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

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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.

How to Identify Success Among Networks That Promote Active Living.

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

2015-11-01

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

Identifying influential spreaders in complex networks through local effective spreading paths

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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.

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

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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.

Identifying Typhoon Tracks based on Event Synchronization derived Spatially Embedded Climate Networks

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Ozturk, Ugur; Marwan, Norbert; Kurths, Jürgen

2017-04-01

Complex networks are commonly used for investigating spatiotemporal dynamics of complex systems, e.g. extreme rainfall. Especially directed networks are very effective tools in identifying climatic patterns on spatially embedded networks. They can capture the network flux, so as the principal dynamics of spreading significant phenomena. Network measures, such as network divergence, bare the source-receptor relation of the directed networks. However, it is still a challenge how to catch fast evolving atmospheric events, i.e. typhoons. In this study, we propose a new technique, namely Radial Ranks, to detect the general pattern of typhoons forward direction based on the strength parameter of the event synchronization over Japan. We suggest to subset a circular zone of high correlation around the selected grid based on the strength parameter. Radial sums of the strength parameter along vectors within this zone, radial ranks are measured for potential directions, which allows us to trace the network flux over long distances. We employed also the delay parameter of event synchronization to identify and separate the frontal storms' and typhoons' individual behaviors.

Identifying influential directors in the United States corporate governance network

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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.

Identifying influential directors in the United States corporate governance network.

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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.

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

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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.

Semantic integration to identify overlapping functional modules in protein interaction networks

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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.

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

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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

Towards Mining Latent Client Identifiers from Network Traffic

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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.

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

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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.

Identifying influential spreaders in complex networks based on kshell hybrid method

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Namtirtha, Amrita; Dutta, Animesh; Dutta, Biswanath

2018-06-01

Influential spreaders are the key players in maximizing or controlling the spreading in a complex network. Identifying the influential spreaders using kshell decomposition method has become very popular in the recent time. In the literature, the core nodes i.e. with the largest kshell index of a network are considered as the most influential spreaders. We have studied the kshell method and spreading dynamics of nodes using Susceptible-Infected-Recovered (SIR) epidemic model to understand the behavior of influential spreaders in terms of its topological location in the network. From the study, we have found that every node in the core area is not the most influential spreader. Even a strategically placed lower shell node can also be a most influential spreader. Moreover, the core area can also be situated at the periphery of the network. The existing indexing methods are only designed to identify the most influential spreaders from core nodes and not from lower shells. In this work, we propose a kshell hybrid method to identify highly influential spreaders not only from the core but also from lower shells. The proposed method comprises the parameters such as kshell power, node's degree, contact distance, and many levels of neighbors' influence potential. The proposed method is evaluated using nine real world network datasets. In terms of the spreading dynamics, the experimental results show the superiority of the proposed method over the other existing indexing methods such as the kshell method, the neighborhood coreness centrality, the mixed degree decomposition, etc. Furthermore, the proposed method can also be applied to large-scale networks by considering the three levels of neighbors' influence potential.

Application of artificial neural networks to identify equilibration in computer simulations

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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.

A neural network to identify neutral mesons

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Lefevre, F.; Lautridou, P.; Marques, M.; Matulewicz, T.; Ostendorf, R.; Schutz, Y.

1994-01-01

Both π 0 and η mesons decay long before they can reach a detector. They predominantly decay by emission of two photons, and are identified by constructing the invariant mass of the photons. Misidentified mesons result from ambiguity in associating photons. Our work tries to select which pair is the most likely to be a physical one rather than a chance one. We first designed a Hopfield neural net, but all the activities converged rapidly towards zero except the highest one. To improve the solution we slew down the computation in order to let the network explore several states and to impose activities to converge towards one for all selected pairs. This was achieved by adding links connecting each cell to itself. The network performance is all the more interesting that the solid angle covered by the detector is greater than 15%. (D.L.). 5 refs

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

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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

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

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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.

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

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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.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

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Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Effectively identifying user profiles in network and host metrics

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Murphy, John P.; Berk, Vincent H.; Gregorio-de Souza, Ian

2010-04-01

This work presents a collection of methods that is used to effectively identify users of computers systems based on their particular usage of the software and the network. Not only are we able to identify individual computer users by their behavioral patterns, we are also able to detect significant deviations in their typical computer usage over time, or compared to a group of their peers. For instance, most people have a small, and relatively unique selection of regularly visited websites, certain email services, daily work hours, and typical preferred applications for mandated tasks. We argue that these habitual patterns are sufficiently specific to identify fully anonymized network users. We demonstrate that with only a modest data collection capability, profiles of individual computer users can be constructed so as to uniquely identify a profiled user from among their peers. As time progresses and habits or circumstances change, the methods presented update each profile so that changes in user behavior can be reliably detected over both abrupt and gradual time frames, without losing the ability to identify the profiled user. The primary benefit of our methodology allows one to efficiently detect deviant behaviors, such as subverted user accounts, or organizational policy violations. Thanks to the relative robustness, these techniques can be used in scenarios with very diverse data collection capabilities, and data privacy requirements. In addition to behavioral change detection, the generated profiles can also be compared against pre-defined examples of known adversarial patterns.

A Visual Analytics Technique for Identifying Heat Spots in Transportation Networks

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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.

Identifying and localizing network problems using the PuNDIT project

International Nuclear Information System (INIS)

Batista, Jorge; McKee, Shawn; Dovrolis, Constantine; Lee, Danny

2015-01-01

In today's world of distributed collaborations of scientists, there are many challenges to providing effective infrastructures to couple these groups of scientists with their shared computing and storage resources. The Pythia Network Diagnostic InfrasTructure (PuNDIT[1]) project is integrating and scaling research tools and creating robust code suitable for operational needs addressing the difficult challenge of automating the detection and location of network problems.PuNDIT is building upon the de-facto standard perfSONAR[2] network measurement infrastructure deployed in Open Science Grid(OSG)[3] and the Worldwide LHC Computing Grid(WLCG)[4]to gather and analyze complex real-world network topologies coupled with their corresponding network metrics to identify possible signatures of network problems from a set of symptoms. The PuNDIT Team is working closely with the perfSONAR developers from ESnet and Internet2 to integrate PuNDIT components as part of the perfSONAR Toolkit. A primary goal for PuNDIT is to convert complex network metrics into easily understood diagnoses in an automated way. We will report on the project progress to-date in working with the OSG and WLCG communities, describe the current implementation including some initial results and discuss future plans and the project timeline. (paper)

The application of particle swarm optimization to identify gamma spectrum with neural network

International Nuclear Information System (INIS)

Shi Dongsheng; Di Yuming; Zhou Chunlin

2006-01-01

Aiming at the shortcomings that BP algorithm is usually trapped to a local optimum and it has a low speed of convergence in the application of neural network to identify gamma spectrum, according to the advantage of the globe optimal searching of particle swarm optimization, this paper put forward a new algorithm for neural network training by combining BP algorithm and Particle Swarm Optimization-mixed PSO-BP algorithm. In the application to identify gamma spectrum, the new algorithm overcomes the shortcoming that BP algorithm is usually trapped to a local optimum and the neural network trained by it has a high ability of generalization with identification result of one hundred percent correct. Practical example shows that the mixed PSO-BP algorithm can effectively and reliably be used to identify gamma spectrum. (authors)

An integrative -omics approach to identify functional sub-networks in human colorectal cancer.

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Rod K Nibbe

2010-01-01

Full Text Available Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC

ICan: an integrated co-alteration network to identify ovarian cancer-related genes.

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Zhou, Yuanshuai; Liu, Yongjing; Li, Kening; Zhang, Rui; Qiu, Fujun; Zhao, Ning; Xu, Yan

2015-01-01

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.

A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components.

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Wang, Chun-Hua; Zhong, Yi; Zhang, Yan; Liu, Jin-Ping; Wang, Yue-Fei; Jia, Wei-Na; Wang, Guo-Cai; Li, Zheng; Zhu, Yan; Gao, Xiu-Mei

2016-02-01

Chinese medicine is known to treat complex diseases with multiple components and multiple targets. However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula (LQF). The LQF is commonly used for the prevention and treatment of viral influenza in China. It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work. In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target (PT) network. An influenza-related protein-protein interaction (PPI) network was constructed and integrated with the PT network. Then the compound-effective target (CET) network and compound-ineffective target network (CIT) were extracted, respectively. A novel approach was developed to identify effective components by comparing CET and CIT networks. As a result, 15 main effective components were identified along with 61 corresponding targets. 7 of these main effective components were further experimentally validated to have antivirus efficacy in vitro. The main effective component-target (MECT) network was further constructed with main effective components and their key targets. Gene Ontology (GO) analysis of the MECT network predicted key functions such as NO production being modulated by the LQF. Interestingly, five effective components were experimentally tested and exhibited inhibitory effects on NO production in the LPS induced RAW 264.7 cell. In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions.

Identifying a set of influential spreaders in complex networks

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

2016-06-01

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

Identifying Gatekeepers in Online Learning Networks

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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…

Identifying the cutting tool type used in excavations using neural networks

Energy Technology Data Exchange (ETDEWEB)

Jonak, J.; Gajewski, J. [Lublin University of Technology, Lublin (Poland). Faculty of Mechanical Engineering

2006-03-15

The paper presents results of preliminary research on utilising neural networks to identify excavating cutting tool's type used in multi-tool excavating heads of mechanical coal miners. Such research is necessary to identify rock excavating process with a given head, and construct adaptation systems for control of excavating process with such a head.

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.

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

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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.

Least-squares methods for identifying biochemical regulatory networks from noisy measurements

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Heslop-Harrison Pat

2007-01-01

Full Text Available Abstract Background We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS. The Total Least Squares (TLS technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. Results The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and mdm2 messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL-6 and (IL-12b messenger RNA expression as a function of ATF3 and NF-κB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL-6 and (IL-12b by ATF3. Conclusion The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable

Identifying vital edges in Chinese air route network via memetic algorithm

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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.

msiDBN: A Method of Identifying Critical Proteins in Dynamic PPI Networks

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Yuan Zhang

2014-01-01

Full Text Available Dynamics of protein-protein interactions (PPIs reveals the recondite principles of biological processes inside a cell. Shown in a wealth of study, just a small group of proteins, rather than the majority, play more essential roles at crucial points of biological processes. This present work focuses on identifying these critical proteins exhibiting dramatic structural changes in dynamic PPI networks. First, a comprehensive way of modeling the dynamic PPIs is presented which simultaneously analyzes the activity of proteins and assembles the dynamic coregulation correlation between proteins at each time point. Second, a novel method is proposed, named msiDBN, which models a common representation of multiple PPI networks using a deep belief network framework and analyzes the reconstruction errors and the variabilities across the time courses in the biological process. Experiments were implemented on data of yeast cell cycles. We evaluated our network construction method by comparing the functional representations of the derived networks with two other traditional construction methods. The ranking results of critical proteins in msiDBN were compared with the results from the baseline methods. The results of comparison showed that msiDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

Emerging Fabric of Science: Persistent Identifiers and Knowledge Networks

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Hugo, W.

2017-12-01

There is an increasing emphasis on the use of persistent identifiers in the description of scientific activity, whether this is done to cite scholarly publications and research output, reliably identify role players such as funders and researchers, or to provide long-lasting references to controlled vocabulary. The ICSU World Data System has been promoting the establishment of a "Knowledge Network" to describe research activity, realising that parts of the network will be established as a federated `system', based on linkages between registries of persistent identifiers. In addition, there is a growing focus on not only the relationship between these major role players and associated digital objects, but also on the processes of science: provenance, reproducibility, and re-usability being significant topics of discussion. The paper will focus on description of the `Fabric of Science' from the perspectives of both structure and processes, review the state of implementation of real services and infrastructure in support of it. A case is made for inclusion of persistent identifiers into the mainstream activities of scientists and data infrastructure managers, and for the development of services, such as Scholix, to make better use of the relationships between digital objects and major role players. A proposal is made for the adoption of a federated system of services that are based on a hybrid graph-object framework similar to Scholix for recording the activity of scientific research. Finally, links to related ideas are explored: novel ways of representing of knowledge (such as Nanopublications) and the possibility that the publication paradigm currently in use may have to be amended.

Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity.

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J R Managbanag

Full Text Available BACKGROUND: Identification of genes that modulate longevity is a major focus of aging-related research and an area of intense public interest. In addition to facilitating an improved understanding of the basic mechanisms of aging, such genes represent potential targets for therapeutic intervention in multiple age-associated diseases, including cancer, heart disease, diabetes, and neurodegenerative disorders. To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking. METHODOLOGY/PRINCIPAL FINDINGS: We have utilized a shortest-path network analysis to identify novel genes that modulate longevity in Saccharomyces cerevisiae. Based on a set of previously reported genes associated with increased life span, we applied a shortest-path network algorithm to a pre-existing protein-protein interaction dataset in order to construct a shortest-path longevity network. To validate this network, the replicative aging potential of 88 single-gene deletion strains corresponding to predicted components of the shortest-path longevity network was determined. Here we report that the single-gene deletion strains identified by our shortest-path longevity analysis are significantly enriched for mutations conferring either increased or decreased replicative life span, relative to a randomly selected set of 564 single-gene deletion strains or to the current data set available for the entire haploid deletion collection. Further, we report the identification of previously unknown longevity genes, several of which function in a conserved longevity pathway believed to mediate life span extension in response to dietary restriction. CONCLUSIONS/SIGNIFICANCE: This work demonstrates that shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity and represents the first application of

Identifying a system of predominant negative symptoms: Network analysis of three randomized clinical trials.

