanced hospitals. Telemedicine in these remote areas allows for timely treatment of emergency cases. Thus, it contributes towards remote emergency critical care in order to save lives in crucial cases. Additionally, the emerging advances have now enabled telemedicine to transfer large amounts of clinical informatics data including images, and test reports to the specifically specialized health professionals in some serious cases. However, as in the case of many emerging technologies, organizing information and understanding the field has significant challenges. The present review article aimed to discuss important aspects of the field with regard to the better management of patients in clinical settings. PMID:27703503

  4. RNEDE: Resilient Network Design Environment

    SciTech Connect

    Venkat Venkatasubramanian, Tanu Malik, Arun Giridh; Craig Rieger; Keith Daum; Miles McQueen

    2010-08-01

    Modern living is more and more dependent on the intricate web of critical infrastructure systems. The failure or damage of such systems can cause huge disruptions. Traditional design of this web of critical infrastructure systems was based on the principles of functionality and reliability. However, it is increasingly being realized that such design objectives are not sufficient. Threats, disruptions and faults often compromise the network, taking away the benefits of an efficient and reliable design. Thus, traditional network design parameters must be combined with self-healing mechanisms to obtain a resilient design of the network. In this paper, we present RNEDEa resilient network design environment that that not only optimizes the network for performance but tolerates fluctuations in its structure that result from external threats and disruptions. The environment evaluates a set of remedial actions to bring a compromised network to an optimal level of functionality. The environment includes a visualizer that enables the network administrator to be aware of the current state of the network and the suggested remedial actions at all times.

  5. Maximal switchability of centralized networks

    NASA Astrophysics Data System (ADS)

    Vakulenko, Sergei; Morozov, Ivan; Radulescu, Ovidiu

    2016-08-01

    We consider continuous time Hopfield-like recurrent networks as dynamical models for gene regulation and neural networks. We are interested in networks that contain n high-degree nodes preferably connected to a large number of N s weakly connected satellites, a property that we call n/N s -centrality. If the hub dynamics is slow, we obtain that the large time network dynamics is completely defined by the hub dynamics. Moreover, such networks are maximally flexible and switchable, in the sense that they can switch from a globally attractive rest state to any structurally stable dynamics when the response time of a special controller hub is changed. In particular, we show that a decrease of the controller hub response time can lead to a sharp variation in the network attractor structure: we can obtain a set of new local attractors, whose number can increase exponentially with N, the total number of nodes of the nework. These new attractors can be periodic or even chaotic. We provide an algorithm, which allows us to design networks with the desired switching properties, or to learn them from time series, by adjusting the interactions between hubs and satellites. Such switchable networks could be used as models for context dependent adaptation in functional genetics or as models for cognitive functions in neuroscience.

  6. Learning from Massive Distributed Data Sets (Invited)

    NASA Astrophysics Data System (ADS)

    Kang, E. L.; Braverman, A. J.

    2013-12-01

    Technologies for remote sensing and ever-expanding computer experiments in climate science are generating massive data sets. Meanwhile, it has been common in all areas of large-scale science to have these 'big data' distributed over multiple different physical locations, and moving large amounts of data can be impractical. In this talk, we will discuss efficient ways for us to summarize and learn from distributed data. We formulate a graphical model to mimic the main characteristics of a distributed-data network, including the size of the data sets and speed of moving data. With this nominal model, we investigate the trade off between prediction accurate and cost of data movement, theoretically and through simulation experiments. We will also discuss new implementations of spatial and spatio-temporal statistical methods optimized for distributed data.

  7. Egocentric Social Network Analysis of Pathological Gambling

    PubMed Central

    Meisel, Matthew K.; Clifton, Allan D.; MacKillop, James; Miller, Joshua D.; Campbell, W. Keith; Goodie, Adam S.

