WorldWideScience

Sample records for network pepnet identified

  1. Test Equity for Individuals Who Are Deaf or Hard of Hearing. PEPNet Test Equity Summit

    Science.gov (United States)

    PEPNet-West, 2010

    2010-01-01

    This paper presents the highlights of the 2008 Test Equity Summit held in Bloomfield, Colorado last August 6-8, 2008. The 2008 Test Equity Summit convened by the Postsecondary Education Programs Network (PEPNet) identified and examined problems, challenges, and issues that academic and psychoeducational tests pose for individuals who are deaf or…

  2. Retencion. Hoja de consejos de PEPNet (Retention. PEPNet Tipsheet)

    Science.gov (United States)

    Walter, Gerard, Comp.

    2010-01-01

    This version of "Retention. PEPNet Tipsheet," written in Spanish, focuses on understanding the factors that research indicates are important for persistence to graduation and suggests some approaches to reducing attrition among students who are deaf/hard of hearing attending postsecondary institutions. Students who are deaf or hard of hearing and…

  3. Sirviendo a los estudiantes sordos que tienen Los implantes cocleares. Hoja de consejos de PEPNet (Serving Deaf Students Who Have Cochlear Implants. PEPNet Tipsheet)

    Science.gov (United States)

    Clark, Catherine

    2010-01-01

    This version of "Serving Deaf Students Who Have Cochlear Implants. PEPNet Tipsheet," written in Spanish, describes how cochlear implants (CIs) work. CIs are complex electronic devices surgically implanted under the skin behind the ear. These devices utilize electrodes placed in the inner ear (the cochlea) to stimulate the auditory nerve of…

  4. Opportunistic Beacon Networks: Information Dissemination via Wireless Network Identifiers

    NARCIS (Netherlands)

    Türkes, Okan; Scholten, Johan; Havinga, Paul J.M.

    2016-01-01

    This paper presents OBN, a universal opportunistic ad hoc networking model particularly intended for smart mobile devices. It enables fast and lightweight data dissemination in wireless community networks through the utilization of universally-available wireless network identifiers. As a ubiquitous

  5. El mejorar del lenguaje y la aprendizaje de los estudiantes que son sordos. Hoja de consejos de PEPNet (Improving the Language and Learning of Students Who Are Deaf. PEPNet Tipsheet)

    Science.gov (United States)

    Livingston, Sue

    2010-01-01

    This version of "Improving the Language and Learning of Students Who Are Deaf. PEPNet Tipsheet," written in Spanish, offers some suggestions on improving the language and learning of students who are deaf. The saying "Good teaching is good teaching" holds considerable truth when thinking about exemplary practices used in educating students who are…

  6. Identifying Tracks Duplicates via Neural Network

    CERN Document Server

    Sunjerga, Antonio; CERN. Geneva. EP Department

    2017-01-01

    The goal of the project is to study feasibility of state of the art machine learning techniques in track reconstruction. Machine learning techniques provide promising ways to speed up the pattern recognition of tracks by adding more intelligence in the algorithms. Implementation of neural network to process of track duplicates identifying will be discussed. Different approaches are shown and results are compared to method that is currently in use.

  7. Identifying Broadband Rotational Spectra with Neural Networks

    Science.gov (United States)

    Zaleski, Daniel P.; Prozument, Kirill

    2017-06-01

    A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

  8. Identifying modular relations in complex brain networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig

    2012-01-01

    and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure......We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility...... are measured within the data driven NPAIRS split-half framework. Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit...

  9. Identifying Geographic Clusters: A Network Analytic Approach

    CERN Document Server

    Catini, Roberto; Penner, Orion; Riccaboni, Massimo

    2015-01-01

    In recent years there has been a growing interest in the role of networks and clusters in the global economy. Despite being a popular research topic in economics, sociology and urban studies, geographical clustering of human activity has often studied been by means of predetermined geographical units such as administrative divisions and metropolitan areas. This approach is intrinsically time invariant and it does not allow one to differentiate between different activities. Our goal in this paper is to present a new methodology for identifying clusters, that can be applied to different empirical settings. We use a graph approach based on k-shell decomposition to analyze world biomedical research clusters based on PubMed scientific publications. We identify research institutions and locate their activities in geographical clusters. Leading areas of scientific production and their top performing research institutions are consistently identified at different geographic scales.

  10. Identifying Gatekeepers in Online Learning Networks

    Science.gov (United States)

    Gursakal, Necmi; Bozkurt, Aras

    2017-01-01

    The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

  11. Identifying familiar strangers in human encounter networks

    Science.gov (United States)

    Liang, Di; Li, Xiang; Zhang, Yi-Qing

    2016-10-01

    Familiar strangers, pairs of individuals who encounter repeatedly but never know each other, have been discovered for four decades yet lack an effective method to identify. Here we propose a novel method called familiar stranger classifier (FSC) to identify familiar strangers from three empirical datasets, and classify human relationships into four types, i.e., familiar stranger (FS), in-role (IR), friend (F) and stranger (S). The analyses of the human encounter networks show that the average number of FS one may encounter is finite but larger than the Dunbar Number, and their encounters are structurally more stable and denser than those of S, indicating the encounters of FS are not limited by the social capacity, and more robust than the random scenario. Moreover, the temporal statistics of encounters between FS over the whole time span show strong periodicity, which are diverse from the bursts of encounters within one day, suggesting the significance of longitudinal patterns of human encounters. The proposed method to identify FS in this paper provides a valid framework to understand human encounter patterns and analyse complex human social behaviors.

  12. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  13. Identifying Jets Using Artifical Neural Networks

    Science.gov (United States)

    Rosand, Benjamin; Caines, Helen; Checa, Sofia

    2017-09-01

    We investigate particle jet interactions with the Quark Gluon Plasma (QGP) using artificial neural networks modeled on those used in computer image recognition. We create jet images by binning jet particles into pixels and preprocessing every image. We analyzed the jets with a Multi-layered maxout network and a convolutional network. We demonstrate each network's effectiveness in differentiating simulated quenched jets from unquenched jets, and we investigate the method that the network uses to discriminate among different quenched jet simulations. Finally, we develop a greater understanding of the physics behind quenched jets by investigating what the network learnt as well as its effectiveness in differentiating samples. Yale College Freshman Summer Research Fellowship in the Sciences and Engineering.

  14. Have No PHEAR: Networks Without Identifiers

    Science.gov (United States)

    2015-12-07

    Have No PHEAR: Networks Without Identifiers∗ Richard Skowyra, Kevin Bauer, Veer Dedhia, Hamed Okhravi, and William Streilein MIT Lincoln Laboratory...Ethernet and TCP/IP were not designed to ensure the security and privacy of users. To protect users’ pri- vacy, anonymity networks such as Tor have been...performance for common applications such as web browsing and file sharing. 1. INTRODUCTION The essential network protocols that have enabled the success of

  15. Identifying community structure in complex networks

    Science.gov (United States)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  16. Identifying network representation issues with the network trip.

    Science.gov (United States)

    2012-04-23

    The purpose of this study was to evaluate the effects of road-network representation on the application of the Network Robustness Index (NRI), using the Chittenden County Regional Transportation Model. The results are expected to improve the requirem...

  17. Identifying Social Communities in Complex Communications for Network Efficiency

    Science.gov (United States)

    Hui, Pan; Yoneki, Eiko; Crowcroft, Jon; Chan, Shu-Yan

    Complex communication networks, more particular Mobile Ad Hoc Networks (MANET) and Pocket Switched Networks (PSN), rely on short range radio and device mobility to transfer data across the network. These kind of mobile networks contain duality in nature: they are radio networks at the same time also human networks, and hence knowledge from social networks can be also applicable here. In this paper, we demonstrate how identifying social communities can significantly improve the forwarding efficiencies in term of delivery ratio and delivery cost. We verify our hypothesis using data from five human mobility experiments and test on two application scenarios, asynchronous messaging and publish/subscribe service.

  18. Identifying Anomalous Port-Specific Network Behavior

    Science.gov (United States)

    2010-05-01

    ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK , OR COPYRIGHT INFRINGEMENT. Use of any trademarks in this report is not intended in any way...to infringe on the rights of the trademark holder. Internal use. Permission to reproduce this document and to prepare derivative works from this...applications (registered or unregistered ). 5 | CMU/SEI-2010-TR-010 Port values play an important role in network flow monitoring. A flow record is an

  19. Identifying Infection Sources and Regions in Large Networks

    OpenAIRE

    Luo, Wuqiong; Tay, Wee Peng; Leng, Mei

    2012-01-01

    Identifying the infection sources in a network, including the index cases that introduce a contagious disease into a population network, the servers that inject a computer virus into a computer network, or the individuals who started a rumor in a social network, plays a critical role in limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources and the infection regions (subsets of nodes infected by each so...

  20. Identifying the Key Weaknesses in Network Security at Colleges.

    Science.gov (United States)

    Olsen, Florence

    2000-01-01

    A new study identifies and ranks the 10 security gaps responsible for most outsider attacks on college computer networks. The list is intended to help campus system administrators establish priorities as they work to increase security. One network security expert urges that institutions utilize multiple security layers. (DB)

  1. Identifying key nodes in multilayer networks based on tensor decomposition

    Science.gov (United States)

    Wang, Dingjie; Wang, Haitao; Zou, Xiufen

    2017-06-01

    The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

  2. Identify Dynamic Network Modules with Temporal and Spatial Constraints

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-09-24

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

  3. Introduction of digital object identifiers (DOI) for seismic networks

    Science.gov (United States)

    Evans, Peter; Strollo, Angelo; Clark, Adam; Ahern, Tim; Newman, Rob; Clinton, John; Pequegnat, Catherine; Pedersen, Helle

    2015-04-01

    Proper attribution for scientific source data is important in promoting transparency and recognising the role of data providers in science. Data sets such as those produced by seismic networks now need to be citable and permanently locatable for research users. Recently the EIDA and IRIS-DMC communities have worked together on development of methods for generation, maintenance and promotion of persistent identifiers for seismic networks. This resulted in a 2014 Recommendation by the International Federation of Digital Seismograph Networks (FDSN) on the use of Digital Object Identifiers (DOI) for seismic networks. These can be cited equivalently to scientific papers, and tools such as DataCite allow the tracking of citations to these datasets. The GEOFON, IRIS and RESIF data centres have now begun to roll-out of these seismic network DOIs. This has involved working with principal investigators to prepare metadata consistent with the FDSN recommendation, preparation of landing pages, and changes to the web sites to promote DOIs where available. This has involved preparing improved descriptions of the data (metadata) and clarifying how individuals and institutions should best be recognised for their contributions to making the data available. We illustrate this process for a few representative networks. We will be in contact with additional network operators to help them establish DOIs for their networks in future.

  4. Structural parameter identifiability analysis for dynamic reaction networks

    DEFF Research Database (Denmark)

    Davidescu, Florin Paul; Jørgensen, Sten Bay

    2008-01-01

    where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...... method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...

  5. Identifying influential directors in the United States corporate governance network.

    Science.gov (United States)

    Huang, Xuqing; Vodenska, Irena; Wang, Fengzhong; Havlin, Shlomo; Stanley, H Eugene

    2011-10-01

    The influence of directors has been one of the most engaging topics recently, but surprisingly little research has been done to quantitatively evaluate the influence and power of directors. We analyze the structure of the US corporate governance network for the 11-year period 1996-2006 based on director data from the Investor Responsibility Research Center director database, and we develop a centrality measure named the influence factor to estimate the influence of directors quantitatively. The US corporate governance network is a network of directors with nodes representing directors and links between two directors representing their service on common company boards. We assume that information flows in the network through information-sharing processes among linked directors. The influence factor assigned to a director is based on the level of information that a director obtains from the entire network. We find that, contrary to commonly accepted belief that directors of large companies, measured by market capitalization, are the most powerful, in some instances, the directors who are influential do not necessarily serve on boards of large companies. By applying our influence factor method to identify the influential people contained in the lists created by popular magazines such as Fortune, Networking World, and Treasury and Risk Management, we find that the influence factor method is consistently either the best or one of the two best methods in identifying powerful people compared to other general centrality measures that are used to denote the significance of a node in complex network theory.

  6. Network Dynamic Connectivity for Identifying Hotspots of Fluvial Geomorphic Change

    Science.gov (United States)

    Czuba, J. A.; Foufoula-Georgiou, E.

    2014-12-01

    The hierarchical branching structure of a river network serves as a template upon which environmental fluxes of water, sediment, nutrients, etc. are conveyed and organized both spatially and temporally within a basin. Dynamical processes occurring on a river network tend to heterogeneously distribute fluxes on the network, often concentrating them into "clusters," i.e., places of excess flux accumulation. Here, we put forward the hypothesis that places in the network predisposed (due to process dynamics and network topology) to accumulate excess bed-material sediment over a considerable river reach and over a considerable period of time reflect locations where a local imbalance in sediment flux may occur thereby highlighting a susceptibility to potential fluvial geomorphic change. We have developed a framework where we are able to track fluxes on a "static" river network using a simplified Lagrangian transport model and use the spatial-temporal distribution of that flux to form a new "dynamic" network of the flux that evolves over time. From this dynamic network we can quantify the dynamic connectivity of the flux and integrate emergent "clusters" over time through a cluster persistence index (CPI) to assess the persistence of mass throughout the network. The framework was applied to sand transport on the Greater Blue Earth River Network in Minnesota where three hotspots of fluvial geomorphic change have been defined based on high rates of channel migration observed from aerial photographic analysis. Locations within the network with high CPI coincided with two of these hotspots, possibly suggesting that channel migration here is driven by sediment deposition "pushing" the stream into and thus eroding the opposite bank. The third hotspot was not identified by high CPI, but instead is believed to be a hotspot of streamflow-driven change based on additional information and the fact that high bed shear stress coincided with this hotspot. The proposed network

  7. Rapid identifying high-influence nodes in complex networks

    Science.gov (United States)

    Song, Bo; Jiang, Guo-Ping; Song, Yu-Rong; Xia, Ling-Ling

    2015-10-01

    A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method (RIM) to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered (SIR) model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness, and eigenvector centrality methods. Project supported by the National Natural Science Foundation of China (Grant Nos. 61374180 and 61373136), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project, China (Grant No. 12YJAZH120), and the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. RLD201212).

  8. Towards Mining Latent Client Identifiers from Network Traffic

    Directory of Open Access Journals (Sweden)

    Jain Sakshi

    2016-04-01

    Full Text Available Websites extensively track users via identifiers that uniquely map to client machines or user accounts. Although such tracking has desirable properties like enabling personalization and website analytics, it also raises serious concerns about online user privacy, and can potentially enable illicit surveillance by adversaries who broadly monitor network traffic.

  9. Faculty Use of Author Identifiers and Researcher Networking Tools

    Science.gov (United States)

    Tran, Clara Y.; Lyon, Jennifer A.

    2017-01-01

    This cross-sectional survey focused on faculty use and knowledge of author identifiers and researcher networking systems, and professional use of social media, at a large state university. Results from 296 completed faculty surveys representing all disciplines (9.3% response rate) show low levels of awareness and variable resource preferences. The…

  10. Identifying node role in social network based on multiple indicators.

    Directory of Open Access Journals (Sweden)

    Shaobin Huang

    Full Text Available It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.

  11. Identifying changes in the support networks of end-of-life carers using social network analysis.

    Science.gov (United States)

    Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie

    2015-06-01

    End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  12. Combination methods for identifying influential nodes in networks

    Science.gov (United States)

    Gao, Chao; Zhong, Lu; Li, Xianghua; Zhang, Zili; Shi, Ning

    2015-11-01

    Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

  13. Identifying node spreading influence for tunable clustering coefficient networks

    Science.gov (United States)

    Wang, Zi-Yi; Han, Jing-Ti; Zhao, Jun

    2017-11-01

    Identifying the node spreading influence is of significant for information and innovation diffusion. In this paper, we argue that the spreading process should be taken into account for identifying the node spreading influence and investigate the effect of the network structure, measured by the clustering coefficient, on the performance of spreading dynamics. Firstly, we generate a series of networks with tunable clustering coefficients. Then, taking into account the spreading process, we explore the performances among the Dynamics-sensitive (DS) index and the degree, between, closeness, eigenvector indices. Comparing with the Susceptible-Infective-Removed (SIR) model, the extensive results show that, for different spreading time steps and clustering coefficients, the DS centrality outperforms the performance, τ > 0 . 97, of degree, betweenness, closeness and eigenvector measures. Moreover, the accuracy of closeness and eigenvector centrality is similar and conducts better in networks with larger spreading rate β = 0 . 20 , τ > 0 . 93. As the clustering coefficient increases, all the performances decrease but DS centrality with least percent of 1.16 at most under β = 0 . 10, and Closeness with the largest percent of 9.75 under β = 0 . 05. This work suggests that the spreading influence not only depends on the network structure, more importantly, the spreading dynamic process also affect the performance greatly, which should be taken into account simultaneously.

  14. A dynamical approach to identify vertices' centrality in complex networks

    Science.gov (United States)

    Guo, Long; Zhang, Wen-Yao; Luo, Zhong-Jie; Gao, Fu-Juan; Zhang, Yi-Cheng

    2017-12-01

    In this paper, we proposed a dynamical approach to assess vertices' centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices' dynamical centrality is calculated based on the Difference of vertices' Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks.

  15. Differential dependency network analysis to identify condition-specific topological changes in biological networks.

    Science.gov (United States)

    Zhang, Bai; Li, Huai; Riggins, Rebecca B; Zhan, Ming; Xuan, Jianhua; Zhang, Zhen; Hoffman, Eric P; Clarke, Robert; Wang, Yue

    2009-02-15

    Significant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets. Here, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics. The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.

  16. Identifying online user reputation of user-object bipartite networks

    Science.gov (United States)

    Liu, Xiao-Lu; Liu, Jian-Guo; Yang, Kai; Guo, Qiang; Han, Jing-Ti

    2017-02-01

    Identifying online user reputation based on the rating information of the user-object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part of all user opinions. The experimental results show that the AUC values of the presented algorithm could reach 0.8929 and 0.8483 for the MovieLens and Netflix data sets, respectively, which is better than the results generated by the CR and IARR methods. Furthermore, the experimental results for different user groups indicate that the presented algorithm outperforms the iterative ranking methods in both ranking accuracy and computation complexity. Moreover, the results for the synthetic networks show that the computation complexity of the presented algorithm is a linear function of the network size, which suggests that the presented algorithm is very effective and efficient for the large scale dynamic online systems.

  17. Identifying a set of influential spreaders in complex networks.

    Science.gov (United States)

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

    2016-06-14

    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.

  18. Identifying gaps in grid middleware on fast networks with the advanced networking initiative

    Energy Technology Data Exchange (ETDEWEB)

    Dykstra, Dave [Fermilab; Garzoglio, Gabriele [Fermilab; Kim, Hyunwoo [Fermilab; Mhashilkar, Parag [Fermilab

    2012-01-01

    As of 2012, a number of US Department of Energy (DOE) National Laboratories have access to a 100 Gb/s wide-area network backbone. The ESnet Advanced Networking Initiative (ANI) project is intended to develop a prototype network, based on emerging 100 Gb/s Ethernet technology. The ANI network will support DOE's science research programs. A 100 Gb/s network test bed is a key component of the ANI project. The test bed offers the opportunity for early evaluation of 100Gb/s network infrastructure for supporting the high impact data movement typical of science collaborations and experiments. In order to make effective use of this advanced infrastructure, the applications and middleware currently used by the distributed computing systems of large-scale science need to be adapted and tested within the new environment, with gaps in functionality identified and corrected. As a user of the ANI test bed, Fermilab aims to study the issues related to end-to-end integration and use of 100 Gb/s networks for the event simulation and analysis applications of physics experiments. In this paper we discuss our findings from evaluating existing HEP Physics middleware and application components, including GridFTP, Globus Online, etc. in the high-speed environment. These will include possible recommendations to the system administrators, application and middleware developers on changes that would make production use of the 100 Gb/s networks, including data storage, caching and wide area access.

  19. Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.

    Science.gov (United States)

    Chairez, Isaac

    2017-05-01

    This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.

  20. Identifying personal health experience tweets with deep neural networks.

    Science.gov (United States)

    Keyuan Jiang; Gupta, Ravish; Gupta, Matrika; Calix, Ricardo A; Bernard, Gordon R

    2017-07-01

    Twitter, as a social media platform, has become an increasingly useful data source for health surveillance studies, and personal health experiences shared on Twitter provide valuable information to the surveillance. Twitter data are known for their irregular usages of languages and informal short texts due to the 140 character limit, and for their noisiness such that majority of the posts are irrelevant to any particular health surveillance. These factors pose challenges in identifying personal health experience tweets from the Twitter data. In this study, we designed deep neural networks with 3 different architectural configurations, and after training them with a corpus of 8,770 annotated tweets, we used them to predict personal experience tweets from a set of 821 annotate tweets. Our results demonstrated a significant amount of improvement in predicting personal health experience tweets by deep neural networks over that by conventional classifiers: 37.5% in accuracy, 31.1% in precision, and 53.6% in recall. We believe that our method can be utilized in various health surveillance studies using Twitter as a data source.

  1. Identifying condition-specific modules by clustering multiple networks.

    Science.gov (United States)

    Ma, Xiaoke; Sun, Penggang; Qin, Guimin

    2017-10-10

    Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the Specific Modules in Multiple Networks (SMMN), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.

  2. Reconstructing context-specific gene regulatory network and identifying modules and network rewiring through data integration.

    Science.gov (United States)

    Ma, Tianle; Zhang, Aidong

    2017-07-15

    Reconstructing context-specific transcriptional regulatory network is crucial for deciphering principles of regulatory mechanisms underlying various conditions. Recently studies that reconstructed transcriptional networks have focused on individual organisms or cell types and relied on data repositories of context-free regulatory relationships. Here we present a comprehensive framework to systematically derive putative regulator-target pairs in any given context by integrating context-specific transcriptional profiling and public data repositories of gene regulatory networks. Moreover, our framework can identify core regulatory modules and signature genes underlying global regulatory circuitry, and detect network rewiring and core rewired modules in different contexts by considering gene modules and edge (gene interaction) modules collaboratively. We applied our methods to analyzing Autism RNA-seq experiment data and produced biologically meaningful results. In particular, all 11 hub genes in a predicted rewired autistic regulatory subnetwork have been linked to autism based on literature review. The predicted rewired autistic regulatory network may shed some new insight into disease mechanism. Published by Elsevier Inc.

  3. Identifying failure in a tree network of a parallel computer

    Science.gov (United States)

    Archer, Charles J.; Pinnow, Kurt W.; Wallenfelt, Brian P.

    2010-08-24

    Methods, parallel computers, and products are provided for identifying failure in a tree network of a parallel computer. The parallel computer includes one or more processing sets including an I/O node and a plurality of compute nodes. For each processing set embodiments include selecting a set of test compute nodes, the test compute nodes being a subset of the compute nodes of the processing set; measuring the performance of the I/O node of the processing set; measuring the performance of the selected set of test compute nodes; calculating a current test value in dependence upon the measured performance of the I/O node of the processing set, the measured performance of the set of test compute nodes, and a predetermined value for I/O node performance; and comparing the current test value with a predetermined tree performance threshold. If the current test value is below the predetermined tree performance threshold, embodiments include selecting another set of test compute nodes. If the current test value is not below the predetermined tree performance threshold, embodiments include selecting from the test compute nodes one or more potential problem nodes and testing individually potential problem nodes and links to potential problem nodes.

  4. Jury-Contestant Bipartite Competition Network: Identifying Biased Scores and Their Impact on Network Structure Inference

    CERN Document Server

    Jeon, Gyuhyeon

    2016-01-01

    A common form of competition is one where judges grade contestants' performances which are then compiled to determine the final ranking of the contestants. Unlike in another common form of competition where two contestants play a head-to-head match to produce a winner as in football or basketball, the objectivity of judges are prone to be questioned, potentially undermining the public's trust in the fairness of the competition. In this work we show, by modeling the judge--contestant competition as a weighted bipartite network, how we can identify biased scores and measure their impact on our inference of the network structure. Analyzing the prestigious International Chopin Piano Competition of 2015 with a well-publicized scoring controversy as an example, we show that even a single statistically uncharacteristic score can be enough to gravely distort our inference of the community structure, demonstrating the importance of detecting and eliminating biases. In the process we also find that there does not exist...

  5. Exploiting The Brain’s Network Structure in Identifying ADHD

    Directory of Open Access Journals (Sweden)

    Soumyabrata eDey

    2012-11-01

    Full Text Available Attention Deficit Hyperactive Disorder (ADHD is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI sequences of the brain. We show that brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis based classifier. We hypothesized that ADHD related differences lie in some specific regions of brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects, and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59% is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.

  6. Identifying influential user communities on the social network

    Science.gov (United States)

    Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

    2015-10-01

    Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

  7. Identifying topological motif patterns of human brain functional networks.

    Science.gov (United States)

    Wei, Yongbin; Liao, Xuhong; Yan, Chaogan; He, Yong; Xia, Mingrui

    2017-05-01

    Recent imaging connectome studies demonstrated that the human functional brain network follows an efficient small-world topology with cohesive functional modules and highly connected hubs. However, the functional motif patterns that represent the underlying information flow remain largely unknown. Here, we investigated motif patterns within directed human functional brain networks, which were derived from resting-state functional magnetic resonance imaging data with controlled confounding hemodynamic latencies. We found several significantly recurring motifs within the network, including the two-node reciprocal motif and five classes of three-node motifs. These recurring motifs were distributed in distinct patterns to support intra- and inter-module functional connectivity, which also promoted integration and segregation in network organization. Moreover, the significant participation of several functional hubs in the recurring motifs exhibited their critical role in global integration. Collectively, our findings highlight the basic architecture governing brain network organization and provide insight into the information flow mechanism underlying intrinsic brain activities. Hum Brain Mapp 38:2734-2750, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. Identifying Discriminating Network Motifs in YouTube Spam

    OpenAIRE

    O'Callaghan, Derek; Harrigan, Martin; Carthy, Joe; Cunningham, Pádraig

    2012-01-01

    Like other social media websites, YouTube is not immune from the attention of spammers. In particular, evidence can be found of attempts to attract users to malicious third-party websites. As this type of spam is often associated with orchestrated campaigns, it has a discernible network signature, based on networks derived from comments posted by users to videos. In this paper, we examine examples of different YouTube spam campaigns of this nature, and use a feature selection process to ident...

  9. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    Energy Technology Data Exchange (ETDEWEB)

    Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  10. Identifying Topology of Power Distribution Networks Based on Smart Meter Data

    OpenAIRE

    Satya, Jayadev P; Bhatt, Nirav; Pasumarthy, Ramkrishna; Rajeswaran, Aravind

    2016-01-01

    In a power distribution network, the network topology information is essential for an efficient operation of the network. This information of network connectivity is not accurately available, at the low voltage level, due to uninformed changes that happen from time to time. In this paper, we propose a novel data--driven approach to identify the underlying network topology including the load phase connectivity from time series of energy measurements. The proposed method involves the applicatio...

  11. Identifying Vulnerabilities and Hardening Attack Graphs for Networked Systems

    Energy Technology Data Exchange (ETDEWEB)

    Saha, Sudip; Vullinati, Anil K.; Halappanavar, Mahantesh; Chatterjee, Samrat

    2016-09-15

    We investigate efficient security control methods for protecting against vulnerabilities in networked systems. A large number of interdependent vulnerabilities typically exist in the computing nodes of a cyber-system; as vulnerabilities get exploited, starting from low level ones, they open up the doors to more critical vulnerabilities. These cannot be understood just by a topological analysis of the network, and we use the attack graph abstraction of Dewri et al. to study these problems. In contrast to earlier approaches based on heuristics and evolutionary algorithms, we study rigorous methods for quantifying the inherent vulnerability and hardening cost for the system. We develop algorithms with provable approximation guarantees, and evaluate them for real and synthetic attack graphs.

  12. Temperature-based Instanton Analysis: Identifying Vulnerability in Transmission Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kersulis, Jonas [Univ. of Michigan, Ann Arbor, MI (United States); Hiskens, Ian [Univ. of Michigan, Ann Arbor, MI (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bienstock, Daniel [Columbia Univ., New York, NY (United States)

    2015-04-08

    A time-coupled instanton method for characterizing transmission network vulnerability to wind generation fluctuation is presented. To extend prior instanton work to multiple-time-step analysis, line constraints are specified in terms of temperature rather than current. An optimization formulation is developed to express the minimum wind forecast deviation such that at least one line is driven to its thermal limit. Results are shown for an IEEE RTS-96 system with several wind-farms.

  13. Identifying key nodes based on improved structural holes in complex networks

    Science.gov (United States)

    Yu, Hui; Cao, Xi; Liu, Zun; Li, Yongjun

    2017-11-01

    Identifying key nodes in complex networks is of theoretical and practical significance. Local metrics such as degree centrality is simplest but cannot effectively identify the important bridging nodes. Global metrics such as betweenness and closeness centrality can better identify important nodes, but they are often restricted by the unknown topology and cannot be conveniently applied in large-scale networks. In this paper, we propose an effective ranking method based on an Improved Structural Holes (ISH) to identify the important nodes. ISH method only uses the degree of nodes and the nearest neighborhood information rather than considering the global structure of a network. Our experimental results on five complex networks show that the proposed method can effectively identify the key nodes in complex networks and can also be applied in large-scale or unconnected networks.

  14. Identifying potentially disruptive trends by means of keyword network analysis

    OpenAIRE

    Dotsika, F.; Watkins, A.

    2017-01-01

    Identifying potentially disruptive technologies is crucial to safeguarding competitive advantage by enabling stakeholders to assign resources in a manner that increases the chances of exploiting the disruption and/or mitigating the ensuing risks. However, disruptive technologies and emergent trends within known disruptive domains are mostly identified ex-post. This paper contributes to the ex-ante prediction of emergent technologies within disruptive domains by proposing a literature-driven m...

  15. Protein networks identify novel symbiogenetic genes resulting from plastid endosymbiosis.

    Science.gov (United States)

    Méheust, Raphaël; Zelzion, Ehud; Bhattacharya, Debashish; Lopez, Philippe; Bapteste, Eric

    2016-03-29

    The integration of foreign genetic information is central to the evolution of eukaryotes, as has been demonstrated for the origin of the Calvin cycle and of the heme and carotenoid biosynthesis pathways in algae and plants. For photosynthetic lineages, this coordination involved three genomes of divergent phylogenetic origins (the nucleus, plastid, and mitochondrion). Major hurdles overcome by the ancestor of these lineages were harnessing the oxygen-evolving organelle, optimizing the use of light, and stabilizing the partnership between the plastid endosymbiont and host through retargeting of proteins to the nascent organelle. Here we used protein similarity networks that can disentangle reticulate gene histories to explore how these significant challenges were met. We discovered a previously hidden component of algal and plant nuclear genomes that originated from the plastid endosymbiont: symbiogenetic genes (S genes). These composite proteins, exclusive to photosynthetic eukaryotes, encode a cyanobacterium-derived domain fused to one of cyanobacterial or another prokaryotic origin and have emerged multiple, independent times during evolution. Transcriptome data demonstrate the existence and expression of S genes across a wide swath of algae and plants, and functional data indicate their involvement in tolerance to oxidative stress, phototropism, and adaptation to nitrogen limitation. Our research demonstrates the "recycling" of genetic information by photosynthetic eukaryotes to generate novel composite genes, many of which function in plastid maintenance.

  16. Network Analysis Identifies Disease-Specific Pathways for Parkinson's Disease.

    Science.gov (United States)

    Monti, Chiara; Colugnat, Ilaria; Lopiano, Leonardo; Chiò, Adriano; Alberio, Tiziana

    2018-01-01

    Neurodegenerative diseases are characterized by the progressive loss of specific neurons in selected regions of the central nervous system. The main clinical manifestation (movement disorders, cognitive impairment, and/or psychiatric disturbances) depends on the neuron population being primarily affected. Parkinson's disease is a common movement disorder, whose etiology remains mostly unknown. Progressive loss of dopaminergic neurons in the substantia nigra causes an impairment of the motor control. Some of the pathogenetic mechanisms causing the progressive deterioration of these neurons are not specific for Parkinson's disease but are shared by other neurodegenerative diseases, like Alzheimer's disease and amyotrophic lateral sclerosis. Here, we performed a meta-analysis of the literature of all the quantitative proteomic investigations of neuronal alterations in different models of Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis to distinguish between general and Parkinson's disease-specific pattern of neurodegeneration. Then, we merged proteomics data with genetics information from the DisGeNET database. The comparison of gene and protein information allowed us to identify 25 proteins involved uniquely in Parkinson's disease and we verified the alteration of one of them, i.e., transaldolase 1 (TALDO1), in the substantia nigra of 5 patients. By using open-source bioinformatics tools, we identified the biological processes specifically affected in Parkinson's disease, i.e., proteolysis, mitochondrion organization, and mitophagy. Eventually, we highlighted four cellular component complexes mostly involved in the pathogenesis: the proteasome complex, the protein phosphatase 2A, the chaperonins CCT complex, and the complex III of the respiratory chain.

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

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Cindy

    2015-07-17

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

  18. Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks.

    Science.gov (United States)

    Park, Heewon; Shimamura, Teppei; Imoto, Seiya; Miyano, Satoru

    2017-10-20

    There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as an L1-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-based L1-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-based L1-type regularization method based on a modified k-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample

  19. Identifying Vulnerable Nodes of Complex Networks in Cascading Failures Induced by Node-Based Attacks

    Directory of Open Access Journals (Sweden)

    Shudong Li

    2013-01-01

    Full Text Available In the research on network security, distinguishing the vulnerable components of networks is very important for protecting infrastructures systems. Here, we probe how to identify the vulnerable nodes of complex networks in cascading failures, which was ignored before. Concerned with random attack (RA and highest load attack (HL on nodes, we model cascading dynamics of complex networks. Then, we introduce four kinds of weighting methods to characterize the nodes of networks including Barabási-Albert scale-free networks (SF, Watts-Strogatz small-world networks (WS, Erdos-Renyi random networks (ER, and two real-world networks. The simulations show that, for SF networks under HL attack, the nodes with small value of the fourth kind of weight are the most vulnerable and the ones with small value of the third weight are also vulnerable. Also, the real-world autonomous system with power-law distribution verifies these findings. Moreover, for WS and ER networks under both RA and HL attack, when the nodes have low tolerant ability, the ones with small value of the fourth kind of weight are more vulnerable and also the ones with high degree are easier to break down. The results give us important theoretical basis for digging the potential safety loophole and making protection strategy.

  20. Mirin: identifying microRNA regulatory modules in protein-protein interaction networks

    National Research Council Canada - National Science Library

    Yang, Ken-Chi; Hsu, Chia-Lang; Lin, Chen-Ching; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-01-01

    .... Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers...

  1. A bio-inspired methodology of identifying influential nodes in complex networks.

    Directory of Open Access Journals (Sweden)

    Cai Gao

    Full Text Available How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.

  2. Identifying and characterizing key nodes among communities based on electrical-circuit networks.

    Directory of Open Access Journals (Sweden)

    Fenghui Zhu

    Full Text Available Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

  3. Identifying and characterizing key nodes among communities based on electrical-circuit networks.

    Science.gov (United States)

    Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

    2014-01-01

    Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

  4. PhysarumSpreader: A New Bio-Inspired Methodology for Identifying Influential Spreaders in Complex Networks.

    Directory of Open Access Journals (Sweden)

    Hongping Wang

    Full Text Available Identifying influential spreaders in networks, which contributes to optimizing the use of available resources and efficient spreading of information, is of great theoretical significance and practical value. A random-walk-based algorithm LeaderRank has been shown as an effective and efficient method in recognizing leaders in social network, which even outperforms the well-known PageRank method. As LeaderRank is initially developed for binary directed networks, further extensions should be studied in weighted networks. In this paper, a generalized algorithm PhysarumSpreader is proposed by combining LeaderRank with a positive feedback mechanism inspired from an amoeboid organism called Physarum Polycephalum. By taking edge weights into consideration and adding the positive feedback mechanism, PhysarumSpreader is applicable in both directed and undirected networks with weights. By taking two real networks for examples, the effectiveness of the proposed method is demonstrated by comparing with other standard centrality measures.

  5. Efficient network reconstruction from dynamical cascades identifies small-world topology of neuronal avalanches.

    Directory of Open Access Journals (Sweden)

    Sinisa Pajevic

    2009-01-01

    Full Text Available Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB and Posterior Weighted Averaging (PWA methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics.

  6. Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

    Science.gov (United States)

    Poudel, R; Jumpponen, A; Schlatter, D C; Paulitz, T C; Gardener, B B McSpadden; Kinkel, L L; Garrett, K A

    2016-10-01

    Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

  7. Network analysis of translocated Takahe populations to identify disease surveillance targets.

    Science.gov (United States)

    Grange, Zoë L; VAN Andel, Mary; French, Nigel P; Gartrell, Brett D

    2014-04-01

    Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final

  8. Quantitative methods of identifying the key nodes in the illegal wildlife trade network.

    Science.gov (United States)

    Patel, Nikkita Gunvant; Rorres, Chris; Joly, Damien O; Brownstein, John S; Boston, Ray; Levy, Michael Z; Smith, Gary

    2015-06-30

    Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions.

  9. Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

    Directory of Open Access Journals (Sweden)

    Taosheng Xu

    Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

  10. Effective spreading from multiple leaders identified by percolation in social networks

    CERN Document Server

    Ji, Shenggong; Yeung, Chi Ho; Hu, Yanqing

    2015-01-01

    Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a group of influential spreaders, such that news propagating from them would lead to an extensive coverage and minimal redundancy. The method well utilizes the similarities between the pre-percolated state and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests on the Facebook networks show that this method outperforms conventional methods based on centrality. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation on social networks.

  11. FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks

    Directory of Open Access Journals (Sweden)

    Massimo Natale

    2014-08-01

    Full Text Available The characterization of the interacting behaviors of complex biological systems is a primary objective in protein–protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein–protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.

  12. A Topology Potential-Based Method for Identifying Essential Proteins from PPI Networks.

    Science.gov (United States)

    Li, Min; Lu, Yu; Wang, Jianxin; Wu, Fang-Xiang; Pan, Yi

    2015-01-01

    Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

  13. Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population.

    Science.gov (United States)

    Iqbal, Khalid; Buijsse, Brian; Wirth, Janine; Schulze, Matthias B; Floegel, Anna; Boeing, Heiner

    2016-03-01

    Data-reduction methods such as principal component analysis are often used to derive dietary patterns. However, such methods do not assess how foods are consumed in relation to each other. Gaussian graphical models (GGMs) are a set of novel methods that can address this issue. We sought to apply GGMs to derive sex-specific dietary intake networks representing consumption patterns in a German adult population. Dietary intake data from 10,780 men and 16,340 women of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were cross-sectionally analyzed to construct dietary intake networks. Food intake for each participant was estimated using a 148-item food-frequency questionnaire that captured the intake of 49 food groups. GGMs were applied to log-transformed intakes (grams per day) of 49 food groups to construct sex-specific food networks. Semiparametric Gaussian copula graphical models (SGCGMs) were used to confirm GGM results. In men, GGMs identified 1 major dietary network that consisted of intakes of red meat, processed meat, cooked vegetables, sauces, potatoes, cabbage, poultry, legumes, mushrooms, soup, and whole-grain and refined breads. For women, a similar network was identified with the addition of fried potatoes. Other identified networks consisted of dairy products and sweet food groups. SGCGMs yielded results comparable to those of GGMs. GGMs are a powerful exploratory method that can be used to construct dietary networks representing dietary intake patterns that reveal how foods are consumed in relation to each other. GGMs indicated an apparent major role of red meat intake in a consumption pattern in the studied population. In the future, identified networks might be transformed into pattern scores for investigating their associations with health outcomes. © 2016 American Society for Nutrition.

  14. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  15. Parallel ICA identifies sub-components of resting state networks that covary with behavioral indices.

    Science.gov (United States)

    Meier, Timothy B; Wildenberg, Joseph C; Liu, Jingyu; Chen, Jiayu; Calhoun, Vince D; Biswal, Bharat B; Meyerand, Mary E; Birn, Rasmus M; Prabhakaran, Vivek

    2012-01-01

    Parallel Independent Component Analysis (para-ICA) is a multivariate method that can identify complex relationships between different data modalities by simultaneously performing Independent Component Analysis on each data set while finding mutual information between the two data sets. We use para-ICA to test the hypothesis that spatial sub-components of common resting state networks (RSNs) covary with specific behavioral measures. Resting state scans and a battery of behavioral indices were collected from 24 younger adults. Group ICA was performed and common RSNs were identified by spatial correlation to publically available templates. Nine RSNs were identified and para-ICA was run on each network with a matrix of behavioral measures serving as the second data type. Five networks had spatial sub-components that significantly correlated with behavioral components. These included a sub-component of the temporo-parietal attention network that differentially covaried with different trial-types of a sustained attention task, sub-components of default mode networks that covaried with attention and working memory tasks, and a sub-component of the bilateral frontal network that split the left inferior frontal gyrus into three clusters according to its cytoarchitecture that differentially covaried with working memory performance. Additionally, we demonstrate the validity of para-ICA in cases with unbalanced dimensions using simulated data.

  16. Matrine Is Identified as a Novel Macropinocytosis Inducer by a Network Target Approach.

    Science.gov (United States)

    Zhang, Bo; Wang, Xin; Li, Yan; Wu, Min; Wang, Shu-Yan; Li, Shao

    2018-01-01

    Comprehensively understanding pharmacological functions of natural products is a key issue to be addressed for the discovery of new drugs. Unlike some single-target drugs, natural products always exert diverse therapeutic effects through acting on a "network" that consists of multiple targets, making it necessary to develop a systematic approach, e.g., network pharmacology, to reveal pharmacological functions of natural products and infer their mechanisms of action. In this work, to identify the "network target" of a natural product, we perform a functional analysis of matrine, a marketed drug in China extracted from a medical herb Ku-Shen ( Radix Sophorae Flavescentis ). Here, the network target of matrine was firstly predicted by drugCIPHER, a genome-wide target prediction method. Based on the network target of matrine, we performed a functional gene set enrichment analysis to computationally identify the potential pharmacological functions of matrine, most of which are supported by the literature evidence, including neurotoxicity and neuropharmacological activities of matrine. Furthermore, computational results demonstrated that matrine has the potential for the induction of macropinocytosis and the regulation of ATP metabolism. Our experimental data revealed that the large vesicles induced by matrine are consistent with the typical characteristics of macropinosome. Our verification results also suggested that matrine could decrease cellular ATP level. These findings demonstrated the availability and effectiveness of the network target strategy for identifying the comprehensive pharmacological functions of natural products.

  17. A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-06-01

    Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

  18. Identifying emergent social networks at a federally qualified health center-based farmers' market.

    Science.gov (United States)

    Alia, Kassandra A; Freedman, Darcy A; Brandt, Heather M; Browne, Teri

    2014-06-01

    Identifying potential mechanisms connecting farmers' market interventions with health, economic, and community outcomes could inform strategies for addressing health disparities. The present study used social network theory to guide the in-depth examination of naturally occurring social interactions at a farmers' market located at a federally qualified health center located in a rural, low-income community. Trained observers recorded 61 observation logs at the market over 18 weeks. Thematic analysis revealed a range of actors and nonhuman facilitators instrumental to the farmers' market context. These actors connected with one another for communication and relationship development, economic and financial exchange, education, resource sharing, community ownership of the farmers' market, and conflict resolution. These interactions provided opportunities for social networks to develop among attendees, which may have facilitated the acquisition of social supports related to improved health, economic and community outcomes. Results provide insight into the role social networks may play in mediating the relationship between a farmers' market intervention and individual benefits. Findings also contribute to defining the typology of social networks, which may further disentangle the complex relationships between social networks and health outcomes. Future research should identify strategies for purposefully targeting social networks as a way to reduce diet-related health disparities.