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Levine, Stephen Z; Leucht, Stefan

2016-12-01

Reasons for the recent mixed success of research into negative symptoms may be informed by conceptualizing negative symptoms as a system that is identifiable from network analysis. We aimed to identify: (I) negative symptom systems; (I) central negative symptoms within each system; and (III) differences between the systems, based on network analysis of negative symptoms for baseline, endpoint and change. Patients with chronic schizophrenia and predominant negative symptoms participated in three clinical trials that compared placebo and amisulpride to 60days (n=487). Networks analyses were computed from the Scale for the Assessment of Negative Symptoms (SANS) scores for baseline and endpoint for severity, and estimated change based on mixed models. Central symptoms to each network were identified. The networks were contrasted for connectivity with permutation tests. Network analysis showed that the baseline and endpoint symptom severity systems formed symptom groups of Affect, Poor responsiveness, Lack of interest, and Apathy-inattentiveness. The baseline and endpoint networks did not significantly differ in terms of connectivity, but both significantly (Psymptom group split into three other groups. The most central symptoms were Decreased Spontaneous Movements at baseline and endpoint, and Poverty of Speech for estimated change. Results provide preliminary evidence for: (I) a replicable negative symptom severity system; and (II) symptoms with high centrality (e.g., Decreased Spontaneous Movement), that may be future treatment targets following replication to ensure the curent results generalize to other samples. Copyright © 2016 Elsevier B.V. All rights reserved.

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

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Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K H; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V; Bean, Yukie T; Beckmann, Matthias W; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S; Cramer, Daniel; Cunningham, Julie M; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F; Edwards, Robert P; Ekici, Arif B; Fasching, Peter A; Fridley, Brooke L; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G; Glasspool, Rosalind; Goode, Ellen L; Goodman, Marc T; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A T; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K; Hosono, Satoyo; Iversen, Edwin S; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K; Kelemen, Linda E; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D; Lee, Alice W; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R; McNeish, Iain A; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B; Narod, Steven A; Nedergaard, Lotte; Ness, Roberta B; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M; Permuth-Wey, Jennifer; Phelan, Catherine M; Pike, Malcolm C; Poole, Elizabeth M; Ramus, Susan J; Risch, Harvey A; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H; Rudolph, Anja; Runnebaum, Ingo B; Rzepecka, Iwona K; Salvesen, Helga B; Schildkraut, Joellen M; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C; Sucheston-Campbell, Lara E; Tangen, Ingvild L; Teo, Soo-Hwang; Terry, Kathryn L; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S; van Altena, Anne M; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S; Wicklund, Kristine G; Wilkens, Lynne R; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A; Monteiro, Alvaro N A; Freedman, Matthew L; Gayther, Simon A; Pharoah, Paul D P

2015-10-01

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 coexpression may also be enriched for additional EOC risk associations. We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. ©2015 American Association for Cancer Research.

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....

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.

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

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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.

Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

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Kese Pontes Freitas Alberton

2015-01-01

Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

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Della Gatta, Giusy; Palomero, Teresa; Perez-Garcia, Arianne; Ambesi-Impiombato, Alberto; Bansal, Mukesh; Carpenter, Zachary W; De Keersmaecker, Kim; Sole, Xavier; Xu, Luyao; Paietta, Elisabeth; Racevskis, Janis; Wiernik, Peter H; Rowe, Jacob M; Meijerink, Jules P; Califano, Andrea; Ferrando, Adolfo A

2012-02-26

The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation. Consistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

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Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

2018-02-16

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

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

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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.

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

Science.gov (United States)

Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying

2017-06-01

Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RPpathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RPpathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017

Application of particle swarm optimization to identify gamma spectrum with neural network

International Nuclear Information System (INIS)

Shi Dongsheng; Di Yuming; Zhou Chunlin

2007-01-01

In applying neural network to identification of gamma spectra back propagation (BP) algorithm is usually trapped to a local optimum and has a low speed of convergence, whereas particle swarm optimization (PSO) is advantageous in terms of globe optimal searching. In this paper, we propose a new algorithm for neural network training, i.e. combined BP and PSO optimization, or PSO-BP algorithm. Practical example shows that the new algorithm can overcome shortcomings of BP algorithm and the neural network trained by it has a high ability of generalization with identification result of 100% correctness. It can be used effectively and reliably to identify gamma spectra. (authors)

Identifying the new Influencers in the Internet Era: Social Media and Social Network Analysis

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MIGUEL DEL FRESNO GARCÍA

2016-01-01

Full Text Available Social media influencers (SMIs can be defined as a new type of independent actor who are able to shape audience attitudes through the use of social media channels in competition and coexistence with professional media. Being able to accurately identify SMIs is critical no matter what is being transmitted in a social system. Social Network Analysis (SNA has been recognized as a powerful tool for representing social network structures and information dissemination. SMIs can be identifi ed by their high-ranking position in a network as the most important or central nodes. The results reveal the existence of three different typologies of SMIs: disseminator, engager and leader. This methodology permits the optimization of resources to create effective online communication strategies.

Identifying the community structure of the food-trade international multi-network

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Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

2018-05-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

Using LTI Dynamics to Identify the Influential Nodes in a Network.

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Goran Murić

Full Text Available Networks are used for modeling numerous technical, social or biological systems. In order to better understand the system dynamics, it is a matter of great interest to identify the most important nodes within the network. For a large set of problems, whether it is the optimal use of available resources, spreading information efficiently or even protection from malicious attacks, the most important node is the most influential spreader, the one that is capable of propagating information in the shortest time to a large portion of the network. Here we propose the Node Imposed Response (NiR, a measure which accurately evaluates node spreading power. It outperforms betweenness, degree, k-shell and h-index centrality in many cases and shows the similar accuracy to dynamics-sensitive centrality. We utilize the system-theoretic approach considering the network as a Linear Time-Invariant system. By observing the system response we can quantify the importance of each node. In addition, our study provides a robust tool set for various protective strategies.

Identifying potential kidney donors using social networking web sites.

Science.gov (United States)

Chang, Alexander; Anderson, Emily E; Turner, Hang T; Shoham, David; Hou, Susan H; Grams, Morgan

2013-01-01

Social networking sites like Facebook may be a powerful tool for increasing rates of live kidney donation. They allow for wide dissemination of information and discussion and could lessen anxiety associated with a face-to-face request for donation. However, sparse data exist on the use of social media for this purpose. We searched Facebook, the most popular social networking site, for publicly available English-language pages seeking kidney donors for a specific individual, abstracting information on the potential recipient, characteristics of the page itself, and whether potential donors were tested. In the 91 pages meeting inclusion criteria, the mean age of potential recipients was 37 (range: 2-69); 88% were US residents. Other posted information included the individual's photograph (76%), blood type (64%), cause of kidney disease (43%), and location (71%). Thirty-two percent of pages reported having potential donors tested, and 10% reported receiving a live-donor kidney transplant. Those reporting donor testing shared more potential recipient characteristics, provided more information about transplantation, and had higher page traffic. Facebook is already being used to identify potential kidney donors. Future studies should focus on how to safely, ethically, and effectively use social networking sites to inform potential donors and potentially expand live kidney donation. © 2013 John Wiley & Sons A/S.

Identifying and tracking attacks on networks: C3I displays and related technologies

Science.gov (United States)

Manes, Gavin W.; Dawkins, J.; Shenoi, Sujeet; Hale, John C.

2003-09-01

Converged network security is extremely challenging for several reasons; expanded system and technology perimeters, unexpected feature interaction, and complex interfaces all conspire to provide hackers with greater opportunities for compromising large networks. Preventive security services and architectures are essential, but in and of themselves do not eliminate all threat of compromise. Attack management systems mitigate this residual risk by facilitating incident detection, analysis and response. There are a wealth of attack detection and response tools for IP networks, but a dearth of such tools for wireless and public telephone networks. Moreover, methodologies and formalisms have yet to be identified that can yield a common model for vulnerabilities and attacks in converged networks. A comprehensive attack management system must coordinate detection tools for converged networks, derive fully-integrated attack and network models, perform vulnerability and multi-stage attack analysis, support large-scale attack visualization, and orchestrate strategic responses to cyber attacks that cross network boundaries. We present an architecture that embodies these principles for attack management. The attack management system described engages a suite of detection tools for various networking domains, feeding real-time attack data to a comprehensive modeling, analysis and visualization subsystem. The resulting early warning system not only provides network administrators with a heads-up cockpit display of their entire network, it also supports guided response and predictive capabilities for multi-stage attacks in converged networks.

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.

Personal identifiers in medical research networks: evaluation of the personal identifier generator in the Competence Network Paediatric Oncology and Haematology

Directory of Open Access Journals (Sweden)

Pommerening, Klaus

2006-06-01

Full Text Available The Society for Paediatric Oncology and Haematology (GPOH and the corresponding Competence Network Paediatric Oncology and Haematology conduct various clinical trials. The comprehensive analysis requires reliable identification of the recruited patients. Therefore, a personal identifier (PID generator is used to assign unambiguous, pseudonymous, non-reversible PIDs to participants in those trials. We tested the matching algorithm of the PID generator using a configuration specific to the GPOH. False data was used to verify the correct processing of PID requests (functionality tests, while test data was used to evaluate the matching outcome. We also assigned PIDs to more than 44,000 data records from the German Childhood Cancer Registry (GCCR and assessed the status of the associated patient list which contains the PIDs, partly encrypted data items and information on the PID generation process for each data record. All the functionality tests showed the expected results. Neither 14,915 test data records nor the GCCR data records yielded any homonyms. Six synonyms were found in the test data, due to erroneous birth dates, and 22 synonyms were found when the GCCR data was run against the actual patient list of 2579 records. In the resulting patient list of 45,693 entries, duplicate record submissions were found for about 7% of all listed patients, while more frequent submissions occurred in less than 1% of cases. The synonym error rate depends mainly on the quality of the input data and on the frequency of multiple submissions. Depending on the requirements on maximally tolerable synonym and homonym error rates, additional measures for securing input data quality might be necessary. The results demonstrate that the PID generator is an appropriate tool for reliably identifying trial participants in medical research networks.

Using neural networks with jet shapes to identify b jets in e+e- interactions

International Nuclear Information System (INIS)

Bellantoni, L.; Conway, J.S.; Jacobsen, J.E.; Pan, Y.B.; Wu Saulan

1991-01-01

A feed-forward neural network trained using backpropagation was used to discriminate between b and light quark jets in e + e - → Z 0 → qanti q events. The information presented to the network consisted of 25 jet shape variables. The network successfully identified b jets in two- and three-jet events modeled using a detector simulation. The jet identification efficiency for two-jet events was 61% and the probability to call a light quark jet a b jet equal to 20%. (orig.)

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.

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...

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.

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.

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

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Bean, Daniel M; Stringer, Clive; Beeknoo, Neeraj; Teo, James; Dobson, Richard J B

2017-01-01

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.

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

Science.gov (United States)

Wang, Hongping; Zhang, Yajuan; Zhang, Zili; Mahadevan, Sankaran; Deng, Yong

2015-01-01

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.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

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Wasserman Wyeth W

2011-03-01

Full Text Available Abstract Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs, microRNAs (miRNAs and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs. Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL. In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT, an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http

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

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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.

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

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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.

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.

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

Science.gov (United States)

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

Science.gov (United States)

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

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.

Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Science.gov (United States)

Yang, Fan; Zhang, Ruisheng; Yang, Zhao; Hu, Rongjing; Li, Mengtian; Yuan, Yongna; Li, Keqin

Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

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…

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

Science.gov (United States)

Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-06

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

A novel method to identify hub pathways of rheumatoid arthritis based on differential pathway networks.

Science.gov (United States)

Wei, Shi-Tong; Sun, Yong-Hua; Zong, Shi-Hua

2017-09-01

The aim of the current study was to identify hub pathways of rheumatoid arthritis (RA) using a novel method based on differential pathway network (DPN) analysis. The present study proposed a DPN where protein‑protein interaction (PPI) network was integrated with pathway‑pathway interactions. Pathway data was obtained from background PPI network and the Reactome pathway database. Subsequently, pathway interactions were extracted from the pathway data by building randomized gene‑gene interactions and a weight value was assigned to each pathway interaction using Spearman correlation coefficient (SCC) to identify differential pathway interactions. Differential pathway interactions were visualized using Cytoscape to construct a DPN. Topological analysis was conducted to identify hub pathways that possessed the top 5% degree distribution of DPN. Modules of DPN were mined according to ClusterONE. A total of 855 pathways were selected to build pathway interactions. By filtrating pathway interactions of weight values >0.7, a DPN with 312 nodes and 791 edges was obtained. Topological degree analysis revealed 15 hub pathways, such as heparan sulfate/heparin‑glycosaminoglycan (HS‑GAG) degradation, HS‑GAG metabolism and keratan sulfate degradation for RA based on DPN. Furthermore, hub pathways were also important in modules, which validated the significance of hub pathways. In conclusion, the proposed method is a computationally efficient way to identify hub pathways of RA, which identified 15 hub pathways that may be potential biomarkers and provide insight to future investigation and treatment of RA.

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

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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.

Co-extinction in a host-parasite network: identifying key hosts for network stability.

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Dallas, Tad; Cornelius, Emily

2015-08-17

Parasites comprise a substantial portion of total biodiversity. Ultimately, this means that host extinction could result in many secondary extinctions of obligate parasites and potentially alter host-parasite network structure. Here, we examined a highly resolved fish-parasite network to determine key hosts responsible for maintaining parasite diversity and network structure (quantified here as nestedness and modularity). We evaluated four possible host extinction orders and compared the resulting co-extinction dynamics to random extinction simulations; including host removal based on estimated extinction risk, parasite species richness and host level contributions to nestedness and modularity. We found that all extinction orders, except the one based on realistic extinction risk, resulted in faster declines in parasite diversity and network structure relative to random biodiversity loss. Further, we determined species-level contributions to network structure were best predicted by parasite species richness and host family. Taken together, we demonstrate that a small proportion of hosts contribute substantially to network structure and that removal of these hosts results in rapid declines in parasite diversity and network structure. As network stability can potentially be inferred through measures of network structure, our findings may provide insight into species traits that confer stability.

WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

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Bayarbaatar Amgalan

Full Text Available Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function over the standard simplex (sparsity constraints. We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.

Applying network theory to animal movements to identify properties of landscape space use.

Science.gov (United States)

Bastille-Rousseau, Guillaume; Douglas-Hamilton, Iain; Blake, Stephen; Northrup, Joseph M; Wittemyer, George

2018-04-01

Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node-level (local) metrics and graph-level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node-level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter-patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph-level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large-scale data sets efficiently, providing a

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

. 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...