    2012-01-01

    Aims To apply social network analysis (SNA) to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family, and co-workers. is an innovative way to look at relationships among individuals; the current study was the first to our knowledge to apply SNA to gambling behaviors. Design Egocentric social network analysis was used to formally characterize the relationships between social network characteristics and gambling pathology. Setting Laboratory-based questionnaire and interview administration. Participants Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. Findings The SNA revealed significant social network compositional differences between the two groups: pathological gamblers (PGs) had more gamblers, smokers, and drinkers in their social networks than did nonpathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked, and drank with than those with who were NPG. Network ties were closer to individuals in their networks who gambled, smoked, and drank more frequently. Associations between gambling severity and structural network characteristics were not significant. Conclusions Pathological gambling is associated with compositional but not structural differences in social networks. Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers, and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention. PMID:23072641

  8. Tectonic setting of kimberlites

    NASA Astrophysics Data System (ADS)

    Jelsma, Hielke; Barnett, Wayne; Richards, Simon; Lister, Gordon

    2009-11-01

    Kimberlites can be viewed as time capsules in a global plate tectonic framework. Their distribution illustrates clustering in time and space. Kimberlite ages span the assembly and break-up of a number of supercontinents, such as Rodinia and Gondwana. These supercontinents show time lines with (i) broad periods devoid of kimberlite magmatism corresponding to times of continent stability, and (ii) narrow kimberlite emplacement windows corresponding to times of fundamental plate reorganizations. This episodicity implies that kimberlite emplacement events are intrinsically related to particular stages in the life cycle of supercontinents. The onset of kimberlite magmatism is closely associated with thermal perturbations (thermal insulation, mantle upwelling?) beneath a stagnant or sluggish supercontinent. These perturbations may have caused uplift and the onset of continental break-up through fracture zones propagating into the supercontinent. Subsequent spreading and ocean floor development is marked by apparent cusps and jogs in plate motion paths. Resultant strain is accommodated along trans-lithospheric corridors with episodic uplift and erosion and focused kimberlite melt migration. The corridors are manifest as discontinuities in the lithosphere mantle, measured as geophysical gradients and as changes in mantle lithosphere composition. Within the crust, these corridors are expressed as (a) terrane boundaries, (b) incipient continental rifts, (c) fracture zones, or (d) major dyke swarms. Some kimberlite populations are clustered along parallel sets of corridors widely distributed across a large part of a subcontinent and repeated magmatism is seen within many of the clusters. The association of kimberlite occurrences with discontinuities may be ascribed to favorable conditions for melt production and to resultant melt focusing along high strain zones that contain fractures and faults. Such conditions may be attained during different stages in the evolution of

  9. Network motif identification in stochastic networks

    NASA Astrophysics Data System (ADS)

    Jiang, Rui; Tu, Zhidong; Chen, Ting; Sun, Fengzhu

    2006-06-01

    Network motifs have been identified in a wide range of networks across many scientific disciplines and are suggested to be the basic building blocks of most complex networks. Nonetheless, many networks come with intrinsic and/or experimental uncertainties and should be treated as stochastic networks. The building blocks in these networks thus may also have stochastic properties. In this article, we study stochastic network motifs derived from families of mutually similar but not necessarily identical patterns of interconnections. We establish a finite mixture model for stochastic networks and develop an expectation-maximization algorithm for identifying stochastic network motifs. We apply this approach to the transcriptional regulatory networks of Escherichia coli and Saccharomyces cerevisiae, as well as the protein-protein interaction networks of seven species, and identify several stochastic network motifs that are consistent with current biological knowledge. expectation-maximization algorithm | mixture model | transcriptional regulatory network | protein-protein interaction network

  10. Decline in the proportion of methicillin resistance among Staphylococcus aureus isolates from non-invasive samples and in outpatient settings, and changes in the co-resistance profiles: an analysis of data collected within the Antimicrobial Resistance Surveillance Network, Germany 2010 to 2015.