  19. Identifying public health policymakers' sources of information: comparing survey and network analyses.

    Science.gov (United States)

    Oliver, Kathryn A; de Vocht, Frank; Money, Annemarie; Everett, Martin

    2017-05-01

    Research suggests that policymakers often use personal contacts to find information and advice. However, the main sources of information for public health policymakers are not known. This study aims to describe policymakers' sources of information. A questionnaire survey of public health policymakers across Greater Manchester (GM) was carried out (response rate 48%). All policy actors above Director level involved in public health policy (finding, analyzing or producing information, producing or implementing policy) in GM were included in the sampling frame. Respondents were provided with a list of sources of information and asked which they used (categorical data) and to name specific individuals who acted as sources of information (network data). Data were analyzed using frequencies and network analysis. The most frequently chosen sources of information from the categorical data were NICE, government websites and Directors of Public Health. However, the network data showed that the main sources of information in the network were actually mid-level managers in the NHS, who had no direct expertise in public health. Academics and researchers did not feature in the network. Both survey and network analyses provide useful insights into how policymakers access information. Network analysis offers practical and theoretical contributions to the evidence-based policy debate. Identifying individuals who act as key users and producers of evidence allows academics to target actors likely to use and disseminate their work.

  20. Using the Contextual Hub Analysis Tool (CHAT) in Cytoscape to Identify Contextually Relevant Network Hubs.

    Science.gov (United States)

    Muetze, Tanja; Lynn, David J

    2017-09-13

    Highly connected nodes in biological networks are called network hubs. Hubs are topologically important to the structure of the network and have been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we provide a step-by-step protocol for using the Contextual Hub Analysis Tool (CHAT), an application within Cytoscape 3, which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene or protein expression data, and identify hub nodes that are more highly connected to contextual nodes than expected by chance. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  1. Identifying vital edges in Chinese air route network via memetic algorithm

    Directory of Open Access Journals (Sweden)

    Wenbo Du

    2017-02-01

    Full Text Available Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.

  2. Using image quality metrics to identify adversarial imagery for deep learning networks

    Science.gov (United States)

    Harguess, Josh; Miclat, Jeremy; Raheema, Julian

    2017-05-01

    Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classification, especially when training data are abundantly available, as is the case with the ImageNet project. However, several researchers have exposed potential vulnerabilities of these networks to carefully crafted adversarial imagery. Additionally, researchers have shown the sensitivity of these networks to some types of noise and distortion. In this paper, we investigate the use of no-reference image quality metrics to identify adversarial imagery and images of poor quality that could potentially fool a deep learning network or dramatically reduce its accuracy. Results are shown on several adversarial image databases with comparisons to popular image classification databases.

  3. NEW CANDIDATE GENES FOR SUSCEPTIBILITY TO TUBERCULOSIS IDENTIFIED THROUGH THE CONSTRUCTION AND ANALYSIS OF ASSOCIATIVE NETWORKS

    Directory of Open Access Journals (Sweden)

    Ye. Yu. Bragina

    2015-01-01

    Full Text Available Tuberculosis (TB is a common disease caused by infection of Mycobacterium tuberculosis and influenced by host hereditary and environmental factors. Accumulated genomic data obtained through the use of new methodological approaches, including analysis of associative networks, contribute to the understanding of the hereditary basis of the disease. In the current study, we carried out the reconstruction and analysis of associative network representing molecular genetic links between proteins/genes involved in the development of TB. In the associative network, well studied proteins and genes with a decisive importance in the efficiency of the human immune response against a pathogen predominated. However, this approach identified 12 new genes encoding for the respective proteins in the associative network polymorphismsof which has not been studied regarding the development of TB.

  4. Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

    Science.gov (United States)

    Hu, Fang; Liu, Yuhua

    2015-02-01

    The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate.

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

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2007-07-01

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

  6. Matrine Is Identified as a Novel Macropinocytosis Inducer by a Network Target Approach

    Directory of Open Access Journals (Sweden)

    Bo Zhang

    2018-01-01

    Full Text Available Comprehensively understanding pharmacological functions of natural products is a key issue to be addressed for the discovery of new drugs. Unlike some single-target drugs, natural products always exert diverse therapeutic effects through acting on a “network” that consists of multiple targets, making it necessary to develop a systematic approach, e.g., network pharmacology, to reveal pharmacological functions of natural products and infer their mechanisms of action. In this work, to identify the “network target” of a natural product, we perform a functional analysis of matrine, a marketed drug in China extracted from a medical herb Ku-Shen (Radix Sophorae Flavescentis. Here, the network target of matrine was firstly predicted by drugCIPHER, a genome-wide target prediction method. Based on the network target of matrine, we performed a functional gene set enrichment analysis to computationally identify the potential pharmacological functions of matrine, most of which are supported by the literature evidence, including neurotoxicity and neuropharmacological activities of matrine. Furthermore, computational results demonstrated that matrine has the potential for the induction of macropinocytosis and the regulation of ATP metabolism. Our experimental data revealed that the large vesicles induced by matrine are consistent with the typical characteristics of macropinosome. Our verification results also suggested that matrine could decrease cellular ATP level. These findings demonstrated the availability and effectiveness of the network target strategy for identifying the comprehensive pharmacological functions of natural products.

  7. Identifying influential spreaders in complex networks through local effective spreading paths

    Science.gov (United States)

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

    2017-05-01

    How to effectively identify a set of influential spreaders in complex networks is of great theoretical and practical value, which can help to inhibit the rapid spread of epidemics, promote the sales of products by word-of-mouth advertising, and so on. A naive strategy is to select the top ranked nodes as identified by some centrality indices, and other strategies are mainly based on greedy methods and heuristic methods. However, most of those approaches did not concern the connections between nodes. Usually, the distances between the selected spreaders are very close, leading to a serious overlapping of their influence. As a consequence, the global influence of the spreaders in networks will be greatly reduced, which largely restricts the performance of those methods. In this paper, a simple and efficient method is proposed to identify a set of discrete yet influential spreaders. By analyzing the spreading paths in the network, we present the concept of effective spreading paths and measure the influence of nodes via expectation calculation. The numerical analysis in undirected and directed networks all show that our proposed method outperforms many other centrality-based and heuristic benchmarks, especially in large-scale networks. Besides, experimental results on different spreading models and parameters demonstrates the stability and wide applicability of our method.

  8. Interpreting & Biomechanics. PEPNet Tipsheet

    Science.gov (United States)

    PEPNet-Northeast, 2001

    2001-01-01

    Cumulative trauma disorder (CTD) refers to a collection of disorders associated with nerves, muscles, tendons, bones, and the neurovascular (nerves and related blood vessels) system. CTD symptoms may involve the neck, back, shoulders, arms, wrists, or hands. Interpreters with CTD may experience a variety of symptoms including: pain, joint…

  9. Interpreting. PEPNet Tipsheet

    Science.gov (United States)

    Darroch, Kathleen

    2010-01-01

    An interpreter's role is to facilitate communication and convey all auditory and signed information so that both hearing and deaf individuals may fully interact. The common types of services provided by interpreters are: (1) American Sign Language (ASL) Interpretation--a visual-gestural language with its own linguistic features; (2) Sign Language…

  10. Oral Transliterating. PEPNet Tipsheet

    Science.gov (United States)

    Troiano, Claire A.

    2010-01-01

    An oral transliterator provides communication access to a person who is deaf or hard of hearing and who uses speechreading and speaking as a means of communicating. The oral transliterator, positioned in front of the speechreader, inaudibly repeats the spoken message, making it as speechreadable as possible. This is called Expressive Oral…

  11. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  12. CRNreals: a toolbox for distinguishability and identifiability analysis of biochemical reaction networks.

    Science.gov (United States)

    Szederkényi, Gábor; Banga, Julio R; Alonso, Antonio A

    2012-06-01

    Chemical reaction network theory is widely used in modeling and analyzing complex biochemical systems such as metabolic networks and cell signalling pathways. Being able to produce all the biologically and chemically important qualitative dynamical features, chemical reaction networks (CRNs) have attracted significant attention in the systems biology community. It is well-known that the reliable inference of CRN models generally requires thorough identifiability and distinguishability analysis together with carefully selected prior modeling assumptions. Here, we present a software toolbox CRNreals that supports the distinguishability and identifiability analysis of CRN models using recently published optimization-based procedures. The CRNreals toolbox and the associated documentation are available at http://www.iim.csic.es/~gingproc/CRNreals/. The toolbox runs under the popular MATLAB computational environment and supports several free and commercial linear programming and mixed integer linear programming solvers.

  13. Social Network Analysis: A Simple but Powerful Tool for Identifying Teacher Leaders

    Science.gov (United States)

    Smith, P. Sean; Trygstad, Peggy J.; Hayes, Meredith L.

    2018-01-01

    Instructional teacher leadership is central to a vision of distributed leadership. However, identifying instructional teacher leaders can be a daunting task, particularly for administrators who find themselves either newly appointed or faced with high staff turnover. This article describes the use of social network analysis (SNA), a simple but…

  14. Discrete particle swarm optimization for identifying community structures in signed social networks.

    Science.gov (United States)

    Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

    2014-10-01

    Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Bo Yang

    2013-01-01

    Full Text Available Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso, the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1 susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes.

  16. Multivariate synchrony modules identified through multiple subject community detection in functional brain networks.

    Science.gov (United States)

    Bolaños, Marcos E; Bernat, Edward M; Aviyente, Selin

    2011-01-01

    The functional connectivity of the human brain may be described by modeling interactions among its neural assemblies as a graph composed of vertices and edges. It has recently been shown that functional brain networks belong to a class of scale-free complex networks for which graphs have helped define an association between function and topology. These networks have been shown to possess a heterogenous structure composed of clusters, dense regions of strongly associated nodes, which represent multivariate relationships among nodes. Network clustering algorithms classify the nodes based on a similarity measure representing the bivariate relationships and similar to unsupervised learning is performed without a priori information. In this paper, we propose a method for partitioning a set of networks representing different subjects and reveal a community structure common to multiple subjects. We apply this community identifying algorithm to functional brain networks during a cognitive control task, in particular the error-related negativity (ERN), to evaluate how the brain organizes itself during error-monitoring.

  17. A Visual Analytics Technique for Identifying Heat Spots in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Marian Sorin Nistor

    2016-12-01

    Full Text Available The decision takers of the public transportation system, as part of urban critical infrastructures, need to increase the system resilience. For doing so, we identified analysis tools for biological networks as an adequate basis for visual analytics in that domain. In the paper at hand we therefore translate such methods for transportation systems and show the benefits by applying them on the Munich subway network. Here, visual analytics is used to identify vulnerable stations from different perspectives. The applied technique is presented step by step. Furthermore, the key challenges in applying this technique on transportation systems are identified. Finally, we propose the implementation of the presented features in a management cockpit to integrate the visual analytics mantra for an adequate decision support on transportation systems.

  18. Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies.

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    Woo Jin Lee

    Full Text Available Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the USPTO and KIPO. To identify the variables that affect convergence, we used quadratic assignment procedures (QAP. From our analysis, we observed the patent network ecology related to convergence and found technologies that have great potential to converge with other robotics technologies. The results of our study are expected to contribute to setting up convergence based R&D policies for robotics, which can lead new innovation.

  19. Identifying protein complexes based on density and modularity in protein-protein interaction network.

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    Ren, Jun; Wang, Jianxin; Li, Min; Wang, Lusheng

    2013-01-01

    Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity. To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, fρ, as fρ= (density)(ρ*)(modularity)(1-ρ), and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0modularity. By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

  20. A modified evidential methodology of identifying influential nodes in weighted networks

    Science.gov (United States)

    Gao, Cai; Wei, Daijun; Hu, Yong; Mahadevan, Sankaran; Deng, Yong

    2013-11-01

    How to identify influential nodes in complex networks is still an open hot issue. In the existing evidential centrality (EVC), node degree distribution in complex networks is not taken into consideration. In addition, the global structure information has also been neglected. In this paper, a new Evidential Semi-local Centrality (ESC) is proposed by modifying EVC in two aspects. Firstly, the Basic Probability Assignment (BPA) of degree generated by EVC is modified according to the actual degree distribution, rather than just following uniform distribution. BPA is the generation of probability in order to model uncertainty. Secondly, semi-local centrality combined with modified EVC is extended to be applied in weighted networks. Numerical examples are used to illustrate the efficiency of the proposed method.

  1. Default-Mode Network Activity Identified by Group Independent Component Analysis

    Science.gov (United States)

    Liu, Conghui; Zhuang, Jie; Peng, Danling; Yu, Guoliang; Yang, Yanhui

    Default-mode network activity refers to some regional increase in blood oxygenation level-dependent (BOLD) signal during baseline than cognitive tasks. Recent functional imaging studies have found co-activation in a distributed network of cortical regions, including ventral anterior cingulate cortex (vACC) and posterior cingulate cortex (PPC) that characterize the default mode of human brain. In this study, general linear model and group independent component analysis (ICA) were utilized to analyze the fMRI data obtained from two language tasks. Both methods yielded similar, but not identical results and detected a resting deactivation network at some midline regions including anterior and posterior cingulate cortex and precuneus. Particularly, the group ICA method segregated functional elements into two separate maps and identified ventral cingulate component and fronto-parietal component. These results suggest that these two components might be linked to different mental function during "resting" baseline.

  2. Identifying influential nodes based on graph signal processing in complex networks

    Science.gov (United States)

    Zhao, Jia; Yu, Li; Li, Jing-Ru; Zhou, Peng

    2015-05-01

    Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity (i.e., different attributes such as interest, energy, age, and so on) ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal processing based centrality (GSPC) method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics. Project supported by the National Natural Science Foundation of China (Grant No. 61231010) and the Fundamental Research Funds for the Central Universities, China (Grant No. HUST No. 2012QN076).

  3. Identifying influential nodes in large-scale directed networks: the role of clustering.

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    Duan-Bing Chen

    Full Text Available Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

  4. Identifying influential nodes in large-scale directed networks: the role of clustering.

    Science.gov (United States)

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

  5. Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

    Science.gov (United States)

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. PMID:24204833

  6. Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks.

    Science.gov (United States)

    Mitra, Jhimli; Shen, Kai-Kai; Ghose, Soumya; Bourgeat, Pierrick; Fripp, Jurgen; Salvado, Olivier; Pannek, Kerstin; Taylor, D Jamie; Mathias, Jane L; Rose, Stephen

    2016-04-01

    Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury. Copyright

  7. Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

    Directory of Open Access Journals (Sweden)

    Xianjun Shen

    Full Text Available How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment. It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

  8. Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

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    Bert van der Zwaag

    Full Text Available The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD, and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.

  9. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma.

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    Rangaprakash, D; Dretsch, Michael N; Venkataraman, Archana; Katz, Jeffrey S; Denney, Thomas S; Deshpande, Gopikrishna

    2017-10-23

    Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley

  10. Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv.

    Science.gov (United States)

    Melak, Tilahun; Gakkhar, Sunita

    2015-01-01

    Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

  11. Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv

    Directory of Open Access Journals (Sweden)

    Tilahun Melak

    2015-01-01

    Full Text Available Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

  12. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

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    Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

  13. Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus

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    Herwig Ralf

    2008-06-01

    Full Text Available Abstract Background Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide. The main goal of these studies is the detailed analysis of a particular aspect of the disease. Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches. Results Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM. Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes. The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study. Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction (128 of yet barely characterized novel candidate genes. Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks. Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any – yet non-associated – gene. Conclusion In our paper we have identified a core set of 213 T2DM candidate genes by a meta-analysis of existing data sources. We have

  14. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

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    Amozegar, M; Khorasani, K

    2016-04-01

    In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network

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    Qiguo Dai

    2014-01-01

    Full Text Available Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity.

  16. Significant Deregulated Pathways in Diabetes Type II Complications Identified through Expression Based Network Biology

    Science.gov (United States)

    Ukil, Sanchaita; Sinha, Meenakshee; Varshney, Lavneesh; Agrawal, Shipra

    Type 2 Diabetes is a complex multifactorial disease, which alters several signaling cascades giving rise to serious complications. It is one of the major risk factors for cardiovascular diseases. The present research work describes an integrated functional network biology approach to identify pathways that get transcriptionally altered and lead to complex complications thereby amplifying the phenotypic effect of the impaired disease state. We have identified two sub-network modules, which could be activated under abnormal circumstances in diabetes. Present work describes key proteins such as P85A and SRC serving as important nodes to mediate alternate signaling routes during diseased condition. P85A has been shown to be an important link between stress responsive MAPK and CVD markers involved in fibrosis. MAPK8 has been shown to interact with P85A and further activate CTGF through VEGF signaling. We have traced a novel and unique route correlating inflammation and fibrosis by considering P85A as a key mediator of signals. The next sub-network module shows SRC as a junction for various signaling processes, which results in interaction between NF-kB and beta catenin to cause cell death. The powerful interaction between these important genes in response to transcriptionally altered lipid metabolism and impaired inflammatory response via SRC causes apoptosis of cells. The crosstalk between inflammation, lipid homeostasis and stress, and their serious effects downstream have been explained in the present analyses.

  17. A local average connectivity-based method for identifying essential proteins from the network level.

    Science.gov (United States)

    Li, Min; Wang, Jianxin; Chen, Xiang; Wang, Huan; Pan, Yi

    2011-06-01

    Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality from the network level. Essential proteins have been found to be more abundant among those highly connected proteins. However, there exist a number of highly connected proteins which are not essential. By analyzing these proteins, we find that few of their neighbors interact with each other. Thus, we propose a new local method, named LAC, to determine a protein's essentiality by evaluating the relationship between a protein and its neighbors. The performance of LAC is validated based on the yeast protein interaction networks obtained from two different databases: DIP and BioGRID. The experimental results of the two networks show that the number of essential proteins predicted by LAC clearly exceeds that explored by Degree Centrality (DC). More over, LAC is also compared with other seven measures of protein centrality (Neighborhood Component (DMNC), Betweenness Centrality (BC), Closeness Centrality (CC), Bottle Neck (BN), Information Centrality (IC), Eigenvector Centrality (EC), and Subgraph Centrality (SC)) in identifying essential proteins. The comparison results based on the validations of sensitivity, specificity, F-measure, positive predictive value, negative predictive value, and accuracy consistently show that LAC outweighs these seven previous methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. Transcriptional Network Analysis Identifies BACH1 as a Master Regulator of Breast Cancer Bone Metastasis

    Science.gov (United States)

    Liang, Yajun; Wu, Heng; Lei, Rong; Chong, Robert A.; Wei, Yong; Lu, Xin; Tagkopoulos, Ilias; Kung, Sun-Yuan; Yang, Qifeng; Hu, Guohong; Kang, Yibin

    2012-01-01

    The application of functional genomic analysis of breast cancer metastasis has led to the identification of a growing number of organ-specific metastasis genes, which often function in concert to facilitate different steps of the metastatic cascade. However, the gene regulatory network that controls the expression of these metastasis genes remains largely unknown. Here, we demonstrate a computational approach for the deconvolution of transcriptional networks to discover master regulators of breast cancer bone metastasis. Several known regulators of breast cancer bone metastasis such as Smad4 and HIF1 were identified in our analysis. Experimental validation of the networks revealed BACH1, a basic leucine zipper transcription factor, as the common regulator of several functional metastasis genes, including MMP1 and CXCR4. Ectopic expression of BACH1 enhanced the malignance of breast cancer cells, and conversely, BACH1 knockdown significantly reduced bone metastasis. The expression of BACH1 and its target genes was linked to the higher risk of breast cancer recurrence in patients. This study established BACH1 as the master regulator of breast cancer bone metastasis and provided a paradigm to identify molecular determinants in complex pathological processes. PMID:22875853

  19. Network analysis identifies SOD2 mRNA as a potential biomarker for Parkinson's disease.

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    Jose A Santiago

    Full Text Available Increasing evidence indicates that Parkinson's disease (PD and type 2 diabetes (T2DM share dysregulated molecular networks. We identified 84 genes shared between PD and T2DM from curated disease-gene databases. Nitric oxide biosynthesis, lipid and carbohydrate metabolism, insulin secretion and inflammation were identified as common dysregulated pathways. A network prioritization approach was implemented to rank genes according to their distance to seed genes and their involvement in common biological pathways. Quantitative polymerase chain reaction assays revealed that a highly ranked gene, superoxide dismutase 2 (SOD2, is upregulated in PD patients compared to healthy controls in 192 whole blood samples from two independent clinical trials, the Harvard Biomarker Study (HBS and the Diagnostic and Prognostic Biomarkers in Parkinson's disease (PROBE. The results from this study reinforce the idea that shared molecular networks between PD and T2DM provides an additional source of biologically meaningful biomarkers. Evaluation of this biomarker in de novo PD patients and in a larger prospective longitudinal study is warranted.

  20. Integrative meta-analysis identifies microRNA-regulated networks in infantile hemangioma.

    Science.gov (United States)

    Bertoni, Natália; Pereira, Lied M S; Severino, Fábio E; Moura, Regina; Yoshida, Winston B; Reis, Patricia P

    2016-01-15

    Hemangioma is a common benign tumor in the childhood; however our knowledge about the molecular mechanisms of hemangioma development and progression are still limited. Currently, microRNAs (miRNAs) have been shown as gene expression regulators with an important role in disease pathogenesis. Our goals were to identify miRNA-mRNA expression networks associated with infantile hemangioma. We performed a meta-analysis of previously published gene expression datasets including 98 hemangioma samples. Deregulated genes were further used to identify microRNAs as potential regulators of gene expression in infantile hemangioma. Data were integrated using bioinformatics methods, and genes were mapped in proteins, which were then used to construct protein-protein interaction networks. Deregulated genes play roles in cell growth and differentiation, cell signaling, angiogenesis and vasculogenesis. Regulatory networks identified included microRNAs miR-9, miR-939 and let-7 family; these microRNAs showed the most number of interactions with deregulated genes in infantile hemangioma, suggesting that they may have an important role in the molecular mechanisms of disease. Additionally, results were used to identify drug-gene interactions and druggable gene categories using Drug-Gene Interaction Database. We show that microRNAs and microRNA-target genes may be useful biomarkers for the development of novel therapeutic strategies for patients with infantile hemangioma. microRNA-regulated pathways may play a role in infantile hemangioma development and progression and may be potentially useful for future development of novel therapeutic strategies for patients with infantile hemangioma.

  1. The use of neural networks in identifying error sources in satellite-derived tropical SST estimates.

    Science.gov (United States)

    Lee, Yung-Hsiang; Ho, Chung-Ru; Su, Feng-Chun; Kuo, Nan-Jung; Cheng, Yu-Hsin

    2011-01-01

    An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%.

  2. The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

    Directory of Open Access Journals (Sweden)

    Yu-Hsin Cheng

    2011-07-01

    Full Text Available An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES. By using the Back Propagation Network (BPN algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%.

  3. Mirin: identifying microRNA regulatory modules in protein-protein interaction networks.

    Science.gov (United States)

    Yang, Ken-Chi; Hsu, Chia-Lang; Lin, Chen-Ching; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-09-01

    Exploring microRNA (miRNA) regulations and protein-protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein-protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer. Mirin is freely available at http://mirin.ym.edu.tw/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. A model to identify urban traffic congestion hotspots in complex networks

    CERN Document Server

    Solé-Ribalta, Albert; Arenas, Alex

    2016-01-01

    The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real-traffic data, show that the proposed model is capable of identifying susceptible junctions that might becomes hotspots if mobility demand increases.

  5. Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network.

    Science.gov (United States)

    Chen, Xi; Wang, Qiao-Ling; Zhang, Meng-Hui

    2017-10-01

    The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including IL6, EPHA2 and GSTT1 and 5 of these 14 key genes were validated by RT-qPCR. Therefore, the current study identified 14 key genes in glaucoma, which may be potential biomarkers to use in the diagnosis of glaucoma and aid in identifying the molecular mechanism of this disease.

  6. Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

    Science.gov (United States)

    Shen, Xianjun; Yi, Li; Yi, Yang; Yang, Jincai; He, Tingting; Hu, Xiaohua

    2015-01-01

    The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization.

  7. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target.

    Science.gov (United States)

    Horvath, S; Zhang, B; Carlson, M; Lu, K V; Zhu, S; Felciano, R M; Laurance, M F; Zhao, W; Qi, S; Chen, Z; Lee, Y; Scheck, A C; Liau, L M; Wu, H; Geschwind, D H; Febbo, P G; Kornblum, H I; Cloughesy, T F; Nelson, S F; Mischel, P S

    2006-11-14

    Glioblastoma is the most common primary malignant brain tumor of adults and one of the most lethal of all cancers. Patients with this disease have a median survival of 15 months from the time of diagnosis despite surgery, radiation, and chemotherapy. New treatment approaches are needed. Recent works suggest that glioblastoma patients may benefit from molecularly targeted therapies. Here, we address the compelling need for identification of new molecular targets. Leveraging global gene expression data from two independent sets of clinical tumor samples (n = 55 and n = 65), we identify a gene coexpression module in glioblastoma that is also present in breast cancer and significantly overlaps with the "metasignature" for undifferentiated cancer. Studies in an isogenic model system demonstrate that this module is downstream of the mutant epidermal growth factor receptor, EGFRvIII, and that it can be inhibited by the epidermal growth factor receptor tyrosine kinase inhibitor Erlotinib. We identify ASPM (abnormal spindle-like microcephaly associated) as a key gene within this module and demonstrate its overexpression in glioblastoma relative to normal brain (or body tissues). Finally, we show that ASPM inhibition by siRNA-mediated knockdown inhibits tumor cell proliferation and neural stem cell proliferation, supporting ASPM as a potential molecular target in glioblastoma. Our weighted gene coexpression network analysis provides a blueprint for leveraging genomic data to identify key control networks and molecular targets for glioblastoma, and the principle eluted from our work can be applied to other cancers.

  8. Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

    Science.gov (United States)

    Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

    2017-08-01

    This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

  9. Using Social Network Analysis to Identify Key Child Care Center Staff for Obesity Prevention Interventions: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Jennifer Marks

    2013-01-01

    Full Text Available Introduction. Interest has grown in how systems thinking could be used in obesity prevention. Relationships between key actors, represented by social networks, are an important focus for considering intervention in systems. Method. Two long day care centers were selected in which previous obesity prevention programs had been implemented. Measures showed ways in which physical activity and dietary policy are conversations and actions transacted through social networks (interrelationships within centers, via an eight item closed-ended social network questionnaire. Questionnaire data were collected from (17/20; response rate 85% long day care center staff. Social network density and centrality statistics were calculated, using UCINET social network software, to examine the role of networks in obesity prevention. Results. “Degree” (influence and “betweeness” (gatekeeper centrality measures of staff inter-relationships about physical activity, dietary, and policy information identified key players in each center. Network density was similar and high on some relationship networks in both centers but markedly different in others, suggesting that the network tool identified unique center social dynamics. These differences could potentially be the focus of future team capacity building. Conclusion. Social network analysis is a feasible and useful method to identify existing obesity prevention networks and key personnel in long day care centers.

  10. Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.

    Science.gov (United States)

    Chen, Lei; Pan, Hongying; Zhang, Yu-Hang; Feng, Kaiyan; Kong, XiangYin; Huang, Tao; Cai, Yu-Dong

    2017-10-02

    Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein-protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

  11. Identifying the main paths of information diffusion in online social networks

    Science.gov (United States)

    Zhu, Hengmin; Yin, Xicheng; Ma, Jing; Hu, Wei

    2016-06-01

    Recently, an increasing number of researches on relationship strength show that there are some socially active links in online social networks. Furthermore, it is likely that there exist main paths which play the most significant role in the process of information diffusion. Although much of previous work has focused on the pathway of a specific event, there are hardly any scholars that have extracted the main paths. To identify the main paths of online social networks, we proposed a method which measures the weights of links based on historical interaction records. The influence of node based on forwarding amount is quantified and top-ranked nodes are selected as the influential users. The path importance is evaluated by calculating the probability that a message would spread via this path. We applied our method to a real-world network and found interesting insights. Each influential user can access another one via a short main path and the distribution of main paths shows significant community effect.

  12. Connecting the Disconnected: Social Work and Social Network Analysis. A Methodological Approach to Identifying Network Peer Leaders

    Directory of Open Access Journals (Sweden)

    del Fresno García, Miguel

    2015-02-01

    Full Text Available Social network theory and analysis (SNA offers a useful conceptual framework and a robust set of methods for understanding, analysing, and representing the pattern of social interactions that surround individuals forming an overall network of ties. SNA provides both insights and applications regarding relational structures that may be consequential for individual and collective agency. Despite the fact that both SNA and social work focus on relationships and behaviour, and that each discipline could substantively inform the other, there remains a significant lack of intersection between the two disciplines. In response to this gap, SNA applied to social work can provide additional ways to both diagnose and intervene behaviourally through the following approaches: a by identifying key players in promoting the dissemination of behavioral changes in networks; b by segmenting and identifying groups, cliques and communities; c by supporting behavioural change through social ties surrounding the individual; and d by aligning and applying specific interventions that draw on mutually interactive processes in terms of individual influences on networks, as well as network influences on individuals. SNA provides social work with an additional lens and set of tools based on the constellation of interactions surrounding individuals, families, groups or communities that supports understanding, diagnosis, and intervention.La Teoría y Análisis de Redes Sociales (SNA ofrece un conjunto de métodos de análisis de las interacciones sociales de los seres humanos, que permiten de forma específica investigar las estructuras relacionales y la representación de éstas como redes. SNA proporciona tanto acceso a nuevo conocimiento como la representación de las estructuras relacionales y como éstas pueden ser consecuencia de la acción individual y colectiva. A pesar de que tanto el SNA como el Trabajo Social tienen su foco en las relaciones y el comportamiento, de

  13. Mapping industrial networks as an approach to identify inter-organisational collaborative potential in new product development

    DEFF Research Database (Denmark)

    Parraguez, Pedro; Maier, Anja

    2012-01-01

    Increased industrial complexity and specialization is pushing organizations to participate more proactively in collaborative networks. These networks enable them to create new products and services requiring a pool of knowledge and capabilities going beyond the traditional organizational boundaries....... Consequently, identifying and selecting potential partners to establish collaboration agreements can be a key activity in the new product development process. This paper explores the implications of mapping industrial networks with the purpose of identifying inter-organisational collaborative potential....... The analysis is contextualized mapping the Danish Cleantech industry and depict the uses of the visualization and analysis of industrial networks in the selection of co-developing partners....

  14. Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding.

    Science.gov (United States)

    Kommadath, Arun; Bao, Hua; Arantes, Adriano S; Plastow, Graham S; Tuggle, Christopher K; Bearson, Shawn M D; Guan, Le Luo; Stothard, Paul

    2014-06-09

    Salmonella enterica serovar Typhimurium is a gram-negative bacterium that can colonise the gut of humans and several species of food producing farm animals to cause enteric or septicaemic salmonellosis. While many studies have looked into the host genetic response to Salmonella infection, relatively few have used correlation of shedding traits with gene expression patterns to identify genes whose variable expression among different individuals may be associated with differences in Salmonella clearance and resistance. Here, we aimed to identify porcine genes and gene co-expression networks that differentiate distinct responses to Salmonella challenge with respect to faecal Salmonella shedding. Peripheral blood transcriptome profiles from 16 pigs belonging to extremes of the trait of faecal Salmonella shedding counts recorded up to 20 days post-inoculation (low shedders (LS), n = 8; persistent shedders (PS), n = 8) were generated using RNA-sequencing from samples collected just before (day 0) and two days after (day 2) Salmonella inoculation. Weighted gene co-expression network analysis (WGCNA) of day 0 samples identified four modules of co-expressed genes significantly correlated with Salmonella shedding counts upon future challenge. Two of those modules consisted largely of innate immunity related genes, many of which were significantly up-regulated at day 2 post-inoculation. The connectivity at both days and the mean gene-wise expression levels at day 0 of the genes within these modules were higher in networks constructed using LS samples alone than those using PS alone. Genes within these modules include those previously reported to be involved in Salmonella resistance such as SLC11A1 (formerly NRAMP1), TLR4, CD14 and CCR1 and those for which an association with Salmonella is novel, for example, SIGLEC5, IGSF6 and TNFSF13B. Our analysis integrates gene co-expression network analysis, gene-trait correlations and differential expression to provide new

  15. [Study of algorithms to identify schizophrenia in the SNIIRAM database conducted by the REDSIAM network].

    Science.gov (United States)

    Quantin, C; Collin, C; Frérot, M; Besson, J; Cottenet, J; Corneloup, M; Soudry-Faure, A; Mariet, A-S; Roussot, A

    2017-10-01

    The aim of the REDSIAM network is to foster communication between users of French medico-administrative databases and to validate and promote analysis methods suitable for the data. Within this network, the working group "Mental and behavioral disorders" took an interest in algorithms to identify adult schizophrenia in the SNIIRAM database and inventoried identification criteria for patients with schizophrenia in these databases. The methodology was based on interviews with nine experts in schizophrenia concerning the procedures they use to identify patients with schizophrenia disorders in databases. The interviews were based on a questionnaire and conducted by telephone. The synthesis of the interviews showed that the SNIIRAM contains various tables which allow coders to identify patients suffering from schizophrenia: chronic disease status, drugs and hospitalizations. Taken separately, these criteria were not sufficient to recognize patients with schizophrenia, an algorithm should be based on all of them. Apparently, only one-third of people living with schizophrenia benefit from the longstanding disease status. Not all patients are hospitalized, and coding for diagnoses at the hospitalization, notably for short stays in medicine, surgery or obstetrics departments, is not exhaustive. As for treatment with antipsychotics, it is not specific enough as such treatments are also prescribed to patients with bipolar disorders, or even other disorders. It seems appropriate to combine these complementary criteria, while keeping in mind out-patient care (every year 80,000 patients are seen exclusively in an outpatient setting), even if these data are difficult to link with other information. Finally, the experts made three propositions for selection algorithms of patients with schizophrenia. Patients with schizophrenia can be relatively accurately identified using SNIIRAM data. Different combinations of the selected criteria must be used depending on the objectives and

  16. Genetic networks inducing invasive growth in Saccharomyces cerevisiae identified through systematic genome-wide overexpression.

    Science.gov (United States)

    Shively, Christian A; Eckwahl, Matthew J; Dobry, Craig J; Mellacheruvu, Dattatreya; Nesvizhskii, Alexey; Kumar, Anuj

    2013-04-01

    The budding yeast Saccharomyces cerevisiae can respond to nutritional and environmental stress by implementing a morphogenetic program wherein cells elongate and interconnect, forming pseudohyphal filaments. This growth transition has been studied extensively as a model signaling system with similarity to processes of hyphal development that are linked with virulence in related fungal pathogens. Classic studies have identified core pseudohyphal growth signaling modules in yeast; however, the scope of regulatory networks that control yeast filamentation is broad and incompletely defined. Here, we address the genetic basis of yeast pseudohyphal growth by implementing a systematic analysis of 4909 genes for overexpression phenotypes in a filamentous strain of S. cerevisiae. Our results identify 551 genes conferring exaggerated invasive growth upon overexpression under normal vegetative growth conditions. This cohort includes 79 genes lacking previous phenotypic characterization. Pathway enrichment analysis of the gene set identifies networks mediating mitogen-activated protein kinase (MAPK) signaling and cell cycle progression. In particular, overexpression screening suggests that nuclear export of the osmoresponsive MAPK Hog1p may enhance pseudohyphal growth. The function of nuclear Hog1p is unclear from previous studies, but our analysis using a nuclear-depleted form of Hog1p is consistent with a role for nuclear Hog1p in repressing pseudohyphal growth. Through epistasis and deletion studies, we also identified genetic relationships with the G2 cyclin Clb2p and phenotypes in filamentation induced by S-phase arrest. In sum, this work presents a unique and informative resource toward understanding the breadth of genes and pathways that collectively constitute the molecular basis of filamentation.

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

    Science.gov (United States)

    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.

  18. Gene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2016-12-01

    Full Text Available Background/Aims: Pediatric sepsis is a disease that threatens life of children. The incidence of pediatric sepsis is higher in developing countries due to various reasons, such as insufficient immunization and nutrition, water and air pollution, etc. Exploring the potential genes via different methods is of significance for the prevention and treatment of pediatric sepsis. This study aimed to identify potential genes associated with pediatric sepsis utilizing analysis of gene network and entropy. Methods: The mRNA expression in the blood samples collected from 20 septic children and 30 healthy controls was quantified by using Affymetrix HG-U133A microarray. Two condition-specific protein-protein interaction networks (PINs, one for the healthy control and the other one for the children with sepsis, were deduced by combining the fundamental human PINs with gene expression profiles in the two phenotypes. Subsequently, distinct modules from the two conditional networks were extracted by adopting a maximal clique-merging approach. Delta entropy (ΔS was calculated between sepsis and control modules. Results: Then, key genes displaying changes in gene composition were identified by matching the control and sepsis modules. Two objective modules were obtained, in which ribosomal protein RPL4 and RPL9 as well as TOP2A were probably considered as the key genes differentiating sepsis from healthy controls. Conclusion: According to previous reports and this work, TOP2A is the potential gene therapy target for pediatric sepsis. The relationship between pediatric sepsis and RPL4 and RPL9 needs further investigation.

  19. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  20. Identifying the greatest team and captain—A complex network approach to cricket matches

    Science.gov (United States)

    Mukherjee, Satyam

    2012-12-01

    We consider all Test matches played between 1877 and 2010 and One Day International (ODI) matches played between 1971 and 2010. We form directed and weighted networks of teams and also of their captains. The success of a team (or captain) is determined by the ‘quality’ of the wins, not simply by the number of wins. We apply the diffusion-based PageRank algorithm to the networks to assess the importance of the wins, and rank the respective teams and captains. Our analysis identifies Australia as the best team in both forms of cricket, Test and ODI. Steve Waugh is identified as the best captain in Test cricket and Ricky Ponting is the best captain in the ODI format. We also compare our ranking scheme with an existing ranking scheme, the Reliance ICC ranking. Our method does not depend on ‘external’ criteria in the ranking of teams (captains). The purpose of this paper is to introduce a revised ranking of cricket teams and to quantify the success of the captains.

  1. An artificial neural network system to identify alleles in reference electropherograms.

    Science.gov (United States)

    Taylor, Duncan; Harrison, Ash; Powers, David

    2017-09-01

    Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Artificial neural network cascade identifies multi-P450 inhibitors in natural compounds

    Directory of Open Access Journals (Sweden)

    Zhangming Li

    2015-12-01

    Full Text Available Substantial evidence has shown that most exogenous substances are metabolized by multiple cytochrome P450 (P450 enzymes instead of by merely one P450 isoform. Thus, multi-P450 inhibition leads to greater drug-drug interaction risk than specific P450 inhibition. Herein, we innovatively established an artificial neural network cascade (NNC model composed of 23 cascaded networks in a ladder-like framework to identify potential multi-P450 inhibitors among natural compounds by integrating 12 molecular descriptors into a P450 inhibition score (PIS. Experimental data reporting in vitro inhibition of five P450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 were obtained for 8,148 compounds from the Cytochrome P450 Inhibitors Database (CPID. The results indicate significant positive correlation between the PIS values and the number of inhibited P450 isoforms (Spearman’s ρ = 0.684, p < 0.0001. Thus, a higher PIS indicates a greater possibility for a chemical to inhibit the enzyme activity of at least three P450 isoforms. Ten-fold cross-validation of the NNC model suggested an accuracy of 78.7% for identifying whether a compound is a multi-P450 inhibitor or not. Using our NNC model, 22.2% of the approximately 160,000 natural compounds in TCM Database@Taiwan were identified as potential multi-P450 inhibitors. Furthermore, chemical similarity calculations suggested that the prevailing parent structures of natural multi-P450 inhibitors were alkaloids. Our findings show that dissection of chemical structure contributes to confident identification of natural multi-P450 inhibitors and provides a feasible method for virtually evaluating multi-P450 inhibition risk for a known structure.

  3. Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis.

    Directory of Open Access Journals (Sweden)

    Marcin J Skwark

    2017-02-01

    Full Text Available Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus and Streptococcus pyogenes (group A Streptococcus. For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery

  4. MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions.

    Directory of Open Access Journals (Sweden)

    Koon-Kiu Yan

    2017-07-01

    Full Text Available Genome-wide proximity ligation based assays such as Hi-C have revealed that eukaryotic genomes are organized into structural units called topologically associating domains (TADs. From a visual examination of the chromosomal contact map, however, it is clear that the organization of the domains is not simple or obvious. Instead, TADs exhibit various length scales and, in many cases, a nested arrangement. Here, by exploiting the resemblance between TADs in a chromosomal contact map and densely connected modules in a network, we formulate TAD identification as a network optimization problem and propose an algorithm, MrTADFinder, to identify TADs from intra-chromosomal contact maps. MrTADFinder is based on the network-science concept of modularity. A key component of it is deriving an appropriate background model for contacts in a random chain, by numerically solving a set of matrix equations. The background model preserves the observed coverage of each genomic bin as well as the distance dependence of the contact frequency for any pair of bins exhibited by the empirical map. Also, by introducing a tunable resolution parameter, MrTADFinder provides a self-consistent approach for identifying TADs at different length scales, hence the acronym "Mr" standing for Multiple Resolutions. We then apply MrTADFinder to various Hi-C datasets. The identified domain boundaries are marked by characteristic signatures in chromatin marks and transcription factors (TF that are consistent with earlier work. Moreover, by calling TADs at different length scales, we observe that boundary signatures change with resolution, with different chromatin features having different characteristic length scales. Furthermore, we report an enrichment of HOT (high-occupancy target regions near TAD boundaries and investigate the role of different TFs in determining boundaries at various resolutions. To further explore the interplay between TADs and epigenetic marks, as tumor mutational

  5. Identifying the origin of waterbird carcasses in Lake Michigan using a neural network source tracking model

    Science.gov (United States)

    Kenow, Kevin P.; Ge, Zhongfu; Fara, Luke J.; Houdek, Steven C.; Lubinski, Brian R.

    2016-01-01

    Avian botulism type E is responsible for extensive waterbird mortality on the Great Lakes, yet the actual site of toxin exposure remains unclear. Beached carcasses are often used to describe the spatial aspects of botulism mortality outbreaks, but lack specificity of offshore toxin source locations. We detail methodology for developing a neural network model used for predicting waterbird carcass motions in response to wind, wave, and current forcing, in lieu of a complex analytical relationship. This empirically trained model uses current velocity, wind velocity, significant wave height, and wave peak period in Lake Michigan simulated by the Great Lakes Coastal Forecasting System. A detailed procedure is further developed to use the model for back-tracing waterbird carcasses found on beaches in various parts of Lake Michigan, which was validated using drift data for radiomarked common loon (Gavia immer) carcasses deployed at a variety of locations in northern Lake Michigan during September and October of 2013. The back-tracing model was further used on 22 non-radiomarked common loon carcasses found along the shoreline of northern Lake Michigan in October and November of 2012. The model-estimated origins of those cases pointed to some common source locations offshore that coincide with concentrations of common loons observed during aerial surveys. The neural network source tracking model provides a promising approach for identifying locations of botulinum neurotoxin type E intoxication and, in turn, contributes to developing an understanding of the dynamics of toxin production and possible trophic transfer pathways.

  6. Resolving Anatomical and Functional Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations

    Science.gov (United States)

    Lohse, Christian; Bassett, Danielle S.; Lim, Kelvin O.; Carlson, Jean M.