Identifying and tracking dynamic processes in social networks

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Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

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

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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.

Identifying the relevant dependencies of the neural network response on characteristics of the input space

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CERN. Geneva

2018-01-01

This talk presents an approach to identify those characteristics of the neural network inputs that are most relevant for the response and therefore provides essential information to determine the systematic uncertainties.

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

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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.

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

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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.

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

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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.

Identifying influential user communities on the social network

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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.

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

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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.

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

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Wufeng Fan

2017-01-01

Full Text Available In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM based on pathway interaction network (PIN which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs, and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

Transition through Teamwork: Professionals Address Student Access

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Bube, Sue Ann; Carrothers, Carol; Johnson, Cinda

2016-01-01

Prior to 2013, there was no collaboration around the transition services for deaf and hard of hearing students in Washington State. Washington had numerous agencies providing excellent support, but those agencies were not working together. It was not until January 29, 2013, when pepnet 2 hosted the Building State Capacity to Address Critical…

Identify and analyze the opportunities and threats of social networks for shahid Beheshti University students

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R. Tavalaee

2017-09-01

Full Text Available Due to the growth of information and communication technology in societies Especially among students, the use of these technologies has become as part of regular working people. Social networks as one of the most important and widely in cyberspace which is Used by many people in various fields. application of social network by students as young and educated population is important.In this regard, this study aimed to investigate and identify the opportunities and threats for shahid Beheshti University students in social network. This study aims to develop a practical and descriptive methodology. Information obtained from the questionnaires using SPSS statistical analysis software in two parts: descriptive and inferential statistics were analyzed.The results indicate that five variables related to social networking opportunities, including e-learning, leisure, organized social groups, the possibility of dialogue and culture, as well as five variables related to social networking threats, including transfer value unethical, abusive, spreading false information, internet & Communications destructive addiction, has a significant positive effect on students.

Correlation Networks for Identifying Changes in Brain Connectivity during Epileptiform Discharges and Transcranial Magnetic Stimulation

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Elsa Siggiridou

2014-07-01

Full Text Available The occurrence of epileptiform discharges (ED in electroencephalographic (EEG recordings of patients with epilepsy signifies a change in brain dynamics and particularly brain connectivity. Transcranial magnetic stimulation (TMS has been recently acknowledged as a non-invasive brain stimulation technique that can be used in focal epilepsy for therapeutic purposes. In this case study, it is investigated whether simple time-domain connectivity measures, namely cross-correlation and partial cross-correlation, can detect alterations in the connectivity structure estimated from selected EEG channels before and during ED, as well as how this changes with the application of TMS. The correlation for each channel pair is computed on non-overlapping windows of 1 s duration forming weighted networks. Further, binary networks are derived by thresholding or statistical significance tests (parametric and randomization tests. The information for the binary networks is summarized by statistical network measures, such as the average degree and the average path length. Alterations of brain connectivity before, during and after ED with or without TMS are identified by statistical analysis of the network measures at each state.

A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

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Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D; Hao, Ke; Summa, Keith C; Yang, He S; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

2015-05-05

Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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

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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.

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses.

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Luo, Jie; Xu, Pei; Cao, Peijian; Wan, Hongjian; Lv, Xiaonan; Xu, Shengchun; Wang, Gangjun; Cook, Melloni N; Jones, Byron C; Lu, Lu; Wang, Xusheng

2018-01-01

Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

Directory of Open Access Journals (Sweden)

Jie Luo

2018-04-01

Full Text Available Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1, down-regulation in NOE but rescue in RSE (pattern 2, up-regulation in both restraint stress followed by a saline injection (RSS and NOE, and further amplification in RSE (pattern 3, and up-regulation in RSS but reduction in both NOE and RSE (pattern 4. We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

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

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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.

“Thanks for sharing”—Identifying users’ roles based on knowledge contribution in Enterprise Social Networks

DEFF Research Database (Denmark)

Cetto, Alexandra; Klier, Mathias; Richter, Alexander

2018-01-01

in the network and help others to get their work done. In this paper, we propose a new methodological approach consisting of three steps, namely “message classification”, “identification of users’ roles” as well as “characterization of users’ roles”. We apply the approach to a dataset from a multinational......, are a central element of the network. In conclusion, the development and application of a new methodological approach allows us to contribute to a more refined understanding of users’ knowledge exchanging behavior in Enterprise Social Networks which can ultimately help companies to take measures to improve......While ever more companies use Enterprise Social Networks for knowledge management, there is still a lack of understanding of users’ knowledge exchanging behavior. In this context, it is important to be able to identify and characterize users who contribute and communicate their knowledge...

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

Science.gov (United States)

Colaprico, Antonio; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-01

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC. PMID:29562723

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

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

2018-06-14

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

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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.

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

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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.

Identifying all moiety conservation laws in genome-scale metabolic networks.

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De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

2014-01-01

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Identifying all moiety conservation laws in genome-scale metabolic networks.

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Andrea De Martino

Full Text Available The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Dissecting molecular stress networks: identifying nodes of divergence between life-history phenotypes.

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Schwartz, Tonia S; Bronikowski, Anne M

2013-02-01

The complex molecular network that underlies physiological stress response is comprised of nodes (proteins, metabolites, mRNAs, etc.) whose connections span cells, tissues and organs. Variable nodes are points in the network upon which natural selection may act. Thus, identifying variable nodes will reveal how this molecular stress network may evolve among populations in different habitats and how it might impact life-history evolution. Here, we use physiological and genetic assays to test whether laboratory-born juveniles from natural populations of garter snakes (Thamnophis elegans), which have diverged in their life-history phenotypes, vary concomitantly at candidate nodes of the stress response network, (i) under unstressed conditions and (ii) in response to an induced stress. We found that two common measures of stress (plasma corticosterone and liver gene expression of heat shock proteins) increased under stress in both life-history phenotypes. In contrast, the phenotypes diverged at four nodes both under unstressed conditions and in response to stress: circulating levels of reactive oxygen species (superoxide, H(2)O(2)); liver gene expression of GPX1 and erythrocyte DNA damage. Additionally, allele frequencies for SOD2 diverge from neutral markers, suggesting diversifying selection on SOD2 alleles. This study supports the hypothesis that these life-history phenotypes have diverged at the molecular level in how they respond to stress, particularly in nodes regulating oxidative stress. Furthermore, the differences between the life-history phenotypes were more pronounced in females. We discuss the responses to stress in the context of the associated life-history phenotype and the evolutionary pressures thought to be responsible for divergence between the phenotypes. © 2012 Blackwell Publishing Ltd.

Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles

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Guo Xiuyun

2011-09-01

Full Text Available Abstract Background Neural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer's disease (AD. While much progress has been made in understanding neural stem cell function, a precise description of the molecular mechanisms regulating neural stem cells is not yet established. This lack of knowledge is a major barrier holding back the discovery of therapeutic uses of neural stem cells. In this paper, the regulatory mechanism of mouse neural stem cell (NSC differentiation by tmem59 is explored on the genome-level. Results We identified regulators of tmem59 during the differentiation of mouse NSCs from a compendium of expression profiles. Based on the microarray experiment, we developed the parallelized SWNI algorithm to reconstruct gene regulatory networks of mouse neural stem cells. From the inferred tmem59 related gene network including 36 genes, pou6f1 was identified to regulate tmem59 significantly and might play an important role in the differentiation of NSCs in mouse brain. There are four pathways shown in the gene network, indicating that tmem59 locates in the downstream of the signalling pathway. The real-time RT-PCR results shown that the over-expression of pou6f1 could significantly up-regulate tmem59 expression in C17.2 NSC line. 16 out of 36 predicted genes in our constructed network have been reported to be AD-related, including Ace, aqp1, arrdc3, cd14, cd59a, cds1, cldn1, cox8b, defb11, folr1, gdi2, mmp3, mgp, myrip, Ripk4, rnd3, and sncg. The localization of tmem59 related genes and functional-related gene groups based on the Gene Ontology (GO annotation was also identified. Conclusions Our findings suggest that the expression of tmem59 is an important factor contributing to AD. The parallelized SWNI algorithm increased the efficiency of network reconstruction significantly. This study enables us to highlight novel genes that may be involved in NSC differentiation and provides a shortcut to

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.

Identifying Controlling Nodes in Neuronal Networks in Different Scales

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Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen

2012-01-01

Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats’ brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats’ brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks. PMID:22848475

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

Science.gov (United States)

Li, Mengtian; Zhang, Ruisheng; Hu, Rongjing; Yang, Fan; Yao, Yabing; Yuan, Yongna

2018-03-01

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible-infectious-recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

Social network characteristics and HIV vulnerability among transgender persons in San Salvador: identifying opportunities for HIV prevention strategies.

Science.gov (United States)

Barrington, Clare; Wejnert, Cyprian; Guardado, Maria Elena; Nieto, Ana Isabel; Bailey, Gabriela Paz

2012-01-01

The purpose of this study is to improve understanding of HIV vulnerability and opportunities for HIV prevention within the social networks of male-to-female transgender persons in San Salvador, El Salvador. We compare HIV prevalence and behavioral data from a sample of gay-identified men who have sex with men (MSM) (n = 279), heterosexual or bisexual identified MSM (n = 229) and transgender persons (n = 67) recruited using Respondent Driven Sampling. Transgender persons consistently reported higher rates of HIV risk behavior than the rest of the study population and were significantly more likely to be involved in sex work. While transgender persons reported the highest rates of exposure to HIV educational activities they had the lowest levels of HIV-related knowledge. Transgender respondents' social networks were homophilous and efficient at recruiting other transgender persons. Findings suggest that transgender social networks could provide an effective and culturally relevant opportunity for HIV prevention efforts in this vulnerable population.

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).

Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.

Directory of Open Access Journals (Sweden)

Bordeaux John M

2011-05-01

Full Text Available Abstract Background Global transcriptional analysis of loblolly pine (Pinus taeda L. is challenging due to limited molecular tools. PtGen2, a 26,496 feature cDNA microarray, was fabricated and used to assess drought-induced gene expression in loblolly pine propagule roots. Statistical analysis of differential expression and weighted gene correlation network analysis were used to identify drought-responsive genes and further characterize the molecular basis of drought tolerance in loblolly pine. Results Microarrays were used to interrogate root cDNA populations obtained from 12 genotype × treatment combinations (four genotypes, three watering regimes. Comparison of drought-stressed roots with roots from the control treatment identified 2445 genes displaying at least a 1.5-fold expression difference (false discovery rate = 0.01. Genes commonly associated with drought response in pine and other plant species, as well as a number of abiotic and biotic stress-related genes, were up-regulated in drought-stressed roots. Only 76 genes were identified as differentially expressed in drought-recovered roots, indicating that the transcript population can return to the pre-drought state within 48 hours. Gene correlation analysis predicts a scale-free network topology and identifies eleven co-expression modules that ranged in size from 34 to 938 members. Network topological parameters identified a number of central nodes (hubs including those with significant homology (E-values ≤ 2 × 10-30 to 9-cis-epoxycarotenoid dioxygenase, zeatin O-glucosyltransferase, and ABA-responsive protein. Identified hubs also include genes that have been associated previously with osmotic stress, phytohormones, enzymes that detoxify reactive oxygen species, and several genes of unknown function. Conclusion PtGen2 was used to evaluate transcriptome responses in loblolly pine and was leveraged to identify 2445 differentially expressed genes responding to severe drought stress in

Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.)

Science.gov (United States)

2011-01-01

Background Global transcriptional analysis of loblolly pine (Pinus taeda L.) is challenging due to limited molecular tools. PtGen2, a 26,496 feature cDNA microarray, was fabricated and used to assess drought-induced gene expression in loblolly pine propagule roots. Statistical analysis of differential expression and weighted gene correlation network analysis were used to identify drought-responsive genes and further characterize the molecular basis of drought tolerance in loblolly pine. Results Microarrays were used to interrogate root cDNA populations obtained from 12 genotype × treatment combinations (four genotypes, three watering regimes). Comparison of drought-stressed roots with roots from the control treatment identified 2445 genes displaying at least a 1.5-fold expression difference (false discovery rate = 0.01). Genes commonly associated with drought response in pine and other plant species, as well as a number of abiotic and biotic stress-related genes, were up-regulated in drought-stressed roots. Only 76 genes were identified as differentially expressed in drought-recovered roots, indicating that the transcript population can return to the pre-drought state within 48 hours. Gene correlation analysis predicts a scale-free network topology and identifies eleven co-expression modules that ranged in size from 34 to 938 members. Network topological parameters identified a number of central nodes (hubs) including those with significant homology (E-values ≤ 2 × 10-30) to 9-cis-epoxycarotenoid dioxygenase, zeatin O-glucosyltransferase, and ABA-responsive protein. Identified hubs also include genes that have been associated previously with osmotic stress, phytohormones, enzymes that detoxify reactive oxygen species, and several genes of unknown function. Conclusion PtGen2 was used to evaluate transcriptome responses in loblolly pine and was leveraged to identify 2445 differentially expressed genes responding to severe drought stress in roots. Many of the

Sex Venue-Based Network Analysis to Identify HIV Prevention Dissemination Targets for Men Who Have Sex with Men.

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Patel, Rupa R; Luke, Douglas A; Proctor, Enola K; Powderly, William G; Chan, Philip A; Mayer, Kenneth H; Harrison, Laura C; Dhand, Amar

2018-01-01

The aim of this study was to identify sex venue-based networks among men who have sex with men (MSM) to inform HIV preexposure prophylaxis (PrEP) dissemination efforts. Using a cross-sectional design, we interviewed MSM about the venues where their recent sexual partners were found. Venues were organized into network matrices grouped by condom use and race. We examined network structure, central venues, and network subgroups. Among 49 participants, the median age was 27 years, 49% were Black and 86% reported condomless anal sex (ncAS). Analysis revealed a map of 54 virtual and physical venues with an overlap in the ncAS and with condom anal sex (cAS) venues. In the ncAS network, virtual and physical locations were more interconnected. The ncAS venues reported by Blacks were more diffusely organized than those reported by Whites. The network structures of sex venues for at-risk MSM differed by race. Network information can enhance HIV prevention dissemination efforts among subpopulations, including PrEP implementation.