    PubMed

    Walter, Jan; Noll, Ines; Feig, Marcel; Weiss, Bettina; Claus, Hermann; Werner, Guido; Eckmanns, Tim; Hermes, Julia; Abu Sin, Muna

    2017-02-23

    Recent analysis of trends of non-invasive infections with methicillin resistant Staphylococcus aureus (MRSA), of trends of MRSA infections in outpatient settings and of co-resistance profiles of MRSA isolates are scarce or lacking in Germany. We analysed data from the Antimicrobial Resistance Surveillance Network (ARS). We included in the analysis the first isolate of S. aureus per patient and year, which had a valid test result for oxacillin resistance and which was not a screening sample. We limited the analysis to isolates from facilities, which contributed to ARS for all six years between 2010 and 2015. We compared the proportion of methicillin resistance among S. aureus isolates by calendar year using Chi-square and Fisher's exact test. We corrected for multiple testing using the Bonferroni correction. We stratified the analysis by sample type including various non-invasive sample types and by type of care (e.g. hospital versus outpatient clinic). We also analysed the non-susceptibility of MRSA to selected antibiotics. The analysis included 148,561 S. aureus isolates. The distribution of these isolates by sex, age, region, sample type, clinical speciality and type of care remained relatively stable over the six years analysed. The proportion of MRSA among S. aureus isolates decreased continuously from 16% in 2010 to 10% in 2015. This decrease was seen for all types of care and for the majority of sample types, including the outpatient clinic (12 to 8%), as well as blood culture (19 to 9%), urine samples (25 to 15%), swabs (14 to 9%), respiratory samples (22 to 11%) and lesions (15 to 10%). The non-susceptibility of MRSA isolates to tobramycin (47 to 32%), ciprofloxacin (95 to 89%), moxifloxacin (94 to 84%), clindamycin (80 to 71%) and erythromycin (81 to 72%) declined markedly, but it increased for tetracyclines (6 to 9%) and gentamicin (3 to 6%). Non-susceptibility of MRSA to linezolid, teicoplanin, tigecycline and vancomycin remained rare. This analysis

  11. Structurally robust biological networks

    PubMed Central

    2011-01-01

    Background The molecular circuitry of living organisms performs remarkably robust regulatory tasks, despite the often intrinsic variability of its components. A large body of research has in fact highlighted that robustness is often a structural property of biological systems. However, there are few systematic methods to mathematically model and describe structural robustness. With a few exceptions, numerical studies are often the preferred approach to this type of investigation. Results In this paper, we propose a framework to analyze robust stability of equilibria in biological networks. We employ Lyapunov and invariant sets theory, focusing on the structure of ordinary differential equation models. Without resorting to extensive numerical simulations, often necessary to explore the behavior of a model in its parameter space, we provide rigorous proofs of robust stability of known bio-molecular networks. Our results are in line with existing literature. Conclusions The impact of our results is twofold: on the one hand, we highlight that classical and simple control theory methods are extremely useful to characterize the behavior of biological networks analytically. On the other hand, we are able to demonstrate that some biological networks are robust thanks to their structure and some qualitative properties of the interactions, regardless of the specific values of their parameters. PMID:21586168

  12. Networking a mobile robot

    NASA Astrophysics Data System (ADS)

    McKee, Gerard T.

    1994-10-01

    Conventional mobile robotic systems are `stand alone'. Program development involves loading programs into the mobile, via an umbilical. Autonomous operation, in this context, means `isolation': the user cannot interact with the program as the robot is moving around. Recent research in `swarm robotics' has exploited wireless networks as a means of providing inter- robot communication, but the population is still isolated from the human user. In this paper we report on research we are conducting into the provision of mobile robots as resources on a local area computer network, and thus breaking the isolation barrier. We are making use of new multimedia workstation and wireless networking technology to link the robots to the network in order to provide a new type of resource for the user. We model the robot as a set of resources and propose a client-server architecture as the basis for providing user access to the robots. We describe the types of resources each robot can provide and we outline the potential for cooperative robotics, human-robot cooperation, and teleoperation and autonomous robot behavior within this context.