    2014-01-01

    Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease. PMID:25275860

  7. Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

    Directory of Open Access Journals (Sweden)

    Christian Lohse

    2014-10-01

    Full Text Available Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

  8. Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

    Science.gov (United States)

    Lohse, Christian; Bassett, Danielle S; Lim, Kelvin O; Carlson, Jean M

    2014-10-01

    Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

  9. An algorithm for identifying the best current friend in a social network

    Directory of Open Access Journals (Sweden)

    Francisco Javier Moreno

    2015-05-01

    Full Text Available A research field in the area of social networks (SNs is the identification of some types of users and groups. To facilitate this process, a SN is usually represented by a graph. The centrality measures, which identify the most important vertices in a graph according to some criterion, are usual tools to analyze a graph. One of these measures is the PageRank (a measure originally designed to classify web pages. Informally, in the context of a SN, the PageRank of a user i represents the probability that another user of the SN is seeing the page of i after a considerable time of navigation in the SN. In this paper, we define a new type of user in a SN: the best current friend. The idea is to identify, among the friends of a user i, who is the friend k that would generate the highest decrease in the PageRank of i if k stops being his/her friend. This may be useful to identify the users/customers whose friendship/relationship should be a priority to keep. We provide formal definitions, algorithms and some experiments for this subject. Our experiments showed that the best current friend of a user is not necessarily the one who has the highest PageRank in the SN nor the one who has more friends.

  10. A new computational strategy for identifying essential proteins based on network topological properties and biological information.

    Science.gov (United States)

    Qin, Chao; Sun, Yongqi; Dong, Yadong

    2017-01-01

    Essential proteins are the proteins that are indispensable to the survival and development of an organism. Deleting a single essential protein will cause lethality or infertility. Identifying and analysing essential proteins are key to understanding the molecular mechanisms of living cells. There are two types of methods for predicting essential proteins: experimental methods, which require considerable time and resources, and computational methods, which overcome the shortcomings of experimental methods. However, the prediction accuracy of computational methods for essential proteins requires further improvement. In this paper, we propose a new computational strategy named CoTB for identifying essential proteins based on a combination of topological properties, subcellular localization information and orthologous protein information. First, we introduce several topological properties of the protein-protein interaction (PPI) network. Second, we propose new methods for measuring orthologous information and subcellular localization and a new computational strategy that uses a random forest prediction model to obtain a probability score for the proteins being essential. Finally, we conduct experiments on four different Saccharomyces cerevisiae datasets. The experimental results demonstrate that our strategy for identifying essential proteins outperforms traditional computational methods and the most recently developed method, SON. In particular, our strategy improves the prediction accuracy to 89, 78, 79, and 85 percent on the YDIP, YMIPS, YMBD and YHQ datasets at the top 100 level, respectively.

  11. Effect of the size of an artificial neural network used as pattern identifier

    Energy Technology Data Exchange (ETDEWEB)

    Reynoso V, M.R.; Vega C, J.J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)

    2003-07-01

    A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)

  12. RNA-seq analysis identifies an intricate regulatory network controlling cluster root development in white lupin

    Science.gov (United States)

    2014-01-01

    Background Highly adapted plant species are able to alter their root architecture to improve nutrient uptake and thrive in environments with limited nutrient supply. Cluster roots (CRs) are specialised structures of dense lateral roots formed by several plant species for the effective mining of nutrient rich soil patches through a combination of increased surface area and exudation of carboxylates. White lupin is becoming a model-species allowing for the discovery of gene networks involved in CR development. A greater understanding of the underlying molecular mechanisms driving these developmental processes is important for the generation of smarter plants for a world with diminishing resources to improve food security. Results RNA-seq analyses for three developmental stages of the CR formed under phosphorus-limited conditions and two of non-cluster roots have been performed for white lupin. In total 133,045,174 high-quality paired-end reads were used for a de novo assembly of the root transcriptome and merged with LAGI01 (Lupinus albus gene index) to generate an improved LAGI02 with 65,097 functionally annotated contigs. This was followed by comparative gene expression analysis. We show marked differences in the transcriptional response across the various cluster root stages to adjust to phosphate limitation by increasing uptake capacity and adjusting metabolic pathways. Several transcription factors such as PLT, SCR, PHB, PHV or AUX/IAA with a known role in the control of meristem activity and developmental processes show an increased expression in the tip of the CR. Genes involved in hormonal responses (PIN, LAX, YUC) and cell cycle control (CYCA/B, CDK) are also differentially expressed. In addition, we identify primary transcripts of miRNAs with established function in the root meristem. Conclusions Our gene expression analysis shows an intricate network of transcription factors and plant hormones controlling CR initiation and formation. In addition

  13. RNA-seq analysis identifies an intricate regulatory network controlling cluster root development in white lupin.

    Science.gov (United States)

    Secco, David; Shou, Huixia; Whelan, James; Berkowitz, Oliver

    2014-03-25

    Highly adapted plant species are able to alter their root architecture to improve nutrient uptake and thrive in environments with limited nutrient supply. Cluster roots (CRs) are specialised structures of dense lateral roots formed by several plant species for the effective mining of nutrient rich soil patches through a combination of increased surface area and exudation of carboxylates. White lupin is becoming a model-species allowing for the discovery of gene networks involved in CR development. A greater understanding of the underlying molecular mechanisms driving these developmental processes is important for the generation of smarter plants for a world with diminishing resources to improve food security. RNA-seq analyses for three developmental stages of the CR formed under phosphorus-limited conditions and two of non-cluster roots have been performed for white lupin. In total 133,045,174 high-quality paired-end reads were used for a de novo assembly of the root transcriptome and merged with LAGI01 (Lupinus albus gene index) to generate an improved LAGI02 with 65,097 functionally annotated contigs. This was followed by comparative gene expression analysis. We show marked differences in the transcriptional response across the various cluster root stages to adjust to phosphate limitation by increasing uptake capacity and adjusting metabolic pathways. Several transcription factors such as PLT, SCR, PHB, PHV or AUX/IAA with a known role in the control of meristem activity and developmental processes show an increased expression in the tip of the CR. Genes involved in hormonal responses (PIN, LAX, YUC) and cell cycle control (CYCA/B, CDK) are also differentially expressed. In addition, we identify primary transcripts of miRNAs with established function in the root meristem. Our gene expression analysis shows an intricate network of transcription factors and plant hormones controlling CR initiation and formation. In addition, functional differences between the

  14. Consensus computational network analysis for identifying candidate outer membrane proteins from Borrelia spirochetes.

    Science.gov (United States)

    Kenedy, Melisha R; Scott, Edgar J; Shrestha, Binu; Anand, Arvind; Iqbal, Henna; Radolf, Justin D; Dyer, David W; Akins, Darrin R

    2016-07-11

    Similar to Gram-negative organisms, Borrelia spirochetes are dual-membrane organisms with both an inner and outer membrane. Although the outer membrane contains integral membrane proteins, few of the borrelial outer membrane proteins (OMPs) have been identified and characterized to date. Therefore, we utilized a consensus computational network analysis to identify novel borrelial OMPs. Using a series of computer-based algorithms, we selected all protein-encoding sequences predicted to be OM-localized and/or to form β-barrels in the borrelial OM. Using this system, we identified 41 potential OMPs from B. burgdorferi and characterized three (BB0838, BB0405, and BB0406) to confirm that our computer-based methodology did, in fact, identify borrelial OMPs. Triton X-114 phase partitioning revealed that BB0838 is found in the detergent phase, which would be expected of a membrane protein. Proteolysis assays indicate that BB0838 is partially sensitive to both proteinase K and trypsin, further indicating that BB0838 is surface-exposed. Consistent with a prior study, we also confirmed that BB0405 is surface-exposed and associates with the borrelial OM. Furthermore, we have shown that BB0406, the product of a co-transcribed downstream gene, also encodes a novel, previously uncharacterized borrelial OMP. Interestingly, while BB0406 has several physicochemical properties consistent with it being an OMP, it was found to be resistant to surface proteolysis. Consistent with BB0405 and BB0406 being OMPs, both were found to be capable of incorporating into liposomes and exhibit pore-forming activity, suggesting that both proteins are porins. Lastly, we expanded our computational analysis to identify OMPs from other borrelial organisms, including both Lyme disease and relapsing fever spirochetes. Using a consensus computer algorithm, we generated a list of candidate OMPs for both Lyme disease and relapsing fever spirochetes and determined that three of the predicted B. burgdorferi

  15. Aspects regarding the use of the INFREP network for identifying possible seismic precursors

    Science.gov (United States)

    Dolea, Paul; Cristea, Octavian; Dascal, Paul Vladut; Moldovan, Iren-Adelina; Biagi, Pier Francesco

    In the last decades, one of the main research directions in identifying seismic precursors involved monitoring VLF (Very Low Frequency) and LF (Low Frequency) radio waves and analysing their propagation characteristics. Essentially this method consists of monitoring different available VLF and LF transmitters from long distance reception points. The received signal has two major components: the ground wave and the sky wave, where the sky wave propagates by reflection on the lower layers of the ionosphere. It is assumed that before and during major earthquakes, unusual changes may occur in the lower layers of the ionosphere, such as the modification of the charged particles number density and the altitude of the reflection zone. Therefore, these unusual changes in the ionosphere may generate unusual variations in the received signal level. The International Network for Frontier Research on Earthquake Precursors (INFREP) was developed starting with 2009 and consists of several dedicated VLF and LF radio receivers used for monitoring various radio transmitters located throughout Europe. The receivers' locations were chosen so that the propagation path from these VLF/LF stations would pass over high seismicity regions while others were chosen to obtain different control paths. The monitoring receivers are capable of continuously measuring the received signal amplitude from the VLF/LF stations of interest. The recorded data is then stored and sent to an INFREP database, which is available on the Internet for scientific researchers. By processing and analysing VLF and LF data samples, collected at different reception points and at different periods of the year, one may be able to identify some distinct patterns in the envelope of the received signal level over time. Significant deviations from these patterns may have local causes such as the electromagnetic pollution at the monitoring point, regional causes like existing electrical storms over the propagation path or

  16. Protocol for a thematic synthesis to identify key themes and messages from a palliative care research network.

    LENUS (Irish Health Repository)

    Nicholson, Emma

    2016-10-21

    Research networks that facilitate collaborative research are increasing both regionally and globally and such collaborations contribute greatly to knowledge transfer particularly in health research. The Palliative Care Research Network is an Irish-based network that seeks to create opportunities and engender a collaborative environment to encourage innovative research that is relevant for policy and practice. The current review outlines a methodology to identify cross-cutting messages to identify how dissemination outputs can be optimized to ensure that key messages from this research reaches all knowledge users.

  17. An Investigation of Bayes Algorithm and Neural Networks for Identifying the Breast Cancer.

    Science.gov (United States)

    Udayakumar, E; Santhi, S; Vetrivelan, P

    2017-01-01

    Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time. To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film. Time factor is important to identify the abnormality in body such as breast cancer and lung cancer. Hence, to detect the tissues and treatment stages, image-processing techniques are improved in several medical areas. In this project, using low-level preprocessing techniques and image segmentation, the breast cancer detection is done. With the help of Bayes algorithm and neural networks (NNs), the type of the mammogram and stages is identified. For segmentation process, region-growing algorithm is used, which helps to find the affected portion, i.e., region of interest. Gray-level co-occurrence matrix (GLCM) and texture feature are used for feature extraction. Bayes algorithm is used for probability of identification, whereas NNs is used to reduce the probability level from 0-1000 to 0-1 in case of classification.

  18. The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

    Directory of Open Access Journals (Sweden)

    Zoran D. Ristovski

    2013-07-01

    Full Text Available Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

  19. Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

    Science.gov (United States)

    Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

    2016-04-26

    Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

  20. A Neural Network Approach for Identifying Particle Pitch Angle Distributions in Van Allen Probes Data

    Science.gov (United States)

    Souza, V. M.; Vieira, L. E. A.; Medeiros, C.; Da Silva, L. A.; Alves, L. R.; Koga, D.; Sibeck, D. G.; Walsh, B. M.; Kanekal, S. G.; Jauer, P. R.; hide

    2016-01-01

    Analysis of particle pitch angle distributions (PADs) has been used as a means to comprehend a multitude of different physical mechanisms that lead to flux variations in the Van Allen belts and also to particle precipitation into the upper atmosphere. In this work we developed a neural network-based data clustering methodology that automatically identifies distinct PAD types in an unsupervised way using particle flux data. One can promptly identify and locate three well-known PAD types in both time and radial distance, namely, 90deg peaked, butterfly, and flattop distributions. In order to illustrate the applicability of our methodology, we used relativistic electron flux data from the whole month of November 2014, acquired from the Relativistic Electron-Proton Telescope instrument on board the Van Allen Probes, but it is emphasized that our approach can also be used with multiplatform spacecraft data. Our PAD classification results are in reasonably good agreement with those obtained by standard statistical fitting algorithms. The proposed methodology has a potential use for Van Allen belt's monitoring.

  1. Exposing the role of exposure : Identifying and evaluating critical links in public transport networks

    NARCIS (Netherlands)

    Cats, O.; Yap, M.; Van Oort, N.

    2015-01-01

    Network vulnerability depends on the probability that adverse events occur and on the impacts of such disruptions on network functionality. Most studies on transport network vulnerability have only analysed vulnerability in terms of the reduction in performance indicators given that a disruption

  2. Networks, Trees, and Treeshrews: Assessing Support and Identifying Conflict with Multiple Loci and a Problematic Root

    Science.gov (United States)

    Roberts, Trina E.; Sargis, Eric J.; Olson, Link E.

    2009-01-01

    Multiple unlinked genetic loci often provide a more comprehensive picture of evolutionary history than any single gene can, but analyzing multigene data presents particular challenges. Differing rates and patterns of nucleotide substitution, combined with the limited information available in any data set, can make it difficult to specify a model of evolution. In addition, conflict among loci can be the result of real differences in evolutionary process or of stochastic variance and errors in reconstruction. We used 6 presumably unlinked nuclear loci to investigate relationships within the mammalian family Tupaiidae (Scandentia), containing all but one of the extant tupaiid genera. We used a phylogenetic mixture model to analyze the concatenated data and compared this with results using partitioned models. We found that more complex models were not necessarily preferred under tests using Bayes factors and that model complexity affected both tree length and parameter variance. We also compared the results of single-gene and multigene analyses and used splits networks to analyze the source and degree of conflict among genes. Networks can show specific relationships that are inconsistent with each other; these conflicting and minority relationships, which are implicitly ignored or collapsed by traditional consensus methods, can be useful in identifying the underlying causes of topological uncertainty. In our data, conflict is concentrated around particular relationships, not widespread throughout the tree. This pattern is further clarified by considering conflict surrounding the root separately from conflict within the ingroup. Uncertainty in rooting may be because of the apparent evolutionary distance separating these genera and our outgroup, the tupaiid genus Dendrogale. Unlike a previous mitochondrial study, these nuclear data strongly suggest that the genus Tupaia is not monophyletic with respect to the monotypic Urogale, even when uncertainty about rooting is

  3. Dissecting the Gene Network of Dietary Restriction to Identify Evolutionarily Conserved Pathways and New Functional Genes

    Science.gov (United States)

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR–essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR–essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR–essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR–essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR–induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple

  4. Identifying functional reorganization of spelling networks: An Individual Peak Probability Comparison Approach.

    Directory of Open Access Journals (Sweden)

    Jeremy Joseph Purcell

    2013-12-01

    Full Text Available Previous research has shown that damage to the neural substrates of orthographic processing can lead to functional reorganization during reading (Tsapkini et al., 2011; in this research we ask if the same is true for spelling. To examine the functional reorganization of spelling networks we present a novel three-stage Individual Peak Probability Comparison (IPPC analysis approach for comparing the activation patterns obtained during fMRI of spelling in a single brain-damaged individual with dysgraphia to those obtained in a set of non-impaired control participants. The first analysis stage characterizes the convergence in activations across non-impaired control participants by applying a technique typically used for characterizing activations across studies: Activation Likelihood Estimate (ALE (Turkeltaub et al., 2002. This method was used to identify locations that have a high likelihood of yielding activation peaks in the non-impaired participants. The second stage provides a characterization of the degree to which the brain-damaged individual’s activations correspond to the group pattern identified in Stage 1. This involves performing a Mahalanobis distance statistics analysis (Tsapkini et al., 2011 that compares each of a control group’s peak activation locations to the nearest peak generated by the brain-damaged individual. The third stage evaluates the extent to which the brain-damaged individual’s peaks are atypical relative to the range of individual variation among the control participants. This IPPC analysis allows for a quantifiable, statistically sound method for comparing an individual’s activation pattern to the patterns observed in a control group and, thus, provides a valuable tool for identifying functional reorganization in a brain-damaged individual with impaired spelling. Furthermore, this approach can be applied more generally to compare any individual’s activation pattern with that of a set of other individuals.

  5. Identifying functional reorganization of spelling networks: an individual peak probability comparison approach.

    Science.gov (United States)

    Purcell, Jeremy J; Rapp, Brenda

    2013-01-01

    Previous research has shown that damage to the neural substrates of orthographic processing can lead to functional reorganization during reading (Tsapkini et al., 2011); in this research we ask if the same is true for spelling. To examine the functional reorganization of spelling networks we present a novel three-stage Individual Peak Probability Comparison (IPPC) analysis approach for comparing the activation patterns obtained during fMRI of spelling in a single brain-damaged individual with dysgraphia to those obtained in a set of non-impaired control participants. The first analysis stage characterizes the convergence in activations across non-impaired control participants by applying a technique typically used for characterizing activations across studies: Activation Likelihood Estimate (ALE) (Turkeltaub et al., 2002). This method was used to identify locations that have a high likelihood of yielding activation peaks in the non-impaired participants. The second stage provides a characterization of the degree to which the brain-damaged individual's activations correspond to the group pattern identified in Stage 1. This involves performing a Mahalanobis distance statistics analysis (Tsapkini et al., 2011) that compares each of a control group's peak activation locations to the nearest peak generated by the brain-damaged individual. The third stage evaluates the extent to which the brain-damaged individual's peaks are atypical relative to the range of individual variation among the control participants. This IPPC analysis allows for a quantifiable, statistically sound method for comparing an individual's activation pattern to the patterns observed in a control group and, thus, provides a valuable tool for identifying functional reorganization in a brain-damaged individual with impaired spelling. Furthermore, this approach can be applied more generally to compare any individual's activation pattern with that of a set of other individuals.

  6. The TAO-Gen Algorithm for Identifying Gene Interaction Networks with Application to SOS Repair in E. coli

    Science.gov (United States)

    Yamanaka, Takeharu; Toyoshiba, Hiroyoshi; Sone, Hideko; Parham, Frederick M.; Portier, Christopher J.

    2004-01-01

    One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts. PMID:15598612

  7. Can one identify karst conduit networks geometry and properties from hydraulic and tracer test data?

    Science.gov (United States)

    Borghi, Andrea; Renard, Philippe; Cornaton, Fabien

    2016-04-01

    Karst aquifers are characterized by extreme heterogeneity due to the presence of karst conduits embedded in a fractured matrix having a much lower hydraulic conductivity. The resulting contrast in the physical properties of the system implies that the system reacts very rapidly to some changes in the boundary conditions and that numerical models are extremely sensitive to small modifications in properties or positions of the conduits. Furthermore, one major issue in all those models is that the location and size of the conduits is generally unknown. For all those reasons, estimating karst network geometry and their properties by solving an inverse problem is a particularly difficult problem. In this paper, two numerical experiments are described. In the first one, 18,000 flow and transport simulations have been computed and used in a systematic manner to assess statistically if one can retrieve the parameters of a model (geometry and radius of the conduits, hydraulic conductivity of the conduits) from head and tracer data. When two tracer test data sets are available, the solution of the inverse problems indicate with high certainty that there are indeed two conduits and not more. The radius of the conduits are usually well identified but not the properties of the matrix. If more conduits are present in the system, but only two tracer test data sets are available, the inverse problem is still able to identify the true solution as the most probable but it also indicates that the data are insufficient to conclude with high certainty. In the second experiment, a more complex model (including non linear flow equations in conduits) is considered. In this example, gradient-based optimization techniques are proved to be efficient for estimating the radius of the conduits and the hydraulic conductivity of the matrix in a promising and efficient manner. These results suggest that, despite the numerical difficulties, inverse methods should be used to constrain numerical

  8. Application of the network robustness index to identifying critical road-network links in Chittenden County, Vermont.

    Science.gov (United States)

    2010-06-01

    The purpose of this project is to conduct a pilot application of the Network : Robustness Index (NRI) for the Chittenden County Regional Transportation Model. : Using the results, improvements to the method to increase its effectiveness for more : wi...

  9. Use of neutron radiography and tomography to identify fracture network connectivity in low permeability carbonates

    Science.gov (United States)

    Lewis, Helen; Zihms, Stephanie; Couples, Gary; Charalampidou, Elma-Maria; Hall, Stephen; Tudisco, Erika; Edlmann, Katriona; Ando, Edward; Etxegarai, Maddi; Tengattini, Alessandro; Atkins, Duncan

    2017-04-01

    For low permeability rocks, open fractures have the potential to provide a dominant flow pathway, but determining effective connectivity, and associated single or multi-phase flow characteristics within an intact sample, is not simple. Flow tests can provide bulk values for any one sample, but general predictability requires considerably more information. X-ray tomography (XRT) has been used to identify fracture patterns and apertures in 3D. Here, in addition to XRT of experimentally-fractured low-permeability laminites, neutron beam radiography and tomography have been used to image flow via sensitivity to protons. To our knowledge this is the first identification of fluid front movement through fracture arrays using neutron tomography. Specifically, samples of a very fine-grained laminite, a layered carbonate rock deposited in a lake bed environment, with a "grain" size of approximately 5µm, were deformed experimentally under conditions representing 1 to 2 km burial depth, creating a series of shear- and extension- fractures that XRT indicated were at least partly open (Fig 1). But only destructive assessment (e.g. SEM) could verify this, destroying the ability to test flow capabilities in the process. Neutron tomography, using deuterated and then distilled water (which have slightly different densities and significantly different Neutron absorption) are introduced into the sample base, under pressure control, enabling observation of the progression of deuterated water into the air-filled laminite matrix- and fracture space-network, following by distilled water that mainly flows in the fractures (Fig. 2). Radiography and tomography identify a complex but rational pattern of initial water movement into the matrix laminae that suggests that in unfractured laminite, the fluid front would progress stepwise from one lamina to the next with a relatively fast filling across an entered lamina and relatively slow progression across the overlying lamina. But in the

  10. Interactome network analysis identifies multiple caspase-6 interactors involved in the pathogenesis of HD.

    Science.gov (United States)

    Riechers, Sean-Patrick; Butland, Stefanie; Deng, Yu; Skotte, Niels; Ehrnhoefer, Dagmar E; Russ, Jenny; Laine, Jean; Laroche, Melissa; Pouladi, Mahmoud A; Wanker, Erich E; Hayden, Michael R; Graham, Rona K

    2016-04-15

    Caspase-6 (CASP6) has emerged as an important player in Huntington disease (HD), Alzheimer disease (AD) and cerebral ischemia, where it is activated early in the disease process. CASP6 also plays a key role in axonal degeneration, further underscoring the importance of this protease in neurodegenerative pathways. As a protein's function is modulated by its protein-protein interactions, we performed a high-throughput yeast-2-hybrid (Y2H) screen against ∼17,000 human proteins to gain further insight into the function of CASP6. We identified a high-confidence list of 87 potential CASP6 interactors. From this list, 61% are predicted to contain a CASP6 recognition site. Of nine candidate substrates assessed, six are cleaved by CASP6. Proteins that did not contain a predicted CASP6 recognition site were assessed using a LUMIER assay approach, and 51% were further validated as interactors by this method. Of note, 54% of the high-confidence interactors identified show alterations in human HD brain at the mRNA level, and there is a significant enrichment for previously validated huntingtin (HTT) interactors. One protein of interest, STK3, a pro-apoptotic kinase, was validated biochemically to be a CASP6 substrate. Furthermore, our results demonstrate that in striatal cells expressing mutant huntingtin (mHTT), an increase in full length and fragment levels of STK3 are observed. We further show that caspase-3 is not essential for the endogenous cleavage of STK3. Characterization of the interaction network provides important new information regarding key pathways of interactors of CASP6 and highlights potential novel therapeutic targets for HD, AD and cerebral ischemia. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  11. RNA sequencing identifies gene regulatory networks controlling extracellular matrix synthesis in intervertebral disk tissues.

    Science.gov (United States)

    Riester, Scott M; Lin, Yang; Wang, Wei; Cong, Lin; Mohamed Ali, Abdel-Moneim; Peck, Sun H; Smith, Lachlan J; Currier, Bradford L; Clark, Michelle; Huddleston, Paul; Krauss, William; Yaszemski, Michael J; Morrey, Mark E; Abdel, Matthew P; Bydon, Mohamad; Qu, Wenchun; Larson, Annalise N; van Wijnen, Andre J; Nassr, Ahmad

    2017-12-11

    Degenerative disk disease of the spine is a major cause of back pain and disability. Optimization of regenerative medical therapies for degenerative disk disease requires a deep mechanistic understanding of the factors controlling the structural integrity of spinal tissues. In this investigation, we sought to identify candidate regulatory genes controlling extracellular matrix synthesis in spinal tissues. To achieve this goal we performed high throughput next generation RNA sequencing on 39 annulus fibrosus and 21 nucleus pulposus human tissue samples. Specimens were collected from patients undergoing surgical discectomy for the treatment of degenerative disk disease. Our studies identified associations between extracellular matrix genes, growth factors, and other important regulatory molecules. The fibrous matrix characteristic of annulus fibrosus was associated with expression of the growth factors platelet derived growth factor beta (PDGFB), vascular endothelial growth factor C (VEGFC), and fibroblast growth factor 9 (FGF9). Additionally we observed high expression of multiple signaling proteins involved in the NOTCH and WNT signaling cascades. Nucleus pulposus extracellular matrix related genes were associated with the expression of numerous diffusible growth factors largely associated with the transforming growth signaling cascade, including transforming factor alpha (TGFA), inhibin alpha (INHA), inhibin beta A (INHBA), bone morphogenetic proteins (BMP2, BMP6), and others. this investigation provides important data on extracellular matrix gene regulatory networks in disk tissues. This information can be used to optimize pharmacologic, stem cell, and tissue engineering strategies for regeneration of the intervertebral disk and the treatment of back pain. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

  12. Gene co-expression network analysis for identifying modules and functionally enriched pathways in SCA2.

    Science.gov (United States)

    Pflieger, Lance T; Dansithong, Warunee; Paul, Sharan; Scoles, Daniel R; Figueroa, Karla P; Meera, Pratap; Otis, Thomas S; Facelli, Julio C; Pulst, Stefan M

    2017-08-15

    Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease caused by CAG repeat expansion in the ATXN2 gene. The repeat resides in an encoded region of the gene resulting in polyglutamine (polyQ) expansion which has been assumed to result in gain of function, predominantly, for the ATXN2 protein. We evaluated temporal cerebellar expression profiles by RNA sequencing of ATXN2Q127 mice versus wild-type (WT) littermates. ATXN2Q127 mice are characterized by a progressive motor phenotype onset, and have progressive cerebellar molecular and neurophysiological (Purkinje cell firing frequency) phenotypes. Our analysis revealed previously uncharacterized early and progressive abnormal patterning of cerebellar gene expression. Weighted Gene Coexpression Network Analysis revealed four gene modules that were significantly correlated with disease status, composed primarily of genes associated with GTPase signaling, calcium signaling and cell death. Of these genes, few overlapped with differentially expressed cerebellar genes that we identified in Atxn2-/- knockout mice versus WT littermates, suggesting that loss-of-function is not a significant component of disease pathology. We conclude that SCA2 is a disease characterized by gain of function for ATXN2. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  13. Identifying socio-ecological networks in rural-urban gradients: Diagnosis of a changing cultural landscape.

    Science.gov (United States)

    Arnaiz-Schmitz, C; Schmitz, M F; Herrero-Jáuregui, C; Gutiérrez-Angonese, J; Pineda, F D; Montes, C

    2018-01-15

    Socio-ecological systems maintain reciprocal interactions between biophysical and socioeconomic structures. As a result of these interactions key essential services for society emerge. Urban expansion is a direct driver of land change and cause serious shifts in socio-ecological relationships and the associated lifestyles. The framework of rural-urban gradients has proved to be a powerful tool for ecological research about urban influences on ecosystems and on sociological issues related to social welfare. However, to date there has not been an attempt to achieve a classification of municipalities in rural-urban gradients based on socio-ecological interactions. In this paper, we developed a methodological approach that allows identifying and classifying a set of socio-ecological network configurations in the Region of Madrid, a highly dynamic cultural landscape considered one of the European hotspots in urban development. According to their socio-ecological links, the integrated model detects four groups of municipalities, ordered along a rural-urban gradient, characterized by their degree of biophysical and socioeconomic coupling and different indicators of landscape structure and social welfare. We propose the developed model as a useful tool to improve environmental management schemes and land planning from a socio-ecological perspective, especially in territories subject to intense urban transformations and loss of rurality. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Mapping the Possibilities: Using Network Analysis to Identify Opportunities for Building Nutrition Partnerships Within Diverse Low-Income Communities.

    Science.gov (United States)

    Buchthal, Opal Vanessa; Maddock, Jason E

    2015-01-01

    To identify communication and collaboration patterns among organizations involved in nutrition education within an ethnically diverse low-income community. A snowball sample methodology was used to identify 27 organizations involved in nutrition activities in the community. The researchers conducted an online survey and network analysis to identify communication and collaboration patterns among these organizations. An urban neighborhood in Honolulu, HI. Individuals responsible for nutrition activities at state, county, and nonprofit organizations. Network structure, betweenness, and centralization. Communication was uncentralized and collaboration was limited. Collaboration was affected by differences in mission, location, and population served. Child care/youth development organizations and community health centers provided links across the community. Agencies serving different ethnic populations were poorly linked and located on the periphery of the network. Ethnic-specific churches expressed strong interest in nutrition partnership but were not identified as potential partners by other agencies in the network. Limited communication between agencies serving different populations in the same community may result in missed opportunities for collaboration. Network analysis is an effective tool for identifying these gaps and helps build community capacity for improving nutrition outcomes. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

  15. Identifying uncertainty of the mean of some water quality variables along water quality monitoring network of Bahr El Baqar drain

    Directory of Open Access Journals (Sweden)

    Hussein G. Karaman

    2013-10-01

    Full Text Available Assigning objectives to the environmental monitoring network is the pillar of the design to these kinds of networks. Conflicting network objectives may affect the adequacy of the design in terms of sampling frequency and the spatial distribution of the monitoring stations which in turn affect the accuracy of the data and the information extracted. The first step in resolving this problem is to identify the uncertainty inherent in the network as the result of the vagueness of the design objective. Entropy has been utilized and adopted over the past decades to identify uncertainty in similar water data sets. Therefore it is used to identify the uncertainties inherent in the water quality monitoring network of Bahr El-Baqar drain located in the Eastern Delta. Toward investigating the applicability of the Entropy methodology, comprehensive analysis at the selected drain as well as their data sets is carried out. Furthermore, the uncertainty calculated by the entropy function will be presented by the means of the geographical information system to give the decision maker a global view to these uncertainties and to open the door to other researchers to find out innovative approaches to lower these uncertainties reaching optimal monitoring network in terms of the spatial distribution of the monitoring stations.

  16. Identifying apple surface defects using principal components analysis and artifical neural networks

    Science.gov (United States)

    Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

  17. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  18. Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks

    Science.gov (United States)

    Akimushkin, Camilo; Amancio, Diego Raphael; Oliveira, Osvaldo Novais

    2017-01-01

    Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network growth mechanisms, but only a few studies have probed the suitability of networks in modeling small chunks of text to grasp stylistic features. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. Since 73% of all series were stationary (ARIMA(p, 0, q)) and the remaining were integrable of first order (ARIMA(p, 1, q)), probability distributions could be obtained for the global network metrics. The metrics exhibit bell-shaped non-Gaussian distributions, and therefore distribution moments were used as learning attributes. With an optimized supervised learning procedure based on a nonlinear transformation performed by Isomap, 71 out of 80 texts were correctly classified using the K-nearest neighbors algorithm, i.e. a remarkable 88.75% author matching success rate was achieved. Hence, purely dynamic fluctuations in network metrics can characterize authorship, thus paving the way for a robust description of large texts in terms of small evolving networks. PMID:28125703

  19. Developing a Precareer Network: An Exercise in Identifying, Using, and Making Investments in Social Capital

    Science.gov (United States)

    Reardon, Karen A.; Walsh, Anne M.

    2017-01-01

    Research on the use of experiential exercises to encourage exploration of professional networks shows that such exercises are effective and provide value for students seeking to enter or navigate careers. Often students in the early phase of their undergraduate experience are unaware of the process involved in building a professional network or…

  20. An in vivo RNA interference screen identifies gene networks controlling Drosophila melanogaster blood cell homeostasis

    Directory of Open Access Journals (Sweden)

    Augé Benoit

    2010-06-01

    Full Text Available Abstract Background In metazoans, the hematopoietic system plays a key role both in normal development and in defense of the organism. In Drosophila, the cellular immune response involves three types of blood cells: plasmatocytes, crystal cells and lamellocytes. This last cell type is barely present in healthy larvae, but its production is strongly induced upon wasp parasitization or in mutant contexts affecting larval blood cell homeostasis. Notably, several zygotic mutations leading to melanotic mass (or "tumor" formation in larvae have been associated to the deregulated differentiation of lamellocytes. To gain further insights into the gene regulatory network and the mechanisms controlling larval blood cell homeostasis, we conducted a tissue-specific loss of function screen using hemocyte-specific Gal4 drivers and UAS-dsRNA transgenic lines. Results By targeting around 10% of the Drosophila genes, this in vivo RNA interference screen allowed us to recover 59 melanotic tumor suppressor genes. In line with previous studies, we show that melanotic tumor formation is associated with the precocious differentiation of stem-cell like blood progenitors in the larval hematopoietic organ (the lymph gland and the spurious differentiation of lamellocytes. We also find that melanotic tumor formation can be elicited by defects either in the fat body, the embryo-derived hemocytes or the lymph gland. In addition, we provide a definitive confirmation that lymph gland is not the only source of lamellocytes as embryo-derived plasmatocytes can differentiate into lamellocytes either upon wasp infection or upon loss of function of the Friend of GATA cofactor U-shaped. Conclusions In this study, we identify 55 genes whose function had not been linked to blood cell development or function before in Drosophila. Moreover our analyses reveal an unanticipated plasticity of embryo-derived plasmatocytes, thereby shedding new light on blood cell lineage relationship, and

  1. NetworkTrail--a web service for identifying and visualizing deregulated subnetworks.

    Science.gov (United States)

    Stöckel, Daniel; Müller, Oliver; Kehl, Tim; Gerasch, Andreas; Backes, Christina; Rurainski, Alexander; Keller, Andreas; Kaufmann, Michael; Lenhof, Hans-Peter

    2013-07-01

    The deregulation of biochemical pathways plays a central role in many diseases like cancer or Parkinsons's disease. In silico tools for calculating these deregulated pathways may help to gain new insights into pathogenic mechanisms and may open novel avenues for therapy stratification in the sense of personalized medicine. Here, we present NetworkTrail, a web service for the detection of deregulated pathways and subgraphs in biological networks. NetworkTrail uses a state-of-the-art integer linear programming-based approach for this task and offers interfaces to the Biological Network Analyzer (BiNA) and Cytoscape Web for visualizing the resulting subnetworks. By providing an accessible interface to otherwise hard-to-use command line tools, the new web service enables non-experts to quickly and reliably carry out this type of network analyses. NetworkTrail is a JavaServer Pages-based web service. The algorithm for finding deregulated subnetworks has been implemented in C++. NetworkTrail is available at http://networktrail.bioinf.uni-sb.de/.

  2. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

    Science.gov (United States)

    Chin, Wei-Chien-Benny; Wen, Tzai-Hung

    2015-01-01

    A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

  3. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

    Directory of Open Access Journals (Sweden)

    Wei-Chien-Benny Chin

    Full Text Available A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR and Geographical PageRank (GPR-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

  4. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    Science.gov (United States)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  5. IDENTIFYING TELECOMMUNICATIONS NETWORK NEEDS TO SUPPORT THE DEPLOYMENT OF SMART METERS IN A WATER UTILITY

    National Research Council Canada - National Science Library

    Samuel de Barros Moraes; Celi Langhi; Marcos Crivelaro

    2015-01-01

      This case study, based on interviews and technical analysis of a Brazilian water utility with more than 10 million clients, aims to understand what kind of adjusts on a telecommunications network...

  6. Identifying vital edges in Chinese air route network via memetic algorithm

    National Research Council Canada - National Science Library

    Du, Wenbo; Liang, Boyuan; Yan, Gang; Lordan, Oriol; Cao, Xianbin

    ... of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges...

  7. Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks

    OpenAIRE

    Choi, Wonsuk; Jo, Hyo Jin; Woo, Samuel; Chun, Ji Young; Park, Jooyoung; Lee, Dong Hoon

    2016-01-01

    In the last several decades, the automotive industry has come to incorporate the latest Information and Communications (ICT) technology, increasingly replacing mechanical components of vehicles with electronic components. These electronic control units (ECUs) communicate with each other in an in-vehicle network that makes the vehicle both safer and easier to drive. Controller Area Networks (CANs) are the current standard for such high quality in-vehicle communication. Unfortunately, however, ...

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

    Directory of Open Access Journals (Sweden)

    Boucher Charles AB

    2010-07-01

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

  9. Identifying Driver Nodes in the Human Signaling Network Using Structural Controllability Analysis.

    Science.gov (United States)

    Liu, Xueming; Pan, Linqiang

    2015-01-01

    Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.

  10. Use of social network analysis in maternity care to identify the profession most suited for case manager role.

    Science.gov (United States)

    Groenen, Carola J M; van Duijnhoven, Noortje T L; Faber, Marjan J; Koetsenruijter, Jan; Kremer, Jan A M; Vandenbussche, Frank P H A

    2017-02-01

    To improve Dutch maternity care, professionals start working in interdisciplinary patient-centred networks, which includes the patients as a member. The introduction of the case manager is expected to work positively on both the individual and the network level. However, case management is new in Dutch maternity care. The present study aims to define the profession that would be most suitable to fulfil the role of case manager. The maternal care network in the Nijmegen region was determined by using Social Network Analysis (SNA). SNA is a quantitative methodology that measures and analyses patient-related connections between different professionals working in a network. To identify the case manager we focused on the position, reach, and connections in the network of the maternal care professionals. Maternity healthcare professionals in a single region of the Netherlands with an average of 4,500 births/year. The participants were 214 individual healthcare workers from eight different professions. The total network showed 3948 connections between 214 maternity healthcare professionals with a density of 0.08. Each profession had some central individuals in the network. The 52 community-based midwives were responsible for 51% of all measured connections. The youth health doctors and nurses were mostly situated on the periphery and less connected. The betweenness centrality had the highest score in obstetricians and community-based midwives. Only the community-based midwives had connections with all other groups of professions. Almost all professionals in the network could reach other professionals in two steps. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Dynamic network connectivity analysis to identify the epileptogenic zones based on stereo-electroencephalography

    Directory of Open Access Journals (Sweden)

    Junwei Mao

    2016-10-01

    Full Text Available ObjectivesAccurate localization of the epileptogenic zones (EZs is essential for the successful surgical treatment of the refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG signals is effective in localizing the EZs.MethodsSEEG data were recorded from seven patients underwent presurgical evaluation for the treatment of refractory focal epilepsy, and the subsequent resective surgery gave the patients good outcome. The time-variant multivariate autoregressive model was constructed by Kalman filter and the time-variant partial directed coherence was computed, which was then used to construct the dynamic directed network of the epileptic brain. Three graph measures, in-degree, out-degree and betweenness centrality, were used to analyze the characteristics of the dynamic network and to find the important nodes in it. ResultsIn all seven patients, the indicative EZs localized by in-degree and betweenness centrality were highly consistent to the clinical diagnosed EZs. However, the out-degree did not indicate significant difference between nodes in the network.ConclusionsIn this work, the method based on ictal SEEG signals and effective connectivity analysis localized the EZs accurately. It suggested that in-degree and betweenness centrality may be better network characteristics to localize the EZs than out-degree.

  12. Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance.

    Directory of Open Access Journals (Sweden)

    Daniel M Bean

    Full Text Available The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King's College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A 'core' subnetwork containing only 13-17% of all edges channelled 83-90% of the patient flow, while an 'ephemeral' network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing.

  13. Identifying candidate drivers of alcohol dependence-induced excessive drinking by assembly and interrogation of brain-specific regulatory networks.

    Science.gov (United States)

    Repunte-Canonigo, Vez; Shin, William; Vendruscolo, Leandro F; Lefebvre, Celine; van der Stap, Lena; Kawamura, Tomoya; Schlosburg, Joel E; Alvarez, Mariano; Koob, George F; Califano, Andrea; Sanna, Pietro Paolo

    2015-02-02

    A systems biology approach based on the assembly and interrogation of gene regulatory networks, or interactomes, was used to study neuroadaptation processes associated with the transition to alcohol dependence at the molecular level. Using a rat model of dependent and non-dependent alcohol self-administration, we reverse engineered a global transcriptional regulatory network during protracted abstinence, a period when relapse rates are highest. We then interrogated the network to identify master regulator genes that mechanistically regulate brain region-specific signatures associated with dependent and non-dependent alcohol self-administration. Among these, the gene coding for the glucocorticoid receptor was independently identified as a master regulator in multiple brain regions, including the medial prefrontal cortex, nucleus accumbens, central nucleus of the amygdala, and ventral tegmental area, consistent with the view that brain reward and stress systems are dysregulated during protracted abstinence. Administration of the glucocorticoid antagonist mifepristone in either the nucleus accumbens or ventral tegmental area selectively decreased dependent, excessive, alcohol self-administration in rats but had no effect on non-dependent, moderate, alcohol self-administration. Our study suggests that assembly and analysis of regulatory networks is an effective strategy for the identification of key regulators of long-term neuroplastic changes within specific brain regions that play a functional role in alcohol dependence. More specifically, our results support a key role for regulatory networks downstream of the glucocorticoid receptor in excessive alcohol drinking during protracted alcohol abstinence.

  14. A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

    Directory of Open Access Journals (Sweden)

    Zhao Yuming

    2011-05-01

    Full Text Available Abstract Background Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. Results We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct ( Conclusions We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.

  15. Identifying Opinion Leaders to Promote Organ Donation on Social Media: Network Study.

    Science.gov (United States)

    Shi, Jingyuan; Salmon, Charles T

    2018-01-09

    In the recent years, social networking sites (SNSs, also called social media) have been adopted in organ donation campaigns, and recruiting opinion leaders for such campaigns has been found effective in promoting behavioral changes. The aim of this paper was to focus on the dissemination of organ donation tweets on Weibo, the Chinese equivalent of Twitter, and to examine the opinion leadership in the retweet network of popular organ donation messages using social network analysis. It also aimed to investigate how personal and social attributes contribute to a user's opinion leadership on the topic of organ donation. All messages about organ donation posted on Weibo from January 1, 2015 to December 31, 2015 were extracted using Python Web crawler. A retweet network with 505,047 nodes and 545,312 edges of the popular messages (n=206) was constructed and analyzed. The local and global opinion leaderships were measured using network metrics, and the roles of personal attributes, professional knowledge, and social positions in obtaining the opinion leadership were examined using general linear model. The findings revealed that personal attributes, professional knowledge, and social positions predicted individual's local opinion leadership in the retweet network of popular organ donation messages. Alternatively, personal attributes and social positions, but not professional knowledge, were significantly associated with global opinion leadership. The findings of this study indicate that health campaign designers may recruit peer leaders in SNS organ donation promotions to facilitate information sharing among the target audience. Users who are unverified, active, well connected, and experienced with information and communications technology (ICT) will accelerate the sharing of organ donation messages in the global environment. Medical professionals such as organ transplant surgeons who can wield a great amount of influence on their direct connections could also effectively

  16. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.