Working with Students Who Are Late-Deafened. PEPNet Tipsheet

Science.gov (United States)

Clark, Mary

2010-01-01

Late-deafness means deafness that happened postlingually, any time after the development of speech and language in a person who has identified with hearing society through schooling, social connections, etc. Students who are late-deafened cannot understand speech without visual aids such as speechreading, sign language, and captioning (although…

Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes.

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Chen, Juan; Xu, Juan; Li, Yongsheng; Zhang, Jinwen; Chen, Hong; Lu, Jianping; Wang, Zishan; Zhao, Xueying; Xu, Kang; Li, Yixue; Li, Xia; Zhang, Yan

2017-02-07

Although competing endogenous RNAs (ceRNAs) have been implicated in many solid tumors, their roles in breast cancer subtypes are not well understood. We therefore generated a ceRNA network for each subtype based on the significance of both, positive co-expression and the shared miRNAs, in the corresponding subtype miRNA dys-regulatory network, which was constructed based on negative regulations between differentially expressed miRNAs and targets. All four subtype ceRNA networks exhibited scale-free architecture and showed that the common ceRNAs were at the core of the networks. Furthermore, the common ceRNA hubs had greater connectivity than the subtype-specific hubs. Functional analysis of the common subtype ceRNA hubs highlighted factors involved in proliferation, MAPK signaling pathways and tube morphogenesis. Subtype-specific ceRNA hubs highlighted unique subtype-specific pathways, like the estrogen response and inflammatory pathways in the luminal subtypes or the factors involved in the coagulation process that participates in the basal-like subtype. Ultimately, we identified 29 critical subtype-specific ceRNA hubs that characterized the different breast cancer subtypes. Our study thus provides new insight into the common and specific subtype ceRNA interactions that define the different categories of breast cancer and enhances our understanding of the pathology underlying the different breast cancer subtypes, which can have prognostic and therapeutic implications in the future.

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.

Identifying essential genes in bacterial metabolic networks with machine learning methods

Science.gov (United States)

2010-01-01

Background Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism. PMID:20438628

Integrative Analysis of Hippocampus Gene Expression Profiles Identifies Network Alterations in Aging and Alzheimer’s Disease

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Vinay Lanke

2018-05-01

Full Text Available Alzheimer’s disease (AD is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20–50 years, aging (70–99 years, and AD (70–99 years. The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein–protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein–protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1 in AD. Further, we found genes that encode proteins

Social Networking Privacy Control: Exploring University Variables Related to Young Adults' Sharing of Personally Identifiable Information

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Zimmerman, Melisa S.

2014-01-01

The growth of the Internet, and specifically social networking sites (SNSs) like Facebook, create opportunities for individuals to share private and identifiable information with a closed or open community. Internet crime has been on the rise and research has shown that criminals are using individuals' personal information pulled from social…

Molecular Epidemiology Identifies HIV Transmission Networks Associated With Younger Age and Heterosexual Exposure Among Korean Individuals

OpenAIRE

Chin, Bum Sik; Chaillon, Antoine; Mehta, Sanjay R.; Wertheim, Joel O.; Kim, Gayeon; Shin, Hyoung-Shik; Smith, Davey M.

2016-01-01

To evaluate if HIV transmission networks could be elucidated from data collected in a short time frame, 131 HIV-1 pol sequences were analyzed which were generated from treatment-naïve Korean individuals who were sequentially identified over 1 year. A transmission linkage was inferred when there was a genetic distance

Deaf Culture. PEPNet Tipsheet

Science.gov (United States)

Siple, Linda; Greer, Leslie; Holcomb, Barbra Ray

2004-01-01

It often comes as a surprise to people that many deaf people refer to themselves as being members of Deaf culture. The American Deaf culture is a unique linguistic minority that uses American Sign Language (ASL) as its primary mode of communication. This tipsheet provides a description of Deaf culture and suggestions for effective communication.

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…

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

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István A Kovács

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

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

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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

Integrative microRNA and proteomic approaches identify novel osteoarthritis genes and their collaborative metabolic and inflammatory networks.

Directory of Open Access Journals (Sweden)

Dimitrios Iliopoulos

Full Text Available BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103 and proteins (PPARA, BMP7, IL1B to be highly correlated with Body Mass Index (BMI. Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic

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.

Application of Network Analysis to Identify and Map Relationships between Information Systems in the context of Arctic Sustainability

Science.gov (United States)

Kontar, Y. Y.

2017-12-01

The Arctic Council is an intergovernmental forum promoting cooperation, coordination and interaction among the Arctic States and indigenous communities on issues of sustainable development and environmental protection in the North. The work of the Council is primarily carried out by six Working Groups: Arctic Contaminants Action Program, Arctic Monitoring and Assessment Programme, Conservation of Arctic Flora and Fauna, Emergency Prevention, Preparedness and Response, Protection of the Arctic Marine Environment, and Sustainable Development Working Group. The Working Groups are composed of researchers and representatives from government agencies. Each Working Group issues numerous scientific assessments and reports on a broad field of subjects, from climate change to emergency response in the Arctic. A key goal of these publications is to contribute to policy-making in the Arctic. Complex networks of information systems and the connections between the diverse elements within the systems have been identified via network analysis. This allowed to distinguish data sources that were used in the composition of the primary publications of the Working Groups. Next step is to implement network analysis to identify and map the relationships between the Working Groups and policy makers in the Arctic.

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…

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

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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.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

Science.gov (United States)

Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

2016-05-01

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

[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

Inverse modelling of fluvial sediment connectivity identifies characteristics and spatial distribution of sediment sources in a large river network.

Science.gov (United States)

Schmitt, R. J. P.; Bizzi, S.; Kondolf, G. M.; Rubin, Z.; Castelletti, A.

2016-12-01

Field and laboratory evidence indicates that the spatial distribution of transport in both alluvial and bedrock rivers is an adaptation to sediment supply. Sediment supply, in turn, depends on spatial distribution and properties (e.g., grain sizes and supply rates) of individual sediment sources. Analyzing the distribution of transport capacity in a river network could hence clarify the spatial distribution and properties of sediment sources. Yet, challenges include a) identifying magnitude and spatial distribution of transport capacity for each of multiple grain sizes being simultaneously transported, and b) estimating source grain sizes and supply rates, both at network scales. Herein, we approach the problem of identifying the spatial distribution of sediment sources and the resulting network sediment fluxes in a major, poorly monitored tributary (80,000 km2) of the Mekong. Therefore, we apply the CASCADE modeling framework (Schmitt et al. (2016)). CASCADE calculates transport capacities and sediment fluxes for multiple grainsizes on the network scale based on remotely-sensed morphology and modelled hydrology. CASCADE is run in an inverse Monte Carlo approach for 7500 random initializations of source grain sizes. In all runs, supply of each source is inferred from the minimum downstream transport capacity for the source grain size. Results for each realization are compared to sparse available sedimentary records. Only 1 % of initializations reproduced the sedimentary record. Results for these realizations revealed a spatial pattern in source supply rates, grain sizes, and network sediment fluxes that correlated well with map-derived patterns in lithology and river-morphology. Hence, we propose that observable river hydro-morphology contains information on upstream source properties that can be back-calculated using an inverse modeling approach. Such an approach could be coupled to more detailed models of hillslope processes in future to derive integrated models

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

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

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.

Identifying tau hadronic decay with neural network

International Nuclear Information System (INIS)

Chen Guoming; Chen Gang

1995-01-01

The identification of tau one prong hadronic decay using neural network is presented. Based on the identification, we measured the branching ratios: Br(π/Kν) = (12.18 +- 0.26 +- 0.42)%, Br(π/Kπ 0 ν) = (25.20 +-0.35 +- 0.50)%, Br(π/K2π 0 ν) = (8.88 +- 0.37 +- 0.38)%, Br(π/K3π 0 ν) = (1.70 +- 0.24 +- 0.39)%

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.

Identifying the starting point of a spreading process in complex networks.

Science.gov (United States)

Comin, Cesar Henrique; Costa, Luciano da Fontoura

2011-11-01

When dealing with the dissemination of epidemics, one important question that can be asked is the location where the contamination began. In this paper, we analyze three spreading schemes and propose and validate an effective methodology for the identification of the source nodes. The method is based on the calculation of the centrality of the nodes on the sampled network, expressed here by degree, betweenness, closeness, and eigenvector centrality. We show that the source node tends to have the highest measurement values. The potential of the methodology is illustrated with respect to three theoretical complex network models as well as a real-world network, the email network of the University Rovira i Virgili.

Two novel antimicrobial defensins from rice identified by gene coexpression network analyses.

Science.gov (United States)

Tantong, Supaluk; Pringsulaka, Onanong; Weerawanich, Kamonwan; Meeprasert, Arthitaya; Rungrotmongkol, Thanyada; Sarnthima, Rakrudee; Roytrakul, Sittiruk; Sirikantaramas, Supaart

2016-10-01

Defensins form an antimicrobial peptides (AMP) family, and have been widely studied in various plants because of their considerable inhibitory functions. However, their roles in rice (Oryza sativa L.) have not been characterized, even though rice is one of the most important staple crops that is susceptible to damaging infections. Additionally, a previous study identified 598 rice genes encoding cysteine-rich peptides, suggesting there are several uncharacterized AMPs in rice. We performed in silico gene expression and coexpression network analyses of all genes encoding defensin and defensin-like peptides, and determined that OsDEF7 and OsDEF8 are coexpressed with pathogen-responsive genes. Recombinant OsDEF7 and OsDEF8 could form homodimers. They inhibited the growth of the bacteria Xanthomonas oryzae pv. oryzae, X. oryzae pv. oryzicola, and Erwinia carotovora subsp. atroseptica with minimum inhibitory concentration (MIC) ranging from 0.6 to 63μg/mL. However, these OsDEFs are weakly active against the phytopathogenic fungi Helminthosporium oryzae and Fusarium oxysporum f.sp. cubense. This study describes a useful method for identifying potential plant AMPs with biological activities. Copyright © 2016 Elsevier Inc. All rights reserved.

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

Science.gov (United States)

Azcorra, A; Chiroque, L F; Cuevas, R; Fernández Anta, A; Laniado, H; Lillo, R E; Romo, J; Sguera, C

2018-05-03

Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. In this report we propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Moreover, it labels the outliers as of shape, magnitude, or amplitude, depending of their features. This allows classifying the outlier users in multiple different classes, which are likely to include different types of influential users. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.

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

The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes

Directory of Open Access Journals (Sweden)

Xinguo Lu

2018-01-01

Full Text Available With advances in next-generation sequencing(NGS technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profiles and copy number variation (CNV data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.

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.

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.

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.

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.

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.

Genome-wide RNAi Screen Identifies Networks Involved in Intestinal Stem Cell Regulation in Drosophila

Directory of Open Access Journals (Sweden)

Xiankun Zeng

2015-02-01

Full Text Available The intestinal epithelium is the most rapidly self-renewing tissue in adult animals and maintained by intestinal stem cells (ISCs in both Drosophila and mammals. To comprehensively identify genes and pathways that regulate ISC fates, we performed a genome-wide transgenic RNAi screen in adult Drosophila intestine and identified 405 genes that regulate ISC maintenance and lineage-specific differentiation. By integrating these genes into publicly available interaction databases, we further developed functional networks that regulate ISC self-renewal, ISC proliferation, ISC maintenance of diploid status, ISC survival, ISC-to-enterocyte (EC lineage differentiation, and ISC-to-enteroendocrine (EE lineage differentiation. By comparing regulators among ISCs, female germline stem cells, and neural stem cells, we found that factors related to basic stem cell cellular processes are commonly required in all stem cells, and stem-cell-specific, niche-related signals are required only in the unique stem cell type. Our findings provide valuable insights into stem cell maintenance and lineage-specific differentiation.

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

DEFF Research Database (Denmark)

Mohammadi, Soma; Bojesen, Carsten

2014-01-01

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...... dynamically while the flow and pressure are calculated on the basis of steady state conditions. The implicit finite element method is applied to simulate the transient temperature behaviour in the network. Pipe network heat losses, pressure drop in the network and return temperature to the plant...... are calculated in the developed model. The model will serve eventually as a basis to find out the optimal supply temperature in an existing DHN in later work. The modelling results are used as decision support for existing DHN; proposing possible modifications to operate at optimal supply temperature....

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

International Nuclear Information System (INIS)

Reynoso V, M.R.; Vega C, J.J.

2003-01-01

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)

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)

A probabilistic approach to identify putative drug targets in biochemical networks.

NARCIS (Netherlands)

Murabito, E.; Smalbone, K.; Swinton, J.; Westerhoff, H.V.; Steuer, R.

2011-01-01

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual

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.

Scientometric methods for identifying emerging technologies

Science.gov (United States)

Abercrombie, Robert K; Schlicher, Bob G; Sheldon, Frederick T

2015-11-03

Provided is a method of generating 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.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

Science.gov (United States)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Identifying diabetes knowledge network nodes as sites for a diabetes prevention program.

Science.gov (United States)

Gesler, Wilbert M; Arcury, Thomas A; Skelly, Anne H; Nash, Sally; Soward, April; Dougherty, Molly

2006-12-01

This paper reports on the methods used and results of a study that identified specific places within a community that have the potential to be sites for a diabetes prevention program. These sites, termed diabetes knowledge network nodes (DKNNs), are based on the concept of socio-spatial knowledge networks (SSKNs), the web of social relationships within which people obtain knowledge about type 2 diabetes. The target population for the study was working poor African Americans, Latinos, and European Americans of both sexes in a small rural southern town who had not been diagnosed with diabetes. Information was collected from a sample of 121 respondents on the places they visited in carrying out their daily activities. Data on number of visits to specific sites, degree of familiarity with these sites, and ratings of sites as places to receive diabetes information were used to develop three categories of DKNNs for six subgroups based on ethnicity and sex. Primary potential sites of importance to one or more subgroups included churches, grocery stores, drugstores, the local library, a beauty salon, laundromats, a community service agency, and a branch of the County Health Department. Secondary potential sites included gas stations, restaurants, banks, and post offices. Latent potential sites included three medical facilities. Most of the DKNNs were located either in the downtown area or in one of two shopping areas along the most used highway that passed through the town. The procedures used in this study can be generalized to other communities and prevention programs for other chronic diseases.