  13. BER Science Network Requirements

    SciTech Connect

    Alapaty, Kiran; Allen, Ben; Bell, Greg; Benton, David; Brettin, Tom; Canon, Shane; Dart, Eli; Cotter, Steve; Crivelli, Silvia; Carlson, Rich; Dattoria, Vince; Desai, Narayan; Egan, Richard; Tierney, Brian; Goodwin, Ken; Gregurick, Susan; Hicks, Susan; Johnston, Bill; de Jong, Bert; Kleese van Dam, Kerstin; Livny, Miron; Markowitz, Victor; McGraw, Jim; McCord, Raymond; Oehmen, Chris; Regimbal, Kevin; Shipman, Galen; Strand, Gary; Flick, Jeff; Turnbull, Susan; Williams, Dean; Zurawski, Jason

    2010-11-01

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the US 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 20 years. In April 2010 ESnet and the Office of Biological and Environmental Research, of the DOE Office of Science, organized a workshop to characterize the networking requirements of the science programs funded by BER. The requirements identified at the workshop are summarized and described in more detail in the case studies and the Findings section. A number of common themes emerged from the case studies and workshop discussions. One is that BER science, like many other disciplines, is becoming more and more distributed and collaborative in nature. Another common theme is that data set sizes are exploding. Climate Science in particular is on the verge of needing to manage exabytes of data, and Genomics is on the verge of a huge paradigm shift in the number of sites with sequencers and the amount of sequencer data being generated.

  14. Determination of optimum structure of backpropagation networks

    NASA Astrophysics Data System (ADS)

    Phien, Huynh N.; Sureerattanan, Songyot

    2000-10-01

    The paper proposes the use of the Baysian Information Criterion (BIC), along with an algorithm to systematically select the appropriate structure of the backpropagation (BP) network for a given set of data. Simulation results with hydrological and economic data show that the algorithm performs very satisfactorily. Moreover, it compares with the method of Daqi and Shouyi for one hidden layer network and it is also used for the networks with more than one hidden layer.

  15. Neural Network False Alarm Filter. Volume 1.

    DTIC Science & Technology

    1994-12-01

    This effort identified, developed and demonstrated a set of approaches for applying neural network learning techniques to the development of a real... neural network models, 9 fault report causes and 12 common groups of BIT techniques was identified. From this space, 4 unique, high-potential...of their strengths and weaknesses were performed along with cost/ benefit analyses. This study concluded that the best candidates for neural network insert

  16. Embarkables Root Cause for Navy Networks

    DTIC Science & Technology

    2012-03-01

    Joint Task Force for Global Network Operations LNSC Local Network Service Center MCEN Marine Corps Enterprise Network MBR Master Boot...hard drive, a common tool is the IBM Rescue and Recover application. 58 This application requires the Master Boot Record ( MBR ) to be properly...configured for the application to function and it replaces it with its own settings. GEHD relies on the MBR and modifying it results in system problems

  17. Quantitative description and modeling of real networks

    NASA Astrophysics Data System (ADS)

    Capocci, Andrea; Caldarelli, Guido; de Los Rios, Paolo

    2003-10-01

    We present data analysis and modeling of two particular cases of study in the field of growing networks. We analyze World Wide Web data set and authorship collaboration networks in order to check the presence of correlation in the data. The results are reproduced with good agreement through a suitable modification of the standard Albert-Barabási model of network growth. In particular, intrinsic relevance of sites plays a role in determining the future degree of the vertex.

  18. Evolving Sensitivity Balances Boolean Networks

    PubMed Central

    Luo, Jamie X.; Turner, Matthew S.

    2012-01-01

    We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman’s Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. Insilico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks. PMID:22586459

  19. Radiation Behavior of Analog Neural Network Chip

    NASA Technical Reports Server (NTRS)

    Langenbacher, H.; Zee, F.; Daud, T.; Thakoor, A.

    1996-01-01

    A neural network experiment conducted for the Space Technology Research Vehicle (STRV-1) 1-b launched in June 1994. Identical sets of analog feed-forward neural network chips was used to study and compare the effects of space and ground radiation on the chips. Three failure mechanisms are noted.

  20. Social Networking on the Semantic Web

    ERIC Educational Resources Information Center

    Finin, Tim; Ding, Li; Zhou, Lina; Joshi, Anupam

    2005-01-01

    Purpose: Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach: The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the…