    Directory of Open Access Journals (Sweden)

    Alex Greenfield

    2010-10-01

    Full Text Available Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations. Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

  17. Identifying the optimal supply temperature in district heating networks - A modelling approach

    DEFF Research Database (Denmark)

    Mohammadi, Soma; Bojesen, Carsten

    2014-01-01

    The number of low-energy and energy renovated buildings with considerably low heating demand has been continuously increasing in recent years. Combined with utilizing low temperature sources, this development raises the necessity of introducing a new generation of District Heating [DH] Systems...... of this study is to develop a model for thermo-hydraulic calculation of low temperature DH system. The modelling is performed with emphasis on transient heat transfer in pipe networks. The pseudo-dynamic approach is adopted to model the District Heating Network [DHN] behaviour which estimates the temperature...

  18. A point-based angular analysis model for identifying attributes of spaces at nodes in street networks

    Science.gov (United States)

    Jeong, Sang Kyu; Ban, Yong Un

    2016-05-01

    The effects of the axial map as the key representation of the original space syntax have been questioned by some researchers because of the map's discontinuity. To address this concern, angular segment analysis (ASA) was introduced. ASA calculates spatial depths by considering the turning angles of path segments in a street network. However, ASA cannot calculate the attributes of nodes connected to path segments in the network because it analyzes spaces by linear representation, as in the original space syntax. Because the attributes of the two ends (nodes) of a given path segment (link) are not equal to each other, and because they can affect pedestrian and vehicle movement and land use in a street network, the identification of the attributes at nodes (points) would be helpful in the detailed analysis of spaces in the network consisting of nodes and the segments connecting them. Accordingly, this study aims to develop an extended analysis model that can calculate the attributes of spaces at the nodes, including terminuses, bends, and junctions, in the network. To achieve this end, in this study we developed algorithms for a point-based angular analysis (PAA) to find the attributes of spaces at nodes (points), in contrast to ASA, which analyzes spaces using linear representations. As a result, this methodology can obtain distinct values for the attributes of two nodes at the ends of a path segment, through the calculation of spatial depths weighted by considering the turning angles and distances (lengths) along consecutive nodes for a route in the network. Through our methodology, it was identified that spatial configurations of street network affect the social and symbolic centralities of nodes in the network.

  19. Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance.

    Science.gov (United States)

    Gordon, Evan M; Stollstorff, Melanie; Vaidya, Chandan J

    2012-07-01

    Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. Copyright © 2011 Wiley-Liss, Inc.

  20. Identifying Central Bank Liquidity Super-Spreaders in Interbank Funds Networks

    NARCIS (Netherlands)

    Léon, C.; Machado, C.; Sarmiento, M.

    2014-01-01

    Evidence suggests that the Colombian interbank funds market exhibits an inhomogeneous and hierarchical network structure, akin to a core-periphery organization, in which a few financial institutions fulfill the role of super-spreaders because of their simultaneous importance as global borrowers and

  1. Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

    Science.gov (United States)

    Wang, Jingying; Wen, Ming-Lee; Jou, Min

    2016-01-01

    Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

  2. A Quantitative Experimental Study of the Effectiveness of Systems to Identify Network Attackers

    Science.gov (United States)

    Handorf, C. Russell

    2016-01-01

    This study analyzed the meta-data collected from a honeypot that was run by the Federal Bureau of Investigation for a period of 5 years. This analysis compared the use of existing industry methods and tools, such as Intrusion Detection System alerts, network traffic flow and system log traffic, within the Open Source Security Information Manager…

  3. Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

    Science.gov (United States)

    Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

    2012-01-01

    Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

  4. Evaluation of neural networks to identify types of activity using accelerometers

    NARCIS (Netherlands)

    Vries, S.I. de; Garre, F.G.; Engbers, L.H.; Hildebrandt, V.H.; Buuren, S. van

    2011-01-01

    Purpose: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults. Methods: Forty-nine subjects (21 men and 28 women; age range

  5. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

  6. Identifying gene-disease associations using centrality on a literature mined gene-interaction network.

    Science.gov (United States)

    Ozgür, Arzucan; Vu, Thuy; Erkan, Günes; Radev, Dragomir R

    2008-07-01

    Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org.

  7. Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

    Science.gov (United States)

    Chen, Zheng; Soutto, Mohammed; Rahman, Bushra; Fazili, Muhammad W; Peng, DunFa; Blanca Piazuelo, Maria; Chen, Heidi; Kay Washington, M; Shyr, Yu; El-Rifai, Wael

    2017-07-01

    Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis. © 2017 Wiley Periodicals, Inc.

  8. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

    Science.gov (United States)

    Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

    2014-01-01

    Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

  9. Identifying and analyzing the most important factors in universities scientific output using neural network

    Directory of Open Access Journals (Sweden)

    Sajjad Mohammadian

    2016-12-01

    Full Text Available For achieving to ideal research system is necessary to have policy and plan. The main aims of this research are knowledge extracted from the existing data gathered by the country research system. This Knowledge is prerequisite for science and technology policy. Population of the present descriptive research includes Universities of the Ministry of Science, Research & Technology. Data is extracted from database of monitoring of science and technology system. Classification algorithms Multilayer Preceptor neural network and neural network-based radius are used for data analysis. This research results showed that among the 15 indicators studied in this research, full-time faculty members, the number of theses, research funding and number of scholarships have more importance than other indicators. Among the four factors studied, the human resource factor is in the first place and has most importance in the scholarly productions. The education factor, financial factor and Structural factor is in the second to fourth rate.

  10. A simplistic model for identifying prominent web users in directed multiplex social networks: a case study using Twitter networks

    Science.gov (United States)

    Loucif, Hemza; Boubetra, Abdelhak; Akrouf, Samir

    2016-10-01

    This paper aims to describe a new simplistic model dedicated to gauge the online influence of Twitter users based on a mixture of structural and interactional features. The model is an additive mathematical formulation which involves two main parts. The first part serves to measure the influence of the Twitter user on just his neighbourhood covering his followers. However, the second part evaluates the potential influence of the Twitter user beyond the circle of his followers. Particularly, it measures the likelihood that the tweets of the Twitter user will spread further within the social graph through the retweeting process. The model is tested on a data set involving four kinds of real-world egocentric networks. The empirical results reveal that an active ordinary user is more prominent than a non-active celebrity one. A simple comparison is conducted between the proposed model and two existing simplistic approaches. The results show that our model generates the most realistic influence scores due to its dealing with both explicit (structural and interactional) and implicit features.

  11. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

    Science.gov (United States)

    Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

    2016-09-09

    Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators

  12. Functional data analysis for identifying nonlinear models of gene regulatory networks.

    Science.gov (United States)

    Summer, Georg; Perkins, Theodore J

    2010-12-02

    A key problem in systems biology is estimating dynamical models of gene regulatory networks. Traditionally, this has been done using regression or other ad-hoc methods when the model is linear. More detailed, realistic modeling studies usually employ nonlinear dynamical models, which lead to computationally difficult parameter estimation problems. Functional data analysis methods, however, offer a means to simplify fitting by transforming the problem from one of matching modeled and observed dynamics to one of matching modeled and observed time derivatives-a regression problem, albeit a nonlinear one. We formulate a functional data analysis approach for estimating the parameters of nonlinear dynamical models and evaluate this approach on data from two real systems, the gap gene system of Drosophila melanogaster and the synthetic IRMA network, which was created expressly as a test case for genetic network inference. We also evaluate the approach on simulated data sets generated by the GeneNetWeaver program, the basis for the annual DREAM reverse engineering challenge. We assess the accuracy with which the correct regulatory relationships within the networks are extracted, and consider alternative methods of regularization for the purpose of overfitting avoidance. We also show that the computational efficiency of the functional data analysis approach, and the decomposability of the resulting regression problem, allow us to explicitly enumerate and evaluate all possible regulator combinations for every gene. This gives deeper insight into the the relevance of different regulators or regulator combinations, and lets one check for alternative regulatory explanations. Functional data analysis is a powerful approach for estimating detailed nonlinear models of gene expression dynamics, allowing efficient and accurate estimation of regulatory architecture.

  13. Downregulation of GNA13-ERK network in prefrontal cortex of schizophrenia brain identified by combined focused and targeted quantitative proteomics.

    Science.gov (United States)

    Hirayama-Kurogi, Mio; Takizawa, Yohei; Kunii, Yasuto; Matsumoto, Junya; Wada, Akira; Hino, Mizuki; Akatsu, Hiroyasu; Hashizume, Yoshio; Yamamoto, Sakon; Kondo, Takeshi; Ito, Shingo; Tachikawa, Masanori; Niwa, Shin-Ichi; Yabe, Hirooki; Terasaki, Tetsuya; Setou, Mitsutoshi; Ohtsuki, Sumio

    2017-03-31

    Schizophrenia is a disabling mental illness associated with dysfunction of the prefrontal cortex, which affects cognition and emotion. The purpose of the present study was to identify altered molecular networks in the prefrontal cortex of schizophrenia patients by comparing protein expression levels in autopsied brains of patients and controls, using a combination of targeted and focused quantitative proteomics. We selected 125 molecules possibly related to schizophrenia for quantification by knowledge-based targeted proteomics. Among the quantified molecules, GRIK4 and MAO-B were significantly decreased in plasma membrane and cytosolic fractions, respectively, of prefrontal cortex. Focused quantitative proteomics identified 15 increased and 39 decreased proteins. Network analysis identified "GNA13-ERK1-eIF4G2 signaling" as a downregulated network, and proteins involved in this network were significantly decreased. Furthermore, searching downstream of eIF4G2 revealed that eIF4A1/2 and CYFIP1 were decreased, suggesting that downregulation of the network suppresses expression of CYFIP1, which regulates actin remodeling and is involved in axon outgrowth and spine formation. Downregulation of this signaling seems likely to impair axon formation and synapse plasticity of neuronal cells, and could be associated with development of cognitive impairment in the pathology of schizophrenia. The present study compared the proteome of the prefrontal cortex between schizophrenia patients and healthy controls by means of targeted proteomics and global quantitative proteomics. Targeted proteomics revealed that GRIK4 and MAOB were significantly decreased among 125 putatively schizophrenia-related proteins in prefrontal cortex of schizophrenia patients. Global quantitative proteomics identified 54 differentially expressed proteins in schizophrenia brains. The protein profile indicates attenuation of "GNA13-ERK signaling" in schizophrenia brain. In particular, EIF4G2 and CYFIP1

  14. Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging.

    Science.gov (United States)

    Di, Xin; Biswal, Bharat B

    2014-02-01

    The default mode network is part of the brain structure that shows higher neural activity and energy consumption when one is at rest. The key regions in the default mode network are highly interconnected as conveyed by both the white matter fiber tracing and the synchrony of resting-state functional magnetic resonance imaging signals. However, the causal information flow within the default mode network is still poorly understood. The current study used the dynamic causal modeling on a resting-state fMRI data set to identify the network structure underlying the default mode network. The endogenous brain fluctuations were explicitly modeled by Fourier series at the low frequency band of 0.01-0.08Hz, and those Fourier series were set as driving inputs of the DCM models. Model comparison procedures favored a model wherein the MPFC sends information to the PCC and the bilateral inferior parietal lobule sends information to both the PCC and MPFC. Further analyses provide evidence that the endogenous connectivity might be higher in the right hemisphere than in the left hemisphere. These data provided insight into the functions of each node in the DMN, and also validate the usage of DCM on resting-state fMRI data. © 2013.

  15. Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy

    Science.gov (United States)

    Inkeles, Megan S.; Teles, Rosane M.B.; Pouldar, Delila; Andrade, Priscila R.; Madigan, Cressida A.; Ambrose, Mike; Sarno, Euzenir N.; Rea, Thomas H.; Ochoa, Maria T.; Iruela-Arispe, M. Luisa; Swindell, William R.; Ottenhoff, Tom H.M.; Geluk, Annemieke; Bloom, Barry R.

    2016-01-01

    Transcriptome profiles derived from the site of human disease have led to the identification of genes that contribute to pathogenesis, yet the complex mixture of cell types in these lesions has been an obstacle for defining specific mechanisms. Leprosy provides an outstanding model to study host defense and pathogenesis in a human infectious disease, given its clinical spectrum, which interrelates with the host immunologic and pathologic responses. Here, we investigated gene expression profiles derived from skin lesions for each clinical subtype of leprosy, analyzing gene coexpression modules by cell-type deconvolution. In lesions from tuberculoid leprosy patients, those with the self-limited form of the disease, dendritic cells were linked with MMP12 as part of a tissue remodeling network that contributes to granuloma formation. In lesions from lepromatous leprosy patients, those with disseminated disease, macrophages were linked with a gene network that programs phagocytosis. In erythema nodosum leprosum, neutrophil and endothelial cell gene networks were identified as part of the vasculitis that results in tissue injury. The present integrated computational approach provides a systems approach toward identifying cell-defined functional networks that contribute to host defense and immunopathology at the site of human infectious disease. PMID:27699251

  16. Unique device identifiers for coronary stent postmarket surveillance and research: a report from the Food and Drug Administration Medical Device Epidemiology Network Unique Device Identifier demonstration.

    Science.gov (United States)

    Tcheng, James E; Crowley, Jay; Tomes, Madris; Reed, Terrie L; Dudas, Joseph M; Thompson, Kweli P; Garratt, Kirk N; Drozda, Joseph P

    2014-10-01

    Although electronic product identification in the consumer sector is ubiquitous, unique identification of medical devices is just being implemented in 2014. To evaluate unique device identifiers (UDIs) in health care, the US Food and Drug Administration (FDA) funded the Medical Device Epidemiology Network initiative, including a demonstration of the implementation of coronary stent UDI data in the information systems of a multihospital system (Mercy Health). This report describes the first phase of the demonstration. An expert panel of interventional cardiologists nominated by the American College of Cardiology and the Society for Cardiovascular Angiography and Interventions was convened with representatives of industry, health system members of the Healthcare Transformation Group, the American College of Cardiology National Cardiovascular Data Registry, and FDA to articulate concepts needed to best use UDI-associated data. The expert panel identified 3: (1) use cases for UDI-associated data (eg, research), (2) a supplemental data set of clinically relevant attributes (eg, stent dimensions), and (3) governance and administrative principles for the authoritative management of these data. Eighteen use cases were identified, encompassing clinical care, supply chain management, consumer information, research, regulatory, and surveillance domains. In addition to the attributes of the FDA Global Unique Device Identification Database, 9 additional coronary stent-specific attributes were required to address use case requirements. Recommendations regarding governance were elucidated as foundational principles for UDI-associated data management. This process for identifying requisite extensions to support the effective use of UDI-associated data should be generalizable. Implementation of a UDI system for medical devices must anticipate both global and device-specific information. Copyright © 2014 Mosby, Inc. All rights reserved.

  17. Network analysis identifies ELF3 as a QTL for the shade avoidance response in Arabidopsis.

    Directory of Open Access Journals (Sweden)

    José M Jiménez-Gómez

    2010-09-01

    Full Text Available Quantitative Trait Loci (QTL analyses in immortal populations are a powerful method for exploring the genetic mechanisms that control interactions of organisms with their environment. However, QTL analyses frequently do not culminate in the identification of a causal gene due to the large chromosomal regions often underlying QTLs. A reasonable approach to inform the process of causal gene identification is to incorporate additional genome-wide information, which is becoming increasingly accessible. In this work, we perform QTL analysis of the shade avoidance response in the Bayreuth-0 (Bay-0, CS954 x Shahdara (Sha, CS929 recombinant inbred line population of Arabidopsis. We take advantage of the complex pleiotropic nature of this trait to perform network analysis using co-expression, eQTL and functional classification from publicly available datasets to help us find good candidate genes for our strongest QTL, SAR2. This novel network analysis detected EARLY FLOWERING 3 (ELF3; AT2G25930 as the most likely candidate gene affecting the shade avoidance response in our population. Further genetic and transgenic experiments confirmed ELF3 as the causative gene for SAR2. The Bay-0 and Sha alleles of ELF3 differentially regulate developmental time and circadian clock period length in Arabidopsis, and the extent of this regulation is dependent on the light environment. This is the first time that ELF3 has been implicated in the shade avoidance response and that different natural alleles of this gene are shown to have phenotypic effects. In summary, we show that development of networks to inform candidate gene identification for QTLs is a promising technique that can significantly accelerate the process of QTL cloning.

  18. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...... tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups....

  19. Application of Neural Network Modeling to Identify Auditory Processing Disorders in School-Age Children

    Directory of Open Access Journals (Sweden)

    Sridhar Krishnamurti

    2015-01-01

    Full Text Available P300 Auditory Event-Related Potentials (P3AERPs were recorded in nine school-age children with auditory processing disorders and nine age- and gender-matched controls in response to tone burst stimuli presented at varying rates (1/second or 3/second under varying levels of competing noise (0 dB, 40 dB, or 60 dB SPL. Neural network modeling results indicated that speed of information processing and task-related demands significantly influenced P3AERP latency in children with auditory processing disorders. Competing noise and rapid stimulus rates influenced P3AERP amplitude in both groups.

  20. Identifying grade/stage-related active modules in human co-regulatory networks: a case study for breast cancer.

    Science.gov (United States)

    Feng, Chenchen; Chen, Lina; Li, Wan; Wang, Hong; Zhang, Liangcai; Jia, Xu; Miao, Zhengqiang; Qu, Xiaoli; Li, Weiguo; He, Weiming

    2012-12-01

    The histological grade/stage of tumor is widely acknowledged as an important clinical prognostic factor for cancer progression. Recent experimental studies have explored the following two topics at the molecular level: (1) whether or not gene expression levels vary by different degrees among different tumor grades/stages, and (2) whether some well-defined modules could distinguish one grade/stage from another. In this article, using breast cancer as an example, we investigated this topic and identified grade/stage-related active modules under the framework of a weighted network integrated from a human protein interaction network and a transcriptional regulatory network. Our results enabled us to draw the conclusion that the gene expression profile could provide more clues about tumor grade, but reveals less evidence about tumor stage. In addition, we found that our modular biomarker method had additional advantages in identifying some tumor grade/stage-related genes with slightly altered expression. According to our case study, the framework we introduced could be used for other cancers to identify their modules during grading or staging.

  1. Identifying the roles of race-based choice and chance in high school friendship network formation.

    Science.gov (United States)

    Currarini, Sergio; Jackson, Matthew O; Pin, Paolo

    2010-03-16

    Homophily, the tendency of people to associate with others similar to themselves, is observed in many social networks, ranging from friendships to marriages to business relationships, and is based on a variety of characteristics, including race, age, gender, religion, and education. We present a technique for distinguishing two primary sources of homophily: biases in the preferences of individuals over the types of their friends and biases in the chances that people meet individuals of other types. We use this technique to analyze racial patterns in friendship networks in a set of American high schools from the Add Health dataset. Biases in preferences and biases in meeting rates are both highly significant in these data, and both types of biases differ significantly across races. Asians and Blacks are biased toward interacting with their own race at rates >7 times higher than Whites, whereas Hispanics exhibit an intermediate bias in meeting opportunities. Asians exhibit the least preference bias, valuing friendships with other types 90% as much as friendships with Asians, whereas Blacks and Hispanics value friendships with other types 55% and 65% as much as same-type friendships, respectively, and Whites fall in between, valuing other-type friendships 75% as much as friendships with Whites. Meetings are significantly more biased in large schools (>1,000 students) than in small schools (schools.

  2. The use of the bicycle compatibility index in identifying gaps and deficiencies in bicycle networks

    Science.gov (United States)

    Ilie, A.; Oprea, C.; Costescu, D.; Roşca, E.; Dinu, O.; Ghionea, F.

    2016-11-01

    Currently, no methodology is widely accepted by engineers, planners, or bicycle coordinators that allow them to determine how compatible a roadway is in providing efficient operation of both bicycles and motor vehicles. Previous studies reported a number of approaches to obtain an appropriate level of service; some authors developed the bicycle level of service (BLOS) and other authors developed the bicycle compatibility indexes (BCI). The level of service (BLOS) for a bicycle route represents an evaluation of safety and commodity perceived by a bicyclist reported to the motorized traffic, while running on the road surface. The bicycle compatibility index (BCI) is used by bicycle coordinators, transportation planners, traffic engineers to evaluate the capability of specific roadways to accommodate both motorists and bicyclists and to plan for and design roadways that are bicycle compatible. After applying BCI and BLOS models for the designed bicycle infrastructure network in the city of Dej, one can see that only few streets are Moderately Low compatible compared to the others with a high degree of compatibility that recommends to include them in the bicycle infrastructure network.

  3. A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.

    Directory of Open Access Journals (Sweden)

    Chao Qin

    Full Text Available Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC and in-degree centrality of complex (IDC. First, we introduce the common centrality measures; second, we propose the densities Den1(v and Den2(v of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC, BC, subgraph centrality (SC, eigenvector centrality (EC, network centrality (NC, and the local average connectivity-based method (LAC. LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.

  4. A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.

    Science.gov (United States)

    Qin, Chao; Sun, Yongqi; Dong, Yadong

    2016-01-01

    Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.

  5. Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study.

    Science.gov (United States)

    Nimmegeers, Philippe; Lauwers, Joost; Telen, Dries; Logist, Filip; Impe, Jan Van

    2017-06-01

    In this work, both the structural and practical identifiability of the Anaerobic Digestion Model no. 1 (ADM1) is investigated, which serves as a relevant case study of large non-linear dynamic network models. The structural identifiability is investigated using the probabilistic algorithm, adapted to deal with the specifics of the case study (i.e., a large-scale non-linear dynamic system of differential and algebraic equations). The practical identifiability is analyzed using a Monte Carlo parameter estimation procedure for a 'non-informative' and 'informative' experiment, which are heuristically designed. The model structure of ADM1 has been modified by replacing parameters by parameter combinations, to provide a generally locally structurally identifiable version of ADM1. This means that in an idealized theoretical situation, the parameters can be estimated accurately. Furthermore, the generally positive structural identifiability results can be explained from the large number of interconnections between the states in the network structure. This interconnectivity, however, is also observed in the parameter estimates, making uncorrelated parameter estimations in practice difficult. Copyright © 2017. Published by Elsevier Inc.

  6. A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants.

    Science.gov (United States)

    Wang, Dong; Amornsiripanitch, Nita; Dong, Xinnian

    2006-11-01

    Many biological processes are controlled by intricate networks of transcriptional regulators. With the development of microarray technology, transcriptional changes can be examined at the whole-genome level. However, such analysis often lacks information on the hierarchical relationship between components of a given system. Systemic acquired resistance (SAR) is an inducible plant defense response involving a cascade of transcriptional events induced by salicylic acid through the transcription cofactor NPR1. To identify additional regulatory nodes in the SAR network, we performed microarray analysis on Arabidopsis plants expressing the NPR1-GR (glucocorticoid receptor) fusion protein. Since nuclear translocation of NPR1-GR requires dexamethasone, we were able to control NPR1-dependent transcription and identify direct transcriptional targets of NPR1. We show that NPR1 directly upregulates the expression of eight WRKY transcription factor genes. This large family of 74 transcription factors has been implicated in various defense responses, but no specific WRKY factor has been placed in the SAR network. Identification of NPR1-regulated WRKY factors allowed us to perform in-depth genetic analysis on a small number of WRKY factors and test well-defined phenotypes of single and double mutants associated with NPR1. Among these WRKY factors we found both positive and negative regulators of SAR. This genomics-directed approach unambiguously positioned five WRKY factors in the complex transcriptional regulatory network of SAR. Our work not only discovered new transcription regulatory components in the signaling network of SAR but also demonstrated that functional studies of large gene families have to take into consideration sequence similarity as well as the expression patterns of the candidates.

  7. A genomic approach to identify regulatory nodes in the transcriptional network of systemic acquired resistance in plants.

    Directory of Open Access Journals (Sweden)

    Dong Wang

    2006-11-01

    Full Text Available Many biological processes are controlled by intricate networks of transcriptional regulators. With the development of microarray technology, transcriptional changes can be examined at the whole-genome level. However, such analysis often lacks information on the hierarchical relationship between components of a given system. Systemic acquired resistance (SAR is an inducible plant defense response involving a cascade of transcriptional events induced by salicylic acid through the transcription cofactor NPR1. To identify additional regulatory nodes in the SAR network, we performed microarray analysis on Arabidopsis plants expressing the NPR1-GR (glucocorticoid receptor fusion protein. Since nuclear translocation of NPR1-GR requires dexamethasone, we were able to control NPR1-dependent transcription and identify direct transcriptional targets of NPR1. We show that NPR1 directly upregulates the expression of eight WRKY transcription factor genes. This large family of 74 transcription factors has been implicated in various defense responses, but no specific WRKY factor has been placed in the SAR network. Identification of NPR1-regulated WRKY factors allowed us to perform in-depth genetic analysis on a small number of WRKY factors and test well-defined phenotypes of single and double mutants associated with NPR1. Among these WRKY factors we found both positive and negative regulators of SAR. This genomics-directed approach unambiguously positioned five WRKY factors in the complex transcriptional regulatory network of SAR. Our work not only discovered new transcription regulatory components in the signaling network of SAR but also demonstrated that functional studies of large gene families have to take into consideration sequence similarity as well as the expression patterns of the candidates.

  8. A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

    Directory of Open Access Journals (Sweden)

    Warren D Anderson

    2017-07-01

    Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

  9. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

    NARCIS (Netherlands)

    Waaijenborg, Sandra; Zwinderman, Aeilko H.

    2009-01-01

    ABSTRACT: BACKGROUND: We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the

  10. AID/APOBEC-network reconstruction identifies pathways associated with survival in ovarian cancer.

    Science.gov (United States)

    Svoboda, Martin; Meshcheryakova, Anastasia; Heinze, Georg; Jaritz, Markus; Pils, Dietmar; Castillo-Tong, Dan Cacsire; Hager, Gudrun; Thalhammer, Theresia; Jensen-Jarolim, Erika; Birner, Peter; Braicu, Ioana; Sehouli, Jalid; Lambrechts, Sandrina; Vergote, Ignace; Mahner, Sven; Zimmermann, Philip; Zeillinger, Robert; Mechtcheriakova, Diana

    2016-08-16

    Building up of pathway-/disease-relevant signatures provides a persuasive tool for understanding the functional relevance of gene alterations and gene network associations in multifactorial human diseases. Ovarian cancer is a highly complex heterogeneous malignancy in respect of tumor anatomy, tumor microenvironment including pro-/antitumor immunity and inflammation; still, it is generally treated as single disease. Thus, further approaches to investigate novel aspects of ovarian cancer pathogenesis aiming to provide a personalized strategy to clinical decision making are of high priority. Herein we assessed the contribution of the AID/APOBEC family and their associated genes given the remarkable ability of AID and APOBECs to edit DNA/RNA, and as such, providing tools for genetic and epigenetic alterations potentially leading to reprogramming of tumor cells, stroma and immune cells. We structured the study by three consecutive analytical modules, which include the multigene-based expression profiling in a cohort of patients with primary serous ovarian cancer using a self-created AID/APOBEC-associated gene signature, building up of multivariable survival models with high predictive accuracy and nomination of top-ranked candidate/target genes according to their prognostic impact, and systems biology-based reconstruction of the AID/APOBEC-driven disease-relevant mechanisms using transcriptomics data from ovarian cancer samples. We demonstrated that inclusion of the AID/APOBEC signature-based variables significantly improves the clinicopathological variables-based survival prognostication allowing significant patient stratification. Furthermore, several of the profiling-derived variables such as ID3, PTPRC/CD45, AID, APOBEC3G, and ID2 exceed the prognostic impact of some clinicopathological variables. We next extended the signature-/modeling-based knowledge by extracting top genes co-regulated with target molecules in ovarian cancer tissues and dissected potential

  11. City networks and commodity chains: identifying global flows and local connections in Ho Chi Minh City

    DEFF Research Database (Denmark)

    Vind, Ingeborg; Fold, Niels

    2010-01-01

    of the role of advanced producer services will strengthen Global Commodity Chain (GCC) analysis. A combination of the literatures on WCN and GCC can contribute to a broader conceptualization of the connections and connectivities of global cities. In addition, a combined approach will improve our understanding...... of globalization processes within many so-called 'third-world' cities that are experiencing booms in export-oriented industrialization and in migration from rural hinterlands as they are being integrated into Global Commodity Chains. We illustrate our argument with insights from GCC analyses of the electronics......Any analytical framework for understanding actual forms of the intensified incorporation of cities into the world economy needs to go beyond the exclusive focus on advanced producer services, which is characteristic of most of the World City Network (WCN) approach. Simultaneously, an account...

  12. An inter-residue network model to identify mutational-constrained regions on the Ebola coat glycoprotein

    Science.gov (United States)

    Quinlan, Devin S.; Raman, Rahul; Tharakaraman, Kannan; Subramanian, Vidya; del Hierro, Gabriella; Sasisekharan, Ram

    2017-01-01

    Recently, progress has been made in the development of vaccines and monoclonal antibody cocktails that target the Ebola coat glycoprotein (GP). Based on the mutation rates for Ebola virus given its natural sequence evolution, these treatment strategies are likely to impose additional selection pressure to drive acquisition of mutations in GP that escape neutralization. Given the high degree of sequence conservation among GP of Ebola viruses, it would be challenging to determine the propensity of acquiring mutations in response to vaccine or treatment with one or a cocktail of monoclonal antibodies. In this study, we analyzed the mutability of each residue using an approach that captures the structural constraints on mutability based on the extent of its inter-residue interaction network within the three-dimensional structure of the trimeric GP. This analysis showed two distinct clusters of highly networked residues along the GP1-GP2 interface, part of which overlapped with epitope surfaces of known neutralizing antibodies. This network approach also permitted us to identify additional residues in the network of the known hotspot residues of different anti-Ebola antibodies that would impact antibody-epitope interactions. PMID:28397835

  13. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

    Directory of Open Access Journals (Sweden)

    Zwinderman Aeilko H

    2009-09-01

    Full Text Available Abstract Background We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes. Results We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes. Conclusion We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

  14. Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

    Science.gov (United States)

    Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

    2016-03-01

    Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. A genome-wide regulatory network identifies key transcription factors for memory CD8⁺ T-cell development

    National Research Council Canada - National Science Library

    Hu, Guangan; Chen, Jianzhu

    2013-01-01

    .... To identify transcription factors and their interactions in memory CD8⁺ T-cell development, we construct a genome-wide regulatory network and apply it to identify key transcription factors that regulate memory signature genes...

  16. Using Social Network Analysis to Identify Sub-Groups in the Operating Room

    NARCIS (Netherlands)

    Listyowardojo, Tita A.; Steglich, Christian; Peuchen, Stephen; Johnson, Addie; de Waard, D.; Godthelp, J.; Kooi, F.L.; Brookhuis, K.A.

    2009-01-01

    The frequency with which operating room (OR) staff work together can impact patient safety because staff who often work together share a set of experiences which may enable them to anticipate each other’s actions and reactions in the future. Identifying sub-groups of staff who frequently work

  17. Predicting General Academic Performance and Identifying the Differential Contribution of Participating Variables Using Artificial Neural Networks

    Science.gov (United States)

    Musso, Mariel F.; Kyndt, Eva; Cascallar, Eduardo C.; Dochy, Filip

    2013-01-01

    Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students'…

  18. Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores.

    Science.gov (United States)

    Parente, Daniel J; Ray, J Christian J; Swint-Kruse, Liskin

    2015-12-01

    As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for coevolution between pairs of positions. Coevolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of coevolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded coevolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; "central" positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise coevolution scores: instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints-detectable by divergent algorithms--that occur at key protein locations. Finally, we discuss the fact that multiple patterns coexist in evolutionary data that, together, give rise to emergent protein functions. © 2015 Wiley Periodicals, Inc.

  19. Amino acid positions subject to multiple co-evolutionary constraints can be robustly identified by their eigenvector network centrality scores

    Science.gov (United States)

    Parente, Daniel J.; Ray, J. Christian J.; Swint-Kruse, Liskin

    2015-01-01

    As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for co-evolution between pairs of positions. Co-evolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of co-evolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded co-evolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; “central” positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise co-evolution scores: Instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints – detectable by divergent algorithms – that occur at key protein locations. Finally, we discuss the fact that multiple patterns co-exist in evolutionary data that, together, give rise to emergent protein functions. PMID:26503808

  20. Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

    Directory of Open Access Journals (Sweden)

    Mengqi Huo

    2017-06-01

    Full Text Available Salvia miltiorrhiza, known as Danshen, has attracted worldwide interest for its substantial effects on coronary heart disease (CHD. Danshensu (DSS is one of the main active ingredients of Danshen on CHD. Although it has been proven to have a good clinical effect on CHD, the action mechanisms remain elusive. In the current study, a coexpression network-based approach was used to illustrate the beneficial properties of DSS in the context of CHD. By integrating the gene expression profile data and protein-protein interactions (PPIs data, two coexpression protein interaction networks (CePIN in a CHD state (CHD CePIN and a non-CHD state (non-CHD CePIN were generated. Then, shared nodes and unique nodes in CHD CePIN were attained by conducting a comparison between CHD CePIN and non-CHD CePIN. By calculating the topological parameters of each shared node and unique node in the networks, and comparing the differentially expressed genes, target proteins involved in disease regulation were attained. Then, Gene Ontology (GO enrichment was utilized to identify biological processes associated to target proteins. Consequently, it turned out that the treatment of CHD with DSS may be partly attributed to the regulation of immunization and blood circulation. Also, it indicated that sodium/hydrogen exchanger 3 (SLC9A3, Prostaglandin G/H synthase 2 (PTGS2, Oxidized low-density lipoprotein receptor 1 (OLR1, and fibrinogen gamma chain (FGG may be potential therapeutic targets for CHD. In summary, this study provided a novel coexpression protein interaction network approach to provide an explanation of the mechanisms of DSS on CHD and identify key proteins which maybe the potential therapeutic targets for CHD.

  1. Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding

    OpenAIRE

    Kommadath, Arun; Bao, Hua; Arantes, Adriano S.; Plastow, Graham S; Tuggle, Christopher K.; Bearson, Shawn MD; Luo Guan, Le; Stothard, Paul

    2014-01-01

    Background Salmonella enterica serovar Typhimurium is a gram-negative bacterium that can colonise the gut of humans and several species of food producing farm animals to cause enteric or septicaemic salmonellosis. While many studies have looked into the host genetic response to Salmonella infection, relatively few have used correlation of shedding traits with gene expression patterns to identify genes whose variable expression among different individuals may be associated with differences in ...

  2. Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations.

    Science.gov (United States)

    Kyeong, Sunghyon; Kim, Jae-Jin; Kim, Eunjoo

    2017-01-01

    Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous condition and identification of clinically meaningful subgroups would open up a new window for personalized medicine. Thus, we aimed to identify new clinical phenotypes in children and adolescents with ADHD and to investigate whether neuroimaging findings validate the identified phenotypes. Neuroimaging and clinical data from 67 children with ADHD and 62 typically developing controls (TDCs) from the ADHD-200 database were selected. Clinical measures of ADHD symptoms and intelligence quotient (IQ) were used as input features into a topological data analysis (TDA) to identify ADHD subgroups within our sample. As external validators, graph theoretical measures obtained from the functional connectome were compared to address the biological meaningfulness of the identified subtypes. The TDA identified two unique subgroups of ADHD, labelled as mild symptom ADHD (mADHD) and severe symptom ADHD (sADHD). The output topology shape was repeatedly observed in the independent validation dataset. The graph theoretical analysis showed a decrease in the degree centrality and PageRank in the bilateral posterior cingulate cortex in the sADHD group compared with the TDC group. The mADHD group showed similar patterns of intra- and inter-module connectivity to the sADHD group. Relative to the TDC group, the inter-module connectivity between the default mode network and executive control network were significantly increased in the sADHD group but not in the mADHD group. Taken together, our results show that the data-driven TDA is potentially useful in identifying objective and biologically relevant disease phenotypes in children and adolescents with ADHD.

  3. Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations.

    Directory of Open Access Journals (Sweden)

    Sunghyon Kyeong

    Full Text Available Attention-deficit/hyperactivity disorder (ADHD is a clinically heterogeneous condition and identification of clinically meaningful subgroups would open up a new window for personalized medicine. Thus, we aimed to identify new clinical phenotypes in children and adolescents with ADHD and to investigate whether neuroimaging findings validate the identified phenotypes. Neuroimaging and clinical data from 67 children with ADHD and 62 typically developing controls (TDCs from the ADHD-200 database were selected. Clinical measures of ADHD symptoms and intelligence quotient (IQ were used as input features into a topological data analysis (TDA to identify ADHD subgroups within our sample. As external validators, graph theoretical measures obtained from the functional connectome were compared to address the biological meaningfulness of the identified subtypes. The TDA identified two unique subgroups of ADHD, labelled as mild symptom ADHD (mADHD and severe symptom ADHD (sADHD. The output topology shape was repeatedly observed in the independent validation dataset. The graph theoretical analysis showed a decrease in the degree centrality and PageRank in the bilateral posterior cingulate cortex in the sADHD group compared with the TDC group. The mADHD group showed similar patterns of intra- and inter-module connectivity to the sADHD group. Relative to the TDC group, the inter-module connectivity between the default mode network and executive control network were significantly increased in the sADHD group but not in the mADHD group. Taken together, our results show that the data-driven TDA is potentially useful in identifying objective and biologically relevant disease phenotypes in children and adolescents with ADHD.

  4. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Directory of Open Access Journals (Sweden)

    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

  5. MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

    Directory of Open Access Journals (Sweden)

    Steffen Sass

    2015-12-01

    Full Text Available MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional

  6. Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor.

    Science.gov (United States)

    Wei, Peng; Pan, Wei

    2012-01-01

    We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.

  7. Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Baoman Wang

    2015-01-01

    Full Text Available Apoptosis is the process of programmed cell death (PCD that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

  8. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Directory of Open Access Journals (Sweden)

    Victor Trevino

    2016-04-01

    Full Text Available The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell

  9. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Science.gov (United States)

    Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antczak, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J; Guindani, Michele; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

    2016-04-01

    The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks

  10. The use of social networking platforms for sexual health promotion: identifying key strategies for successful user engagement.

    Science.gov (United States)

    Veale, Hilary J; Sacks-Davis, Rachel; Weaver, Emma Rn; Pedrana, Alisa E; Stoové, Mark A; Hellard, Margaret E

    2015-02-06

    Online social networking platforms such as Facebook and Twitter have grown rapidly in popularity, with opportunities for interaction enhancing their health promotion potential. Such platforms are being used for sexual health promotion but with varying success in reaching and engaging users. We aimed to identify Facebook and Twitter profiles that were able to engage large numbers of users, and to identify strategies used to successfully attract and engage users in sexual health promotion on these platforms. We identified active Facebook (n = 60) and Twitter (n = 40) profiles undertaking sexual health promotion through a previous systematic review, and assessed profile activity over a one-month period. Quantitative measures of numbers of friends and followers (reach) and social media interactions were assessed, and composite scores used to give profiles an 'engagement success' ranking. Associations between host activity, reach and interaction metrics were explored. Content of the top ten ranked Facebook and Twitter profiles was analysed using a thematic framework and compared with five poorly performing profiles to identify strategies for successful user engagement. Profiles that were able to successfully engage large numbers of users were more active and had higher levels of interaction per user than lower-ranked profiles. Strategies used by the top ten ranked profiles included: making regular posts/tweets (median 46 posts or 124 tweets/month for top-ranked profiles versus six posts or six tweets for poorly-performing profiles); individualised interaction with users (85% of top-ranked profiles versus 0% for poorly-performing profiles); and encouraging interaction and conversation by posing questions (100% versus 40%). Uploading multimedia material (80% versus 30%) and highlighting celebrity involvement (70% versus 10%) were also key strategies. Successful online engagement on social networking platforms can be measured through quantitative (user numbers and

  11. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Energy Technology Data Exchange (ETDEWEB)

    Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

    2011-10-15

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  12. EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

    Directory of Open Access Journals (Sweden)

    Chun-Chuan Chen

    Full Text Available Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation. In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

  13. Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

    Directory of Open Access Journals (Sweden)

    Zhidong Tu

    Full Text Available Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6 and diabetes-susceptible (BTBR mouse strains made genetically obese by the Leptin(ob/ob mutation (Lep(ob. High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

  14. Use of Microarray Datasets to generate Caco-2-dedicated Networks and to identify Reporter Genes of Specific Pathway Activity.

    Science.gov (United States)

    Venkatasubramanian, Prashanna Balaji; Toydemir, Gamze; de Wit, Nicole; Saccenti, Edoardo; Martins Dos Santos, Vitor A P; van Baarlen, Peter; Wells, Jerry M; Suarez-Diez, Maria; Mes, Jurriaan J

    2017-07-28

    Intestinal epithelial cells, like Caco-2, are commonly used to study the interaction between food, other luminal factors and the host, often supported by microarray analysis to study the changes in gene expression as a result of the exposure. However, no compiled dataset for Caco-2 has ever been initiated and Caco-2-dedicated gene expression networks are barely available. Here, 341 Caco-2-specific microarray samples were collected from public databases and from in-house experiments pertaining to Caco-2 cells exposed to pathogens, probiotics and several food compounds. Using these datasets, a gene functional association network specific for Caco-2 was generated containing 8937 nodes 129711 edges. Two in silico methods, a modified version of biclustering and the new Differential Expression Correlation Analysis, were developed to identify Caco-2-specific gene targets within a pathway of interest. These methods were subsequently applied to the AhR and Nrf2 signalling pathways and altered expression of the predicted target genes was validated by qPCR in Caco-2 cells exposed to coffee extracts, known to activate both AhR and Nrf2 pathways. The datasets and in silico method(s) to identify and predict responsive target genes can be used to more efficiently design experiments to study Caco-2/intestinal epithelial-relevant biological processes.

  15. LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network.

    Science.gov (United States)

    Wei, Pi-Jing; Zhang, Di; Xia, Junfeng; Zheng, Chun-Hou

    2016-12-23

    Cancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient's lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations. In this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver (LNDriver). Experiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes.

  16. A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize.

    Directory of Open Access Journals (Sweden)

    Matt eGeisler

    2015-06-01

    Full Text Available Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6,004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize.

  17. Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma.

    Science.gov (United States)

    Randhawa, Vinay; Acharya, Vishal

    2015-07-16

    Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful. The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset. The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy. The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.

  18. Finding meaning in social media: content-based social network analysis of QuitNet to identify new opportunities for health promotion.

    Science.gov (United States)

    Myneni, Sahiti; Cobb, Nathan K; Cohen, Trevor

    2013-01-01

    Unhealthy behaviors increase individual health risks and are a socioeconomic burden. Harnessing social influence is perceived as fundamental for interventions to influence health-related behaviors. However, the mechanisms through which social influence occurs are poorly understood. Online social networks provide the opportunity to understand these mechanisms as they digitally archive communication between members. In this paper, we present a methodology for content-based social network analysis, combining qualitative coding, automated text analysis, and formal network analysis such that network structure is determined by the content of messages exchanged between members. We apply this approach to characterize the communication between members of QuitNet, an online social network for smoking cessation. Results indicate that the method identifies meaningful theme-based social sub-networks. Modeling social network data using this method can provide us with theme-specific insights such as the identities of opinion leaders and sub-community clusters. Implications for design of targeted social interventions are discussed.

  19. Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions.