Adverse Outcome Pathway Networks II: Network Analytics.

Science.gov (United States)

Villeneuve, Daniel L; Angrish, Michelle M; Fortin, Marie C; Katsiadaki, Ioanna; Leonard, Marc; Margiotta-Casaluci, Luigi; Munn, Sharon; O'Brien, Jason M; Pollesch, Nathan L; Smith, L Cody; Zhang, Xiaowei; Knapen, Dries

2018-02-28

Toxicological responses to stressors are more complex than the simple one biological perturbation to one adverse outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present paper introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using two example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses, or previously undefined emergent patterns of response, are introduced. Along with a companion article (Knapen et al. part I), these concepts set the stage for development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. Collectively, this work addresses one of the major themes identified through a SETAC Horizon Scanning effort focused on advancing the AOP framework. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A new centrality measure for identifying influential nodes in social networks

Science.gov (United States)

Rhouma, Delel; Ben Romdhane, Lotfi

2018-04-01

The identification of central nodes has been a key problem in the field of social network analysis. In fact, it is a measure that accounts the popularity or the visibility of an actor within a network. In order to capture this concept, various measures, either sample or more elaborate, has been developed. Nevertheless, many of "traditional" measures are not designed to be applicable to huge data. This paper sets out a new node centrality index suitable for large social network. It uses the amount of the neighbors of a node and connections between them to characterize a "pivot" node in the graph. We presented experimental results on real data sets which show the efficiency of our proposal.

Identifying quantum phase transitions with adversarial neural networks

Science.gov (United States)

Huembeli, Patrick; Dauphin, Alexandre; Wittek, Peter

2018-04-01

The identification of phases of matter is a challenging task, especially in quantum mechanics, where the complexity of the ground state appears to grow exponentially with the size of the system. Traditionally, physicists have to identify the relevant order parameters for the classification of the different phases. We here follow a radically different approach: we address this problem with a state-of-the-art deep learning technique, adversarial domain adaptation. We derive the phase diagram of the whole parameter space starting from a fixed and known subspace using unsupervised learning. This method has the advantage that the input of the algorithm can be directly the ground state without any ad hoc feature engineering. Furthermore, the dimension of the parameter space is unrestricted. More specifically, the input data set contains both labeled and unlabeled data instances. The first kind is a system that admits an accurate analytical or numerical solution, and one can recover its phase diagram. The second type is the physical system with an unknown phase diagram. Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture. Once these layers are trained, we can attach an unsupervised learner to the network to find phase transitions. We show the success of this technique by applying it on several paradigmatic models: the Ising model with different temperatures, the Bose-Hubbard model, and the Su-Schrieffer-Heeger model with disorder. The method finds unknown transitions successfully and predicts transition points in close agreement with standard methods. This study opens the door to the classification of physical systems where the phase boundaries are complex such as the many-body localization problem or the Bose glass phase.

Identifying failure in a tree network of a parallel computer

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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.

A network biology approach evaluating the anticancer effects of bortezomib identifies SPARC as a therapeutic target in adult T-cell leukemia cells

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Yu Zhang

2008-10-01

Full Text Available Junko H Ohyashiki1, Ryoko Hamamura2, Chiaki Kobayashi2, Yu Zhang2, Kazuma Ohyashiki21Intractable Immune System Disease Research Center, Tokyo Medical University, Tokyo, Japan; 2First Department of Internal Medicine, Tokyo Medical University, Tokyo, JapanAbstract: There is a need to identify the regulatory gene interaction of anticancer drugs on target cancer cells. Whole genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by hundreds to thousands of genes that induce changes in expression. A proteasome inhibitor, bortezomib, could be a potential therapeutic agent in treating adult T-cell leukemia (ATL patients, however, the underlying mechanism by which bortezomib induces cell death in ATL cells via gene regulatory network has not been fully elucidated. Here we show that a Bayesian statistical framework by VoyaGene® identified a secreted protein acidic and rich in cysteine (SPARC gene, a tumor-invasiveness related gene, as a possible modulator of bortezomib-induced cell death in ATL cells. Functional analysis using RNAi experiments revealed that inhibition of the expression SPARC by siRNA enhanced the apoptotic effect of bortezomib on ATL cells in accordance with an increase of cleaved caspase 3. Targeting SPARC may help to treat ATL patients in combination with bortezomib. This work shows that a network biology approach can be used advantageously to identify the genetic interaction related to anticancer effects.Keywords: network biology, adult T cell leukemia, bortezomib, SPARC

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

Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

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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

Improving network management with Software Defined Networking

International Nuclear Information System (INIS)

Dzhunev, Pavel

2013-01-01

Software-defined networking (SDN) is developed as an alternative to closed networks in centers for data processing by providing a means to separate the control layer data layer switches, and routers. SDN introduces new possibilities for network management and configuration methods. In this article, we identify problems with the current state-of-the-art network configuration and management mechanisms and introduce mechanisms to improve various aspects of network management

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

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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. PMID:24399981

Demonstration of statistical approaches to identify component's ageing by operational data analysis-A case study for the ageing PSA network

International Nuclear Information System (INIS)

Rodionov, Andrei; Atwood, Corwin L.; Kirchsteiger, Christian; Patrik, Milan

2008-01-01

The paper presents some results of a case study on 'Demonstration of statistical approaches to identify the component's ageing by operational data analysis', which was done in the frame of the EC JRC Ageing PSA Network. Several techniques: visual evaluation, nonparametric and parametric hypothesis tests, were proposed and applied in order to demonstrate the capacity, advantages and limitations of statistical approaches to identify the component's ageing by operational data analysis. Engineering considerations are out of the scope of the present study

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

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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.

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

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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

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

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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

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

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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...

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

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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.

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

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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.

Network Transformations in Economy

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Bolychev O.

2014-09-01

Full Text Available In the context of ever-increasing market competition, networked interactions play a special role in the economy. The network form of entrepreneurship is increasingly viewed as an effective organizational structure to create a market value embedded in innovative business solutions. The authors study the characteristics of a network as an economic category and emphasize certain similarities between Rus sian and international approaches to identifying interactions of economic systems based on the network principle. The paper focuses on the types of networks widely used in the economy. The authors analyze the transformation of business networks along two lines: from an intra- to an inter-firm network and from an inter-firm to an inter-organizational network. The possible forms of network formation are described depending on the strength of connections and the type of integration. The drivers and reasons behind process of transition from a hierarchical model of the organizational structure to a network type are identified. The authors analyze the advantages of creating inter-firm networks and discuss the features of inter-organizational networks as compares to inter-firm ones. The article summarizes the reasons for and advantages of participation in inter-rganizational networks and identifies the main barriers to the formation of inter-organizational network.

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

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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.

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

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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.

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

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Salmon, Charles T

2018-01-01

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

Identifying the Effective Factors in Making Trust in Online Social Networks on the perspective of Iranian experts Using Fuzzy ELECTRE

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Elham Haghighi

2015-12-01

Full Text Available this paper attempts to rank the effective factors in making trust in social networks to provide the possibility of attracting and increasing users’ trust on these social networks for providers and designers of online social networks. Identifying the effective factors in making trust in social networks is a multi-criteria decision making problem and most of effective factors are ambiguous and uncertain, thereby this article uses Fuzzy ELECTRE to rank them. By implementing Fuzzy ELECTRE on gathered data, respectively «usability factor», «supporting up to date technology factor», «integrity» and «the rate of ethics factor» are on the top of effective factors in making trust in users. In general, «web features» and «technology features» have a higher degree of importance than «security features», «individual-social features» and «cultural features». Ranking of Fuzzy ELECTRE comparison ranking of Fuzzy TOPSIS and Fuzzy ELECTRE method becomes validate because Spearman correlation coefficients is 0/867. Result of sensitivity analysis on changing weight of criteria shows that Fuzzy ELECTRE isn’t affected by ambiguity and uncertainty in inputs.

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

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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.;

Identification of important nodes in directed biological networks: a network motif approach.

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Pei Wang

Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

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

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

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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.

Identifying Central Bank Liquidity Super-Spreaders in Interbank Funds Networks

NARCIS (Netherlands)

Leon Rincon, C.E.; Machado, C.; Sarmiento Paipilla, N.M.

2015-01-01

We model the allocation of central bank liquidity among the participants of the interbank market by using network analysis’ metrics. Our analytical framework considers that a super-spreader simultaneously excels at receiving (borrowing) and distributing (lending) central bank’s liquidity for the

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

On Identities in Modern Networks

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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.

Identifying the borders of mathematical knowledge

International Nuclear Information System (INIS)

Silva, Filipi Nascimento; Travencolo, Bruno A N; Viana, Matheus P; Costa, Luciano da Fontoura

2010-01-01

Based on a divide and conquer approach, knowledge about nature has been organized into a set of interrelated facts, allowing a natural representation in terms of graphs: each 'chunk' of knowledge corresponds to a node, while relationships between such chunks are expressed as edges. This organization becomes particularly clear in the case of mathematical theorems, with their intense cross-implications and relationships. We have derived a web of mathematical theorems from Wikipedia and, thanks to the powerful concept of entropy, identified its more central and frontier elements. Our results also suggest that the central nodes are the oldest theorems, while the frontier nodes are those recently added to the network. The network communities have also been identified, allowing further insights about the organization of this network, such as its highly modular structure.

Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance.

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Carey, Maureen A; Papin, Jason A; Guler, Jennifer L

2017-07-19

Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms. Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood. Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.

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.

A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

Directory of Open Access Journals (Sweden)

Tong Qiao

2018-04-01

Full Text Available Measuring centrality has recently attracted increasing attention, with algorithms ranging from those that simply calculate the number of immediate neighbors and the shortest paths to those that are complicated iterative refinement processes and objective dynamical approaches. Indeed, vital nodes identification allows us to understand the roles that different nodes play in the structure of a network. However, quantifying centrality in complex networks with various topological structures is not an easy task. In this paper, we introduce a novel definition of entropy-based centrality, which can be applicable to weighted directed networks. By design, the total power of a node is divided into two parts, including its local power and its indirect power. The local power can be obtained by integrating the structural entropy, which reveals the communication activity and popularity of each node, and the interaction frequency entropy, which indicates its accessibility. In addition, the process of influence propagation can be captured by the two-hop subnetworks, resulting in the indirect power. In order to evaluate the performance of the entropy-based centrality, we use four weighted real-world networks with various instance sizes, degree distributions, and densities. Correspondingly, these networks are adolescent health, Bible, United States (US airports, and Hep-th, respectively. Extensive analytical results demonstrate that the entropy-based centrality outperforms degree centrality, betweenness centrality, closeness centrality, and the Eigenvector centrality.

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

Science.gov (United States)

Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

2016-02-01

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

Searching for Communities in Bipartite Networks

OpenAIRE

Barber, Michael J.; Faria, Margarida; Streit, Ludwig; Strogan, Oleg

2008-01-01

Bipartite networks are a useful tool for representing and investigating interaction networks. We consider methods for identifying communities in bipartite networks. Intuitive notions of network community groups are made explicit using Newman's modularity measure. A specialized version of the modularity, adapted to be appropriate for bipartite networks, is presented; a corresponding algorithm is described for identifying community groups through maximizing this measure. The algorithm is applie...

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.

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.

Identifying the best locations to install flow control devices in sewer networks to enable in-sewer storage

Science.gov (United States)

Leitão, J. P.; Carbajal, J. P.; Rieckermann, J.; Simões, N. E.; Sá Marques, A.; de Sousa, L. M.

2018-01-01

The activation of available in-sewer storage volume has been suggested as a low-cost flood and combined sewer overflow mitigation measure. However, it is currently unknown what the attributes for suitable objective functions to identify the best location for flow control devices are and the impact of those attributes on the results. In this study, we present a novel location model and efficient algorithm to identify the best location(s) to install flow limiters. The model is a screening tool that does not require hydraulic simulations but rather considers steady state instead of simplistic static flow conditions. It also maximises in-sewer storage according to different reward functions that also considers the potential impact of flow control device failure. We demonstrate its usefulness on two real sewer networks, for which an in-sewer storage potential of approximately 2,000 m3 and 500 m3 was estimated with five flow control devices installed.

Perception of Communication Network Fraud Dynamics by Network ...

African Journals Online (AJOL)

In considering the implications of the varied nature of the potential targets, the paper identifies the view to develop effective intelligence analysis methodologies for network fraud detection and prevention by network administrators and stakeholders. The paper further notes that organizations are fighting an increasingly ...

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...... model can be used during the network planning process for facilitating cost- effective network survivability design....

Identifying options for regulating the coordination of network investments with investments in distributed electricity generation

International Nuclear Information System (INIS)

Nisten, E.

2010-02-01

The increase in the distributed generation of electricity, with wind turbines and solar panels, necessitates investments in the distribution network. The current tariff regulation in the Dutch electricity industry, with its ex post evaluation of the efficiency of investments and the frontier shift in the x-factor, delays these investments. In the unbundled electricity industry, the investments in the network need to be coordinated with those in the distributed generation of electricity to enable the DSOs to build enough network capacity. The current Dutch regulations do not provide for a sufficient information exchange between the generators and the system operators to coordinate the investments. This paper analyses these two effects of the Dutch regulation, and suggests improvements to the regulation of the network connection and transportation tariffs to allow for sufficient network capacity and coordination between the investments in the network and in the generation of electricity. These improvements include locally differentiated tariffs that increase with an increasing concentration of distributed generators.

Identifying colon cancer risk modules with better classification performance based on human signaling network.

Science.gov (United States)

Qu, Xiaoli; Xie, Ruiqiang; Chen, Lina; Feng, Chenchen; Zhou, Yanyan; Li, Wan; Huang, Hao; Jia, Xu; Lv, Junjie; He, Yuehan; Du, Youwen; Li, Weiguo; Shi, Yuchen; He, Weiming

2014-10-01

Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer. Copyright © 2013. Published by Elsevier Inc.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

Science.gov (United States)

Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

2017-08-01

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Networking Ethics: A Survey of Bioethics Networks Across the U.S.