    Science.gov (United States)

    da Silva, Aleksandra do Socorro; de Brito, Silvana Rossy; Vijaykumar, Nandamudi Lankalapalli; da Rocha, Cláudio Alex Jorge; Monteiro, Maurílio de Abreu; Costa, João Crisóstomo Weyl Albuquerque; Francês, Carlos Renato Lisboa

    2016-01-01

    The published literature reveals several arguments concerning the strategic importance of information and communication technology (ICT) interventions for developing countries where the digital divide is a challenge. Large-scale ICT interventions can be an option for countries whose regions, both urban and rural, present a high number of digitally excluded people. Our goal was to monitor and identify problems in interventions aimed at certification for a large number of participants in different geographical regions. Our case study is the training at the Telecentros.BR, a program created in Brazil to install telecenters and certify individuals to use ICT resources. We propose an approach that applies social network analysis and mining techniques to data collected from Telecentros.BR dataset and from the socioeconomics and telecommunications infrastructure indicators of the participants' municipalities. We found that (i) the analysis of interactions in different time periods reflects the objectives of each phase of training, highlighting the increased density in the phase in which participants develop and disseminate their projects; (ii) analysis according to the roles of participants (i.e., tutors or community members) reveals that the interactions were influenced by the center (or region) to which the participant belongs (that is, a community contained mainly members of the same region and always with the presence of tutors, contradicting expectations of the training project, which aimed for intense collaboration of the participants, regardless of the geographic region); (iii) the social network of participants influences the success of the training: that is, given evidence that the degree of the community member is in the highest range, the probability of this individual concluding the training is 0.689; (iv) the North region presented the lowest probability of participant certification, whereas the Northeast, which served municipalities with similar

  20. Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions.

    Directory of Open Access Journals (Sweden)

    Aleksandra do Socorro da Silva

    Full Text Available The published literature reveals several arguments concerning the strategic importance of information and communication technology (ICT interventions for developing countries where the digital divide is a challenge. Large-scale ICT interventions can be an option for countries whose regions, both urban and rural, present a high number of digitally excluded people. Our goal was to monitor and identify problems in interventions aimed at certification for a large number of participants in different geographical regions. Our case study is the training at the Telecentros.BR, a program created in Brazil to install telecenters and certify individuals to use ICT resources. We propose an approach that applies social network analysis and mining techniques to data collected from Telecentros.BR dataset and from the socioeconomics and telecommunications infrastructure indicators of the participants' municipalities. We found that (i the analysis of interactions in different time periods reflects the objectives of each phase of training, highlighting the increased density in the phase in which participants develop and disseminate their projects; (ii analysis according to the roles of participants (i.e., tutors or community members reveals that the interactions were influenced by the center (or region to which the participant belongs (that is, a community contained mainly members of the same region and always with the presence of tutors, contradicting expectations of the training project, which aimed for intense collaboration of the participants, regardless of the geographic region; (iii the social network of participants influences the success of the training: that is, given evidence that the degree of the community member is in the highest range, the probability of this individual concluding the training is 0.689; (iv the North region presented the lowest probability of participant certification, whereas the Northeast, which served municipalities with similar

  1. Analysis of serum inflammatory mediators identifies unique dynamic networks associated with death and spontaneous survival in pediatric acute liver failure.

    Science.gov (United States)

    Azhar, Nabil; Ziraldo, Cordelia; Barclay, Derek; Rudnick, David A; Squires, Robert H; Vodovotz, Yoram

    2013-01-01

    Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx) decisions in pediatric acute liver failure (PALF), but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF. We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG) collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA), and Dynamic Bayesian Network (DBN) inference. Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA. The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.

  2. Analysis of serum inflammatory mediators identifies unique dynamic networks associated with death and spontaneous survival in pediatric acute liver failure.

    Directory of Open Access Journals (Sweden)

    Nabil Azhar

    Full Text Available Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx decisions in pediatric acute liver failure (PALF, but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF.We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA, and Dynamic Bayesian Network (DBN inference.Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA.The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.

  3. Applying Social Network Analysis to Identify the Social Support Needs of Adolescent and Young Adult Cancer Patients and Survivors.

    Science.gov (United States)

    Koltai, Kolina; Walsh, Casey; Jones, Barbara; Berkelaar, Brenda L

    2017-11-06

    This article examines how theoretical and clinical applications of social network analysis (SNA) can inform opportunities for innovation and advancement of social support programming for adolescent and young adult (AYA) cancer patients and survivors. SNA can help address potential barriers and challenges to initiating and sustaining AYA peer support by helping to identify the diverse psychosocial needs among individuals in the AYA age range; find strategic ways to support and connect AYAs at different phases of the cancer trajectory with resources and services; and increase awareness of psychosocial resources and referrals from healthcare providers. Network perspectives on homophily, proximity, and evolution provide a foundational basis to explore the utility of SNA in AYA clinical care and research initiatives. The uniqueness of the AYA oncology community can also provide insight into extending and developing current SNA theories. Using SNA in AYA psychosocial cancer research has the potential to create new ideas and pathways for supporting AYAs across the continuum of care, while also extending theories of SNA. SNA may also prove to be a useful tool for examining social support resources for AYAs with various chronic health conditions and other like groups.

  4. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2017-09-05

    In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  5. Using Network Extracted Ontologies to Identify Novel Genes with Roles in Appressorium Development in the Rice Blast Fungus Magnaporthe oryzae.

    Science.gov (United States)

    Ames, Ryan M

    2017-01-17

    Magnaporthe oryzae is the causal agent of rice blast disease, the most important infection of rice worldwide. Half the world's population depends on rice for its primary caloric intake and, as such, rice blast poses a serious threat to food security. The stages of M. oryzae infection are well defined, with the formation of an appressorium, a cell type that allows penetration of the plant cuticle, particularly well studied. However, many of the key pathways and genes involved in this disease stage are yet to be identified. In this study, I have used network-extracted ontologies (NeXOs), hierarchical structures inferred from RNA-Seq data, to identify pathways involved in appressorium development, which in turn highlights novel genes with potential roles in this process. This study illustrates the use of NeXOs for pathway identification from large-scale genomics data and also identifies novel genes with potential roles in disease. The methods presented here will be useful to study disease processes in other pathogenic species and these data represent predictions of novel targets for intervention in M. oryzae.

  6. Using Network Extracted Ontologies to Identify Novel Genes with Roles in Appressorium Development in the Rice Blast Fungus Magnaporthe oryzae

    Directory of Open Access Journals (Sweden)

    Ryan M. Ames

    2017-01-01

    Full Text Available Magnaporthe oryzae is the causal agent of rice blast disease, the most important infection of rice worldwide. Half the world’s population depends on rice for its primary caloric intake and, as such, rice blast poses a serious threat to food security. The stages of M. oryzae infection are well defined, with the formation of an appressorium, a cell type that allows penetration of the plant cuticle, particularly well studied. However, many of the key pathways and genes involved in this disease stage are yet to be identified. In this study, I have used network-extracted ontologies (NeXOs, hierarchical structures inferred from RNA-Seq data, to identify pathways involved in appressorium development, which in turn highlights novel genes with potential roles in this process. This study illustrates the use of NeXOs for pathway identification from large-scale genomics data and also identifies novel genes with potential roles in disease. The methods presented here will be useful to study disease processes in other pathogenic species and these data represent predictions of novel targets for intervention in M. oryzae.

  7. Application of network properties and signal strength to identify face-to-face links in an electronic dataset

    CERN Document Server

    Sekara, Vedran

    2014-01-01

    Understanding how people interact and socialize is important in many contexts, from disease control to urban planning. Datasets that capture this specific aspect of human life have increased in size and availability over the last few years. We have yet to understand, however, to what extent such electronic datasets may serve as a valid proxy for real life face-to-face interactions. For an observational dataset, gathered by mobile phones, we attack the problem of identifying transient and non-important links, as well as how to highlight important interactions. Using the Bluetooth signal strength parameter to distinguish between observations, we demonstrate that weak links, compared to strong links, have a lower probability of being observed at later times, while such links--on average--also have lower link-weights and a lower probability of sharing an online friendship. Further, the role of link-strength is investigated in relation to social network properties.

  8. Regulatory network analysis of Epstein-Barr virus identifies functional modules and hub genes involved in infectious mononucleosis.

    Science.gov (United States)

    Poorebrahim, Mansour; Salarian, Ali; Najafi, Saeideh; Abazari, Mohammad Foad; Aleagha, Maryam Nouri; Dadras, Mohammad Nasr; Jazayeri, Seyed Mohammad; Ataei, Atousa; Poortahmasebi, Vahdat

    2017-05-01

    Epstein-Barr virus (EBV) is the most common cause of infectious mononucleosis (IM) and establishes lifetime infection associated with a variety of cancers and autoimmune diseases. The aim of this study was to develop an integrative gene regulatory network (GRN) approach and overlying gene expression data to identify the representative subnetworks for IM and EBV latent infection (LI). After identifying differentially expressed genes (DEGs) in both IM and LI gene expression profiles, functional annotations were applied using gene ontology (GO) and BiNGO tools, and construction of GRNs, topological analysis and identification of modules were carried out using several plugins of Cytoscape. In parallel, a human-EBV GRN was generated using the Hu-Vir database for further analyses. Our analysis revealed that the majority of DEGs in both IM and LI were involved in cell-cycle and DNA repair processes. However, these genes showed a significant negative correlation in the IM and LI states. Furthermore, cyclin-dependent kinase 2 (CDK2) - a hub gene with the highest centrality score - appeared to be the key player in cell cycle regulation in IM disease. The most significant functional modules in the IM and LI states were involved in the regulation of the cell cycle and apoptosis, respectively. Human-EBV network analysis revealed several direct targets of EBV proteins during IM disease. Our study provides an important first report on the response to IM/LI EBV infection in humans. An important aspect of our data was the upregulation of genes associated with cell cycle progression and proliferation.

  9. Network modeling identifies molecular functions targeted by miR-204 to suppress head and neck tumor metastasis.

    Directory of Open Access Journals (Sweden)

    Younghee Lee

    2010-04-01

    Full Text Available Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC. We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1-22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy

  10. Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

    Science.gov (United States)

    Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

    2017-10-01

    Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

  11. Time-resolved human kinome RNAi screen identifies a network regulating mitotic-events as early regulators of cell proliferation.

    Directory of Open Access Journals (Sweden)

    Jitao David Zhang

    Full Text Available Analysis of biological processes is frequently performed with the help of phenotypic assays where data is mostly acquired in single end-point analysis. Alternative phenotypic profiling techniques are desired where time-series information is essential to the biological question, for instance to differentiate early and late regulators of cell proliferation in loss-of-function studies. So far there is no study addressing this question despite of high unmet interests, mostly due to the limitation of conventional end-point assaying technologies. We present the first human kinome screen with a real-time cell analysis system (RTCA to capture dynamic RNAi phenotypes, employing time-resolved monitoring of cell proliferation via electrical impedance. RTCA allowed us to investigate the dynamics of phenotypes of cell proliferation instead of using conventional end-point analysis. By introducing data transformation with first-order derivative, i.e. the cell-index growth rate, we demonstrate this system suitable for high-throughput screenings (HTS. The screen validated previously identified inhibitor genes and, additionally, identified activators of cell proliferation. With the information of time kinetics available, we could establish a network of mitotic-event related genes to be among the first displaying inhibiting effects after RNAi knockdown. The time-resolved screen captured kinetics of cell proliferation caused by RNAi targeting human kinome, serving as a resource for researchers. Our work establishes RTCA technology as a novel robust tool with biological and pharmacological relevance amenable for high-throughput screening.

  12. microRNA Expression in Sentinel Nodes from Progressing Melanoma Patients Identifies Networks Associated with Dysfunctional Immune Response

    Directory of Open Access Journals (Sweden)

    Viviana Vallacchi

    2016-12-01

    Full Text Available Sentinel node biopsy (SNB is a main staging biomarker in melanoma and is the first lymph node to drain the tumor, thus representing the immunological site where anti-tumor immune dysfunction is established and where potential prognostic immune markers can be identified. Here we analyzed microRNA (miR profiles in archival tumor-positive SNBs derived from melanoma patients with different outcomes and performed an integrated analysis of transcriptional data to identify deregulated immune signaling networks. Twenty-six miRs were differentially expressed in melanoma-positive SNB samples between patients with disease progression and non-progressing patients, the majority being previously reported in the regulation of immune responses. A significant variation in miR expression levels was confirmed in an independent set of SNB samples. Integrated information from genome-wide transcriptional profiles and in vitro assessment in immune cells led to the identification of miRs associated with the regulation of the TNF receptor superfamily member 8 (TNFRSF8 gene encoding the CD30 receptor, a marker increased in lymphocytes of melanoma patients with progressive disease. These findings indicate that miRs are involved in the regulation of pathways leading to immune dysfunction in the sentinel node and may provide valuable markers for developing prognostic molecular signatures for the identification of stage III melanoma patients at risk of recurrence.

  13. Validity of Preoperative Clinical Findings to Identify Dental Pulp Status: A National Dental Practice-Based Research Network Study.

    Science.gov (United States)

    Pigg, Maria; Nixdorf, Donald R; Nguyen, Ruby H N; Law, Alan S

    2016-06-01

    Endodontic diagnostic tests are often used clinically to assess pulp status as a basis for the diagnosis and determination of whether root canal treatment (RCT) is indicated. Response to cold and pain on percussion are 2 common tests, yet their validity in identifying nonvital pulp in regular dental practice has not been reported. We assessed the validity of cold and percussion tests to identify nonvital pulp in teeth requiring RCT in a dental practice setting performed by 46 general dentists and 16 endodontists in the National Dental Practice-Based Research Network. The influence of patient-, tooth-, and dentist-related characteristics was investigated. Observed bleeding from the pulp chamber was the clinical reference. Sensitivity (SN), specificity (SP), overall test accuracy (TA), positive (PPV) and negative (NPV) predictive values, and likelihood and diagnostic odds ratios (LR+, LR-, dORs) were calculated for each single test and the combined cold and percussion tests. Seven hundred eight patient teeth were included. Cold test showed high validity to identify a nonvital pulp status (SN = 89%, SP = 80%, TA = 84%, PPV = 81%, NPV = 88%, LR+ = 4.35, LR- = 0.14, dOR = 31.4), whereas pain on percussion had lower validity (SN = 72%, SP = 41%, TA = 56%, PPV = 54%, NPV = 60%, LR+ = 1.22, LR- = 0.69, dOR = 1.78). Combining the 2 tests did not increase validity, whereas preoperative pain, medication intake, patient age and sex, and dentist training level affected test validity significantly. In regular dental practice, the cold test exhibits higher validity to discriminate between vital and nonvital pulp than the tooth percussion test. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  14. ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network.

    Science.gov (United States)

    Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Ritchie, Marylyn D

    2013-12-20

    Gene expression profiles have been broadly used in cancer research as a diagnostic or prognostic signature for the clinical outcome prediction such as stage, grade, metastatic status, recurrence, and patient survival, as well as to potentially improve patient management. However, emerging evidence shows that gene expression-based prediction varies between independent data sets. One possible explanation of this effect is that previous studies were focused on identifying genes with large main effects associated with clinical outcomes. Thus, non-linear interactions without large individual main effects would be missed. The other possible explanation is that gene expression as a single level of genomic data is insufficient to explain the clinical outcomes of interest since cancer can be dysregulated by multiple alterations through genome, epigenome, transcriptome, and proteome levels. In order to overcome the variability of diagnostic or prognostic predictors from gene expression alone and to increase its predictive power, we need to integrate multi-levels of genomic data and identify interactions between them associated with clinical outcomes. Here, we proposed an integrative framework for identifying interactions within/between multi-levels of genomic data associated with cancer clinical outcomes using the Grammatical Evolution Neural Networks (GENN). In order to demonstrate the validity of the proposed framework, ovarian cancer data from TCGA was used as a pilot task. We found not only interactions within a single genomic level but also interactions between multi-levels of genomic data associated with survival in ovarian cancer. Notably, the integration model from different levels of genomic data achieved 72.89% balanced accuracy and outperformed the top models with any single level of genomic data. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different levels of genomic data is

  15. Using a network-based approach to identify interactions structure for innovation in a low-technology intensive sector

    Energy Technology Data Exchange (ETDEWEB)

    Aouinait, C.; Lepori, B.; Christen, D.; Carlen, C.; Foray, D.

    2016-07-01

    Knowledge transfer in the agricultural network is realized through interactions between stakeholders, inducing innovation development and diffusion. The aim of the paper was to trace interactions in the Swiss apricot sector. Identification of collaborations using face-toface interviews of knowledge producers and knowledge users were conducted. The study showed that informal collaborations are exclusively used to transfer knowledge and create innovation. Personal ties have been established between internal actors of the value chain (e.g. professionals like producers, transformers and wholesalers). External partners like public research organizations have created strong ties with agricultural stakeholders. However, the spatial proximity does not guarantee higher rate of collaborations. The links with the Universities of Applied Sciences, closely located, are sparse. Hence, in order to warrant innovation success, spatial proximity has to be balanced with organizational proximity. Despite the educational background of producers, there are a few connections with universities. Human capital formation and education in the agricultural sector should be examined to design innovation policy. Besides, the public research center for agriculture catalyzes knowledge transfer and facilitates innovation adoption. A suitable ecology of actors through the value chain from research to application is necessary. Furthermore, productive interactions should be investigated to identify the efficiency of knowledge and innovation transfer mechanisms and potential gaps in this process. (Author)

  16. Deep sequencing and in silico analyses identify MYB-regulated gene networks and signaling pathways in pancreatic cancer.

    Science.gov (United States)

    Azim, Shafquat; Zubair, Haseeb; Srivastava, Sanjeev K; Bhardwaj, Arun; Zubair, Asif; Ahmad, Aamir; Singh, Seema; Khushman, Moh'd; Singh, Ajay P

    2016-06-29

    We have recently demonstrated that the transcription factor MYB can modulate several cancer-associated phenotypes in pancreatic cancer. In order to understand the molecular basis of these MYB-associated changes, we conducted deep-sequencing of transcriptome of MYB-overexpressing and -silenced pancreatic cancer cells, followed by in silico pathway analysis. We identified significant modulation of 774 genes upon MYB-silencing (p MYB-silenced pancreatic cancer cells exhibiting suppression of EGFR and NF-κB. Decreased expression of EGFR and RELA was validated by both qPCR and immunoblotting and they were both shown to be under direct transcriptional control of MYB. These observations were further confirmed in a converse approach wherein MYB was overexpressed ectopically in a MYB-null pancreatic cancer cell line. Our findings thus suggest that MYB potentially regulates growth and genomic stability of pancreatic cancer cells via targeting complex gene networks and signaling pathways. Further in-depth functional studies are warranted to fully understand MYB signaling in pancreatic cancer.

  17. Identifying clinical and support-service resources and network practices for cancer patients and survivors in southern Puerto Rico

    Science.gov (United States)

    Castro, Eida M.; Jiménez, Julio C.; Quinn, Gwendolyn; García, Myra; Colón, Yesenia; Ramos, Axel; Brandon, Thomas; Simmons, Vani; Gwede, Clement; Vadaparampil, Susan; Nazario, Cruz María

    2015-01-01

    Objective The objectives of this study were to identify cancer-related health care services and to explore the presence of inter-organizational interactions among clinical and support oncology services in southern Puerto Rico. Methods From January through July of 2010, a survey was completed by 54 health care organizations offering clinical, supportive, or both services to cancer patients/survivors (CPS) in southern PR. Survey data were compiled and descriptive analyses performed using the software Statistical Package for a Social Science (SPSS), version 18.0. Results The distribution of the primary services provided by the participating organizations was the following: 26 had clinical services, 16 had support services, and 12 offered a combination of clinical and support services. Only 24% of the surveyed organizations offered their services exclusively to patients diagnosed with cancer. In terms of referral practices, 61% of the responses were for medical specialists, 43% were for mental health services, and 37% were referrals for primary care services. The most common reason for interacting (n = 27) was to provide a given patient both an referral and information. Conclusion Findings suggest gaps in both the availability of oncology services and the delivery of integrated health care. Lack of communication among clinical and support organizations (for cancer patients, specifically) could negatively impact the quality of the services that they offer. Further network analysis studies are needed to confirm these gaps. Until systemic, structural changes occur, more efforts are needed to facilitate communication and collaboration among these kinds of organization. PMID:25249352

  18. Incorporation of Spatial Interactions in Location Networks to Identify Critical Geo-Referenced Routes for Assessing Disease Control Measures on a Large-Scale Campus

    Directory of Open Access Journals (Sweden)

    Tzai-Hung Wen

    2015-04-01

    Full Text Available Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission.

  19. Use of social network analysis in maternity care to identify the profession most suited for case manager role

    NARCIS (Netherlands)

    Groenen, C.J.M.; Duijnhoven, N.T.L. van; Faber, M.J.; Koetsenruijter, J.; Kremer, J.A.M.; Vandenbussche, F.P.H.A.

    2017-01-01

    OBJECTIVE: To improve Dutch maternity care, professionals start working in interdisciplinary patient-centred networks, which includes the patients as a member. The introduction of the case manager is expected to work positively on both the individual and the network level. However, case management

  20. Constructing Integrated Networks for Identifying New Secondary Metabolic Pathway Regulators in Grapevine: Recent Applications and Future Opportunities.

    Science.gov (United States)

    Wong, Darren C J; Matus, José Tomás

    2017-01-01

    Representing large biological data as networks is becoming increasingly adopted for predicting gene function while elucidating the multifaceted organization of life processes. In grapevine ( Vitis vinifera L.), network analyses have been mostly adopted to contribute to the understanding of the regulatory mechanisms that control berry composition. Whereas, some studies have used gene co-expression networks to find common pathways and putative targets for transcription factors related to development and metabolism, others have defined networks of primary and secondary metabolites for characterizing the main metabolic differences between cultivars throughout fruit ripening. Lately, proteomic-related networks and those integrating genome-wide analyses of promoter regulatory elements have also been generated. The integration of all these data in multilayered networks allows building complex maps of molecular regulation and interaction. This perspective article describes the currently available network data and related resources for grapevine. With the aim of illustrating data integration approaches into network construction and analysis in grapevine, we searched for berry-specific regulators of the phenylpropanoid pathway. We generated a composite network consisting of overlaying maps of co-expression between structural and transcription factor genes, integrated with the presence of promoter cis -binding elements, microRNAs, and long non-coding RNAs (lncRNA). This approach revealed new uncharacterized transcription factors together with several microRNAs potentially regulating different steps of the phenylpropanoid pathway, and one particular lncRNA compromising the expression of nine stilbene synthase (STS) genes located in chromosome 10. Application of network-based approaches into multi-omics data will continue providing supplementary resources to address important questions regarding grapevine fruit quality and composition.

  1. Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

    Directory of Open Access Journals (Sweden)

    Guo Shuixia

    2010-06-01

    Full Text Available Abstract Background Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE, Bayesian networks, information theory and Granger Causality. Results Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins. For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. Conclusions The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

  2. Influence of the Training Set Value on the Quality of the Neural Network to Identify Selected Moulding Sand Properties

    National Research Council Canada - National Science Library

    J. Jakubski; St. M. Dobosz; K. Major-Gabryś

    2013-01-01

    .... This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast...

  3. Using Social Network Analysis to Identify Key Child Care Center Staff for Obesity Prevention Interventions: A Pilot Study

    OpenAIRE

    Jennifer Marks; Barnett, Lisa M.; Chad Foulkes; Penelope Hawe; Steven Allender

    2013-01-01

    Introduction. Interest has grown in how systems thinking could be used in obesity prevention. Relationships between key actors, represented by social networks, are an important focus for considering intervention in systems. Method. Two long day care centers were selected in which previous obesity prevention programs had been implemented. Measures showed ways in which physical activity and dietary policy are conversations and actions transacted through social networks (interrelationships) with...

  4. Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

    Directory of Open Access Journals (Sweden)

    Urmi Sengupta

    Full Text Available Type 2 diabetes mellitus (T2D is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present study allows detection of molecular targets by unravelling their role in altered biological pathways during diabetes and its associated risk factors and complications. We have used an integrated functional networks concept by merging co-expression network and interaction network to detect the transcriptionally altered pathways and regulations involved in the disease. Our analysis reports four novel significant networks which could lead to the development of diabetes and other associated dysfunctions. (a The first network illustrates the up regulation of TGFBRII facilitating oxidative stress and causing the expression of early transcription genes via MAPK pathway leading to cardiovascular and kidney related complications. (b The second network demonstrates novel interactions between GAPDH and inflammatory and proliferation candidate genes i.e., SUMO4 and EGFR indicating a new link between obesity and diabetes. (c The third network portrays unique interactions PTPN1 with EGFR and CAV1 which could lead to an impaired vascular function in diabetic nephropathy condition. (d Lastly, from our fourth network we have inferred that the interaction of beta-catenin with CDH5 and TGFBR1 through Smad molecules could contribute to endothelial dysfunction. A probability of emergence of kidney complication might be suggested in T2D condition. An experimental investigation on this aspect may further provide more decisive observation in drug target identification and better

  5. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

    Directory of Open Access Journals (Sweden)

    Kandasamy Suganthi

    2010-06-01

    Full Text Available Abstract Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown

  6. Soft computing model for optimized siRNA design by identifying off target possibilities using artificial neural network model.

    Science.gov (United States)

    Murali, Reena; John, Philips George; Peter S, David

    2015-05-15

    The ability of small interfering RNA (siRNA) to do posttranscriptional gene regulation by knocking down targeted genes is an important research topic in functional genomics, biomedical research and in cancer therapeutics. Many tools had been developed to design exogenous siRNA with high experimental inhibition. Even though considerable amount of work has been done in designing exogenous siRNA, design of effective siRNA sequences is still a challenging work because the target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. In some cases, siRNAs may tolerate mismatches with the target mRNA, but knockdown of genes other than the intended target could make serious consequences. Hence to design siRNAs, two important concepts must be considered: the ability in knocking down target genes and the off target possibility on any nontarget genes. So before doing gene silencing by siRNAs, it is essential to analyze their off target effects in addition to their inhibition efficacy against a particular target. Only a few methods have been developed by considering both efficacy and off target possibility of siRNA against a gene. In this paper we present a new design of neural network model with whole stacking energy (ΔG) that enables to identify the efficacy and off target effect of siRNAs against target genes. The tool lists all siRNAs against a particular target with their inhibition efficacy and number of matches or sequence similarity with other genes in the database. We could achieve an excellent performance of Pearson Correlation Coefficient (R=0. 74) and Area Under Curve (AUC=0.906) when the threshold of whole stacking energy is ≥-34.6 kcal/mol. To the best of the author's knowledge, this is one of the best score while considering the "combined efficacy and off target possibility" of siRNA for silencing a gene. The proposed model

  7. Method for identifying drivers, barriers and synergies related to the deployment of a CO2 pipeline network : A case study for the Iberian Peninsula and Morocco

    NARCIS (Netherlands)

    Berghout, Niels; Cabal, Helena; Gouveia, João Pedro; van den Broek, Machteld; Faaij, André

    2015-01-01

    This paper provides a method to identify drivers, barriers and synergies (DBS) related to the deployment of a CO2 pipeline network. The method was demonstrated for the West Mediterranean region (WMR) (i.e. Spain, Portugal and Morocco). The method comprises a literature review, analysis of

  8. Identifying Differences in Abiotic Stress Gene Networks between Lowland and Upland Ecotypes of Switchgrass (DE-SC0008338)

    Energy Technology Data Exchange (ETDEWEB)

    Childs, Kevin [Michigan State Univ., East Lansing, MI (United States); Buell, Robin [Michigan State Univ., East Lansing, MI (United States); Zhao, Bingyu [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Zhang, Xunzhong [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

    2016-11-10

    reduce the stress (e.g., transmembrane pumps that partition Na+) and mitigate the effects of the stress (e.g., synthesis of osmoprotectant metabolites and stress-related signaling compounds). Prior to the start of this project, no gene expression analysis had been performed on switchgrass under conditions of drought or salt stress, and therefore, relevant gene networks responding to drought and salt stress were unknown in switchgrass. In this project, we performed drought, salt and alkali-salt screens on 49 switchgrass cultivars (Liu et al 2014; Liu et al 2015; Hu et al 2015; Kim et al 2016). These experiments demonstrated that a wide range of variation exists within switchgrass for drought, salt and alkali-salt tolerance and that, while the lowland ecotype of switchgrass is often considered more tolerant of abiotic stresses, there are some upland switchgrass lines that are also very tolerant of drought, salt and alkali-salt stress. We also conducted drought and salt time course experiments with Alamo and Dacotah. We have identified modules of coexpressed genes that differentiate Alamo and Dacotah drought responses. We are continuing to analyze these results and plan to submit manuscripts describing this work in early 2017. In an effort to show how drought- and salt-related gene modules could be dissected, we generated transgenic switchgrass overexpressing either PvGTγ-1 or ZmDREB2. Increased expression of PvGTγ-1 does confer increased salt tolerance, and we were able to identify genes that are induced and suppressed by PvGTγ-1. Overexpression of ZmDREB2 increases drought tolerance in switchgrass. Analysis of the PvGTγ-1 and ZmDREB2 overexpression work is ongoing, and we plan to prepare manuscripts about these experiments for submission in early 2017.

  9. Sox17-Mediated XEN Cell Conversion Identifies Dynamic Networks Controlling Cell-Fate Decisions in Embryo-Derived Stem Cells

    Directory of Open Access Journals (Sweden)

    Angela C.H. McDonald

    2014-10-01

    Full Text Available Little is known about the gene regulatory networks (GRNs distinguishing extraembryonic endoderm (ExEn stem (XEN cells from those that maintain the extensively characterized embryonic stem cell (ESC. An intriguing network candidate is Sox17, an essential transcription factor for XEN derivation and self-renewal. Here, we show that forced Sox17 expression drives ESCs toward ExEn, generating XEN cells that contribute to ExEn when placed back into early mouse embryos. Transient Sox17 expression is sufficient to drive this fate change during which time cells transit through distinct intermediate states prior to the generation of functional XEN-like cells. To orchestrate this conversion process, Sox17 acts in autoregulatory and feedforward network motifs, regulating dynamic GRNs directing cell fate. Sox17-mediated XEN conversion helps to explain the regulation of cell-fate changes and reveals GRNs regulating lineage decisions in the mouse embryo.

  10. An integrative analysis of gene expression and molecular interaction data to identify dys-regulated sub-networks in inflammatory bowel disease.

    Science.gov (United States)

    Muraro, Daniele; Simmons, Alison

    2016-01-19

    Inflammatory bowel disease (IBD) consists of two main disease-subtypes, Crohn's disease (CD) and ulcerative colitis (UC); these subtypes share overlapping genetic and clinical features. Genome-wide microarray data enable unbiased documentation of alterations in gene expression that may be disease-specific. As genetic diseases are believed to be caused by genetic alterations affecting the function of signalling pathways, module-centric optimisation algorithms, whose aim is to identify sub-networks that are dys-regulated in disease, are emerging as promising approaches. In order to account for the topological structure of molecular interaction networks, we developed an optimisation algorithm that integrates databases of known molecular interactions with gene expression data; such integration enables identification of differentially regulated network modules. We verified the performance of our algorithm by testing it on simulated networks; we then applied the same method to study experimental data derived from microarray analysis of CD and UC biopsies and human interactome databases. This analysis allowed the extraction of dys-regulated subnetworks under different experimental conditions (inflamed and uninflamed tissues in CD and UC). Optimisation was performed to highlight differentially expressed network modules that may be common or specific to the disease subtype. We show that the selected subnetworks include genes and pathways of known relevance for IBD; in particular, the solutions found highlight cross-talk among enriched pathways, mainly the JAK/STAT signalling pathway and the EGF receptor signalling pathway. In addition, integration of gene expression with molecular interaction data highlights nodes that, although not being differentially expressed, interact with differentially expressed nodes and are part of pathways that are relevant to IBD. The method proposed here may help identifying dys-regulated sub-networks that are common in different diseases and

  11. Towards a Diagnostic Instrument to Identify Improvement Opportunities for Quality Controlled Logistics in Agrifood Supply Chain Networks

    NARCIS (Netherlands)

    Vorst, van der J.G.A.J.; Kooten, van O.; Luning, P.A.

    2011-01-01

    Western-European consumers have become not only more demanding on product availability in retail outlets but also on other food attributes such as quality, integrity, and safety. When (re)designing food supply-chain networks, from a logistics point of view, one has to consider these demands next to

  12. Characterization of the CLASP2 Protein Interaction Network Identifies SOGA1 as a Microtubule-Associated Protein

    DEFF Research Database (Denmark)

    Sørensen, Rikke Kruse; Krantz, James; Barker, Natalie

    2017-01-01

    and built a CLASP2 protein network in 3T3-L1 adipocytes. Using two different commercially available antibodies for CLASP2 and an antibody for epitope-tagged, overexpressed CLASP2, we performed multiple affinity purification coupled with mass spectrometry (AP-MS) experiments in combination with label...

  13. A Systems genetics approach identifies gene regulatory networks associated with fatty acid composition in brassica rapa seed

    NARCIS (Netherlands)

    Basnet, Ram Kumar; Pino Del Carpio, Dunia; Xiao, Dong; Bucher, Johan; Jin, Mina; Boyle, Kerry; Fobert, Pierre; Visser, R.G.F.; Maliepaard, Chris; Bonnema, Guusje

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation

  14. Architecture of the global land acquisition system: applying the tools of network science to identify key vulnerabilities

    Science.gov (United States)

    Seaquist, J. W.; Li Johansson, Emma; Nicholas, Kimberly A.

    2014-11-01

    Global land acquisitions, often dubbed ‘land grabbing’ are increasingly becoming drivers of land change. We use the tools of network science to describe the connectivity of the global acquisition system. We find that 126 countries participate in this form of global land trade. Importers are concentrated in the Global North, the emerging economies of Asia, and the Middle East, while exporters are confined to the Global South and Eastern Europe. A small handful of countries account for the majority of land acquisitions (particularly China, the UK, and the US), the cumulative distribution of which is best described by a power law. We also find that countries with many land trading partners play a disproportionately central role in providing connectivity across the network with the shortest trading path between any two countries traversing either China, the US, or the UK over a third of the time. The land acquisition network is characterized by very few trading cliques and therefore characterized by a low degree of preferential trading or regionalization. We also show that countries with many export partners trade land with countries with few import partners, and vice versa, meaning that less developed countries have a large array of export partnerships with developed countries, but very few import partnerships (dissassortative relationship). Finally, we find that the structure of the network is potentially prone to propagating crises (e.g., if importing countries become dependent on crops exported from their land trading partners). This network analysis approach can be used to quantitatively analyze and understand telecoupled systems as well as to anticipate and diagnose the potential effects of telecoupling.

  15. A Systems Genetics Approach Identifies Gene Regulatory Networks Associated with Fatty Acid Composition in Brassica rapa Seed1

    Science.gov (United States)

    Xiao, Dong; Bucher, Johan; Jin, Mina; Boyle, Kerry; Fobert, Pierre; Maliepaard, Chris

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation of seed fatty acid composition in a Brassica rapa doubled haploid population from a cross between a yellow sarson oil type and a black-seeded pak choi. The distribution of major QTLs for fatty acids showed a relationship with the fatty acid types: linkage group A03 for monounsaturated fatty acids, A04 for saturated fatty acids, and A05 for polyunsaturated fatty acids. Using a genetical genomics approach, expression quantitative trait locus (eQTL) hotspots were found at major fatty acid QTLs on linkage groups A03, A04, A05, and A09. An eQTL-guided gene coexpression network of lipid metabolism-related genes showed major hubs at the genes BrPLA2-ALPHA, BrWD-40, a number of seed storage protein genes, and the transcription factor BrMD-2, suggesting essential roles for these genes in lipid metabolism. Three subnetworks were extracted for the economically important and most abundant fatty acids erucic, oleic, linoleic, and linolenic acids. Network analysis, combined with comparison of the genome positions of cis- or trans-eQTLs with fatty acid QTLs, allowed the identification of candidate genes for genetic regulation of these fatty acids. The generated insights in the genetic architecture of fatty acid composition and the underlying complex gene regulatory networks in B. rapa seeds are discussed. PMID:26518343

  16. A Systems Genetics Approach Identifies Gene Regulatory Networks Associated with Fatty Acid Composition in Brassica rapa Seed.

    Science.gov (United States)

    Basnet, Ram Kumar; Del Carpio, Dunia Pino; Xiao, Dong; Bucher, Johan; Jin, Mina; Boyle, Kerry; Fobert, Pierre; Visser, Richard G F; Maliepaard, Chris; Bonnema, Guusje

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation of seed fatty acid composition in a Brassica rapa doubled haploid population from a cross between a yellow sarson oil type and a black-seeded pak choi. The distribution of major QTLs for fatty acids showed a relationship with the fatty acid types: linkage group A03 for monounsaturated fatty acids, A04 for saturated fatty acids, and A05 for polyunsaturated fatty acids. Using a genetical genomics approach, expression quantitative trait locus (eQTL) hotspots were found at major fatty acid QTLs on linkage groups A03, A04, A05, and A09. An eQTL-guided gene coexpression network of lipid metabolism-related genes showed major hubs at the genes BrPLA2-ALPHA, BrWD-40, a number of seed storage protein genes, and the transcription factor BrMD-2, suggesting essential roles for these genes in lipid metabolism. Three subnetworks were extracted for the economically important and most abundant fatty acids erucic, oleic, linoleic, and linolenic acids. Network analysis, combined with comparison of the genome positions of cis- or trans-eQTLs with fatty acid QTLs, allowed the identification of candidate genes for genetic regulation of these fatty acids. The generated insights in the genetic architecture of fatty acid composition and the underlying complex gene regulatory networks in B. rapa seeds are discussed. © 2016 American Society of Plant Biologists. All Rights Reserved.

  17. Characterizing Radiation-Aged Polysiloxane-Silica Composites: Identifying Changes in Network Topology via 1H NMR

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B; Chinn, S C; Maxwell, R S; Reimer, J

    2008-11-03

    Characterizing and quantifying changes in elastomeric materials upon exposure to harsh environments is important in the estimation of device lifetimes. Nuclear magnetic resonance (NMR) spectroscopy has been used effectively in the analysis of such materials and has proved to be both sensitive to micro- and macroscopic changes associated with material 'aging'. Traditional analyses, however, rely on empirical formulae containing a large number of (often arbitrary) independent variables. This ambiguity can be circumvented largely by developing models of NMR observables that are based on basic polymer physics. We compare two such models, one previously published and one derived herein, along with empirical expressions that describe the proton transverse magnetization decay associated with complex polymer networks. One particular extracted parameter, the proton-proton residual dipolar coupling (RDC), can be directly related to network topology, and a comparison of the extracted RDCs reveals high consistency among the models. An expression derived from the properties of a static Gaussian chain can minimize the number of parameters necessarily to describe the solid-like, networked proton population to a single independent parameter, the average residual dipolar coupling, D{sub avg}.

  18. PCE-FR: A Novel Method for Identifying Overlapping Protein Complexes in Weighted Protein-Protein Interaction Networks Using Pseudo-Clique Extension Based on Fuzzy Relation.

    Science.gov (United States)

    Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Ding, Pingjian

    2016-10-01

    Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a novel method to detect protein complexes using pseudo-clique extension based on fuzzy relation (PCE-FR). Our algorithm operates in three stages: it first forms the nonoverlapping protein substructure based on fuzzy relation and then expands each substructure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. Particularly, our algorithm employs the biological significance hidden in protein pairs to construct edge weight for protein interaction networks. The experiment results show that our method can not only outperform classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN, and ProRank +, but also achieve ideal overall performance in most of the yeast PPI datasets in terms of composite score consisting of precision, accuracy, and separation. We further apply our method to a human PPI network from the HPRD dataset and demonstrate it is very effective in detecting protein complexes compared to other algorithms.

  19. Flexible logical-link-identifier assignment policy for Ethernet passive optical networks based on extended multipoint-control-protocol du flow control

    Science.gov (United States)

    Hajduczenia, Marek; da Silva, Henrique J.; Monteiro, Paulo P.

    2006-09-01

    We present a novel proposal for the extension of the currently approved multipoint control protocol (MPCP), as defined in the IEEE 802.3ah standard, clause 64, in the form of two new MPCP messages (extended GATE and extended REPORT), maintaining full backward compatibility with the already-deployed Ethernet passive optical network (EPON) systems and allowing for coexistence with legacy MPCP flow-control messages within the same network structure. Both currently existing logical-link identifier (LLID) assignment systems [namely, one LLID per optical network unit (ONU) and one LLID per queue] are examined in depth, and a missing scenario (one LLID per multiqueue) is discussed. Simulation results conducted using C++ based implementation of standard EPON networks with support for extended GATE-REPORT MPCP data units (DUs) prove that the design assumptions for both new flow-control messages were met to their fullest extent. The eGATE/eREPORT MPCP DUs allow for per-queue scheduling from the central packet controller in the optical line terminal (OLT) at the cost of ONU-based operation, thereby maintaining the benefits of two standard solutions. The obtained simulation results indicate superiority of the proposed IEEE 802.3ah, clause 64 extension in terms of network resource management, bandwidth efficiency, and system setup flexibility.

  20. An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning.

    Science.gov (United States)

    Lu, Junfeng; Zhang, Han; Hameed, N U Farrukh; Zhang, Jie; Yuan, Shiwen; Qiu, Tianming; Shen, Dinggang; Wu, Jinsong

    2017-10-23

    As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

  1. Brief Report: The Prevalence of Neurofibromatosis Type 1 among Children with Autism Spectrum Disorder Identified by the Autism and Developmental Disabilities Monitoring Network

    OpenAIRE

    Bilder, Deborah A.; Bakian, Amanda V.; Stevenson, David A.; Carbone, Paul S.; Cunniff, Christopher; Goodman, Alyson B.; McMahon, William M.; Fisher, Nicole P.; Viskochil, David

    2016-01-01

    Neurofibromatosis type 1 (NF1) is an inherited neurocutaneous disorder associated with neurodevelopmental disorders including autism spectrum disorder (ASD). The frequency of ASD/NF1 co-occurrence has been subject to debate since the 1980s. This relationship was investigated in a large population-based sample of 8-year-old children identified with ASD (N = 12,271) by the Centers for Disease Control and Prevention’s Autism and Developmental Disabilities Monitoring (ADDM) Network. Twenty-two (1...

  2. A versatile mass spectrometry-based method to both identify kinase client-relationships and characterize signaling network topology

    Science.gov (United States)

    While more than a thousand protein kinases (PK) have been identified in the Arabidopsis thaliana genome, relatively little progress has been made towards identifying their individual client proteins. Herein we describe use of a mass spectrometry-based in vitro phosphorylation strategy, termed Kinase...

  3. Using Respondent Driven Sampling to Identify Malaria Risks and Occupational Networks among Migrant Workers in Ranong, Thailand.

    Directory of Open Access Journals (Sweden)

    Piyaporn Wangroongsarb

    Full Text Available Ranong Province in southern Thailand is one of the primary entry points for migrants entering Thailand from Myanmar, and borders Kawthaung Township in Myanmar where artemisinin resistance in malaria parasites has been detected. Areas of high population movement could increase the risk of spread of artemisinin resistance in this region and beyond.A respondent-driven sampling (RDS methodology was used to compare migrant populations coming from Myanmar in urban (Site 1 vs. rural (Site 2 settings in Ranong, Thailand. The RDS methodology collected information on knowledge, attitudes, and practices for malaria, travel and occupational histories, as well as social network size and structure. Individuals enrolled were screened for malaria by microscopy, Real Time-PCR, and serology.A total of 619 participants were recruited in Ranong City and 623 participants in Kraburi, a rural sub-district. By PCR, a total of 14 (1.1% samples were positive (2 P. falciparum in Site 1; 10 P. vivax, 1 Pf, and 1 P. malariae in Site 2. PCR analysis demonstrated an overall weighted prevalence of 0.5% (95% CI, 0-1.3% in the urban site and 1.0% (95% CI, 0.5-1.7% in the rural site for all parasite species. PCR positivity did not correlate with serological positivity; however, as expected there was a strong association between antibody prevalence and both age and exposure. Access to long-lasting insecticidal treated nets remains low despite relatively high reported traditional net use among these populations.The low malaria prevalence, relatively smaller networks among migrants in rural settings, and limited frequency of travel to and from other areas of malaria transmission in Myanmar, suggest that the risk for the spread of artemisinin resistance from this area may be limited in these networks currently but may have implications for regional malaria elimination efforts.

  4. Animal Health Challenges and Veterinary Aspects of Organic Livestock Farming Identified Through a 3 Year EU Network Project

    DEFF Research Database (Denmark)

    Vaarst, Mette; Padel, Susanne; Younie, David

    2008-01-01

    From 2003-2006, an EU network project ‘Sustaining Animal Health and Food Safety in Organic Farming‘ (SAFO), was carried out with 26 partners from 20 EU-countries and 4 related partners from 4 candidate or new member states. The focus was the integration of animal health and welfare issues...... in organic farming with food safety aspects. Four very consistent conclusions became apparent: 1) The climatic, physical and socio-economic conditions vary considerably throughout Europe, leading to different livestock farming systems. This limits the possibility for technology transfer between regions...

  5. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

  6. Gene co-expression network analysis identifies porcine genes associated with variation in metabolizing fenbendazole and flunixin meglumine in the liver.

    Science.gov (United States)

    Howard, Jeremy T; Ashwell, Melissa S; Baynes, Ronald E; Brooks, James D; Yeatts, James L; Maltecca, Christian

    2017-05-02

    Identifying individual genetic variation in drug metabolism pathways is of importance not only in livestock, but also in humans in order to provide the ultimate goal of giving the right drug at the right dose at the right time. Our objective was to identify individual genes and gene networks involved in metabolizing fenbendazole (FBZ) and flunixin meglumine (FLU) in swine liver. The population consisted of female and castrated male pigs that were sired by boars represented by 4 breeds. Progeny were randomly placed into groups: no drug (UNT), FLU or FBZ administered. Liver transcriptome profiles from 60 animals with extreme (i.e. fast or slow drug metabolism) pharmacokinetic (PK) profiles were generated from RNA sequencing. Multiple cytochrome P450 (CYP1A1, CYP2A19 and CYP2C36) genes displayed different transcript levels across treated versus UNT. Weighted gene co-expression network analysis identified 5 and 3 modules of genes correlated with PK parameters and a portion of these were enriched for biological processes relevant to drug metabolism for FBZ and FLU, respectively. Genes within identified modules were shown to have a higher transcript level relationship (i.e. connectivity) in treated versus UNT animals. Investigation into the identified genes would allow for greater insight into FBZ and FLU metabolism.

  7. Contextual Hub Analysis Tool (CHAT: A Cytoscape app for identifying contextually relevant hubs in biological networks [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Tanja Muetze

    2016-08-01

    Full Text Available Highly connected nodes (hubs in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT, which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest.   Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store (http://apps.cytoscape.org/apps/chat.

  8. Yeast Augmented Network Analysis (YANA: a new systems approach to identify therapeutic targets for human genetic diseases [v1; ref status: indexed, http://f1000r.es/3gk

    Directory of Open Access Journals (Sweden)

    David J. Wiley

    2014-06-01

    Full Text Available Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets. Here we introduce our Yeast Augmented Network Analysis (YANA approach and test it with the X-linked spinal muscular atrophy (SMA disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.

  9. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

    Directory of Open Access Journals (Sweden)

    Daniel Durstewitz

    2017-06-01

    Full Text Available The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast maximum-likelihood estimation framework for PLRNNs that may enable to recover

  10. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

    Science.gov (United States)

    Durstewitz, Daniel

    2017-06-01

    The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects

  11. An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

    Directory of Open Access Journals (Sweden)

    Arunachalam Vinayagam

    2016-09-01

    Full Text Available Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

  12. Using regulatory and epistatic networks to extend the findings of a genome scan: identifying the gene drivers of pigmentation in merino sheep.

    Directory of Open Access Journals (Sweden)

    Elsa García-Gámez

    Full Text Available Extending genome wide association analysis by the inclusion of gene expression data may assist in the dissection of complex traits. We examined piebald, a pigmentation phenotype in both human and Merino sheep, by analysing multiple data types using a systems approach. First, a case control analysis of 49,034 ovine SNP was performed which confirmed a multigenic basis for the condition. We combined these results with gene expression data from five tissue types analysed with a skin-specific microarray. Promoter sequence analysis of differentially expressed genes allowed us to reverse-engineer a regulatory network. Likewise, by testing two-loci models derived from all pair-wise comparisons across piebald-associated SNP, we generated an epistatic network. At the intersection of both networks, we identified thirteen genes with insulin-like growth factor binding protein 7 (IGFBP7, platelet-derived growth factor alpha (PDGFRA and the tetraspanin platelet activator CD9 at the kernel of the intersection. Further, we report a number of differentially expressed genes in regions containing highly associated SNP including ATRN, DOCK7, FGFR1OP, GLI3, SILV and TBX15. The application of network theory facilitated co-analysis of genetic variation with gene expression, recapitulated aspects of the known molecular biology of skin pigmentation and provided insights into the transcription regulation and epistatic interactions involved in piebald Merino sheep.

  13. Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk

    DEFF Research Database (Denmark)

    Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan

    2015-01-01

    BACKGROUND: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by...

  14. Robust Selection Algorithm (RSA for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

    Directory of Open Access Journals (Sweden)

    Vasudha Sehgal

    Full Text Available MicroRNAs (miRNAs play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

  15. Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

    Science.gov (United States)

    Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T

    2015-01-01

    MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

  16. Transcriptomic network analyses of leaf dehydration responses identify highly connected ABA and ethylene signaling hubs in three grapevine species differing in drought tolerance.

    Science.gov (United States)

    Hopper, Daniel W; Ghan, Ryan; Schlauch, Karen A; Cramer, Grant R

    2016-05-23

    Grapevine is a major food crop that is affected by global climate change. Consistent with field studies, dehydration assays of grapevine leaves can reveal valuable information of the plant's response at physiological, transcript, and protein levels. There are well-known differences in grapevine rootstocks responses to dehydration. We used time-series transcriptomic approaches combined with network analyses to elucidate and identify important physiological processes and network hubs that responded to dehydration in three different grapevine species differing in their drought tolerance. Transcriptomic analyses of the leaves of Cabernet Sauvignon, Riparia Gloire, and Ramsey were evaluated at different times during a 24-h controlled dehydration. Analysis of variance (ANOVA) revealed that approximately 11,000 transcripts changed significantly with respect to the genotype x treatment interaction term and approximately 6000 transcripts changed significantly according to the genotype x treatment x time interaction term indicating massive differential changes in gene expression over time. Standard analyses determined substantial effects on the transcript abundance of genes involved in the metabolism and signaling of two known plant stress hormones, abscisic acid (ABA) and ethylene. ABA and ethylene signaling maps were constructed and revealed specific changes in transcript abundance that were associated with the known drought tolerance of the genotypes including genes such as VviABI5, VviABF2, VviACS2, and VviWRKY22. Weighted-gene coexpression network analysis (WGCNA) confirmed these results. In particular, WGCNA identified 30 different modules, some of which had highly enriched gene ontology (GO) categories for photosynthesis, phenylpropanoid metabolism, ABA and ethylene signaling. The ABA signaling transcription factors, VviABI5 and VviABF2, were highly connected hubs in two modules, one being enriched in gaseous transport and the other in ethylene signaling. VviABI5 was

  17. Factor analysis in the Genetics of Asthma International Network family study identifies five major quantitative asthma phenotypes

    NARCIS (Netherlands)

    Pillai, S. G.; Tang, Y.; van den Oord, E.; Klotsman, M.; Barnes, K.; Carlsen, K.; Gerritsen, J.; Lenney, W.; Silverman, M.; Sly, P.; Sundy, J.; Tsanakas, J.; von Berg, A.; Whyte, M.; Ortega, H. G.; Anderson, W. H.; Helms, P. J.

    Background Asthma is a clinically heterogeneous disease caused by a complex interaction between genetic susceptibility and diverse environmental factors. In common with other complex diseases the lack of a standardized scheme to evaluate the phenotypic variability poses challenges in identifying the

  18. The Art of Athlete Leadership: Identifying High-Quality Athlete Leadership at the Individual and Team Level Through Social Network Analysis.

    Science.gov (United States)

    Fransen, Katrien; Van Puyenbroeck, Stef; Loughead, Todd M; Vanbeselaere, Norbert; De Cuyper, Bert; Vande Broek, Gert; Boen, Filip

    2015-06-01

    This research aimed to introduce social network analysis as a novel technique in sports teams to identify the attributes of high-quality athlete leadership, both at the individual and at the team level. Study 1 included 25 sports teams (N = 308 athletes) and focused on athletes' general leadership quality. Study 2 comprised 21 sports teams (N = 267 athletes) and focused on athletes' specific leadership quality as a task, motivational, social, and external leader. The extent to which athletes felt connected with their leader proved to be most predictive for athletes' perceptions of that leader's quality on each leadership role. Also at the team level, teams with higher athlete leadership quality were more strongly connected. We conclude that social network analysis constitutes a valuable tool to provide more insight in the attributes of high-quality leadership both at the individual and at the team level.

  19. Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes.

    Science.gov (United States)

    North, B V; Curtis, D; Cassell, P G; Hitman, G A; Sham, P C

    2003-07-01

    Biallelic markers, such as single nucleotide polymorphisms (SNPs), provide greater information for localising disease loci when treated as multilocus haplotypes, but often haplotypes are not immediately available from multilocus genotypes in case-control studies. An artificial neural network allows investigation of association between disease phenotype and tightly linked markers without requiring haplotype phase and without modelling any evolutionary history for the disease-related haplotypes. The network assesses whether marker haplotypes differ between cases and controls to the extent that classification of disease status based on multi-marker genotypes is achievable. The network is "trained" to "recognise" affection status based on supplied marker genotypes, and then for each multi-marker genotype it produces outputs which aim to approximate the associated affection status. Next, the genotypes are permuted relative to affection status to produce many random datasets and the process of training and recording of outputs is repeated. The extent to which the ability to predict affection for the real dataset exceeds that for the random datasets measures the statistical significance of the association between multi-marker genotype and affection. This permutation test performs well with simulated case-control datasets, particularly when major gene effects are present. We have explored the effects of systematically varying different network parameters in order to identify their optimal values. We have applied the permutation test to 4 SNPs of the calpain 10 (CAPN10) gene typed in a case-control sample of subjects with type 2 diabetes, impaired glucose tolerance, and controls. We show that the neural network produces more highly significant evidence for association than do single marker tests corrected for the number of markers genotyped. The use of a permutation test could potentially allow conditional analyses which could incorporate known risk factors alongside marker

  20. m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.

    Science.gov (United States)

    Zhang, Song-Yao; Zhang, Shao-Wu; Liu, Lian; Meng, Jia; Huang, Yufei

    2016-12-01

    As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

  1. m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Song-Yao Zhang

    2016-12-01

    Full Text Available As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq data using a Random Walk with Restart (RWR algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (demethylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

  2. Artificial neural network (ANN velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity

    Directory of Open Access Journals (Sweden)

    Lein Michael

    2008-09-01

    Full Text Available Abstract Background To validate an artificial neural network (ANN based on the combination of PSA velocity (PSAV with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa and benign prostate hyperplasia (BPH. Methods The study comprised 199 patients with PCa (n = 49 or BPH (n = 150 with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values. Results 71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65% and increasing values are less frequently (11% vs. 21% if the ANNV is used instead of the PSAV. Conclusion PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11–17% of unnecessary prostate biopsies in known BPH patients.

  3. Modeling of the spatial state of the ionosphere using regular definitions of the VTEC identifier at the network of continuously operating GNSS stations of Ukraine

    Science.gov (United States)

    Yankiv-Vitkovska, Liubov; Dzhuman, Bogdan

    2017-04-01

    Due to the wide application of global navigation satellite systems (GNSS), the development of the modern GNSS infrastructure moved the monitoring of the Earth's ionosphere to a new methodological and technological level. The peculiarity of such monitoring is that it allows conducting different experimental studies including the study of the ionosphere directly while using the existing networks of reference GNSS stations intended for solving other problems. The application of the modern GNSS infrastructure is another innovative step in the ionospheric studies as such networks allow to conduct measurements continuously over time in any place. This is used during the monitoring of the ionosphere and allows studying the global and regional phenomena in the ionosphere in real time. Application of a network of continuously operating reference stations to determine numerical characteristics of the Earth's ionosphere allows creating an effective technology to monitor the ionosphere regionally. This technology is intended to solve both scientific problems concerning the space weather, and practical tasks such as providing coordinates of the geodetic level accuracy. For continuously operating reference GNSS stations, the results of the determined ionization identifier TEC (Total Electron Content). On the one hand, this data reflects the state of the ionosphere during the observation; on the other hand, it is a substantial tool for accuracy improvement and reliable determination of coordinates of the observation place. Thus, it was decided to solve a problem of restoring the spatial position of the ionospheric state or its ionization field according to the regular definitions of the TEC identifier, i.e. VTEC (Vertical TEC). The description below shows one of the possible solutions that is based on the spherical cap harmonic analysis method for modeling VTEC parameter. This method involves transformation of the initial data to a spherical cap and construction of model using

  4. Application of gene network analysis techniques identifies AXIN1/PDIA2 and endoglin haplotypes associated with bicuspid aortic valve.

    Directory of Open Access Journals (Sweden)

    Eric C Wooten

    2010-01-01

    Full Text Available Bicuspid Aortic Valve (BAV is a highly heritable congenital heart defect. The low frequency of BAV (1% of general population limits our ability to perform genome-wide association studies. We present the application of four a priori SNP selection techniques, reducing the multiple-testing penalty by restricting analysis to SNPs relevant to BAV in a genome-wide SNP dataset from a cohort of 68 BAV probands and 830 control subjects. Two knowledge-based approaches, CANDID and STRING, were used to systematically identify BAV genes, and their SNPs, from the published literature, microarray expression studies and a genome scan. We additionally tested Functionally Interpolating SNPs (fitSNPs present on the array; the fourth consisted of SNPs selected by Random Forests, a machine learning approach. These approaches reduced the multiple testing penalty by lowering the fraction of the genome probed to 0.19% of the total, while increasing the likelihood of studying SNPs within relevant BAV genes and pathways. Three loci were identified by CANDID, STRING, and fitSNPS. A haplotype within the AXIN1-PDIA2 locus (p-value of 2.926x10(-06 and a haplotype within the Endoglin gene (p-value of 5.881x10(-04 were found to be strongly associated with BAV. The Random Forests approach identified a SNP on chromosome 3 in association with BAV (p-value 5.061x10(-06. The results presented here support an important role for genetic variants in BAV and provide support for additional studies in well-powered cohorts. Further, these studies demonstrate that leveraging existing expression and genomic data in the context of GWAS studies can identify biologically relevant genes and pathways associated with a congenital heart defect.

  5. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    Science.gov (United States)

    Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

    2014-01-01

    Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co

  6. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    Directory of Open Access Journals (Sweden)

    Xiaolin Xiao

    2014-01-01

    Full Text Available Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states. Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based and humans (mRNA-sequencing-based and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi

  7. Pediatric Emergency Care Applied Research Network (PECARN) prediction rules in identifying high risk children with mild traumatic brain injury.

    Science.gov (United States)

    Nakhjavan-Shahraki, B; Yousefifard, M; Hajighanbari, M J; Oraii, A; Safari, S; Hosseini, M

    2017-06-22

    Pediatric Emergency Care Applied Research Network (PECARN) traumatic brain injury (TBI) prognostic rules is a scoring system for prediction of the need for computed tomography (CT) scanning in children with mild TBI. However, its validation has not been assessed in developing countries. Therefore, the present study was designed to assess the value of PECARN rule in identification of children with clinically important TBI (ciTBI). In this prospective cross-sectional study, 594 children (mean age: 7.9 ± 5.3 years; 79.3% boys) with mild TBI brought to emergency ward of two healthcare centers in Tehran, Iran were assessed. PECARN checklist was filled for all patients and children were divided to three groups of low, intermediate and high risks. Patients were followed for 2 weeks by phone to assess their ciTBI status. At the end, discrimination power, calibration and overall performance of PECARN rule were assessed. PECARN had a sensitivity and specificity of 92.3 and 40.6%, respectively, in predicting ciTBI in children under 2 years and 100.0 and 57.8%, respectively, in individuals between the ages of 2 and 18. PECARN rule had a proper calibration in prediction of ciTBI and CT scan findings. Brier score (overall performance) of PECARN rule in predicting ciTBI in children under 2 and 2-18 years were 1.5 and 1.2, respectively. PECARN prediction rule has a proper validity in the prediction of ciTBI. Therefor it can be used for screening and identification of high risk children with mild TBI.

  8. Comprehensive meta-analysis, co-expression, and miRNA nested network analysis identifies gene candidates in citrus against Huanglongbing disease.

    Science.gov (United States)

    Rawat, Nidhi; Kiran, Sandhya P; Du, Dongliang; Gmitter, Fred G; Deng, Zhanao

    2015-07-28

    Huanglongbing (HLB), the most devastating disease of citrus, is associated with infection by Candidatus Liberibacter asiaticus (CaLas) and is vectored by the Asian citrus psyllid (ACP). Recently, the molecular basis of citrus-HLB interactions has been examined using transcriptome analyses, and these analyses have identified many probe sets and pathways modulated by CaLas infection among different citrus cultivars. However, lack of consistency among reported findings indicates that an integrative approach is needed. This study was designed to identify the candidate probe sets in citrus-HLB interactions using meta-analysis and gene co-expression network modelling. Twenty-two publically available transcriptome studies on citrus-HLB interactions, comprising 18 susceptible (S) datasets and four resistant (R) datasets, were investigated using Limma and RankProd methods of meta-analysis. A combined list of 7,412 differentially expressed probe sets was generated using a Teradata in-house Structured Query Language (SQL) script. We identified the 65 most common probe sets modulated in HLB disease among different tissues from the S and R datasets. Gene ontology analysis of these probe sets suggested that carbohydrate metabolism, nutrient transport, and biotic stress were the core pathways that were modulated in citrus by CaLas infection and HLB development. We also identified R-specific probe sets, which encoded leucine-rich repeat proteins, chitinase, constitutive disease resistance (CDR), miraculins, and lectins. Weighted gene co-expression network analysis (WGCNA) was conducted on 3,499 probe sets, and 21 modules with major hub probe sets were identified. Further, a miRNA nested network was created to examine gene regulation of the 3,499 target probe sets. Results suggest that csi-miR167 and csi-miR396 could affect ion transporters and defence response pathways, respectively. Most of the potential candidate hub probe sets were co-expressed with gibberellin pathway (GA

  9. Transcriptome analysis of ripe and unripe fruit tissue of banana identifies major metabolic networks involved in fruit ripening process.

    Science.gov (United States)

    Asif, Mehar Hasan; Lakhwani, Deepika; Pathak, Sumya; Gupta, Parul; Bag, Sumit K; Nath, Pravendra; Trivedi, Prabodh Kumar

    2014-12-02

    Banana is one of the most important crop plants grown in the tropics and sub-tropics. It is a climacteric fruit and undergoes ethylene dependent ripening. Once ripening is initiated, it proceeds at a fast rate making postharvest life short, which can result in heavy economic losses. During the fruit ripening process a number of physiological and biochemical changes take place and thousands of genes from various metabolic pathways are recruited to produce a ripe and edible fruit. To better understand the underlying mechanism of ripening, we undertook a study to evaluate global changes in the transcriptome of the fruit during the ripening process. We sequenced the transcriptomes of the unripe and ripe stages of banana (Musa accuminata; Dwarf Cavendish) fruit. The transcriptomes were sequenced using a 454 GSFLX-Titanium platform that resulted in more than 7,00,000 high quality (HQ) reads. The assembly of the reads resulted in 19,410 contigs and 92,823 singletons. A large number of the differentially expressed genes identified were linked to ripening dependent processes including ethylene biosynthesis, perception and signalling, cell wall degradation and production of aromatic volatiles. In the banana fruit transcriptomes, we found transcripts included in 120 pathways described in the KEGG database for rice. The members of the expansin and xyloglucan transglycosylase/hydrolase (XTH) gene families were highly up-regulated during ripening, which suggests that they might play important roles in the softening of the fruit. Several genes involved in the synthesis of aromatic volatiles and members of transcription factor families previously reported to be involved in ripening were also identified. A large number of differentially regulated genes were identified during banana fruit ripening. Many of these are associated with cell wall degradation and synthesis of aromatic volatiles. A large number of differentially expressed genes did not align with any of the databases and

  10. A sperm-specific proteome-scale metabolic network model identifies non-glycolytic genes for energy deficiency in asthenozoospermia.

    Science.gov (United States)

    Asghari, Arvand; Marashi, Sayed-Amir; Ansari-Pour, Naser

    2017-04-01

    About 15% of couples experience difficulty in conceiving a child, of which half of the cases are thought to be male-related. Asthenozoospermia, or low sperm motility, is one of the frequent types of male infertility. Although energy metabolism is suggested to be central to the etiology of asthenozoospermia, very few attempts have been made to identify its underlying metabolic pathways. Here, we reconstructed SpermNet, the first proteome-scale model of the sperm cell by using whole-proteome data and the mCADRE algorithm. The reconstructed model was then analyzed using the COBRA toolbox. Genes were knocked-out in the model to investigate their effect on ATP production. A total of 78 genes elevated ATP production rate considerably of which most encode components of oxidative phosphorylation, fatty acid oxidation, the Krebs cycle, and members of the solute carrier 25 family. Among them, we identified 11 novel genes which have previously not been associated with sperm cell energy metabolism and may thus be implicated in asthenozoospermia. We further examined the reconstructed model by in silico knock out of currently known asthenozoospermia implicated-genes that were not predicted by our model. The pathways affected by knocking out these genes were also related to energy metabolism, confirming previous findings. Therefore, our model not only predicts the known pathways, it also identifies several non-glycolytic genes for deficient energy metabolism in asthenozoospermia. Finally, this model supports the notion that metabolic pathways besides glycolysis such as oxidative phosphorylation and fatty acid oxidation are essential for sperm energy metabolism and if validated, may form a basis for fertility recovery. mCADRE: metabolic context-specificity assessed by deterministic reaction evaluation; ATP: adenosine triphosphate; RNA: ribonucleic acid; FBA: flux balance analysis; FVA: flux variability analysis; DAVID: database for annotation, visualization and integrated

  11. Una red neuronal binaria para la identificación de mecanismos isomorfos. // A binary Neural network for identifying isomorphic mechanisms.

    Directory of Open Access Journals (Sweden)

    G. Galán Marín

    2002-05-01

    Full Text Available Un problema de importancia primordial en el diseño mecánico es identificar los mecanismos isomorfos, puesto que los isomorfismos nodetectados generan soluciones duplicadas y por tanto suponen un esfuerzo innecesario en el proceso de diseño. Desde 1960, una grancantidad de métodos han sido propuestos para la detección de mecanismos isomorfos. Sin embargo, diversos trabajos han mostrado queaunque pueden existir algoritmos eficaces para casos particulares, en el caso general los métodos tradicionales para la detección deisomorfismos en cadenas cinemáticas no proporcionan usualmente soluciones eficientes para este problema, que ha sido clasificado comoNP-duro. Un eficaz método alternativo para la resolución de problemas NP-duros ha surgido recientemente con las redes neuronales. Eneste trabajo proponemos un nuevo modelo de red neuronal binaria diseñado para la resolución del problema de detección de mecanismosisomorfos. El modelo propuesto se halla basado en unas dinámicas discretas que garantizan una rápida y correcta convergencia de la redhacia soluciones aceptables. Las simulaciones numéricas muestran en los mecanismos analizados que la red neuronal propuestaproporciona excelentes resultados, mostrándose además muy superior a la red de Hopfield continua tradicional en lo que respecta al tiempode computación y en la facilidad de su implementación.Palabras claves: Mecanismos isomorfos, síntesis de mecanismos, isomorfismo de grafos, red neuronal binaria,redes de Hopfield.____________________________________________________________________________AbstractAn important step in the kinematic mechanism synthesis process is to identify graph isomorphism whilegenerating new mechanisms. Undetected isomorphisms result in duplicate solutions and unnecessary effort.Since 1960, a lot of methods have been proposed for the graph isomorphism identification. Some authors haveconcluded that, in the general case, the traditional methods of

  12. Integrating ChIP-sequencing and digital gene expression profiling to identify BRD7 downstream genes and construct their regulating network.

    Science.gov (United States)

    Xu, Ke; Xiong, Wei; Zhou, Ming; Wang, Heran; Yang, Jing; Li, Xiayu; Chen, Pan; Liao, Qianjin; Deng, Hao; Li, Xiaoling; Li, Guiyuan; Zeng, Zhaoyang

    2016-01-01

    BRD7 is a single bromodomain-containing protein that functions as a subunit of the SWI/SNF chromatin-remodeling complex to regulate transcription. It also interacts with the well-known tumor suppressor protein p53 to trans-activate genes involved in cell cycle arrest. In this paper, we report an integrative analysis of genome-wide chromatin occupancy of BRD7 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and digital gene expression (DGE) profiling by RNA-sequencing upon the overexpression of BRD7 in human cells. We localized 156 BRD7-binding peaks representing 184 genes by ChIP-sequencing, and most of these peaks were co-localized with histone modification sites. Four novel motifs were significantly represented in these BRD7-enriched regions. Ingenuity pathway analysis revealed that 22 of these BRD7 target genes were involved in a network regulating cell death and survival. DGE profiling identified 560 up-regulated genes and 1088 down-regulated genes regulated by BRD7. Using Gene Ontology and pathway analysis, we found significant enrichment of the cell cycle and apoptosis pathway genes. For the integrative analysis of the ChIP-seq and DEG data, we constructed a regulating network of BRD7 downstream genes, and this network suggests multiple feedback regulations of the pathways. Furthermore, we validated BIRC2, BIRC3, TXN2, and NOTCH1 genes as direct, functional BRD7 targets, which were involved in the cell cycle and apoptosis pathways. These results provide a genome-wide view of chromatin occupancy and the gene regulation network of the BRD7 signaling pathway.

  13. Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

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    Cielito C Reyes-Gibby

    Full Text Available Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA, a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive and thymine degradation pathways (p = 1.06-08 were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis. The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67. In conclusion, gene network analysis identified novel molecules and

  14. Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

    Science.gov (United States)

    Reyes-Gibby, Cielito C; Melkonian, Stephanie C; Wang, Jian; Yu, Robert K; Shelburne, Samuel A; Lu, Charles; Gunn, Gary Brandon; Chambers, Mark S; Hanna, Ehab Y; Yeung, Sai-Ching J; Shete, Sanjay

    2017-01-01

    Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA), a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive) and thymine degradation pathways (p = 1.06-08) were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis). The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67). In conclusion, gene network analysis identified novel molecules and biological

  15. Comparative Transcriptional Analysis of Loquat Fruit Identifies Major Signal Networks Involved in Fruit Development and Ripening Process

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    Huwei Song

    2016-11-01

    Full Text Available Loquat (Eriobotrya japonica Lindl. is an important non-climacteric fruit and rich in essential nutrients such as minerals and carotenoids. During fruit development and ripening, thousands of the differentially expressed genes (DEGs from various metabolic pathways cause a series of physiological and biochemical changes. To better understand the underlying mechanism of fruit development, the Solexa/Illumina RNA-seq high-throughput sequencing was used to evaluate the global changes of gene transcription levels. More than 51,610,234 high quality reads from ten runs of fruit development were sequenced and assembled into 48,838 unigenes. Among 3256 DEGs, 2304 unigenes could be annotated to the Gene Ontology database. These DEGs were distributed into 119 pathways described in the Kyoto Encyclopedia of Genes and Genomes (KEGG database. A large number of DEGs were involved in carbohydrate metabolism, hormone signaling, and cell-wall degradation. The real-time reverse transcription (qRT-PCR analyses revealed that several genes related to cell expansion, auxin signaling and ethylene response were differentially expressed during fruit development. Other members of transcription factor families were also identified. There were 952 DEGs considered as novel genes with no annotation in any databases. These unigenes will serve as an invaluable genetic resource for loquat molecular breeding and postharvest storage.

  16. Comparative transcription analysis of different Antirrhinum phyllotaxy nodes identifies major signal networks involved in vegetative-reproductive transition.

    Directory of Open Access Journals (Sweden)

    Dongliang Wang

    Full Text Available Vegetative-reproductive phase change is an indispensable event which guarantees several aspects of successful meristem behaviour and organ development. Antirrhinum majus undergoes drastic changes of shoot architecture during the phase change, including phyllotactic change and leaf type alteration from opposite decussate to spiral. However, the regulation mechanism in both of phyllotactic morphology changes is still unclear. Here, the Solexa/Illumina RNA-seq high-throughput sequencing was used to evaluate the global changes of transcriptome levels among four node regions during phyllotactic development. More than 86,315,782 high quality reads were sequenced and assembled into 58,509 unigenes. These differentially expressed genes (DEGs were classified into 118 pathways described in the KEGG database. Based on the heat-map analysis, a large number of DEGs were overwhelmingly distributed in the hormone signal pathway as well as the carbohydrate biosynthesis and metabolism. The quantitative real time (qRT-PCR results indicated that most of DEGs were highly up-regulated in the swapping regions of phyllotactic morphology. Moreover, transcriptions factors (TFs with high transcripts were also identified, controlling the phyllotactic morphology by the regulation of hormone and sugar-metabolism signal pathways. A number of DEGs did not align with any databases and might be novel genes involved in the phyllotactic development. These genes will serve as an invaluable genetic resource for understanding the molecular mechanism of the phyllotactic development.

  17. Comparative Transcriptional Analysis of Loquat Fruit Identifies Major Signal Networks Involved in Fruit Development and Ripening Process.

    Science.gov (United States)

    Song, Huwei; Zhao, Xiangxiang; Hu, Weicheng; Wang, Xinfeng; Shen, Ting; Yang, Liming

    2016-11-04

    Loquat (Eriobotrya japonica Lindl.) is an important non-climacteric fruit and rich in essential nutrients such as minerals and carotenoids. During fruit development and ripening, thousands of the differentially expressed genes (DEGs) from various metabolic pathways cause a series of physiological and biochemical changes. To better understand the underlying mechanism of fruit development, the Solexa/Illumina RNA-seq high-throughput sequencing was used to evaluate the global changes of gene transcription levels. More than 51,610,234 high quality reads from ten runs of fruit development were sequenced and assembled into 48,838 unigenes. Among 3256 DEGs, 2304 unigenes could be annotated to the Gene Ontology database. These DEGs were distributed into 119 pathways described in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A large number of DEGs were involved in carbohydrate metabolism, hormone signaling, and cell-wall degradation. The real-time reverse transcription (qRT)-PCR analyses revealed that several genes related to cell expansion, auxin signaling and ethylene response were differentially expressed during fruit development. Other members of transcription factor families were also identified. There were 952 DEGs considered as novel genes with no annotation in any databases. These unigenes will serve as an invaluable genetic resource for loquat molecular breeding and postharvest storage.

  18. A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease.

    Science.gov (United States)

    Li, Lin; Wang, YanShu; An, Lifeng; Kong, XiangYin; Huang, Tao

    2017-01-01

    As a chronic illness derived from hair cells of the inner ear, Menière's disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

  19. Contact networks in a wildlife-livestock host community: identifying high-risk individuals in the transmission of bovine TB among badgers and cattle.

    Science.gov (United States)

    Böhm, Monika; Hutchings, Michael R; White, Piran C L

    2009-01-01

    The management of many pathogens, which are of concern to humans and their livestock, is complicated by the pathogens' ability to cross-infect multiple host species, including wildlife. This has major implications for the management of such diseases, since the dynamics of infection are dependent on the rates of both intra- and inter-specific transmission. However, the difficulty of studying transmission networks in free-living populations means that the relative opportunities for intra- versus inter-specific disease transmission have not previously been demonstrated empirically within any wildlife-livestock disease system. Using recently-developed proximity data loggers, we quantify both intra- and inter-specific contacts in a wildlife-livestock disease system, using bovine tuberculosis (bTB) in badgers and cattle in the UK as our example. We assess the connectedness of individuals within the networks in order to identify whether there are certain 'high-risk' individuals or groups of individuals for disease transmission within and between species. Our results show that contact patterns in both badger and cattle populations vary widely, both between individuals and over time. We recorded only infrequent interactions between badger social groups, although all badgers fitted with data loggers were involved in these inter-group contacts. Contacts between badgers and cattle occurred more frequently than contacts between different badger groups. Moreover, these inter-specific contacts involved those individual cows, which were highly connected within the cattle herd. This work represents the first continuous time record of wildlife-host contacts for any free-living wildlife-livestock disease system. The results highlight the existence of specific individuals with relatively high contact rates in both livestock and wildlife populations, which have the potential to act as hubs in the spread of disease through complex contact networks. Targeting testing or preventive

  20. An Integrative Developmental Genomics and Systems Biology Approach to Identify an In Vivo Sox Trio-Mediated Gene Regulatory Network in Murine Embryos

    Science.gov (United States)

    Lee, Wenqing Jean; Chatterjee, Sumantra; Yap, Sook Peng; Lim, Siew Lan; Xing, Xing; Kraus, Petra; Sun, Wenjie; Hu, Xiaoming; Sivakamasundari, V.; Chan, Hsiao Yun; Kolatkar, Prasanna R.; Prabhakar, Shyam

    2017-01-01

    Embryogenesis is an intricate process involving multiple genes and pathways. Some of the key transcription factors controlling specific cell types are the Sox trio, namely, Sox5, Sox6, and Sox9, which play crucial roles in organogenesis working in a concerted manner. Much however still needs to be learned about their combinatorial roles during this process. A developmental genomics and systems biology approach offers to complement the reductionist methodology of current developmental biology and provide a more comprehensive and integrated view of the interrelationships of complex regulatory networks that occur during organogenesis. By combining cell type-specific transcriptome analysis and in vivo ChIP-Seq of the Sox trio using mouse embryos, we provide evidence for the direct control of Sox5 and Sox6 by the transcriptional trio in the murine model and by Morpholino knockdown in zebrafish and demonstrate the novel role of Tgfb2, Fbxl18, and Tle3 in formation of Sox5, Sox6, and Sox9 dependent tissues. Concurrently, a complete embryonic gene regulatory network has been generated, identifying a wide repertoire of genes involved and controlled by the Sox trio in the intricate process of normal embryogenesis. PMID:28630873

  1. A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

    Science.gov (United States)

    Wang, Nizhuan; Chang, Chunqi; Zeng, Weiming; Shi, Yuhu; Yan, Hongjie

    2017-01-01

    Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

  2. Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI

    DEFF Research Database (Denmark)

    Wang, Weijing; Jiang, Wenjie; Hou, Lin

    2017-01-01

    .04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very...... and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. RESULTS: In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs......-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub...

  3. Using geographic information systems (GIS) to identify communities in need of health insurance outreach: An OCHIN practice-based research network (PBRN) report.

    Science.gov (United States)

    Angier, Heather; Likumahuwa, Sonja; Finnegan, Sean; Vakarcs, Trisha; Nelson, Christine; Bazemore, Andrew; Carrozza, Mark; DeVoe, Jennifer E

    2014-01-01

    Our practice-based research network (PBRN) is conducting an outreach intervention to increase health insurance coverage for patients seen in the network. To assist with outreach site selection, we sought an understandable way to use electronic health record (EHR) data to locate uninsured patients. Health insurance information was displayed within a web-based mapping platform to demonstrate the feasibility of using geographic information systems (GIS) to visualize EHR data. This study used EHR data from 52 clinics in the OCHIN PBRN. We included cross-sectional coverage data for patients aged 0 to 64 years with at least 1 visit to a study clinic during 2011 (n = 228,284). Our PBRN was successful in using GIS to identify intervention sites. Through use of the maps, we found geographic variation in insurance rates of patients seeking care in OCHIN PBRN clinics. Insurance rates also varied by age: The percentage of adults without insurance ranged from 13.2% to 86.8%; rates of children lacking insurance ranged from 1.1% to 71.7%. GIS also showed some areas of households with median incomes that had low insurance rates. EHR data can be imported into a web-based GIS mapping tool to visualize patient information. Using EHR data, we were able to observe smaller areas than could be seen using only publicly available data. Using this information, we identified appropriate OCHIN PBRN clinics for dissemination of an EHR-based insurance outreach intervention. GIS could also be used by clinics to visualize other patient-level characteristics to target clinic outreach efforts or interventions. © Copyright 2014 by the American Board of Family Medicine.

  4. Urinary Metabolomics Identifies a Molecular Correlate of Interstitial Cystitis/Bladder Pain Syndrome in a Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP Research Network Cohort

    Directory of Open Access Journals (Sweden)

    Kaveri S. Parker

    2016-05-01

    Full Text Available Interstitial cystitis/bladder pain syndrome (IC/BPS is a poorly understood syndrome affecting up to 6.5% of adult women in the U.S. The lack of broadly accepted objective laboratory markers for this condition hampers efforts to diagnose and treat this condition. To identify biochemical markers for IC/BPS, we applied mass spectrometry-based global metabolite profiling to urine specimens from a cohort of female IC/BPS subjects from the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP Research Network. These analyses identified multiple metabolites capable of discriminating IC/BPS and control subjects. Of these candidate markers, etiocholan-3α-ol-17-one sulfate (Etio-S, a sulfoconjugated 5-β reduced isomer of testosterone, distinguished female IC/BPS and control subjects with a sensitivity and specificity >90%. Among IC/BPS subjects, urinary Etio-S levels are correlated with elevated symptom scores (symptoms, pelvic pain, and number of painful body sites and could resolve high- from low-symptom IC/BPS subgroups. Etio-S-associated biochemical changes persisted through 3–6 months of longitudinal follow up. These results raise the possibility that an underlying biochemical abnormality contributes to symptoms in patients with severe IC/BPS.

  5. Transcriptome analysis of an apple (Malus × domestica) yellow fruit somatic mutation identifies a gene network module highly associated with anthocyanin and epigenetic regulation.

    Science.gov (United States)

    El-Sharkawy, Islam; Liang, Dong; Xu, Kenong

    2015-12-01

    Using RNA-seq, this study analysed an apple (Malus×domestica) anthocyanin-deficient yellow-skin somatic mutant 'Blondee' (BLO) and its red-skin parent 'Kidd's D-8' (KID), the original name of 'Gala', to understand the molecular mechanisms underlying the mutation. A total of 3299 differentially expressed genes (DEGs) were identified between BLO and KID at four developmental stages and/or between two adjacent stages within BLO and/or KID. A weighted gene co-expression network analysis (WGCNA) of the DEGs uncovered a network module of 34 genes highly correlated (r=0.95, P=9.0×10(-13)) with anthocyanin contents. Although 12 of the 34 genes in the WGCNA module were characterized and known of roles in anthocyanin, the remainder 22 appear to be novel. Examining the expression of ten representative genes in the module in 14 diverse apples revealed that at least eight were significantly correlated with anthocyanin variation. MdMYB10 (MDP0000259614) and MdGST (MDP0000252292) were among the most suppressed module member genes in BLO despite being undistinguishable in their corresponding sequences between BLO and KID. Methylation assay of MdMYB10 and MdGST in fruit skin revealed that two regions (MR3 and MR7) in the MdMYB10 promoter exhibited remarkable differences between BLO and KID. In particular, methylation was high and progressively increased alongside fruit development in BLO while was correspondingly low and constant in KID. The methylation levels in both MR3 and MR7 were negatively correlated with anthocyanin content as well as the expression of MdMYB10 and MdGST. Clearly, the collective repression of the 34 genes explains the loss-of-colour in BLO while the methylation in MdMYB10 promoter is likely causal for the mutation. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  6. Triumph of hope over experience: learning from interventions to reduce avoidable hospital admissions identified through an Academic Health and Social Care Network

    Directory of Open Access Journals (Sweden)

    Woodhams Victoria

    2012-06-01

    Full Text Available Abstract Background Internationally health services are facing increasing demands due to new and more expensive health technologies and treatments, coupled with the needs of an ageing population. Reducing avoidable use of expensive secondary care services, especially high cost admissions where no procedure is carried out, has become a focus for the commissioners of healthcare. Method We set out to identify, evaluate and share learning about interventions to reduce avoidable hospital admission across a regional Academic Health and Social Care Network (AHSN. We conducted a service evaluation identifying initiatives that had taken place across the AHSN. This comprised a literature review, case studies, and two workshops. Results We identified three types of intervention: pre-hospital; within the emergency department (ED; and post-admission evaluation of appropriateness. Pre-hospital interventions included the use of predictive modelling tools (PARR – Patients at risk of readmission and ACG – Adjusted Clinical Groups sometimes supported by community matrons or virtual wards. GP-advisers and outreach nurses were employed within the ED. The principal post-hoc interventions were the audit of records in primary care or the application of the Appropriateness Evaluation Protocol (AEP within the admission ward. Overall there was a shortage of independent evaluation and limited evidence that each intervention had an impact on rates of admission. Conclusions Despite the frequency and cost of emergency admission there has been little independent evaluation of interventions to reduce avoidable admission. Commissioners of healthcare should consider interventions at all stages of the admission pathway, including regular audit, to ensure admission thresholds don’t change.

  7. Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI.

    Science.gov (United States)

    Wang, Weijing; Jiang, Wenjie; Hou, Lin; Duan, Haiping; Wu, Yili; Xu, Chunsheng; Tan, Qihua; Li, Shuxia; Zhang, Dongfeng

    2017-11-13

    The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly

  8. Interpretacion y Biomecanica. Hoja de consejos de PEPNet (Interpreting and Biomechanics. PEPNet Tipsheet)

    Science.gov (United States)

    DeGroote, Bill; Morrison, Carolyn

    2010-01-01

    This publication, written in Spanish, describes cumulative trauma disorder (CTD), which refers to a collection of disorders associated with nerves, muscles, tendons, bones, and the neurovascular (nerves and related blood vessels) system. CTD symptoms may involve the neck, back, shoulders, arms, wrists, or hands. Interpreters with CTD may…

  9. The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

    Science.gov (United States)

    Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

    2017-06-15

    Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis

    Directory of Open Access Journals (Sweden)

    Barker Jeffery L

    2009-08-01

    Full Text Available Abstract Background Cortical development is a complex process that includes sequential generation of neuronal progenitors, which proliferate and migrate to form the stratified layers of the developing cortex. To identify the individual microRNAs (miRNAs and mRNAs that may regulate the genetic network guiding the earliest phase of cortical development, the expression profiles of rat neuronal progenitors obtained at embryonic day 11 (E11, E12 and E13 were analyzed. Results Neuronal progenitors were purified from telencephalic dissociates by a positive-selection strategy featuring surface labeling with tetanus-toxin and cholera-toxin followed by fluorescence-activated cell sorting. Microarray analyses revealed the fractions of miRNAs and mRNAs that were up-regulated or down-regulated in these neuronal progenitors at the beginning of cortical development. Nearly half of the dynamically expressed miRNAs were negatively correlated with the expression of their predicted target mRNAs. Conclusion These data support a regulatory role for miRNAs during the transition from neuronal progenitors into the earliest differentiating cortical neurons. In addition, by supplying a robust data set in which miRNA and mRNA profiles originate from the same purified cell type, this empirical study may facilitate the development of new algorithms to integrate various "-omics" data sets.

  11. Harnessing Social Networks along with Consumer-Driven Electronic Communication Technologies to Identify and Engage Members of 'Hard-to-Reach' Populations: A Methodological Case Report

    Directory of Open Access Journals (Sweden)

    Rock Melanie J

    2010-01-01

    Full Text Available Abstract Background Sampling in the absence of accurate or comprehensive information routinely poses logistical, ethical, and resource allocation challenges in social science, clinical, epidemiological, health service and population health research. These challenges are compounded if few members of a target population know each other or regularly interact. This paper reports on the sampling methods adopted in ethnographic case study research with a 'hard-to-reach' population. Methods To identify and engage a small yet diverse sample of people who met an unusual set of criteria (i.e., pet owners who had been treating cats or dogs for diabetes, four sampling strategies were used. First, copies of a recruitment letter were posted in pet-friendly places. Second, information about the study was diffused throughout the study period via word of mouth. Third, the lead investigator personally sent the recruitment letter via email to a pet owner, who then circulated the information to others, and so on. Fourth, veterinarians were enlisted to refer people who had diabetic pets. The second, third and fourth strategies rely on social networks and represent forms of chain referral sampling. Results Chain referral sampling via email proved to be the most efficient and effective, yielding a small yet diverse group of respondents within one month, and at negligible cost. Conclusions The widespread popularity of electronic communication technologies offers new methodological opportunities for researchers seeking to recruit from hard-to-reach populations.