Science.gov (United States)

Fausett, Jennifer Kleiner; Gilmore-Szott, Eleanor; Hester, D Micah

2016-06-01

Ethics networks have emerged over the last few decades as a mechanism for individuals and institutions over various regions, cities and states to converge on healthcare-related ethical issues. However, little is known about the development and nature of such networks. In an effort to fill the gap in the knowledge about such networks, a survey was conducted that evaluated the organizational structure, missions and functions, as well as the outcomes/products of ethics networks across the country. Eighteen established bioethics networks were identified via consensus of three search processes and were approached for participation. The participants completed a survey developed for the purposes of this study and distributed via SurveyMonkey. Responses were obtained from 10 of the 18 identified and approached networks regarding topic areas of: Network Composition and Catchment Areas; Network Funding and Expenses; Personnel; Services; and Missions and Accomplishments. Bioethics networks are designed primarily to bring ethics education and support to professionals and hospitals. They do so over specifically defined areas-states, regions, or communities-and each is concerned about how to stay financially healthy. At the same time, the networks work off different organizational models, either as stand-alone organizations or as entities within existing organizational structures.

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.

Science.gov (United States)

Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-12-01

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. © 2014 American Society of Plant Biologists. All rights reserved.

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.

A method to identify important dynamical states in Boolean models of regulatory networks: application to regulation of stomata closure by ABA in A. thaliana.

Science.gov (United States)

Bugs, Cristhian A; Librelotto, Giovani R; Mombach, José C M

2011-12-22

We introduce a method to analyze the states of regulatory Boolean models that identifies important network states and their biological influence on the global network dynamics. It consists in (1) finding the states of the network that are most frequently visited and (2) the identification of variable and frozen nodes of the network. The method, along with a simulation that includes random features, is applied to the study of stomata closure by abscisic acid (ABA) in A. thaliana proposed by Albert and coworkers. We find that for the case of study, that the dynamics of wild and mutant networks have just two states that are highly visited in their space of states and about a third of all nodes of the wild network are variable while the rest remain frozen in True or False states. This high number of frozen elements explains the low cardinality of the space of states of the wild network. Similar results are observed in the mutant networks. The application of the method allowed us to explain how wild and mutants behave dynamically in the SS and determined an essential feature of the activation of the closure node (representing stomata closure), i.e. its synchronization with the AnionEm node (representing anion efflux at the plasma membrane). The dynamics of this synchronization explains the efficiency reached by the wild and each of the mutant networks. For the biological problem analyzed, our method allows determining how wild and mutant networks differ 'phenotypically'. It shows that the different efficiencies of stomata closure reached among the simulated wild and mutant networks follow from a dynamical behavior of two nodes that are always synchronized. Additionally, we predict that the involvement of the anion efflux at the plasma membrane is crucial for the plant response to ABA. The algorithm used in the simulations is available upon request.

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Directory of Open Access Journals (Sweden)

Tong Qiao

2017-11-01

Full Text Available Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be “vital” to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary’s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

Directory of Open Access Journals (Sweden)

Jiang Wei

2008-08-01

Full Text Available Abstract Background With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. Results The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8 (p ≈ 0, desmin (DES (p = 2.71 × 10-6 and enolase 1 (ENO1 (p = 4.19 × 10-5], while two novel hub genes [RNA binding motif protein 9 (RBM9 (p = 1.50 × 10-4 and ribosomal protein L30 (RPL30 (p = 1.50 × 10-4] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO based analysis of the colon cancer-specific gene network and

Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.

Science.gov (United States)

Jiang, Wei; Li, Xia; Rao, Shaoqi; Wang, Lihong; Du, Lei; Li, Chuanxing; Wu, Chao; Wang, Hongzhi; Wang, Yadong; Yang, Baofeng

2008-08-10

With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p approximately 0), desmin (DES) (p = 2.71 x 10(-6)) and enolase 1 (ENO1) (p = 4.19 x 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 x 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 x 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that

Optimization-based topology identification of complex networks

International Nuclear Information System (INIS)

Tang Sheng-Xue; Chen Li; He Yi-Gang

2011-01-01

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

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.

Do Policy Networks lead to Network Governing?

DEFF Research Database (Denmark)

Damgaard, Bodil

This paper challenges the notion that creation of local policy networks necessarily leads to network governing. Through actor-centred case studies in the area of municipally implemented employment policy in Denmark it was found that the local governing mode is determined mainly by the municipality......’s approach to local co-governing as well as by the capacity and interest of key private actors. It is argued that national legislation requesting the creation of local policy networks was not enough to assure network governing and the case studies show that local policy networks may subsist also under...... hierarchical governing modes. Reasons why hierarchical governing modes prevail over network governing in some settings are identified pointing to both actor borne and structural factors. Output indicators of the four cases do not show that a particular governing mode is more efficient in its employment policy...

Northern emporia and maritime networks. Modelling past communication using archaeological network analysis

DEFF Research Database (Denmark)

Sindbæk, Søren Michael

2015-01-01

preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...

Correlated network of networks enhances robustness against catastrophic failures.

Science.gov (United States)

Min, Byungjoon; Zheng, Muhua

2018-01-01

Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule.

Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis

Directory of Open Access Journals (Sweden)

Cristina Pina

2015-06-01

Full Text Available We explore cell heterogeneity during spontaneous and transcription-factor-driven commitment for network inference in hematopoiesis. Since individual genes display discrete OFF states or a distribution of ON levels, we compute and combine pairwise gene associations from binary and continuous components of gene expression in single cells. Ddit3 emerges as a regulatory node with positive linkage to erythroid regulators and negative association with myeloid determinants. Ddit3 loss impairs erythroid colony output from multipotent cells, while forcing Ddit3 in granulo-monocytic progenitors (GMPs enhances self-renewal and impedes differentiation. Network analysis of Ddit3-transduced GMPs reveals uncoupling of myeloid networks and strengthening of erythroid linkages. RNA sequencing suggests that Ddit3 acts through development or stabilization of a precursor upstream of GMPs with inherent Meg-E potential. The enrichment of Gata2 target genes in Ddit3-dependent transcriptional responses suggests that Ddit3 functions in an erythroid transcriptional network nucleated by Gata2.

Interchange. Program Improvement Products Identified through Networking.

Science.gov (United States)

Ohio State Univ., Columbus. National Center for Research in Vocational Education.

This catalog lists exemplary field-based program improvement products identified by the Dissemination and Utilization Products and Services Program (D&U) at the National Center for Research in Vocational Education. It is designed to increase awareness of these products among vocational educators and to provide information about them that…

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.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

Science.gov (United States)

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

2015-01-01

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

Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways

Directory of Open Access Journals (Sweden)

Q. Wang

Full Text Available Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC based on the functional dependency among pathways. Protein-protein interaction (PPI information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN, where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.

Use of neural networks to identify transient operating conditions in nuclear power plants

International Nuclear Information System (INIS)

Uhrig, R.E.; Guo, Z.

1989-01-01

A technique using neural networks as a means of diagnosing specific abnormal conditions or problems in nuclear power plants is investigated and found to be feasible. The technique is based on the fact that each physical state of the plant can be represented by a unique pattern of instrument readings, which can be related to the condition of the plant. Neural networks are used to relate this pattern to the fault or problem. 3 refs., 2 figs., 4 tabs

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

International Nuclear Information System (INIS)

Zio, E.; Golea, L.R.; Rocco S, C.M.

2012-01-01

In this paper, an analysis of the vulnerability of the Italian high-voltage (380 kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information.

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

Directory of Open Access Journals (Sweden)

Jack G.A.J. van der Vorst

2011-10-01

Full Text Available  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 (redesigning food supply-chain networks, from a logistics point of view, one has to consider these demands next to traditional efficiency and responsiveness requirements. The concept ‘quality controlled logistics’ (QCL hypothesizes that if product quality in each step of the supply chain can be predicted in advance, goods flows can be controlled in a pro-active manner and better chain designs can be established resulting in higher product availability, constant quality, and less product losses. The paper discusses opportunities of using real-time product quality information for improvement of the design and management of ‘AgriFood Supply Chain Networks’, and presents a preliminary diagnostic instrument for assessment of ‘critical quality’ and ‘logistics control’ points in the supply chain network. Results of a tomato-chain case illustrate the added value of the QCL concept for identifying improvement opportunities in the supply chain as to increase both product availability and quality. Future research aims for the further development of the diagnostic instrument and the quantification of costs and benefits of QCL scenarios.

Network Restoration for Next-Generation Communication and Computing Networks

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B. S. Awoyemi

2018-01-01

Full Text Available Network failures are undesirable but inevitable occurrences for most modern communication and computing networks. A good network design must be robust enough to handle sudden failures, maintain traffic flow, and restore failed parts of the network within a permissible time frame, at the lowest cost achievable and with as little extra complexity in the network as possible. Emerging next-generation (xG communication and computing networks such as fifth-generation networks, software-defined networks, and internet-of-things networks have promises of fast speeds, impressive data rates, and remarkable reliability. To achieve these promises, these complex and dynamic xG networks must be built with low failure possibilities, high network restoration capacity, and quick failure recovery capabilities. Hence, improved network restoration models have to be developed and incorporated in their design. In this paper, a comprehensive study on network restoration mechanisms that are being developed for addressing network failures in current and emerging xG networks is carried out. Open-ended problems are identified, while invaluable ideas for better adaptation of network restoration to evolving xG communication and computing paradigms are discussed.

An examination of a reciprocal relationship between network governance and network structure

DEFF Research Database (Denmark)

Bergenholtz, Carsten; Goduscheit, René Chester

The present article examines the network structure and governance of inter-organisational innovation networks. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals with the overall...... structural relations between the actors in the network. These streams of research do contain references to each other but mostly rely on a static conception of the relationship between network structure and the applied network governance. The paper is based on a primarily qualitative case study of a loosely...... coupled Danish inter-organisational innovation network. The proposition is that a reciprocal relation between network governance and network structure can be identified....

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

. The GTPase-activating proteins AGAP1 and AGAP3 were also enriched in the CLASP2 interactome, although subsequent AGAP3 and CLIP2 interactome analysis suggests a preference of AGAP3 for CLIP2. Follow-up MARK2 interactome analysis confirmed reciprocal co-IP of CLASP2 and also revealed MARK2 can co-IP SOGA1......, glycogen synthase, and glycogenin. Investigating the SOGA1 interactome confirmed SOGA1 can reciprocal co-IP both CLASP2 and MARK2 as well as glycogen synthase and glycogenin. SOGA1 was confirmed to colocalize with CLASP2 and also with tubulin, which identifies SOGA1 as a new microtubule-associated protein....... These results introduce the metabolic function of these proposed novel protein networks and their relationship with microtubules as new fields of cytoskeleton-associated protein biology....

Network Learning and Innovation in SME Formal Networks

Directory of Open Access Journals (Sweden)

Jivka Deiters

2013-02-01

Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

Identifying Chaotic FitzHugh–Nagumo Neurons Using Compressive Sensing

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Ri-Qi Su

2014-07-01

Full Text Available We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state. However, due to couplings among the nodes, the measured time series, even from non-chaotic neurons, would appear random, rendering inapplicable traditional nonlinear time-series analysis, such as the delay-coordinate embedding method, which yields information about the global dynamics of the entire network. Our method is based on compressive sensing. In particular, we demonstrate that identifying chaotic elements can be formulated as a general problem of reconstructing the nodal dynamical systems, network connections and all coupling functions, as well as their weights. The working and efficiency of the method are illustrated by using networks of non-identical FitzHugh–Nagumo neurons with randomly-distributed coupling weights.

Trimming of mammalian transcriptional networks using network component analysis

Directory of Open Access Journals (Sweden)

Liao James C

2010-10-01

Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm

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

Machine-to-Machine networks: integration of M2M networks into companies' administrative networks

OpenAIRE

Pointereau, Romain

2013-01-01

This analysis will address the technical, economic and regulatory aspects and will identify the position taken by the various market actors. Integration of M2M Networks into Companies' Administrative Networks. Integración de redes M2M en redes administrativas de las empresas. Integració de xarxes M2M en xarxes administratives de les empreses.

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

Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

Directory of Open Access Journals (Sweden)

Ettore Mosca

2017-09-01

Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

Topology of the European Network of Earth Observation Networks and the need for an European Network of Networks

Science.gov (United States)

Masó, Joan; Serral, Ivette; McCallum, Ian; Blonda, Palma; Plag, Hans-Peter

2016-04-01

ConnectinGEO (Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations" is an H2020 Coordination and Support Action with the primary goal of linking existing Earth Observation networks with science and technology (S&T) communities, the industry sector, the Group on Earth Observations (GEO), and Copernicus. The project will end in February 2017. ConnectinGEO will initiate a European Network of Earth Observation Networks (ENEON) that will encompass space-based, airborne and in-situ observations networks. ENEON will be composed of project partners representing thematic observation networks along with the GEOSS Science and Technology Stakeholder Network, GEO Communities of Practices, Copernicus services, Sentinel missions and in-situ support data representatives, representatives of the European space-based, airborne and in-situ observations networks. This communication presents the complex panorama of Earth Observations Networks in Europe. The list of networks is classified by discipline, variables, geospatial scope, etc. We also capture the membership and relations with other networks and umbrella organizations like GEO. The result is a complex interrelation between networks that can not be clearly expressed in a flat list. Technically the networks can be represented as nodes with relations between them as lines connecting the nodes in a graph. We have chosen RDF as a language and an AllegroGraph 3.3 triple store that is visualized in several ways using for example Gruff 5.7. Our final aim is to identify gaps in the EO Networks and justify the need for a more structured coordination between them.

Sparse Linear Identifiable Multivariate Modeling

DEFF Research Database (Denmark)

Henao, Ricardo; Winther, Ole

2011-01-01

and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...