  12. Harnessing social networks along with consumer-driven electronic communication technologies to identify and engage members of 'hard-to-reach' populations: a methodological case report.

    Science.gov (United States)

    Rock, Melanie J

    2010-01-20

    Sampling in the absence of accurate or comprehensive information routinely poses logistical, ethical, and resource allocation challenges in social science, clinical, epidemiological, health service and population health research. These challenges are compounded if few members of a target population know each other or regularly interact. This paper reports on the sampling methods adopted in ethnographic case study research with a 'hard-to-reach' population. To identify and engage a small yet diverse sample of people who met an unusual set of criteria (i.e., pet owners who had been treating cats or dogs for diabetes), four sampling strategies were used. First, copies of a recruitment letter were posted in pet-friendly places. Second, information about the study was diffused throughout the study period via word of mouth. Third, the lead investigator personally sent the recruitment letter via email to a pet owner, who then circulated the information to others, and so on. Fourth, veterinarians were enlisted to refer people who had diabetic pets. The second, third and fourth strategies rely on social networks and represent forms of chain referral sampling. Chain referral sampling via email proved to be the most efficient and effective, yielding a small yet diverse group of respondents within one month, and at negligible cost. The widespread popularity of electronic communication technologies offers new methodological opportunities for researchers seeking to recruit from hard-to-reach populations.

  13. Linking concepts in the ecology and evolution of invasive plants: network analysis shows what has been most studied and identifies knowledge gaps.

    Science.gov (United States)

    Vanderhoeven, Sonia; Brown, Cynthia S; Tepolt, Carolyn K; Tsutsui, Neil D; Vanparys, Valérie; Atkinson, Sheryl; Mahy, Grégory; Monty, Arnaud

    2010-03-01

    In recent decades, a growing number of studies have addressed connections between ecological and evolutionary concepts in biologic invasions. These connections may be crucial for understanding the processes underlying invaders' success. However, the extent to which scientists have worked on the integration of the ecology and evolution of invasive plants is poorly documented, as few attempts have been made to evaluate these efforts in invasion biology research. Such analysis can facilitate recognize well-documented relationships and identify gaps in our knowledge. In this study, we used a network-based method for visualizing the connections between major aspects of ecology and evolution in the primary research literature. Using the family Poaceae as an example, we show that ecological concepts were more studied and better interconnected than were evolutionary concepts. Several possible connections were not documented at all, representing knowledge gaps between ecology and evolution of invaders. Among knowledge gaps, the concepts of plasticity, gene flow, epigenetics and human influence were particularly under-connected. We discuss five possible research avenues to better understand the relationships between ecology and evolution in the success of Poaceae, and of alien plants in general.

  14. Assessing Optimal Neural Network Architecture for Identifying Disease-associated Multi-marker Genotypes using a Permutation Test, and Application to Calpain 10 Polymorphisms Associated with Diabetes

    National Research Council Canada - National Science Library

    North B.V; Curtis D; Cassell P.G; Hitman G.A; Sham P.C

    2003-01-01

    .... An artificial neural network allows investigation of association between disease phenotype and tightly linked markers without requiring haplotype phase and without modelling any evolutionary history...

  15. A network of networks.

    Science.gov (United States)

    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

  16. A systems biology approach identified different regulatory networks targeted by KSHV miR-K12-11 in B cells and endothelial cells.

    Science.gov (United States)

    Yang, Yajie; Boss, Isaac W; McIntyre, Lauren M; Renne, Rolf

    2014-08-08

    Kaposi's sarcoma associated herpes virus (KSHV) is associated with tumors of endothelial and lymphoid origin. During latent infection, KSHV expresses miR-K12-11, an ortholog of the human tumor gene hsa-miR-155. Both gene products are microRNAs (miRNAs), which are important post-transcriptional regulators that contribute to tissue specific gene expression. Advances in target identification technologies and molecular interaction databases have allowed a systems biology approach to unravel the gene regulatory networks (GRNs) triggered by miR-K12-11 in endothelial and lymphoid cells. Understanding the tissue specific function of miR-K12-11 will help to elucidate underlying mechanisms of KSHV pathogenesis. Ectopic expression of miR-K12-11 differentially affected gene expression in BJAB cells of lymphoid origin and TIVE cells of endothelial origin. Direct miRNA targeting accounted for a small fraction of the observed transcriptome changes: only 29 genes were identified as putative direct targets of miR-K12-11 in both cell types. However, a number of commonly affected biological pathways, such as carbohydrate metabolism and interferon response related signaling, were revealed by gene ontology analysis. Integration of transcriptome profiling, bioinformatic algorithms, and databases of protein-protein interactome from the ENCODE project identified different nodes of GRNs utilized by miR-K12-11 in a tissue-specific fashion. These effector genes, including cancer associated transcription factors and signaling proteins, amplified the regulatory potential of a single miRNA, from a small set of putative direct targets to a larger set of genes. This is the first comparative analysis of miRNA-K12-11's effects in endothelial and B cells, from tissues infected with KSHV in vivo. MiR-K12-11 was able to broadly modulate gene expression in both cell types. Using a systems biology approach, we inferred that miR-K12-11 establishes its GRN by both repressing master TFs and influencing

  17. Organizational Networks

    DEFF Research Database (Denmark)

    Grande, Bård; Sørensen, Ole Henning

    1998-01-01

    The paper focuses on the concept of organizational networks. Four different uses of the concept of organizational network are identified and critically discussed. Special focus is placed on how information and communication technologies as communication mediators and cognitive pictures influence...

  18. A real-time in-memory discovery service leveraging hierarchical packaging information in a unique identifier network to retrieve track and trace information

    CERN Document Server

    Müller, Jürgen

    2014-01-01

    This book examines how to efficiently retrieve track and trace information for an item that took a certain path through a complex network of manufacturers, wholesalers, retailers and consumers. It includes valuable tips on in-memory data management.

  19. Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network1[OPEN

    Science.gov (United States)

    Kim, Taehyong; Dreher, Kate; Nilo-Poyanco, Ricardo; Lee, Insuk; Fiehn, Oliver; Lange, Bernd Markus; Nikolau, Basil J.; Sumner, Lloyd; Welti, Ruth; Wurtele, Eve S.; Rhee, Seung Y.

    2015-01-01

    Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes. PMID:25670818

  20. PBMC transcriptome profiles identifies potential candidate genes and functional networks controlling the innate and the adaptive immune response to PRRSV vaccine in Pietrain pig

    Science.gov (United States)

    Islam, Md. Aminul; Große-Brinkhaus, Christine; Pröll, Maren Julia; Uddin, Muhammad Jasim; Aqter Rony, Sharmin; Tesfaye, Dawit; Tholen, Ernst; Hoelker, Michael; Schellander, Karl; Neuhoff, Christiane

    2017-01-01

    The porcine reproductive and respiratory syndrome (PRRS) is a devastating viral disease affecting swine production, health and welfare throughout the world. A synergistic action of the innate and the adaptive immune system of the host is essential for mounting a durable protective immunity through vaccination. Therefore, the current study aimed to investigate the transcriptome profiles of peripheral blood mononuclear cells (PBMCs) to characterize the innate and the adaptive immune response to PRRS Virus (PRRSV) vaccination in Pietrain pigs. The Affymetrix gene chip porcine gene 1.0 ST array was used for the transcriptome profiling of PBMCs collected at immediately before (D0), at one (D1) and 28 days (D28) post PRRSV vaccination with three biological replications. With FDR activation, cytokine activity and inflammatory response were enriched during the innate immunity; cytolysis, T cell mediated cytotoxicity, immunoglobulin production were enriched during adaptive immunity to PRRSV vaccination. Significant enrichment of cytokine-cytokine receptor interaction, signaling by interleukins, signaling by the B cell receptor (BCR), viral mRNA translation, IFN-gamma pathway and AP-1 transcription factor network pathways were indicating the involvement of altered genes in the antiviral defense. Network analysis revealed that four network modules were functionally involved with the transcriptional network of innate immunity, and five modules were linked to adaptive immunity in PBMCs. The innate immune transcriptional network was found to be regulated by LCK, STAT3, ATP5B, UBB and RSP17. While TGFß1, IL7R, RAD21, SP1 and GZMB are likely to be predictive for the adaptive immune transcriptional response to PRRSV vaccine in PBMCs. Results of the current immunogenomics study advances our understanding of PRRS in term of host-vaccine interaction, and thereby contribute to design a rationale for disease control strategy. PMID:28278192

  1. European Surveillance Network for Influenza in Pigs: Surveillance Programs, Diagnostic Tools and Swine Influenza Virus Subtypes Identified in 14 European Countries from 2010 to 2013

    DEFF Research Database (Denmark)

    Simon, Gaelle; Larsen, Lars Erik; Duerrwald, Ralf

    2014-01-01

    Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs) circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs......, mainly conducted through passive surveillance programs, resulted in the examination of more than 9 000 herds in 17 countries. Influenza A viruses were detected in 31% of herds examined from which 1887 viruses were preliminary characterized. The dominating subtypes were the three European enzootic SIVs......: avian-like swine H1N1 (53.6%), human-like reassortant swine H1N2 (13%) and human-like reassortant swine H3N2 (9.1%), as well as pandemic A/H1N1 2009 (H1N1pdm) virus (10.3%). Viruses from these four lineages co-circulated in several countries but with very different relative levels of incidence...

  2. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.

    Directory of Open Access Journals (Sweden)

    Andreas Kuehne

    2017-06-01

    Full Text Available In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

  3. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.

    Science.gov (United States)

    Kuehne, Andreas; Mayr, Urs; Sévin, Daniel C; Claassen, Manfred; Zamboni, Nicola

    2017-06-01

    In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

  4. PBMC transcriptome profiles identifies potential candidate genes and functional networks controlling the innate and the adaptive immune response to PRRSV vaccine in Pietrain pig.

    Science.gov (United States)

    Islam, Md Aminul; Große-Brinkhaus, Christine; Pröll, Maren Julia; Uddin, Muhammad Jasim; Aqter Rony, Sharmin; Tesfaye, Dawit; Tholen, Ernst; Hoelker, Michael; Schellander, Karl; Neuhoff, Christiane

    2017-01-01

    The porcine reproductive and respiratory syndrome (PRRS) is a devastating viral disease affecting swine production, health and welfare throughout the world. A synergistic action of the innate and the adaptive immune system of the host is essential for mounting a durable protective immunity through vaccination. Therefore, the current study aimed to investigate the transcriptome profiles of peripheral blood mononuclear cells (PBMCs) to characterize the innate and the adaptive immune response to PRRS Virus (PRRSV) vaccination in Pietrain pigs. The Affymetrix gene chip porcine gene 1.0 ST array was used for the transcriptome profiling of PBMCs collected at immediately before (D0), at one (D1) and 28 days (D28) post PRRSV vaccination with three biological replications. With FDR <0.05 and log2 fold change ±1.5 as cutoff criteria, 295 and 115 transcripts were found to be differentially expressed in PBMCs during the stage of innate and adaptive response, respectively. The microarray expression results were technically validated by qRT-PCR. The gene ontology terms such as viral life cycle, regulation of lymphocyte activation, cytokine activity and inflammatory response were enriched during the innate immunity; cytolysis, T cell mediated cytotoxicity, immunoglobulin production were enriched during adaptive immunity to PRRSV vaccination. Significant enrichment of cytokine-cytokine receptor interaction, signaling by interleukins, signaling by the B cell receptor (BCR), viral mRNA translation, IFN-gamma pathway and AP-1 transcription factor network pathways were indicating the involvement of altered genes in the antiviral defense. Network analysis revealed that four network modules were functionally involved with the transcriptional network of innate immunity, and five modules were linked to adaptive immunity in PBMCs. The innate immune transcriptional network was found to be regulated by LCK, STAT3, ATP5B, UBB and RSP17. While TGFß1, IL7R, RAD21, SP1 and GZMB are likely to

  5. Integrated Network Analysis Identifies Fight-Club Nodes as a Class of Hubs Encompassing Key Putative Switch Genes That Induce Major Transcriptome Reprogramming during Grapevine Development[W][OPEN

    Science.gov (United States)

    Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

    2014-01-01

    We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named “fight-club hubs” characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named “switch genes” was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. PMID:25490918

  6. Organizational Networks

    DEFF Research Database (Denmark)

    Sørensen, Ole Henning; Grande, Bård

    1996-01-01

    The paper focuses on the concept of organizational networks. Four different uses of the concept are identified and critically discussed.......The paper focuses on the concept of organizational networks. Four different uses of the concept are identified and critically discussed....

  7. m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.

    OpenAIRE

    Song-Yao Zhang; Shao-Wu Zhang; Lian Liu; Jia Meng; Yufei Huang

    2016-01-01

    As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated ...

  8. The regulatory network of cluster-root function and development in phosphate-deficient white lupin (Lupinus albus) identified by transcriptome sequencing.

    Science.gov (United States)

    Wang, Zhengrui; Straub, Daniel; Yang, Huaiyu; Kania, Angelika; Shen, Jianbo; Ludewig, Uwe; Neumann, Günter

    2014-07-01

    Lupinus albus serves as model plant for root-induced mobilization of sparingly soluble soil phosphates via the formation of cluster-roots (CRs) that mediate secretion of protons, citrate, phenolics and acid phosphatases (APases). This study employed next-generation sequencing to investigate the molecular mechanisms behind these complex adaptive responses at the transcriptome level. We compared different stages of CR development, including pre-emergent (PE), juvenile (JU) and the mature (MA) stages. The results confirmed that the primary metabolism underwent significant modifications during CR maturation, promoting the biosynthesis of organic acids, as had been deduced from physiological studies. Citrate catabolism was downregulated, associated with citrate accumulation in MA clusters. Upregulation of the phenylpropanoid pathway reflected the accumulation of phenolics. Specific transcript expression of ALMT and MATE transporter genes correlated with the exudation of citrate and flavonoids. The expression of transcripts related to nucleotide degradation and APases in MA clusters coincided with the re-mobilization and hydrolysis of organic phosphate resources. Most interestingly, hormone-related gene expression suggested a central role of ethylene during CR maturation. This was associated with the upregulation of the iron (Fe)-deficiency regulated network that mediates ethylene-induced expression of Fe-deficiency responses in other species. Finally, transcripts related to abscisic acid and jasmonic acid were upregulated in MA clusters, while auxin- and brassinosteroid-related genes and cytokinin receptors were most strongly expressed during CR initiation. Key regulations proposed by the RNA-seq data were confirmed by quantitative real-time polymerase chain reaction (RT-qPCR) and some physiological analyses. A model for the gene network regulating CR development and function is presented. © 2014 Scandinavian Plant Physiology Society.

  9. Brief Report: The Prevalence of Neurofibromatosis Type 1 among Children with Autism Spectrum Disorder Identified by the Autism and Developmental Disabilities Monitoring Network

    Science.gov (United States)

    Bilder, Deborah A.; Bakian, Amanda V.; Stevenson, David A.; Carbone, Paul S.; Cunniff, Christopher; Goodman, Alyson B.; McMahon, William M.; Fisher, Nicole P.; Viskochil, David

    2016-01-01

    Neurofibromatosis type 1 (NF1) is an inherited neurocutaneous disorder associated with neurodevelopmental disorders including autism spectrum disorder (ASD). The frequency of ASD/NF1 co-occurrence has been subject to debate since the 1980s. This relationship was investigated in a large population-based sample of 8-year-old children identified with…

  10. European surveillance network for influenza in pigs: surveillance programs, diagnostic tools and Swine influenza virus subtypes identified in 14 European countries from 2010 to 2013.

    Directory of Open Access Journals (Sweden)

    Gaëlle Simon

    Full Text Available Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs (ESNIP3, 2010-2013 aimed to expand widely the knowledge of the epidemiology of European SIVs. ESNIP3 stimulated programs of harmonized SIV surveillance in European countries and supported the coordination of appropriate diagnostic tools and subtyping methods. Thus, an extensive virological monitoring, mainly conducted through passive surveillance programs, resulted in the examination of more than 9 000 herds in 17 countries. Influenza A viruses were detected in 31% of herds examined from which 1887 viruses were preliminary characterized. The dominating subtypes were the three European enzootic SIVs: avian-like swine H1N1 (53.6%, human-like reassortant swine H1N2 (13% and human-like reassortant swine H3N2 (9.1%, as well as pandemic A/H1N1 2009 (H1N1pdm virus (10.3%. Viruses from these four lineages co-circulated in several countries but with very different relative levels of incidence. For instance, the H3N2 subtype was not detected at all in some geographic areas whereas it was still prevalent in other parts of Europe. Interestingly, H3N2-free areas were those that exhibited highest frequencies of circulating H1N2 viruses. H1N1pdm viruses were isolated at an increasing incidence in some countries from 2010 to 2013, indicating that this subtype has become established in the European pig population. Finally, 13.9% of the viruses represented reassortants between these four lineages, especially between previous enzootic SIVs and H1N1pdm. These novel viruses were detected at the same time in several countries, with increasing prevalence. Some of them might become established in pig herds, causing implications for zoonotic infections.

  11. European Surveillance Network for Influenza in Pigs: Surveillance Programs, Diagnostic Tools and Swine Influenza Virus Subtypes Identified in 14 European Countries from 2010 to 2013

    Science.gov (United States)

    Simon, Gaëlle; Larsen, Lars E.; Dürrwald, Ralf; Foni, Emanuela; Harder, Timm; Van Reeth, Kristien; Markowska-Daniel, Iwona; Reid, Scott M.; Dan, Adam; Maldonado, Jaime; Huovilainen, Anita; Billinis, Charalambos; Davidson, Irit; Agüero, Montserrat; Vila, Thaïs; Hervé, Séverine; Breum, Solvej Østergaard; Chiapponi, Chiara; Urbaniak, Kinga; Kyriakis, Constantinos S.; Brown, Ian H.; Loeffen, Willie

    2014-01-01

    Swine influenza causes concern for global veterinary and public health officials. In continuing two previous networks that initiated the surveillance of swine influenza viruses (SIVs) circulating in European pigs between 2001 and 2008, a third European Surveillance Network for Influenza in Pigs (ESNIP3, 2010–2013) aimed to expand widely the knowledge of the epidemiology of European SIVs. ESNIP3 stimulated programs of harmonized SIV surveillance in European countries and supported the coordination of appropriate diagnostic tools and subtyping methods. Thus, an extensive virological monitoring, mainly conducted through passive surveillance programs, resulted in the examination of more than 9 000 herds in 17 countries. Influenza A viruses were detected in 31% of herds examined from which 1887 viruses were preliminary characterized. The dominating subtypes were the three European enzootic SIVs: avian-like swine H1N1 (53.6%), human-like reassortant swine H1N2 (13%) and human-like reassortant swine H3N2 (9.1%), as well as pandemic A/H1N1 2009 (H1N1pdm) virus (10.3%). Viruses from these four lineages co-circulated in several countries but with very different relative levels of incidence. For instance, the H3N2 subtype was not detected at all in some geographic areas whereas it was still prevalent in other parts of Europe. Interestingly, H3N2-free areas were those that exhibited highest frequencies of circulating H1N2 viruses. H1N1pdm viruses were isolated at an increasing incidence in some countries from 2010 to 2013, indicating that this subtype has become established in the European pig population. Finally, 13.9% of the viruses represented reassortants between these four lineages, especially between previous enzootic SIVs and H1N1pdm. These novel viruses were detected at the same time in several countries, with increasing prevalence. Some of them might become established in pig herds, causing implications for zoonotic infections. PMID:25542013

  12. Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders

    KAUST Repository

    Marquet, Pierre

    2014-09-22

    Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed.

  13. A census of P. longum's phytochemicals and their network pharmacological evaluation for identifying novel drug-like molecules against various diseases, with a special focus on neurological disorders.

    Science.gov (United States)

    Choudhary, Neha; Singh, Vikram

    2018-01-01

    Piper longum (P. longum, also called as long pepper) is one of the common culinary herbs that has been extensively used as a crucial constituent in various indigenous medicines, specifically in traditional Indian medicinal system known as Ayurveda. For exploring the comprehensive effect of its constituents in humans at proteomic and metabolic levels, we have reviewed all of its known phytochemicals and enquired about their regulatory potential against various protein targets by developing high-confidence tripartite networks consisting of phytochemical-protein target-disease association. We have also (i) studied immunomodulatory potency of this herb; (ii) developed subnetwork of human PPI regulated by its phytochemicals and could successfully associate its specific modules playing important role in diseases, and (iii) reported several novel drug targets. P10636 (microtubule-associated protein tau, that is involved in diseases like dementia etc.) was found to be the commonly screened target by about seventy percent of these phytochemicals. We report 20 drug-like phytochemicals in this herb, out of which 7 are found to be the potential regulators of 5 FDA approved drug targets. Multi-targeting capacity of 3 phytochemicals involved in neuroactive ligand receptor interaction pathway was further explored via molecular docking experiments. To investigate the molecular mechanism of P. longum's action against neurological disorders, we have developed a computational framework that can be easily extended to explore its healing potential against other diseases and can also be applied to scrutinize other indigenous herbs for drug-design studies.

  14. In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

    Directory of Open Access Journals (Sweden)

    Kristopher J. L. Irizarry

    2016-01-01

    Full Text Available Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1 that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.

  15. The Brassica rapa FLC homologue FLC2 is a key regulator of flowering time, identified through transcriptional co-expression networks.

    Science.gov (United States)

    Xiao, Dong; Zhao, Jian J; Hou, Xi L; Basnet, Ram K; Carpio, Dunia P D; Zhang, Ning W; Bucher, Johan; Lin, Ke; Cheng, Feng; Wang, Xiao W; Bonnema, Guusje

    2013-11-01

    The role of many genes and interactions among genes involved in flowering time have been studied extensively in Arabidopsis, and the purpose of this study was to investigate how effectively results obtained with the model species Arabidopsis can be applied to the Brassicacea with often larger and more complex genomes. Brassica rapa represents a very close relative, with its triplicated genome, with subgenomes having evolved by genome fractionation. The question of whether this genome fractionation is a random process, or whether specific genes are preferentially retained, such as flowering time (Ft) genes that play a role in the extreme morphological variation within the B. rapa species (displayed by the diverse morphotypes), is addressed. Data are presented showing that indeed Ft genes are preferentially retained, so the next intriguing question is whether these different orthologues of Arabidopsis Ft genes play similar roles compared with Arabidopsis, and what is the role of these different orthologues in B. rapa. Using a genetical-genomics approach, co-location of flowering quantitative trait loci (QTLs) and expression QTLs (eQTLs) resulted in identification of candidate genes for flowering QTLs and visualization of co-expression networks of Ft genes and flowering time. A major flowering QTL on A02 at the BrFLC2 locus co-localized with cis eQTLs for BrFLC2, BrSSR1, and BrTCP11, and trans eQTLs for the photoperiod gene BrCO and two paralogues of the floral integrator genes BrSOC1 and BrFT. It is concluded that the BrFLC2 Ft gene is a major regulator of flowering time in the studied doubled haploid population.

  16. The network researchers' network

    DEFF Research Database (Denmark)

    Henneberg, Stephan C.; Jiang, Zhizhong; Naudé, Peter

    2009-01-01

    The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987). In thi......The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...

  17. Global mapping of binding sites for Nrf2 identifies novel targets in cell survival response through ChIP-Seq profiling and network analysis.

    Science.gov (United States)

    Malhotra, Deepti; Portales-Casamar, Elodie; Singh, Anju; Srivastava, Siddhartha; Arenillas, David; Happel, Christine; Shyr, Casper; Wakabayashi, Nobunao; Kensler, Thomas W; Wasserman, Wyeth W; Biswal, Shyam

    2010-09-01

    The Nrf2 (nuclear factor E2 p45-related factor 2) transcription factor responds to diverse oxidative and electrophilic environmental stresses by circumventing repression by Keap1, translocating to the nucleus, and activating cytoprotective genes. Nrf2 responses provide protection against chemical carcinogenesis, chronic inflammation, neurodegeneration, emphysema, asthma and sepsis in murine models. Nrf2 regulates the expression of a plethora of genes that detoxify oxidants and electrophiles and repair or remove damaged macromolecules, such as through proteasomal processing. However, many direct targets of Nrf2 remain undefined. Here, mouse embryonic fibroblasts (MEF) with either constitutive nuclear accumulation (Keap1(-/-)) or depletion (Nrf2(-/-)) of Nrf2 were utilized to perform chromatin-immunoprecipitation with parallel sequencing (ChIP-Seq) and global transcription profiling. This unique Nrf2 ChIP-Seq dataset is highly enriched for Nrf2-binding motifs. Integrating ChIP-Seq and microarray analyses, we identified 645 basal and 654 inducible direct targets of Nrf2, with 244 genes at the intersection. Modulated pathways in stress response and cell proliferation distinguish the inducible and basal programs. Results were confirmed in an in vivo stress model of cigarette smoke-exposed mice. This study reveals global circuitry of the Nrf2 stress response emphasizing Nrf2 as a central node in cell survival response.

  18. Investigation of naphthofuran moiety as potential dual inhibitor against BACE-1 and GSK-3β: molecular dynamics simulations, binding energy, and network analysis to identify first-in-class dual inhibitors against Alzheimer's disease.

    Science.gov (United States)

    Kumar, Akhil; Srivastava, Gaurava; Srivastava, Swati; Verma, Seema; Negi, Arvind S; Sharma, Ashok

    2017-08-01

    BACE-1 and GSK-3β are potential therapeutic drug targets for Alzheimer's disease. Recently, both the targets received attention for designing dual inhibitors for Alzheimer's disease. Until now, only two-scaffold triazinone and curcumin have been reported as BACE-1 and GSK-3β dual inhibitors. Docking, molecular dynamics, clustering, binding energy, and network analysis of triazinone derivatives with BACE-1 and GSK-3β was performed to get molecular insight into the first reported dual inhibitor. Further, we designed and evaluated a naphthofuran series for its ability to inhibit BACE-1 and GSK-3β with the computational approaches. Docking study of naphthofuran series showed a good binding affinity towards both the targets. Molecular dynamics, binding energy, and network analysis were performed to compare their binding with the targets and amino acids responsible for binding. Naphthofuran series derivatives showed good interaction within the active site residues of both of the targets. Hydrogen bond occupancy and binding energy suggested strong binding with the targets. Dual-inhibitor binding was mostly governed by the hydrophobic interactions for both of the targets. Per residue energy decomposition and network analysis identified the key residues involved in the binding and inhibiting BACE-1 and GSK-3β. The results indicated that naphthofuran series derivative 11 may be a promising first-in-class dual inhibitor against BACE-1 and GSK-3β. This naphthofuran series may be further explored to design better dual inhibitors. Graphical abstract Naphthofuran derivative as a dual inhibitor for BACE-1 and GSK-3β.

  19. Counseling Students Who Have Usher Syndrome. PEPNet Tipsheet

    Science.gov (United States)

    Lago-Avery, Patricia, Comp.

    2010-01-01

    Usher Syndrome is an autosomal recessive genetic disorder characterized by congenital hearing loss and gradually developing retinitis pigmentosa leading to the loss of vision. Approximately 27,000 people in the United States have some form of Usher Syndrome. Most of these individuals have either Type I (11,000) or Type II (16,000). Type I Usher…

  20. Author Identifiers in Scholarly Repositories

    CERN Document Server

    Warner, Simeon

    2010-01-01

    Bibliometric and usage-based analyses and tools highlight the value of information about scholarship contained within the network of authors, articles and usage data. Less progress has been made on populating and using the author side of this network than the article side, in part because of the difficulty of unambiguously identifying authors. I briefly review a sample of author identifier schemes, and consider use in scholarly repositories. I then describe preliminary work at arXiv to implement public author identifiers, services based on them, and plans to make this information useful beyond the boundaries of arXiv.

  1. Gene Expression Profiling Identifies Lobe-Specific and Common Disruptions of Multiple Gene Networks in Testosterone-Supported, 17β-Estradiol- or Diethylstilbestrol-Induced Prostate Dysplasia in Noble Rats

    Directory of Open Access Journals (Sweden)

    Neville N.C. Tam

    2008-01-01

    Full Text Available The xenoestrogen diethylstilbestrol (DES is commonly believed to mimic the action of the natural estrogen 17β-estradiol (E2. To determine if these two estrogens exert similar actions in prostate carcinogenesis, we elevated circulating levels of estrogen in Noble (NBL rats with E2/DES-filled implants, while maintaining physiological levels of testosterone (T in the animals with T-filled implants. The two estrogens induced dysplasia in a lobe-specific manner, with E2 targeting only the lateral prostate (LP and DES impacting only the ventral prostate (VP. Gene expression profiling identified distinct and common E2-disrupted versus DES-disrupted gene networks in each lobe. More importantly, hierarchical clustering analyses revealed that T + E2 treatment primarily affected the gene expression pattern in the LP, whereas T + DES treatment primarily affected the gene expression profile in the VP. Gene ontology analyses and pathway mapping suggest that the two hormone treatments disrupt unique and/or common cellular processes, including cell development, proliferation, motility, apoptosis, and estrogen signaling, which may be linked to dysplasia development in the rat prostate. These findings suggest that the effects of xenoestrogens and natural estrogens on the rat prostate are more divergent than previously suspected and that these differences may explain the lobe-specific carcinogenic actions of the hormones.

  2. Identifying polymer states by machine learning

    Science.gov (United States)

    Wei, Qianshi; Melko, Roger G.; Chen, Jeff Z. Y.

    2017-03-01

    The ability of a feed-forward neural network to learn and classify different states of polymer configurations is systematically explored. Performing numerical experiments, we find that a simple network model can, after adequate training, recognize multiple structures, including gaslike coil, liquidlike globular, and crystalline anti-Mackay and Mackay structures. The network can be trained to identify the transition points between various states, which compare well with those identified by independent specific-heat calculations. Our study demonstrates that neural networks provide an unconventional tool to study the phase transitions in polymeric systems.

  3. Digital Identifier Systems: Comparative Evaluation

    Directory of Open Access Journals (Sweden)

    Hamid Reza Khedmatgozar

    2015-02-01

    Full Text Available Identifier is one of the main elements in identifying an object in digital environment. Digital identifier systems were developed followed by a lot of problems such as violation of persistency and uniqueness of physical identifiers and URL in digital environment. These identifiers try to guarantee uniqueness and persistency of hostnames by using indirect names for Domain Name System (DNS. The main objective of this research is to identify qualified digital identifier system among other systems. To achieve the research objective, researchers have considered two major steps: first, identifying main criteria for distinguishing digital identifier based on literature review and focus group interview; and second, performing a comparative evaluation on common identifier systems in the world. Findings of first step demonstrated seven main criteria in three domains for distinguishing digital identifier systems: identifier uniqueness and persistency in the identifier features domain, digital identification, digital uniqueness, digital persistency and digital actionability in the digital coverage domain, and globality in the comprehensiveness of scope domain. In the second step, results of the comparative evaluation on common identifier systems indicated that six identifier systems, included, DOI, Handle, UCI, URN, ARK and PURL, are appropriate choices for using as a digital identifier system. Also, according to these results, three identification systems Including NBN, MARIAM and ISNI were identified as suitable choices for digital identification in certain specialized fields. According to many benefits of using these identifiers in important applied fields, such as, digital content chains and networks integration, digital right management, cross referencing, digital libraries and citation analysis, results of this study can help digital environment experts to diagnose digital identifier and their effective use in applied fields.

  4. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  5. Exploring network organization in practice

    DEFF Research Database (Denmark)

    Hu, Yimei; Sørensen, Olav Jull

    that there are different logical considerations when designing a network organization to facilitate innovation. I identify three types of network organizations: market-led, directed and culture-led network organizations. Different types of network organizations show that organizations are dual and even ternary systems...

  6. Identifying Power Elites

    DEFF Research Database (Denmark)

    Grau Larsen, Anton; Ellersgaard, Christoph Houman

    2017-01-01

    Specifying network boundaries is fundamental in the study of social structures of elite networks. However, traditional methods do not offer clear criteria on either size or composition of the elite, and rely on numerous ad hoc decisions. A methodological framework that is inductive, reproducible...... and suitable for comparative research is proposed. First, a comprehensive dataset of the 5079 affiliation networks of all potentially powerful sectors in Denmark was assembled. Second, these heterogeneous affiliation networks were weighted to account for potential level of social integration. Third, a weighted...

  7. Managing Innovation Networks

    DEFF Research Database (Denmark)

    Awate, Snehal Suyash; Møller Larsen, Marcus; Mudambi, Ram

    In this article, we treat innovation as a multidimensional construct spanning people, technologies, and geographies. We study how these dimensions interact and impact firms' inventor networks and the ultimate innovation performance. We identify five distinct planes in which inventor networks reside...... of the costs and benefits of being centrally located on multiplex network versus having an equal distribution of ties between the different network layers. Using patent data on the global wind power industry, we find evidence suggesting that being centrally positioned on a multiplex network with an equal...

  8. Network topology analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Kalb, Jeffrey L.; Lee, David S.

    2008-01-01

    Emerging high-bandwidth, low-latency network technology has made network-based architectures both feasible and potentially desirable for use in satellite payload architectures. The selection of network topology is a critical component when developing these multi-node or multi-point architectures. This study examines network topologies and their effect on overall network performance. Numerous topologies were reviewed against a number of performance, reliability, and cost metrics. This document identifies a handful of good network topologies for satellite applications and the metrics used to justify them as such. Since often multiple topologies will meet the requirements of the satellite payload architecture under development, the choice of network topology is not easy, and in the end the choice of topology is influenced by both the design characteristics and requirements of the overall system and the experience of the developer.

  9. Identifying Clusters of Complex Urban–Rural Issues as Part of Policy Making Process Using a Network Analysis Approach: A Case Study in Bahía de Los Ángeles, Mexico

    Directory of Open Access Journals (Sweden)

    Javier Sandoval

    2017-06-01

    Full Text Available Improving human settlements diagnosis is a key factor in effective urban planning and the design of efficient policy making. In this paper, we illustrate how network theory concepts can be applied to reveal the topological structure of functional relationships in a network of heterogeneous urban–rural issues. This mapping is done using clustering algorithms and centrality value techniques. By analyzing emergent groups of urban–rural related issues, our methodology was applied to a rural community, considering in this exercise environmental matters and real estate interests as a way to better understand the structure of salient issues in the context of its urban development program design. Results show clusters that arrange themselves not by an obvious similarity in their constituent components, but by relations observed in urban–rural settings that hint on the issues that the urban development program must focus. Due to its complex nature, the classification of these emerging clusters and how they must be treated in traditional planning instruments is a new challenge that this novel methodology reveals.

  10. In vivo genome-wide analysis of multiple tissues identifies gene regulatory networks, novel functions and downstream regulatory genes for Bapx1 and its co-regulation with Sox9 in the mammalian vertebral column.

    Science.gov (United States)

    Chatterjee, Sumantra; Sivakamasundari, V; Yap, Sook Peng; Kraus, Petra; Kumar, Vibhor; Xing, Xing; Lim, Siew Lan; Sng, Joel; Prabhakar, Shyam; Lufkin, Thomas

    2014-12-05

    Vertebrate organogenesis is a highly complex process involving sequential cascades of transcription factor activation or repression. Interestingly a single developmental control gene can occasionally be essential for the morphogenesis and differentiation of tissues and organs arising from vastly disparate embryological lineages. Here we elucidated the role of the mammalian homeobox gene Bapx1 during the embryogenesis of five distinct organs at E12.5 - vertebral column, spleen, gut, forelimb and hindlimb - using expression profiling of sorted wildtype and mutant cells combined with genome wide binding site analysis. Furthermore we analyzed the development of the vertebral column at the molecular level by combining transcriptional profiling and genome wide binding data for Bapx1 with similarly generated data sets for Sox9 to assemble a detailed gene regulatory network revealing genes previously not reported to be controlled by either of these two transcription factors. The gene regulatory network appears to control cell fate decisions and morphogenesis in the vertebral column along with the prevention of premature chondrocyte differentiation thus providing a detailed molecular view of vertebral column development.

  11. Declarative Networking

    CERN Document Server

    Loo, Boon Thau

    2012-01-01

    Declarative Networking is a programming methodology that enables developers to concisely specify network protocols and services, which are directly compiled to a dataflow framework that executes the specifications. Declarative networking proposes the use of a declarative query language for specifying and implementing network protocols, and employs a dataflow framework at runtime for communication and maintenance of network state. The primary goal of declarative networking is to greatly simplify the process of specifying, implementing, deploying and evolving a network design. In addition, decla

  12. Dominating biological networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

    Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

  13. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

    ... (extended from Leavitt, 1965). This text identifies aspects of network-based effectiveness that can benefit from a better understanding of leadership and management development of people, procedures, technology, and organizations...

  14. Collective network routing

    Science.gov (United States)

    Hoenicke, Dirk

    2014-12-02

    Disclosed are a unified method and apparatus to classify, route, and process injected data packets into a network so as to belong to a plurality of logical networks, each implementing a specific flow of data on top of a common physical network. The method allows to locally identify collectives of packets for local processing, such as the computation of the sum, difference, maximum, minimum, or other logical operations among the identified packet collective. Packets are injected together with a class-attribute and an opcode attribute. Network routers, employing the described method, use the packet attributes to look-up the class-specific route information from a local route table, which contains the local incoming and outgoing directions as part of the specifically implemented global data flow of the particular virtual network.

  15. Serial Network Flow Monitor

    Science.gov (United States)

    Robinson, Julie A.; Tate-Brown, Judy M.

    2009-01-01

    Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.

  16. Network cosmology.

    Science.gov (United States)

    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.

  17. Network Cosmology

    Science.gov (United States)

    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

  18. Brief report: Identifying defenders of peer victimization.

    Science.gov (United States)

    Meter, Diana J; Card, Noel A

    2016-06-01

    Betweenness centrality quantifies the amount of network flow that a network member controls as hypothetical traffic passes between network members. Those with high betweenness centrality within the peer social network based on nominations of liking may be especially important connectors between individuals who do not like each other. This study tested the hypothesis that individuals' betweenness centrality would predict their defending of victimized peers. After controlling for popularity, perception of being liked, and defenders' victimization, betweenness centrality predicted defending. Those found to be connectors within the peer group were more likely to be those who defend peer victims. This investigation showed that analysis of betweenness centrality is a viable way to identify potential defenders in research and also those who could potentially act as mediators. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  19. On Identities in Modern Networks

    OpenAIRE

    Libor Polcak; Radek Hranicky; Tomas Martinek

    2014-01-01

    Communicating parties inside computer networks use different kind of identifiers. Some of these identifiers are stable, e.g., logins used to access a specific service, some are only temporary, e.g., dynamically assigned IP addresses. This paper tackles several challenges of lawful interception that emerged in modern networks. The main contribution is the graph model that links identities learnt from various sources distributed in a network. The inferred identities result into an interception ...

  20. Telecommunication networks

    CERN Document Server

    Iannone, Eugenio

    2011-01-01

    Many argue that telecommunications network infrastructure is the most impressive and important technology ever developed. Analyzing the telecom market's constantly evolving trends, research directions, infrastructure, and vital needs, Telecommunication Networks responds with revolutionized engineering strategies to optimize network construction. Omnipresent in society, telecom networks integrate a wide range of technologies. These include quantum field theory for the study of optical amplifiers, software architectures for network control, abstract algebra required to design error correction co

  1. Identifying Depression on Twitter

    OpenAIRE

    Nadeem, Moin

    2016-01-01

    Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a crowdsourced method to compile a list of Twitter users who profess to being diagnosed with depression. Using up to a year of pri...

  2. Thoughts on identifiers

    CERN Multimedia

    CERN. Geneva

    2005-01-01

    As business processes and information transactions have become an inextricably intertwined with the Web, the importance of assignment, registration, discovery, and maintenance of identifiers has increased. In spite of this, integrated frameworks for managing identifiers have been slow to emerge. Instead, identification systems arise (quite naturally) from immediate business needs without consideration for how they fit into larger information architectures. In addition, many legacy identifier systems further complicate the landscape, making it difficult for content managers to select and deploy identifier systems that meet both the business case and long term information management objectives. This presentation will outline a model for evaluating identifier applications and the functional requirements of the systems necessary to support them. The model is based on a layered analysis of the characteristics of identifier systems, including: * Functional characteristics * Technology * Policy * Business * Social T...

  3. Identifiability in stochastic models

    CERN Document Server

    1992-01-01

    The problem of identifiability is basic to all statistical methods and data analysis, occurring in such diverse areas as Reliability Theory, Survival Analysis, and Econometrics, where stochastic modeling is widely used. Mathematics dealing with identifiability per se is closely related to the so-called branch of ""characterization problems"" in Probability Theory. This book brings together relevant material on identifiability as it occurs in these diverse fields.

  4. Location-Based Services in Vehicular Networks

    Science.gov (United States)

    Wu, Di

    2013-01-01

    Location-based services have been identified as a promising communication paradigm in highly mobile and dynamic vehicular networks. However, existing mobile ad hoc networking cannot be directly applied to vehicular networking due to differences in traffic conditions, mobility models and network topologies. On the other hand, hybrid architectures…

  5. Phenology drives mutualistic network structure and diversity

    NARCIS (Netherlands)

    Encinas Viso, Francisco; Revilla, Tomas A; Etienne, Rampal S.

    Several network properties have been identified as determinants of the stability and complexity of mutualistic networks. However, it is unclear which mechanisms give rise to these network properties. Phenology seems important, because it shapes the topology of mutualistic networks, but its effects

  6. Metadata, Identifiers, and Physical Samples

    Science.gov (United States)

    Arctur, D. K.; Lenhardt, W. C.; Hills, D. J.; Jenkyns, R.; Stroker, K. J.; Todd, N. S.; Dassie, E. P.; Bowring, J. F.