Identifying multiple influential spreaders in term of the distance-based coloring

Energy Technology Data Exchange (ETDEWEB)

Guo, Lei; Lin, Jian-Hong; Guo, Qiang [Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093 (China); Liu, Jian-Guo, E-mail: liujg004@ustc.edu.cn [Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093 (China); Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433 (China)

2016-02-22

Identifying influential nodes is of significance for understanding the dynamics of information diffusion process in complex networks. In this paper, we present an improved distance-based coloring method to identify the multiple influential spreaders. In our method, each node is colored by a kind of color with the rule that the distance between initial nodes is close to the average distance of a network. When all nodes are colored, nodes with the same color are sorted into an independent set. Then we choose the nodes at the top positions of the ranking list according to their centralities. The experimental results for an artificial network and three empirical networks show that, comparing with the performance of traditional coloring method, the improvement ratio of our distance-based coloring method could reach 12.82%, 8.16%, 4.45%, 2.93% for the ER, Erdős, Polblogs and Routers networks respectively. - Highlights: • We present an improved distance-based coloring method to identify the multiple influential spreaders. • Each node is colored by a kind of color where the distance between initial nodes is close to the average distance. • For three empirical networks show that the improvement ratio of our distance-based coloring method could reach 8.16% for the Erdos network.

Identifying multiple influential spreaders in term of the distance-based coloring

International Nuclear Information System (INIS)

Guo, Lei; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-01-01

Identifying influential nodes is of significance for understanding the dynamics of information diffusion process in complex networks. In this paper, we present an improved distance-based coloring method to identify the multiple influential spreaders. In our method, each node is colored by a kind of color with the rule that the distance between initial nodes is close to the average distance of a network. When all nodes are colored, nodes with the same color are sorted into an independent set. Then we choose the nodes at the top positions of the ranking list according to their centralities. The experimental results for an artificial network and three empirical networks show that, comparing with the performance of traditional coloring method, the improvement ratio of our distance-based coloring method could reach 12.82%, 8.16%, 4.45%, 2.93% for the ER, Erdős, Polblogs and Routers networks respectively. - Highlights: • We present an improved distance-based coloring method to identify the multiple influential spreaders. • Each node is colored by a kind of color where the distance between initial nodes is close to the average distance. • For three empirical networks show that the improvement ratio of our distance-based coloring method could reach 8.16% for the Erdos network.

Community detection using preference networks

Science.gov (United States)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

Science.gov (United States)

Tomasi, Dardo G; Shokri-Kojori, Ehsan; Wiers, Corinde E; Kim, Sunny W; Demiral, Şukru B; Cabrera, Elizabeth A; Lindgren, Elsa; Miller, Gregg; Wang, Gene-Jack; Volkow, Nora D

2017-12-01

It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.

A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease

Science.gov (United States)

Huan, Tianxiao; Zhang, Bin; Wang, Zhi; Joehanes, Roby; Zhu, Jun; Johnson, Andrew D.; Ying, Saixia; Munson, Peter J.; Raghavachari, Nalini; Wang, Richard; Liu, Poching; Courchesne, Paul; Hwang, Shih-Jen; Assimes, Themistocles L.; McPherson, Ruth; Samani, Nilesh J.; Schunkert, Heribert; Meng, Qingying; Suver, Christine; O'Donnell, Christopher J.; Derry, Jonathan; Yang, Xia; Levy, Daniel

2013-01-01

Objective Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. Approach and Results We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. 24 coexpression modules were identified including one case-specific and one control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with altered gene expression associated SNPs (eSNPs) and with results of GWAS of CHD and its risk factors, the control-specific DM was implicated as CHD-causal based on its significant enrichment for both CHD and lipid eSNPs. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver (KD) genes. Multi-tissue KDs (SPIB and TNFRSF13C) and tissue-specific KDs (e.g. EBF1) were identified. Conclusions Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk. PMID:23539213

Physician Networks and Ambulatory Care-sensitive Admissions.

Science.gov (United States)

Casalino, Lawrence P; Pesko, Michael F; Ryan, Andrew M; Nyweide, David J; Iwashyna, Theodore J; Sun, Xuming; Mendelsohn, Jayme; Moody, James

2015-06-01

Research on the quality and cost of care traditionally focuses on individual physicians or medical groups. Social network theory suggests that the care a patient receives also depends on the network of physicians with whom a patient's physician is connected. The objectives of the study are: (1) identify physician networks; (2) determine whether the rate of ambulatory care-sensitive hospital admissions (ACSAs) varies across networks--even different networks at the same hospital; and (3) determine the relationship between ACSA rates and network characteristics. We identified networks by applying network detection algorithms to Medicare 2008 claims for 987,000 beneficiaries in 5 states. We estimated a fixed-effects model to determine the relationship between networks and ACSAs and a multivariable model to determine the relationship between network characteristics and ACSAs. We identified 417 networks. Mean size: 129 physicians; range, 26-963. In the fixed-effects model, ACSA rates varied significantly across networks: there was a 46% difference in rates between networks at the 25th and 75th performance percentiles. At 95% of hospitals with admissions from 2 networks, the networks had significantly different ACSA rates; the mean difference was 36% of the mean ACSA rate. Networks with a higher percentage of primary-care physicians and networks in which patients received care from a larger number of physicians had higher ACSA rates. Physician networks have a relationship with ACSAs that is independent of the physicians in the network. Physician networks could be an important focus for understanding variations in medical care and for intervening to improve care.

Coordination of locomotor and cardiorespiratory networks of Lymnaea stagnalis by a pair of identified interneurones.

Science.gov (United States)

Syed, N I; Winlow, W

1991-07-01

1. The morphology and electrophysiology of a newly identified bilateral pair of interneurones in the central nervous system of the pulmonate pond snail Lymnaea stagnalis is described. 2. These interneurones, identified as left and right pedal dorsal 11 (L/RPeD11), are electrically coupled to each other as well as to a large number of foot and body wall motoneurones, forming a fast-acting neural network which coordinates the activities of foot and body wall muscles. 3. The left and right sides of the body wall of Lymnaea are innervated by left and right cerebral A cluster neurones. Although these motoneurones have only ipsilateral projections, they are indirectly electrically coupled to their contralateral homologues via their connections with L/RPeD11. Similarly, the activities of left and right pedal G cluster neurones, which are known to be involved in locomotion, are also coordinated by L/RPeD11. 4. Selective ablation of both neurones PeD11 results in the loss of coordination between the bilateral cerebral A clusters. 5. Interneurones L/RPeD11 are multifunctional. In addition to coordinating motoneuronal activity, they make chemical excitatory connections with heart motoneurones. They also synapse upon respiratory motoneurones, hyperpolarizing those involved in pneumostome opening (expiration) and depolarizing those involved in pneumostome closure (inspiration). 6. An identified respiratory interneurone involved in pneumostome closure (visceral dorsal 4) inhibits L/RPeD11 together with all their electrically coupled follower cells. 7. Both L/RPeD11 have strong excitatory effects on another pair of electrically coupled neurones, visceral dorsal 1 and right parietal dorsal 2, which have previously been shown to be sensitive to changes in the partial pressure of environmental oxygen (PO2). 8. Although L/RPeD11 participate in whole-body withdrawal responses, electrical stimulation applied directly to these neurones was not sufficient to induce this behaviour.

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

Science.gov (United States)

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

2017-12-28

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

Analyzing the factors affecting network lifetime cluster-based wireless sensor network

International Nuclear Information System (INIS)

Malik, A.S.; Qureshi, A.

2010-01-01

Cluster-based wireless sensor networks enable the efficient utilization of the limited energy resources of the deployed sensor nodes and hence prolong the node as well as network lifetime. Low Energy Adaptive Clustering Hierarchy (Leach) is one of the most promising clustering protocol proposed for wireless sensor networks. This paper provides the energy utilization and lifetime analysis for cluster-based wireless sensor networks based upon LEACH protocol. Simulation results identify some important factors that induce unbalanced energy utilization between the sensor nodes and hence affect the network lifetime in these types of networks. These results highlight the need for a standardized, adaptive and distributed clustering technique that can increase the network lifetime by further balancing the energy utilization among sensor nodes. (author)

Optimal stabilization of Boolean networks through collective influence

Science.gov (United States)

Wang, Jiannan; Pei, Sen; Wei, Wei; Feng, Xiangnan; Zheng, Zhiming

2018-03-01

Boolean networks have attracted much attention due to their wide applications in describing dynamics of biological systems. During past decades, much effort has been invested in unveiling how network structure and update rules affect the stability of Boolean networks. In this paper, we aim to identify and control a minimal set of influential nodes that is capable of stabilizing an unstable Boolean network. For locally treelike Boolean networks with biased truth tables, we propose a greedy algorithm to identify influential nodes in Boolean networks by minimizing the largest eigenvalue of a modified nonbacktracking matrix. We test the performance of the proposed collective influence algorithm on four different networks. Results show that the collective influence algorithm can stabilize each network with a smaller set of nodes compared with other heuristic algorithms. Our work provides a new insight into the mechanism that determines the stability of Boolean networks, which may find applications in identifying virulence genes that lead to serious diseases.

Community Based Networks and 5G

DEFF Research Database (Denmark)

Williams, Idongesit

2016-01-01

The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways.

Directory of Open Access Journals (Sweden)

Francesca eSacco

2014-05-01

Full Text Available Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI#K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26 respectively.

Maximum entropy networks are more controllable than preferential attachment networks

International Nuclear Information System (INIS)

Hou, Lvlin; Small, Michael; Lao, Songyang

2014-01-01

A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution. - Highlights: • The controllability of maximum entropy (ME) and Barabási–Albert (BA) networks is investigated. • ME networks are significantly more easily controlled than BA networks of the same degree distribution. • The properties of the neighbors of driver node sensitively affect the network controllability. • Subtle and important structural differences exist between BA networks and typical scale-free networks

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...

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

2018-04-01

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.

Ecological network analysis: network construction

NARCIS (Netherlands)

Fath, B.D.; Scharler, U.M.; Ulanowicz, R.E.; Hannon, B.

2007-01-01

Ecological network analysis (ENA) is a systems-oriented methodology to analyze within system interactions used to identify holistic properties that are otherwise not evident from the direct observations. Like any analysis technique, the accuracy of the results is as good as the data available, but

Designing Green Networks and Network Operations Saving Run-the-Engine Costs

CERN Document Server

Minoli, Daniel

2011-01-01

In recent years the confluence of socio-political trends toward environmental responsibility and the pressing need to reduce Run-the-Engine (RTE) costs has given birth to a nascent discipline of Green IT. A clear and concise introduction to green networks and green network operations, this book examines analytical measures and discusses virtualization, network computing, and web services as approaches for green data centers and networks. It identifies some strategies for green appliance and end devices and examines the methodical steps that can be taken over time to achieve a seamless migratio

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

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

Science.gov (United States)

Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

2009-12-21

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. 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. We provide a computational framework to reconstruct

A Theory of Network Tracing

Science.gov (United States)

Acharya, Hrishikesh B.; Gouda, Mohamed G.

Traceroute is a widely used program for computing the topology of any network in the Internet. Using Traceroute, one starts from a node and chooses any other node in the network. Traceroute obtains the sequence of nodes that occur between these two nodes, as specified by the routing tables in these nodes. Each use of Traceroute in a network produces a trace of nodes that constitute a simple path in this network. In every trace that is produced by Traceroute, each node occurs either by its unique identifier, or by the anonymous identifier"*". In this paper, we introduce the first theory aimed at answering the following important question. Is there an algorithm to compute the topology of a network N from a trace set T that is produced by using Traceroute in network N, assuming that each edge in N occurs in at least one trace in T, and that each node in N occurs by its unique identifier in at least one trace in T? We prove that the answer to this question is "No" if N is an even ring or a general network. However, it is "Yes" if N is a tree or an odd ring. The answer is also "No" if N is mostly-regular, but "Yes" if N is a mostly-regular even ring.

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

Identifying important nodes by adaptive LeaderRank

Science.gov (United States)

Xu, Shuang; Wang, Pei

2017-03-01

Spreading process is a common phenomenon in complex networks. Identifying important nodes in complex networks is of great significance in real-world applications. Based on the spreading process on networks, a lot of measures have been proposed to evaluate the importance of nodes. However, most of the existing measures are appropriate to static networks, which are fragile to topological perturbations. Many real-world complex networks are dynamic rather than static, meaning that the nodes and edges of such networks may change with time, which challenge numerous existing centrality measures. Based on a new weighted mechanism and the newly proposed H-index and LeaderRank (LR), this paper introduces a variant of the LR measure, called adaptive LeaderRank (ALR), which is a new member of the LR-family. Simulations on six real-world networks reveal that the new measure can well balance between prediction accuracy and robustness. More interestingly, the new measure can better adapt to the adjustment or local perturbations of network topologies, as compared with the existing measures. By discussing the detailed properties of the measures from the LR-family, we illustrate that the ALR has its competitive advantages over the other measures. The proposed algorithm enriches the measures to understand complex networks, and may have potential applications in social networks and biological systems.

Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets.

Science.gov (United States)

Bogenpohl, James W; Mignogna, Kristin M; Smith, Maren L; Miles, Michael F

2017-01-01

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x

Complex Network Analysis of Guangzhou Metro

Directory of Open Access Journals (Sweden)

Yasir Tariq Mohmand

2015-11-01

Full Text Available The structure and properties of public transportation networks can provide suggestions for urban planning and public policies. This study contributes a complex network analysis of the Guangzhou metro. The metro network has 236 kilometers of track and is the 6th busiest metro system of the world. In this paper topological properties of the network are explored. We observed that the network displays small world properties and is assortative in nature. The network possesses a high average degree of 17.5 with a small diameter of 5. Furthermore, we also identified the most important metro stations based on betweenness and closeness centralities. These could help in identifying the probable congestion points in the metro system and provide policy makers with an opportunity to improve the performance of the metro system.

The Network Information Management System (NIMS) in the Deep Space Network

Science.gov (United States)

Wales, K. J.

1983-01-01

In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.

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...