    2016-12-01

    Physical samples are integral to much of the research conducted by geoscientists. The samples used in this research are often obtained at significant cost and represent an important investment for future research. However, making information about samples - whether considered data or metadata - available for researchers to enable discovery is difficult: a number of key elements related to samples are difficult to characterize in common ways, such as classification, location, sample type, sampling method, repository information, subsample distribution, and instrumentation, because these differ from one domain to the next. Unifying these elements or developing metadata crosswalks is needed. The iSamples (Internet of Samples) NSF-funded Research Coordination Network (RCN) is investigating ways to develop these types of interoperability and crosswalks. Within the iSamples RCN, one of its working groups, WG1, has focused on the metadata related to physical samples. This includes identifying existing metadata standards and systems, and how they might interoperate with the International Geo Sample Number (IGSN) schema (schema.igsn.org) in order to help inform leading practices for metadata. For example, we are examining lifecycle metadata beyond the IGSN `birth certificate.' As a first step, this working group is developing a list of relevant standards and comparing their various attributes. In addition, the working group is looking toward technical solutions to facilitate developing a linked set of registries to build the web of samples. Finally, the group is also developing a comparison of sample identifiers and locators. This paper will provide an overview and comparison of the standards identified thus far, as well as an update on the technical solutions examined for integration. We will discuss how various sample identifiers might work in complementary fashion with the IGSN to more completely describe samples, facilitate retrieval of contextual information, and

  7. Distributed Persistent Identifiers System Design

    Directory of Open Access Journals (Sweden)

    Pavel Golodoniuc

    2017-06-01

    Full Text Available The need to identify both digital and physical objects is ubiquitous in our society. Past and present persistent identifier (PID systems, of which there is a great variety in terms of technical and social implementation, have evolved with the advent of the Internet, which has allowed for globally unique and globally resolvable identifiers. PID systems have, by in large, catered for identifier uniqueness, integrity, and persistence, regardless of the identifier’s application domain. Trustworthiness of these systems has been measured by the criteria first defined by Bütikofer (2009 and further elaborated by Golodoniuc 'et al'. (2016 and Car 'et al'. (2017. Since many PID systems have been largely conceived and developed by a single organisation they faced challenges for widespread adoption and, most importantly, the ability to survive change of technology. We believe that a cause of PID systems that were once successful fading away is the centralisation of support infrastructure – both organisational and computing and data storage systems. In this paper, we propose a PID system design that implements the pillars of a trustworthy system – ensuring identifiers’ independence of any particular technology or organisation, implementation of core PID system functions, separation from data delivery, and enabling the system to adapt for future change. We propose decentralisation at all levels — persistent identifiers and information objects registration, resolution, and data delivery — using Distributed Hash Tables and traditional peer-to-peer networks with information replication and caching mechanisms, thus eliminating the need for a central PID data store. This will increase overall system fault tolerance thus ensuring its trustworthiness. We also discuss important aspects of the distributed system’s governance, such as the notion of the authoritative source and data integrity

  8. The robustness and restoration of a network of ecological networks.

    Science.gov (United States)

    Pocock, Michael J O; Evans, Darren M; Memmott, Jane

    2012-02-24

    Understanding species' interactions and the robustness of interaction networks to species loss is essential to understand the effects of species' declines and extinctions. In most studies, different types of networks (such as food webs, parasitoid webs, seed dispersal networks, and pollination networks) have been studied separately. We sampled such multiple networks simultaneously in an agroecosystem. We show that the networks varied in their robustness; networks including pollinators appeared to be particularly fragile. We show that, overall, networks did not strongly covary in their robustness, which suggests that ecological restoration (for example, through agri-environment schemes) benefitting one functional group will not inevitably benefit others. Some individual plant species were disproportionately well linked to many other species. This type of information can be used in restoration management, because it identifies the plant taxa that can potentially lead to disproportionate gains in biodiversity.

  9. Interconnected networks

    CERN Document Server

    2016-01-01

    This volume provides an introduction to and overview of the emerging field of interconnected networks which include multi layer or multiplex networks, as well as networks of networks. Such networks present structural and dynamical features quite different from those observed in isolated networks. The presence of links between different networks or layers of a network typically alters the way such interconnected networks behave – understanding the role of interconnecting links is therefore a crucial step towards a more accurate description of real-world systems. While examples of such dissimilar properties are becoming more abundant – for example regarding diffusion, robustness and competition – the root of such differences remains to be elucidated. Each chapter in this topical collection is self-contained and can be read on its own, thus making it also suitable as reference for experienced researchers wishing to focus on a particular topic.

  10. On Identities in Modern Networks

    Directory of Open Access Journals (Sweden)

    Libor Polcak

    2014-09-01

    Full Text Available Communicating parties inside computer networks use different kind of identifiers. Some of these identifiers are stable, e.g., logins used to access a specific service, some are only temporary, e.g., dynamically assigned IP addresses. This paper tackles several challenges of lawful interception that emerged in modern networks. The main contribution is the graph model that links identities learnt from various sources distributed in a network. The inferred identities result into an interception of more detailed data in conformance with the issued court order. The approach deals with network address translation, short-lived identifiers and simultaneous usage of different identities. The approach was evaluated to be viable during real network testing based on various means to learn identities of users connected to a network.

  11. Network maintenance

    CERN Multimedia

    IT Department

    2009-01-01

    A site wide network maintenance has been scheduled for Saturday 28 February. Most of the network devices of the General Purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites along this day. This upgrade will not affect: the Computer centre itself, building 613, the Technical Network and the LHC experiments dedicated networks at the pits. Should you need more details on this intervention, please contact Netops by phone 74927 or email mailto:Netops@cern.ch. IT/CS Group

  12. Network maintenance

    CERN Multimedia

    GS Department

    2009-01-01

    A site-wide network maintenance operation has been scheduled for Saturday 28 February. Most of the network devices of the general purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites throughout the day. This upgrade will not affect the Computer Centre itself, Building 613, the Technical Network and the LHC experiments, dedicated networks at the pits. For further details of this intervention, please contact Netops by phone 74927 or e-mail mailto:Netops@cern.ch. IT/CS Group

  13. Network Performance Improvement under Epidemic Failures in Optical Transport Networks

    DEFF Research Database (Denmark)

    Fagertun, Anna Manolova; Ruepp, Sarah Renée

    2013-01-01

    In this paper we investigate epidemic failure spreading in large- scale GMPLS-controlled transport networks. By evaluating the effect of the epidemic failure spreading on the network, we design several strategies for cost-effective network performance improvement via differentiated repair times....... First we identify the most vulnerable and the most strategic nodes in the network. Then, via extensive simulations we show that strategic placement of resources for improved failure recovery has better performance than randomly assigning lower repair times among the network nodes. Our OPNET simulation...

  14. The EH network

    DEFF Research Database (Denmark)

    Santolini, E; Salcini, A E; Kay, B K

    1999-01-01

    . Moreover, a number of cellular ligands of the domain have been identified and demonstrated to define a complex network of protein-protein interactions in the eukaryotic cell. Interestingly, many of the EH-containing and EH-binding proteins display characteristics of endocytic "accessory" proteins......, suggesting that the principal function of the EH network is to regulate various steps in endocytosis. In addition, recent evidence suggests that the EH network might work as an "integrator" of signals controlling cellular pathways as diverse as endocytosis, nucleocytosolic export, and ultimately cell...

  15. Network Ambivalence

    Directory of Open Access Journals (Sweden)

    Patrick Jagoda

    2015-08-01

    Full Text Available The language of networks now describes everything from the Internet to the economy to terrorist organizations. In distinction to a common view of networks as a universal, originary, or necessary form that promises to explain everything from neural structures to online traffic, this essay emphasizes the contingency of the network imaginary. Network form, in its role as our current cultural dominant, makes scarcely imaginable the possibility of an alternative or an outside uninflected by networks. If so many things and relationships are figured as networks, however, then what is not a network? If a network points towards particular logics and qualities of relation in our historical present, what others might we envision in the future? In  many ways, these questions are unanswerable from within the contemporary moment. Instead of seeking an avant-garde approach (to move beyond networks or opting out of networks (in some cases, to recover elements of pre-networked existence, this essay proposes a third orientation: one of ambivalence that operates as a mode of extreme presence. I propose the concept of "network aesthetics," which can be tracked across artistic media and cultural forms, as a model, style, and pedagogy for approaching interconnection in the twenty-first century. The following essay is excerpted from Network Ambivalence (Forthcoming from University of Chicago Press. 

  16. Identifiability of nonlinear systems

    Energy Technology Data Exchange (ETDEWEB)

    Tunali, E.T.

    1985-01-01

    The parameter identifiability problem of deterministic, nonlinear dynamical control systems is studied in the framework of differential geometric systems theory. The relations between nonlinear observability, nonlinear functional expansions and identifiability are investigated and necessary and sufficient conditions are obtained for a class of nonlinear systems. In a different approach, by using the uniqueness theorem of nonlinear system realization theory, necessary and sufficient conditions are obtained for another class of nonlinear systems. These results provide an insight to the identifiability problem of nonlinear systems. The results are illustrated by examples that also show the effectiveness of the conditions obtained. Finally, some possible research topics in this area are suggested.

  17. Network Access Control For Dummies

    CERN Document Server

    Kelley, Jay; Wessels, Denzil

    2009-01-01

    Network access control (NAC) is how you manage network security when your employees, partners, and guests need to access your network using laptops and mobile devices. Network Access Control For Dummies is where you learn how NAC works, how to implement a program, and how to take real-world challenges in stride. You'll learn how to deploy and maintain NAC in your environment, identify and apply NAC standards, and extend NAC for greater network security. Along the way you'll become familiar with what NAC is (and what it isn't) as well as the key business drivers for deploying NAC.Learn the step

  18. Network neuroscience.

    Science.gov (United States)

    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.

  19. Network workshop

    DEFF Research Database (Denmark)

    Bruun, Jesper; Evans, Robert Harry

    2014-01-01

    This paper describes the background for, realisation of and author reflections on a network workshop held at ESERA2013. As a new research area in science education, networks offer a unique opportunity to visualise and find patterns and relationships in complicated social or academic network data...... research community. With this workshop, participants were offered a way into network science based on authentic educational research data. The workshop was constructed as an inquiry lesson with emphasis on user autonomy. Learning activities had participants choose to work with one of two cases of networks...... network methodology in one’s research might supersede the perceived benefits of doing so. As a response to that problem, we argue that workshops can act as a road towards meaningful engagement with networks and highlight that network methodology promises new ways of interpreting data to answer questions...

  20. Social Networks

    OpenAIRE

    Martí, Joan; Zenou, Yves

    2009-01-01

    We survey the literature on social networks by putting together the economics, sociological and physics/applied mathematics approaches, showing their similarities and differences. We expose, in particular, the two main ways of modeling network formation. While the physics/applied mathematics approach is capable of reproducing most observed networks, it does not explain why they emerge. On the contrary, the economics approach is very precise in explaining why networks emerge but does a poor jo...

  1. Network Coding

    Indian Academy of Sciences (India)

    Network coding is a technique to increase the amount of information °ow in a network by mak- ing the key observation that information °ow is fundamentally different from commodity °ow. Whereas, under traditional methods of opera- tion of data networks, intermediate nodes are restricted to simply forwarding their incoming.

  2. How exotic plants integrate into pollination networks

    National Research Council Canada - National Science Library

    Stouffer, Daniel B; Cirtwill, Alyssa R; Bascompte, Jordi; Bartomeus, Ignasi

    2014-01-01

    ...–pollinator communities. In this manuscript, we have studied a global data base of empirical pollination networks to determine whether community, network, species or interaction characteristics can help identify invaded communities...

  3. Identifying Knowledge and Communication

    Directory of Open Access Journals (Sweden)

    Eduardo Coutinho Lourenço de Lima

    2006-12-01

    Full Text Available In this paper, I discuss how the principle of identifying knowledge which Strawson advances in ‘Singular Terms and Predication’ (1961, and in ‘Identifying Reference and Truth-Values’ (1964 turns out to constrain communication. The principle states that a speaker’s use of a referring expression should invoke identifying knowledge on the part of the hearer, if the hearer is to understand what the speaker is saying, and also that, in so referring, speakers are attentive to hearers’ epistemic states. In contrasting it with Russell’s Principle (Evans 1982, as well as with the principle of identifying descriptions (Donnellan 1970, I try to show that the principle of identifying knowledge, ultimately a condition for understanding, makes sense only in a situation of conversation. This allows me to conclude that the cooperative feature of communication (Grice 1975 and reference (Clark andWilkes-Gibbs 1986 holds also at the understanding level. Finally, I discuss where Strawson’s views seem to be unsatisfactory, and suggest how they might be improved.

  4. Technical Network

    CERN Multimedia

    2007-01-01

    In order to optimise the management of the Technical Network (TN), to facilitate understanding of the purpose of devices connected to the TN and to improve security incident handling, the Technical Network Administrators and the CNIC WG have asked IT/CS to verify the "description" and "tag" fields of devices connected to the TN. Therefore, persons responsible for systems connected to the TN will receive e-mails from IT/CS asking them to add the corresponding information in the network database at "network-cern-ch". Thank you very much for your cooperation. The Technical Network Administrators & the CNIC WG

  5. Identifying learning styles.

    Science.gov (United States)

    Hughes, Grace

    2016-12-14

    What was the nature of the CPD activity, practice-related feedback and/or event and/or experience in your practice? The article explored different learning styles and outlined some of the models that can be used to identify them. It discussed the limitations of these models, indicating that although they can be helpful in identifying a student's preferred learning style, this is not 'fixed' and might change over time. Learning is also influenced by other factors, such as culture and age.

  6. Network science

    CERN Document Server

    Barabasi, Albert-Laszlo

    2016-01-01

    Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network sci...

  7. Vulnerability of network of networks

    Science.gov (United States)

    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.

  8. Identifying and Managing Risk.

    Science.gov (United States)

    Abraham, Janice M.

    1999-01-01

    The role of the college or university chief financial officer in institutional risk management is (1) to identify risk (physical, casualty, fiscal, business, reputational, workplace safety, legal liability, employment practices, general liability), (2) to develop a campus plan to reduce and control risk, (3) to transfer risk, and (4) to track and…

  9. Networked Microgrids Scoping Study

    Energy Technology Data Exchange (ETDEWEB)

    Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Dobriansky, Larisa [General MicroGrids, San Diego, CA (United States); Glover, Steve [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Liu, Chen-Ching [Washington State Univ., Pullman, WA (United States); Looney, Patrick [Brookhaven National Lab. (BNL), Upton, NY (United States); Mashayekh, Salman [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Pratt, Annabelle [National Renewable Energy Lab. (NREL), Golden, CO (United States); Schneider, Kevin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Starke, Michael [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States); Yue, Meng [Brookhaven National Lab. (BNL), Upton, NY (United States)

    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.

  10. Identifying Bitcoin users by transaction behavior

    Science.gov (United States)

    Monaco, John V.

    2015-05-01

    Digital currencies, such as Bitcoin, offer convenience and security to criminals operating in the black marketplace. Some Bitcoin marketplaces, such as Silk Road, even claim anonymity. This claim contradicts the findings in this work, where long term transactional behavior is used to identify and verify account holders. Transaction timestamps and network properties observed over time contribute to this finding. The timestamp of each transaction is the result of many factors: the desire purchase an item, daily schedule and activities, as well as hardware and network latency. Dynamic network properties of the transaction, such as coin flow and the number of edge outputs and inputs, contribute further to reveal account identity. In this paper, we propose a novel methodology for identifying and verifying Bitcoin users based on the observation of Bitcoin transactions over time. The behavior we attempt to quantify roughly occurs in the social band of Newell's time scale. A subset of the Blockchain 230686 is taken, selecting users that initiated between 100 and 1000 unique transactions per month for at least 6 different months. This dataset shows evidence of being nonrandom and nonlinear, thus a dynamical systems approach is taken. Classification and authentication accuracies are obtained under various representations of the monthly Bitcoin samples: outgoing transactions, as well as both outgoing and incoming transactions are considered, along with the timing and dynamic network properties of transaction sequences. The most appropriate representations of monthly Bitcoin samples are proposed. Results show an inherent lack of anonymity by exploiting patterns in long-term transactional behavior.

  11. NP Science Network Requirements

    Energy Technology Data Exchange (ETDEWEB)

    Dart, Eli [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Rotman, Lauren [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Tierney, Brian [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

    2011-08-26

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. To support SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 20 years. In August 2011, ESnet and the Office of Nuclear Physics (NP), of the DOE SC, organized a workshop to characterize the networking requirements of the programs funded by NP. The requirements identified at the workshop are summarized in the Findings section, and are described in more detail in the body of the report.

  12. Network Coded Software Defined Networking

    DEFF Research Database (Denmark)

    Hansen, Jonas; Roetter, Daniel Enrique Lucani; Krigslund, Jeppe

    2015-01-01

    . The inherent flexibility of both SDN and NC provides fertile ground to envision more efficient, robust, and secure networking designs, which may also incorporate content caching and storage, all of which are key challenges of the upcoming 5G networks. This article not only proposes the fundamentals......Software defined networking has garnered large attention due to its potential to virtualize services in the Internet, introducing flexibility in the buffering, scheduling, processing, and routing of data in network routers. SDN breaks the deadlock that has kept Internet network protocols stagnant...... for decades, while applications and physical links have evolved. This article advocates for the use of SDN to bring about 5G network services by incorporating network coding (NC) functionalities. The latter constitutes a major leap forward compared to the state-of-the- art store and forward Internet paradigm...

  13. Network Coded Software Defined Networking

    DEFF Research Database (Denmark)

    Krigslund, Jeppe; Hansen, Jonas; Roetter, Daniel Enrique Lucani

    2015-01-01

    Software Defined Networking (SDN) and Network Coding (NC) are two key concepts in networking that have garnered a large attention in recent years. On the one hand, SDN's potential to virtualize services in the Internet allows a large flexibility not only for routing data, but also to manage....... This paper advocates for the use of SDN to bring about future Internet and 5G network services by incorporating network coding (NC) functionalities. The inherent flexibility of both SDN and NC provides a fertile ground to envision more efficient, robust, and secure networking designs, that may also...... incorporate content caching and storage, all of which are key challenges of the future Internet and the upcoming 5G networks. This paper proposes some of the keys behind this intersection and supports it with use cases as well as a an implementation that integrated the Kodo library (NC) into OpenFlow (SDN...

  14. TCGA study identifies genomic features of cervical cancer

    Science.gov (United States)

    Investigators with The Cancer Genome Atlas (TCGA) Research Network have identified novel genomic and molecular characteristics of cervical cancer that will aid in subclassification of the disease and may help target therapies that are most appropriate for each patient.

  15. Telecommunication Networks

    DEFF Research Database (Denmark)

    Olsen, Rasmus Løvenstein; Balachandran, Kartheepan; Hald, Sara Ligaard

    2014-01-01

    In this chapter, we look into the role of telecommunication networks and their capability of supporting critical infrastructure systems and applications. The focus is on smart grids as the key driving example, bearing in mind that other such systems do exist, e.g., water management, traffic control......, etc. First, the role of basic communication is examined with a focus on critical infrastructures. We look at heterogenic networks and standards for smart grids, to give some insight into what has been done to ensure inter-operability in this direction. We then go to the physical network, and look...... at the deployment of the physical layout of the communication network and the related costs. This is an important aspect as one option to use existing networks is to deploy dedicated networks. Following this, we look at some generic models that describe reliability for accessing dynamic information. This part...

  16. Networked Identities

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Larsen, Malene Charlotte

    2008-01-01

    In this article we take up a critique of the concept of Communities of Practice (CoP) voiced by several authors, who suggest that networks may provide a better metaphor to understand social forms of organisation and learning. Through a discussion of the notion of networked learning and the critique...... of CoPs we shall argue that the metaphor or theory of networked learning is itself confronted with some central tensions and challenges that need to be addressed. We then explore these theoretical and analytic challenges to the network metaphor, through an analysis of a Danish social networking site. We...... argue that understanding meaning-making and ‘networked identities’ may be relevant analytic entry points in navigating the challenges....

  17. Computerized Library Networking in Canada.

    Science.gov (United States)

    Duchesne, Roderick M.; Islam, Mazharul

    1979-01-01

    Reviews a study which examined computerized bibliographic centers in Canada identifying three types: (1) library processing facility; (2) library network user group; and (3) information retrieval facility. The study also reported on ways to promote a computerized library network with emphasis on national location service. (CWM)

  18. Financial Network Systemic Risk Contributions

    NARCIS (Netherlands)

    Hautsch, N.; Schaumburg, J.; Schienle, M.

    2015-01-01

    We propose the realized systemic risk beta as a measure of financial companies' contribution to systemic risk, given network interdependence between firms' tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define

  19. Discovering universal statistical laws of complex networks

    CERN Document Server

    Cardanobile, Stefano; Deger, Moritz; Rotter, Stefan

    2011-01-01

    Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their generalisation power, which we identify with large structural variability and absence of constraints imposed by the construction scheme. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This allows, for instance, to infer global features from local ones usi...

  20. Wireless Networks

    OpenAIRE

    Samaka, Mohammed; Khan, Khaled M.D.

    2007-01-01

    Wireless communication is the fastest-growing field in the telecommunication industry. Wireless networks have grown significantly as an important segment of the communications industry. They have become popular networks with the potential to provide high-speed, high-quality information exchange between two or more portable devices without any wire or conductors. Wireless networks can simply be characterized as the technology that provides seamless access to information, anywhere, anyplace, an...

  1. Enterpreneurial network

    OpenAIRE

    Thoma, Antonela; Nguyen, Lien; Kupsyte, Valdone

    2014-01-01

    Network has become more and more indispensable in the entrepreneurial world. Especially in startup businesses, network is crucial for new entrepreneurs. This project looks at how entrepreneurs in different sectors use network to become successful. We chose to work with three entrepreneurs from three companies that have been operational for a few years and conducted face to face interviews with them. Through the data from the interviews, we analyzed firstly what type of entrepreneurs they are,...

  2. Network security

    CERN Document Server

    Perez, André

    2014-01-01

    This book introduces the security mechanisms deployed in Ethernet, Wireless-Fidelity (Wi-Fi), Internet Protocol (IP) and MultiProtocol Label Switching (MPLS) networks. These mechanisms are grouped throughout the book according to the following four functions: data protection, access control, network isolation, and data monitoring. Data protection is supplied by data confidentiality and integrity control services. Access control is provided by a third-party authentication service. Network isolation is supplied by the Virtual Private Network (VPN) service. Data monitoring consists of applying

  3. Networking Japan

    DEFF Research Database (Denmark)

    Hansen, Annette Skovsted

    HIDA). Many of these alumni have and will in the future exchange ideas and keep contact not only to Japan, but also to fellow alumni around the globe and, thereby, practice south-south exchanges, which are made possible and traceable by their established alumni network and the World Network of Friends...... (WNF). Through the alumni network, Japan continues to infuse ideas to participants and alumni, who interpret and disseminate these ideas through alumni society networks and activities, but their discussions nationally and regionally also get reported back to Japan and affect future policies...

  4. Technical Network

    CERN Multimedia

    2007-01-01

    In order to optimize the management of the Technical Network (TN), to ease the understanding and purpose of devices connected to the TN, and to improve security incident handling, the Technical Network Administrators and the CNIC WG have asked IT/CS to verify the "description" and "tag" fields of devices connected to the TN. Therefore, persons responsible for systems connected to the TN will receive email notifications from IT/CS asking them to add the corresponding information in the network database. Thank you very much for your cooperation. The Technical Network Administrators & the CNIC WG

  5. Future Network Architectures

    DEFF Research Database (Denmark)

    Wessing, Henrik; Bozorgebrahimi, Kurosh; Belter, Bartosz

    2015-01-01

    This study identifies key requirements for NRENs towards future network architectures that become apparent as users become more mobile and have increased expectations in terms of availability of data. In addition, cost saving requirements call for federated use of, in particular, the optical...

  6. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    Science.gov (United States)

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  7. Random Cell Identifiers Assignment

    Directory of Open Access Journals (Sweden)

    Robert Bestak

    2012-01-01

    Full Text Available Despite integration of advanced functions that enable Femto Access Points (FAPs to be deployed in a plug-and-play manner, the femtocell concept still cause several opened issues to be resolved. One of them represents an assignment of Physical Cell Identifiers (PCIs to FAPs. This paper analyses a random based assignment algorithm in LTE systems operating in diverse femtocell scenarios. The performance of the algorithm is evaluated by comparing the number of confusions for various femtocell densities, PCI ranges and knowledge of vicinity. Simulation results show that better knowledge of vicinity can significantly reduce the number of confusions events.

  8. Cloud Radio Access Network architecture. Towards 5G mobile networks

    DEFF Research Database (Denmark)

    Checko, Aleksandra

    Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges that mobile operators face while trying to support ever-growing end-users’ needs towards 5th generation of mobile networks (5G). The main idea behind C-RAN is to split the base......, and for the analyzed scenario it can assure synchronization on the nanosecond level, fulfilling mobile network requirements. Furthermore, mechanisms to lower delay and jitter have been identified, namely: source scheduling and preemption. An innovative source scheduling scheme which can minimize jitter has been...

  9. Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy.

    Science.gov (United States)

    Widjaja, E; Zamyadi, M; Raybaud, C; Snead, O C; Smith, M L

    2013-12-01

    Epilepsy is considered a disorder of neural networks. The aims of this study were to assess functional connectivity within resting-state networks and functional network connectivity across resting-state networks by use of resting-state fMRI in children with frontal lobe epilepsy and to relate changes in resting-state networks with neuropsychological function. Fifteen patients with frontal lobe epilepsy and normal MR imaging and 14 healthy control subjects were recruited. Spatial independent component analysis was used to identify the resting-state networks, including frontal, attention, default mode network, sensorimotor, visual, and auditory networks. The Z-maps of resting-state networks were compared between patients and control subjects. The relation between abnormal connectivity and neuropsychological function was assessed. Correlations from all pair-wise combinations of independent components were performed for each group and compared between groups. The frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-state networks in patients. There was a weak association between connectivity in frontal network and executive function (P = .029) and a significant association between sensorimotor network and fine motor function (P = .004). Control subjects had 79 pair-wise independent components that showed significant temporal coherence across all resting-state networks except for default mode network-auditory network. Patients had 66 pairs of independent components that showed significant temporal coherence across all resting-state networks. Group comparison showed reduced functional network connectivity between default mode network-attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network-sensorimotor, and frontal

  10. Knowledge sharing in horizontal networks

    National Research Council Canada - National Science Library

    Juliano Nunes Alves; Breno Augusto Diniz Pereira; Augusto Diniz

    2012-01-01

      The present study aimed to identify the process of sharing knowledge between the partners involved in the network, as well as the dimensions on sharing of knowledge between enterprises belonging...

  11. Vulnerability of critical infrastructures : identifying critical nodes.

    Energy Technology Data Exchange (ETDEWEB)

    Cox, Roger Gary; Robinson, David Gerald

    2004-06-01

    The objective of this research was the development of tools and techniques for the identification of critical nodes within critical infrastructures. These are nodes that, if disrupted through natural events or terrorist action, would cause the most widespread, immediate damage. This research focuses on one particular element of the national infrastructure: the bulk power system. Through the identification of critical elements and the quantification of the consequences of their failure, site-specific vulnerability analyses can be focused at those locations where additional security measures could be effectively implemented. In particular, with appropriate sizing and placement within the grid, distributed generation in the form of regional power parks may reduce or even prevent the impact of widespread network power outages. Even without additional security measures, increased awareness of sensitive power grid locations can provide a basis for more effective national, state and local emergency planning. A number of methods for identifying critical nodes were investigated: small-world (or network theory), polyhedral dynamics, and an artificial intelligence-based search method - particle swarm optimization. PSO was found to be the only viable approach and was applied to a variety of industry accepted test networks to validate the ability of the approach to identify sets of critical nodes. The approach was coded in a software package called Buzzard and integrated with a traditional power flow code. A number of industry accepted test networks were employed to validate the approach. The techniques (and software) are not unique to power grid network, but could be applied to a variety of complex, interacting infrastructures.

  12. Network coding for multi-resolution multicast

    DEFF Research Database (Denmark)

    2013-01-01

    A method, apparatus and computer program product for utilizing network coding for multi-resolution multicast is presented. A network source partitions source content into a base layer and one or more refinement layers. The network source receives a respective one or more push-back messages from one...... or more network destination receivers, the push-back messages identifying the one or more refinement layers suited for each one of the one or more network destination receivers. The network source computes a network code involving the base layer and the one or more refinement layers for at least one...... of the one or more network destination receivers, and transmits the network code to the one or more network destination receivers in accordance with the push-back messages....

  13. The use of pollination networks in conservation

    National Research Council Canada - National Science Library

    Elle, Elizabeth; Elwell, Sherri L; Gielens, Grahame A

    2012-01-01

    .... One promising approach at the community scale is network analysis, which allows actual interactions to be assessed, unlike biodiversity surveys, which only identify the potentially interacting organisms...

  14. Network analysis applications in hydrology

    Science.gov (United States)

    Price, Katie

    2017-04-01

    Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

  15. Global Microbial Identifier

    DEFF Research Database (Denmark)

    Wielinga, Peter; Hendriksen, Rene S.; Aarestrup, Frank Møller

    2017-01-01

    Human and animal populations are increasingly confronted with emerging and re-emerging infections and often such infections are exchanged between these populations, e.g. through food. A more effective and uniform approach to the prevention of these microbial threats is essential. The technological......-source systems. There is therefore an obvious need to develop a global system of whole microbial genome databases to aggregate, share, mine and use microbiological genomic data, to address global public health and clinical challenges, and most importantly to identify and diagnose infectious diseases. The global...... of microorganisms, for the identification of relevant genes and for the comparison of genomes to detect outbreaks and emerging pathogens. To harness the full potential of WGS, a shared global database of genomes linked to relevant metadata and the necessary software tools needs to be generated, hence the global...

  16. Actionable Persistent Identifier Collections

    Directory of Open Access Journals (Sweden)

    Tobias Weigel

    2014-01-01

    Full Text Available Persistent Identifiers (PIDs have lately received a lot of attention from scientific infrastructure projects and communities that aim to employ them for management of massive amounts of research data and metadata objects. Such usage scenarios, however, require additional facilities to enable automated data management with PIDs. In this article, we present a conceptual framework that is based on the idea of using common abstract data types (ADTs in combination with PIDs. This provides a well-defined interface layer that abstracts from both underlying PID systems and higher-level applications. Our practical implementation is based on the Handle System, yet the fundamental concept of PID-based ADTs is transferable to other infrastructures, and it is well suited to achieve interoperability between them.

  17. Value Creation in Cryptocurrency Networks

    DEFF Research Database (Denmark)

    Kazan, Erol; Tan, Chee-Wee; Lim, Eric T. K.

    2015-01-01

    Cryptocurrency networks have given birth to a diversity of start-ups and attracted a huge influx of venture capital to invest in these start-ups for creating and capturing value within and between such networks. Synthesizing strategic management and information systems (IS) literature, this study...... advances a unified theoretical framework for identifying and investigating how cryptocurrency companies configure value through digital business models. This framework is then employed, via multiple case studies, to examine digital business models of companies within the bitcoin network. Findings suggest...... on value configurations and digital businesses models within the emerging and increasingly pervasive domain of cryptocurrency networks....

  18. Overlay networks toward information networking

    CERN Document Server

    Tarkoma, Sasu

    2010-01-01

    With their ability to solve problems in massive information distribution and processing, while keeping scaling costs low, overlay systems represent a rapidly growing area of R&D with important implications for the evolution of Internet architecture. Inspired by the author's articles on content based routing, Overlay Networks: Toward Information Networking provides a complete introduction to overlay networks. Examining what they are and what kind of structures they require, the text covers the key structures, protocols, and algorithms used in overlay networks. It reviews the current state of th

  19. Identifying potential academic leaders

    Science.gov (United States)

    White, David; Krueger, Paul; Meaney, Christopher; Antao, Viola; Kim, Florence; Kwong, Jeffrey C.

    2016-01-01

    Objective To identify variables associated with willingness to undertake leadership roles among academic family medicine faculty. Design Web-based survey. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with willingness to undertake leadership roles. Setting Department of Family and Community Medicine at the University of Toronto in Ontario. Participants A total of 687 faculty members. Main outcome measures Variables related to respondents’ willingness to take on various academic leadership roles. Results Of all 1029 faculty members invited to participate in the survey, 687 (66.8%) members responded. Of the respondents, 596 (86.8%) indicated their level of willingness to take on various academic leadership roles. Multivariable analysis revealed that the predictors associated with willingness to take on leadership roles were as follows: pursuit of professional development opportunities (odds ratio [OR] 3.79, 95% CI 2.29 to 6.27); currently holding at least 1 leadership role (OR 5.37, 95% CI 3.38 to 8.53); a history of leadership training (OR 1.86, 95% CI 1.25 to 2.78); the perception that mentorship is important for one’s current role (OR 2.25, 95% CI 1.40 to 3.60); and younger age (OR 0.97, 95% CI 0.95 to 0.99). Conclusion Willingness to undertake new or additional leadership roles was associated with 2 variables related to leadership experiences, 2 variables related to perceptions of mentorship and professional development, and 1 demographic variable (younger age). Interventions that support opportunities in these areas might expand the pool and strengthen the academic leadership potential of faculty members. PMID:27331226

  20. Heterodox networks

    DEFF Research Database (Denmark)

    Lala, Purnima; Kumar, Ambuj

    2016-01-01

    It is imperative for the service providers to bring innovation in the network design to meet the exponential growth of mobile subscribers for multi-technology future wireless networks. As a matter of research, studies on providing services to moving subscriber groups aka ‘Place Time Capacity (PTC...

  1. Sensor networks

    NARCIS (Netherlands)

    Chatterjea, Supriyo; Thurston, J.; Kininmonth, S.; Havinga, Paul J.M.

    2006-01-01

    This article describes the details of a sensor network that is currently being deployed at the Great Barrier Reef in Australia. The sensor network allows scientists to retrieve sensor data that has a high spatial and temporal resolution. We give an overview of the energy-efficient data aggregation

  2. Network Protocols

    NARCIS (Netherlands)

    Tanenbaum, A.S.

    1981-01-01

    Dunng the last ten years, many computer networks have been designed, implemented, and put into service in the United States, Canada, Europe, Japan, and elsewhere. From the experience obtamed with these networks, certain key design principles have begun to emerge, principles that can be used to

  3. Probabilistic Networks

    DEFF Research Database (Denmark)

    Jensen, Finn Verner; Lauritzen, Steffen Lilholt

    2001-01-01

    This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs.......This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs....

  4. Affective Networks

    OpenAIRE

    Jodi Dean

    2010-01-01

    This article sets out the idea of affective networks as a constitutive feature of communicative capitalism. It explores the circulation of intensities in contemporary information and communication networks, arguing that this circulation should be theorized in terms of the psychoanalytic notion of the drive. The article includes critical engagements with theorists such as Guy Debord, Jacques Lacan, Tiziana Terranova, and Slavoj Zizek.

  5. Network chemistry, network toxicology, network informatics, and network behavioristics: A scientific outline

    OpenAIRE

    WenJun Zhang

    2016-01-01

    In present study, I proposed some new sciences: network chemistry, network toxicology, network informatics, and network behavioristics. The aims, scope and scientific foundation of these sciences are outlined.

  6. Network Affordances

    DEFF Research Database (Denmark)

    Samson, Audrey; Soon, Winnie

    2015-01-01

    This paper examines the notion of network affordance within the context of network art. Building on Gibson's theory (Gibson, 1979) we understand affordance as the perceived and actual parameters of a thing. We expand on Gaver's affordance of predictability (Gaver, 1996) to include ecological...... and computational parameters of unpredictability. We illustrate the notion of unpredictability by considering four specific works that were included in a network art exhibiton, SPEED SHOW [2.0] Hong Kong. The paper discusses how the artworks are contingent upon the parameteric relations (Parisi, 2013......), of the network. We introduce network affordance as a dynamic framework that could articulate the experienced tension arising from the (visible) symbolic representation of computational processes and its hidden occurrences. We base our proposal on the experience of both organising the SPEED SHOW and participating...

  7. Controllability of Complex Networks

    Science.gov (United States)

    Liu, Yang; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2011-03-01

    The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered systems towards a desired state, we lack a general framework to control complex self-organized systems, like the regulatory network of a cell or the Internet. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes whose time-dependent control can guide the system's dynamics. We apply these tools to real and model networks, finding that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control. In contrast, dense and homogeneous networks can be controlled via a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the hubs. We show that the robustness of control to link failure is determined by a core percolation problem, helping us understand why many complex systems are relatively insensitive to link deletion. The developed approach offers a framework to address the controllability of an arbitrary network, representing a key step towards the eventual control of complex systems.

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

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

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

  9. Interorganizational Innovation in Systemic Networks

    DEFF Research Database (Denmark)

    Seemann, Janne; Dinesen, Birthe; Gustafsson, Jeppe

    2013-01-01

    that linear n-stage models by reducing complexity and flux end up focusing only on the surface of the network and are thus unable to grasp important aspects of network dynamics. The paper suggests that there is a need for a more dynamic innovation model able to grasp the whole picture of dynamics in systemic...... patients with chronic obstructive pulmonary disease (COPD) to avoid readmission, perform self monitoring and to maintain rehabilitation in their homes. The aim of the paper is to identify, analyze and discuss innovation dynamics in the COPD network and on a preliminary basis to identify implications...... for managing innovations in systemic networks. The main argument of this paper is that innovation dynamics in systemic networks should be understood as a complex interplay of four logics: 1) Fragmented innovation, 2) Interface innovation, 3) Competing innovation, 4) Co-innovation. The findings indicate...

  10. A Study of Scientometric Methods to Identify Emerging Technologies

    Energy Technology Data Exchange (ETDEWEB)

    Abercrombie, Robert K [ORNL; Udoeyop, Akaninyene W [ORNL

    2011-01-01

    This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.

  11. Harnessing the Power of Teacher Networks

    Science.gov (United States)

    Farley-Ripple, Elizabeth N.; Buttram, Joan L.

    2013-01-01

    Teacher networks are an important lever for helping schools make change. In order to take advantage of teacher networks, principals must map the existing networks in their schools, identifying teachers and others who serve as experts or advice givers, brokers, and advice seekers. Once these are known, principals can decide on a strategy for…

  12. Networks as Drivers for Innovation – Experiences from Food Networks in Canada and New Zealand

    Directory of Open Access Journals (Sweden)

    Karen Hamann

    2013-02-01

    Full Text Available A common feature among networks is the focus on innovation but the approach to driving innovation and to supporting companies’ innovation work differs widely between networks. Some networks strive to be THE forum of an industry, andthese networks generally focus on promoting innovations that are market‐ready. Networks with a defined objective of promoting research‐driven innovation must have different network organisations from the forum‐oriented networks. Research shows that networks promoting research‐driven innovation also lead to patent applications and should have activities towards commercialisation support. This paper compares four networks from Canada and New Zealand in order to identify examples of how networks with different structures and objectives can support innovation in agri‐food companies. The paper is an empirical contribution to the research area of networks and innovation.

  13. Centrality measures in social networks

    Science.gov (United States)

    Moreau, Michele

    Complex networks represent an extensive variety of systems in nature and human interactions. Networks are graphs that describe the structures of interacting systems and give substantial information about the patterns of connections between the nodes in a particular system. In turn, knowing about the structure of networks and their arrangements enables one to make certain types of predictions about their behavior. With that larger motivation, this thesis research emphasizes different measurement metrics such as degree distribution, assortativity and clustering coefficients, transitivity, modularity, network diameter, and the average path length to associate the configurations of the different networks to determine certain types of behavior. The main focus of this thesis is on social networks, where the assortative patterns of social networks were identified. The various parameters used in the study of the networks were calculated and defined using the software packages Networkx and Gephi. The different types of networks are from the Stanford Network Analysis Project (SNAP) website. In particular, the focus is on using the numerical values of the coefficients to infer differences in the forms of contact in different social networks. The ability to do so has implications for detecting preferences when it comes to the relations between groups of people in social networks. As a result of social networks displaying assortative behaviors, the data indicates that these networks could also project some traits of 'narrow-mindedness' due to the formation of different clusters. Another significant repercussion of this research is the ability of a community to thrive successfully based on the interactions of the people with one another.

  14. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

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

  15. Introduction to Social Network Analysis

    Science.gov (United States)

    Zaphiris, Panayiotis; Ang, Chee Siang

    Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

  16. Network science and cybersecurity

    CERN Document Server

    Pino, Robinson E

    2014-01-01

    Network Science and Cybersecurity introduces new research and development efforts for cybersecurity solutions and applications taking place within various U.S. Government Departments of  Defense, industry and academic laboratories. This book examines new algorithms and tools, technology platforms and reconfigurable technologies for cybersecurity systems. Anomaly-based intrusion detection systems (IDS) are explored as a key component of any general network intrusion detection service, complementing signature-based IDS components by attempting to identify novel attacks.  These attacks  may not y

  17. Identifying Adolescent Sleep Problems

    Science.gov (United States)

    Short, Michelle A.; Gradisar, Michael; Gill, Jason; Camfferman, Danny

    2013-01-01

    Objectives To examine the efficacy of self-report and parental report of adolescent sleep problems and compare these findings to the incidence of adolescents who fulfill clinical criteria for a sleep problem. Sleep and daytime functioning factors that predict adolescents’ self-identification of a sleep problem will also be examined. Method 308 adolescents (aged 13–17 years) from eight socioeconomically diverse South Australian high schools participated in this study. Participants completed a survey battery during class time, followed by a 7-day Sleep Diary and the Flinders Fatigue Scale completed on the final day of the study. Parents completed a Sleep, Medical, Education and Family History Survey. Results The percentage of adolescents fulfilling one or more of the criteria for a sleep problem was inordinately high at 66%. Adolescent self-reporting a sleep problem was significantly lower than the adolescents who had one or more of the clinical criteria for a sleep problem (23.1% vs. 66.6%; χ2 = 17.46, padolescent having a sleep problem was significantly lower than adolescent self-report (14.3% vs. 21.1%, pAdolescents who reported unrefreshing sleep were 4.81 times more likely to report a sleep problem. For every hour that bedtime was delayed, the odds of self-reporting a sleep problem increased by 1.91 times, while each additional 10 minutes taken to fall asleep increased the odds 1.40 times. Conclusion While many adolescents were found to have sleep patterns indicative of a sleep problem, only a third of this number self-identify having a sleep problem, while only a sixth of this number are indicated by parental report. This study highlights important features to target in future sleep education and intervention strategies for both adolescents and parents. PMID:24086501

  18. Counseling Services for Students Who Are Deaf and Hard of Hearing. PEPNet Tipsheet

    Science.gov (United States)

    Searls, J. Matt, Comp.

    2001-01-01

    A recent "Time" magazine article noted that college counseling centers are experiencing an increasing number of students seeking services for a range of mental health and developmental needs. For many students, the college experience can be stressful, which may contribute to poor eating habits, irregular sleeping patterns, or experimentation with…

  19. Social networks

    CERN Document Server

    Etaner-Uyar, A Sima

    2014-01-01

    The present volume provides a comprehensive resource for practitioners and researchers alike-both those new to the field as well as those who already have some experience. The work covers Social Network Analysis theory and methods with a focus on current applications and case studies applied in various domains such as mobile networks, security, machine learning and health. With the increasing popularity of Web 2.0, social media has become a widely used communication platform. Parallel to this development, Social Network Analysis gained in importance as a research field, while opening up many

  20. Network Warrior

    CERN Document Server

    Donahue, Gary

    2011-01-01

    Pick up where certification exams leave off. With this practical, in-depth guide to the entire network infrastructure, you'll learn how to deal with real Cisco networks, rather than the hypothetical situations presented on exams like the CCNA. Network Warrior takes you step by step through the world of routers, switches, firewalls, and other technologies based on the author's extensive field experience. You'll find new content for MPLS, IPv6, VoIP, and wireless in this completely revised second edition, along with examples of Cisco Nexus 5000 and 7000 switches throughout. Topics include: An

  1. Sensitive Dependence of Optimal Network Dynamics on Network Structure

    Directory of Open Access Journals (Sweden)

    Takashi Nishikawa

    2017-11-01

    Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

  2. An improved tarpit for network deception

    OpenAIRE

    Shing, Leslie

    2016-01-01

    Approved for public release; distribution is unlimited Networks are constantly bombarded with malicious or suspicious network traffic by attackers attempting to execute their attack operations. One of the most prevalent types of traffic observed on the network is scanning traffic from reconnaissance efforts. This thesis investigates the use of network tarpits to slow automated scanning or confuse human adversaries. We identify distinguishing tarpit signatures and shortcomings of existing t...

  3. Critical Node Location in De Bruijn Networks

    Science.gov (United States)

    2016-10-01

    algorithms that are normally time- consuming perform exceptionally well on de Bruijn networks. This class of networks has yet to be considered from an... exceptionally well on de Bruijn networks. This class of networks has yet to be considered from an identifying code perspective, and a complete...Note that this provides only an upper bound on our t-prefixes - if we have repeated letters than we may have double -counted. Now we are ready to

  4. Troubleshooting Assistance Services in Community Wireless Networks

    Directory of Open Access Journals (Sweden)

    P. Kriz

    2012-01-01

    Full Text Available We have identified new services intended for users and administrators of community wireless networks. Troubleshooting assistance services will assist the users during solution of communication problems, gathering data for expert analysis, informing the user about the state of the network (including outages, and so forth. Network administrators will be provided with a unique tool supporting the network analysis, operation, and development. We have mainly focused on the use cases and prerequirements—the problem of topology discovery.