Identifying and Analyzing Strong Components of an Industrial Network Based on Cycle Degree

Directory of Open Access Journals (Sweden)

Zhiying Zhang

2016-01-01

Full Text Available In the era of big data and cloud computing, data research focuses not only on describing the individual characteristics but also on depicting the relationships among individuals. Studying dependence and constraint relationships among industries has aroused significant interest in the academic field. From the network perspective, this paper tries to analyze industrial relational structures based on cycle degree. The cycle degree of a vertex, that is, the number of cycles through a vertex in an industrial network, can describe the roles of the vertices of strong components in industrial circulation. In most cases, different vertices in a strong component have different cycle degrees, and the one with a larger cycle degree plays more important roles. However, the concept of cycle degree does not involve the lengths of the cycles, which are also important for circulations. The more indirect the relationship between two industries is, the weaker it is. In order to analyze strong components thoroughly, this paper proposes the concept of circular centrality taking into consideration the influence by two factors: the lengths and the numbers of cycles through a vertex. Exemplification indicates that a profound analysis of strong components in an industrial network can reveal the features of an economy.

Identifying Network Motifs that Buffer Front-to-Back Signaling in Polarized Neutrophils

Directory of Open Access Journals (Sweden)

Yanqin Wang

2013-05-01

Full Text Available Neutrophil polarity relies on local, mutual inhibition to segregate incompatible signaling circuits to the leading and trailing edges. Mutual inhibition alone should lead to cells having strong fronts and weak backs or vice versa. However, analysis of cell-to-cell variation in human neutrophils revealed that back polarity remains consistent despite changes in front strength. How is this buffering achieved? Pharmacological perturbations and mathematical modeling revealed a functional role for microtubules in buffering back polarity by mediating positive, long-range crosstalk from front to back; loss of microtubules inhibits buffering and results in anticorrelation between front and back signaling. Furthermore, a systematic, computational search of network topologies found that a long-range, positive front-to-back link is necessary for back buffering. Our studies suggest a design principle that can be employed by polarity networks: short-range mutual inhibition establishes distinct signaling regions, after which directed long-range activation insulates one region from variations in the other.

A systems biology pipeline identifies new immune and disease related molecular signatures and networks in human cells during microgravity exposure.

Science.gov (United States)

Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram

2016-05-17

Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.

A systems biology pipeline identifies new immune and disease related molecular signatures and networks in human cells during microgravity exposure

Science.gov (United States)

Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram

2016-05-01

Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.

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.

Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study

Directory of Open Access Journals (Sweden)

Yuhui Du

2018-01-01

Full Text Available Although individuals at clinical high risk (CHR for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ, it remains unclear whether their resting-state brain intrinsic functional networks (INs show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs, 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive, and 58 early illness SZ (ESZ patients (among which 53 patients took antipsychotic medication within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a

Identifying Cytochrome P450 Functional Networks and Their Allosteric Regulatory Elements

Science.gov (United States)

2013-12-03

based on physiochemical features not captured by residue co-evolution. In all the networks we characterized, it was evident that some residues were...corresponding iron and sulphur related parameters, were obtained from Bathelt et al. [46]. These parameters are based on QM/MM calculations and have been...2007) Adaptations for the oxidation of polycyclic aromatic hydrocarbons exhibited by the structure of human P450 1A2. J Biol Chem 282: 14348-14355. doi

Integration analysis of microRNA and mRNA paired expression profiling identifies deregulated microRNA-transcription factor-gene regulatory networks in ovarian endometriosis.

Science.gov (United States)

Zhao, Luyang; Gu, Chenglei; Ye, Mingxia; Zhang, Zhe; Li, Li'an; Fan, Wensheng; Meng, Yuanguang

2018-01-22

The etiology and pathophysiology of endometriosis remain unclear. Accumulating evidence suggests that aberrant microRNA (miRNA) and transcription factor (TF) expression may be involved in the pathogenesis and development of endometriosis. This study therefore aims to survey the key miRNAs, TFs and genes and further understand the mechanism of endometriosis. Paired expression profiling of miRNA and mRNA in ectopic endometria compared with eutopic endometria were determined by high-throughput sequencing techniques in eight patients with ovarian endometriosis. Binary interactions and circuits among the miRNAs, TFs, and corresponding genes were identified by the Pearson correlation coefficients. miRNA-TF-gene regulatory networks were constructed using bioinformatic methods. Eleven selected miRNAs and TFs were validated by quantitative reverse transcription-polymerase chain reaction in 22 patients. Overall, 107 differentially expressed miRNAs and 6112 differentially expressed mRNAs were identified by comparing the sequencing of the ectopic endometrium group and the eutopic endometrium group. The miRNA-TF-gene regulatory network consists of 22 miRNAs, 12 TFs and 430 corresponding genes. Specifically, some key regulators from the miR-449 and miR-34b/c cluster, miR-200 family, miR-106a-363 cluster, miR-182/183, FOX family, GATA family, and E2F family as well as CEBPA, SOX9 and HNF4A were suggested to play vital regulatory roles in the pathogenesis of endometriosis. Integration analysis of the miRNA and mRNA expression profiles presents a unique insight into the regulatory network of this enigmatic disorder and possibly provides clues regarding replacement therapy for endometriosis.

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

Science.gov (United States)

Zou, Cunlu; Ladroue, Christophe; Guo, Shuixia; Feng, Jianfeng

2010-06-21

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. 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. 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.

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.

Manufacturing network evolution

DEFF Research Database (Denmark)

Yang, Cheng; Farooq, Sami; Johansen, John

2011-01-01

Purpose – This paper examines the effect of changes at the manufacturing plant level on other plants in the manufacturing network and also investigates the role of manufacturing plants on the evolution of a manufacturing network. Design/methodology/approach –The research questions are developed...... different manufacturing plants in the network and their impact on network transformation. Findings – The paper highlights the dominant role of manufacturing plants in the continuously changing shape of a manufacturing network. The paper demonstrates that a product or process change at one manufacturing...... by identifying the gaps in the reviewed literature. The paper is based on three case studies undertaken in Danish manufacturing companies to explore in detail their manufacturing plants and networks. The cases provide a sound basis for developing the research questions and explaining the interaction between...

Patent citation network in nanotechnology (1976-2004)

International Nuclear Information System (INIS)

Li Xin; Chen Hsinchun; Huang Zan; Roco, Mihail C.

2007-01-01

The patent citation networks are described using critical node, core network, and network topological analysis. The main objective is understanding of the knowledge transfer processes between technical fields, institutions and countries. This includes identifying key influential players and subfields, the knowledge transfer patterns among them, and the overall knowledge transfer efficiency. The proposed framework is applied to the field of nanoscale science and engineering (NSE), including the citation networks of patent documents, submitting institutions, technology fields, and countries. The NSE patents were identified by keywords 'full-text' searching of patents at the United States Patent and Trademark Office (USPTO). The analysis shows that the United States is the most important citation center in NSE research. The institution citation network illustrates a more efficient knowledge transfer between institutions than a random network. The country citation network displays a knowledge transfer capability as efficient as a random network. The technology field citation network and the patent document citation network exhibit a less efficient knowledge diffusion capability than a random network. All four citation networks show a tendency to form local citation clusters

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

Identifying maternity services in public hospitals in rural and remote Australia.

Science.gov (United States)

Longman, Jo; Pilcher, Jennifer M; Donoghue, Deborah A; Rolfe, Margaret; Kildea, Sue V; Kruske, Sue; Oats, Jeremy J N; Morgan, Geoffrey G; Barclay, Lesley M

2014-06-01

This paper articulates the importance of accurately identifying maternity services. It describes the process and challenges of identifying the number, level and networks of rural and remote maternity services in public hospitals serving communities of between 1000 and 25000 people across Australia, and presents the findings of this process. Health departments and the national government's websites, along with lists of public hospitals, were used to identify all rural and remote Australian public hospitals offering maternity services in small towns. State perinatal reports were reviewed to establish numbers of births by hospital. The level of maternity services and networks of hospitals within which services functioned were determined via discussion with senior jurisdictional representatives. In all, 198 rural and remote public hospitals offering maternity services were identified. There were challenges in sourcing information on maternity services to generate an accurate national picture. The nature of information about maternity services held centrally by jurisdictions varied, and different frameworks were used to describe minimum requirements for service levels. Service networks appeared to be based on a combination of individual links, geography and transport infrastructure. The lack of readily available centralised and comparable information on rural and remote maternity services has implications for policy review and development, equity, safety and quality, network development and planning. Accountability for services and capacity to identify problems is also compromised.

Weighted Complex Network Analysis of Pakistan Highways

Directory of Open Access Journals (Sweden)

Yasir Tariq Mohmand

2013-01-01

Full Text Available The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.

Software Defined Networking Demands on Software Technologies

DEFF Research Database (Denmark)

Galinac Grbac, T.; Caba, Cosmin Marius; Soler, José

2015-01-01

Software Defined Networking (SDN) is a networking approach based on a centralized control plane architecture with standardised interfaces between control and data planes. SDN enables fast configuration and reconfiguration of the network to enhance resource utilization and service performances....... This new approach enables a more dynamic and flexible network, which may adapt to user needs and application requirements. To this end, systemized solutions must be implemented in network software, aiming to provide secure network services that meet the required service performance levels. In this paper......, we review this new approach to networking from an architectural point of view, and identify and discuss some critical quality issues that require new developments in software technologies. These issues we discuss along with use case scenarios. Here in this paper we aim to identify challenges...

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...... the organizational forms discussed in the paper. It is asserted that the underlying organizational phenomena are not changing but that the manifestations and representations are shifting due to technological developments....

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…

Interorganizational Innovation in Systemic Networks

DEFF Research Database (Denmark)

Seemann, Janne; Dinesen, Birthe; Gustafsson, Jeppe

2013-01-01

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...... 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...

In Silico Functional Networks Identified in Fish Nucleated Red Blood Cells by Means of Transcriptomic and Proteomic Profiling.

Science.gov (United States)

Puente-Marin, Sara; Nombela, Iván; Ciordia, Sergio; Mena, María Carmen; Chico, Verónica; Coll, Julio; Ortega-Villaizan, María Del Mar

2018-04-09

Nucleated red blood cells (RBCs) of fish have, in the last decade, been implicated in several immune-related functions, such as antiviral response, phagocytosis or cytokine-mediated signaling. RNA-sequencing (RNA-seq) and label-free shotgun proteomic analyses were carried out for in silico functional pathway profiling of rainbow trout RBCs. For RNA-seq, a de novo assembly was conducted, in order to create a transcriptome database for RBCs. For proteome profiling, we developed a proteomic method that combined: (a) fractionation into cytosolic and membrane fractions, (b) hemoglobin removal of the cytosolic fraction, (c) protein digestion, and (d) a novel step with pH reversed-phase peptide fractionation and final Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC ESI-MS/MS) analysis of each fraction. Combined transcriptome- and proteome- sequencing data identified, in silico, novel and striking immune functional networks for rainbow trout nucleated RBCs, which are mainly linked to innate and adaptive immunity. Functional pathways related to regulation of hematopoietic cell differentiation, antigen presentation via major histocompatibility complex class II (MHCII), leukocyte differentiation and regulation of leukocyte activation were identified. These preliminary findings further implicate nucleated RBCs in immune function, such as antigen presentation and leukocyte activation.

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

Science.gov (United States)

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Identifying Interbank Loans, Rates, and Claims Networks from Transactional Data

NARCIS (Netherlands)

Leon Rincon, C.E.; Cely, Jorge; Cadena, Carlos

2015-01-01

We identify interbank (i.e. non-collateralized) loans from the Colombian large-value payment system by implementing Furfine’s method. After identifying interbank loans from transactional data we obtain the interbank rates and claims without relying on financial institutions’ reported data.

A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology.

Science.gov (United States)

Goekoop, Rutger; Goekoop, Jaap G

2014-01-01

The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "Depression", "Mania", "Anxiety", "Psychosis", "Retardation", and "Behavioral Disorganization". Network metrics were used to quantify the continuities between the elementary syndromes. We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a 'Psychopathology Web'. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

Brokerage in SME networks

NARCIS (Netherlands)

Kirkels, Y.E.M.; Duysters, G.M.

2010-01-01

This study focuses on SME networks of design and high-tech companies in Southeast Netherlands. By highlighting the personal networks of members across design and high-tech industries, the study attempts to identify the main brokers in this dynamic environment. In addition, we investigate whether

Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network

OpenAIRE

Yoshi Nishitani; Chie Hosokawa; Yuko Mizuno-Matsumoto; Tomomitsu Miyoshi; Shinichi Tamura

2017-01-01

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using ...

Use intensity of social networks in southern Brazil

Directory of Open Access Journals (Sweden)

Rafaele Matte Wojahn

2017-08-01

Full Text Available A social network implies in connect people. This article aims to identify the use intensity of social network in Southern Brazil. The research was characterized by quantitative approach, descriptive, cross-sectional and survey, with a sample of 372 respondents. To data analysis was used descriptive analysis to characterize the sample, verify the access frequency of social networks and the daily access time, and Pearson’s Correlation to identify the daily access time and the social networks. The results indicated the social network used in more intensity is the Facebook and then Whatsapp, and the access occurs at home. However, all the social networks promote interactions toward users.

Inferring general relations between network characteristics from specific network ensembles.

Science.gov (United States)

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

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 ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. 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 makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

A Review of the Topologies Used in Smart Water Meter Networks: A Wireless Sensor Network Application

OpenAIRE

Marais, Jaco; Malekian, Reza; Ye, Ning; Wang, Ruchuan

2016-01-01

This paper presents several proposed and existing smart utility meter systems as well as their communication networks to identify the challenges of creating scalable smart water meter networks. Network simulations are performed on 3 network topologies (star, tree, and mesh) to determine their suitability for smart water meter networks. The simulations found that once a number of nodes threshold is exceeded the network’s delay increases dramatically regardless of implemented topology. This thr...

Network structure and travel time perception.

Science.gov (United States)

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

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.

EEG classification of emotions using emotion-specific brain functional network.

Science.gov (United States)

Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

2015-08-01

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

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...

Contributions of Social Networking for Innovation

Directory of Open Access Journals (Sweden)

Daniela Maria Cartoni

2013-04-01

Full Text Available This paper investigates the role of virtual social networks as a mechanism complementary to formal channels of technology transfer represented by ICT and by private centers of R & D in industry. The strengthening of Web 2.0 has provided the expansion of collaborative tools, in particular the social networks, with a strong influence on the spread of knowledge and innovation. To evaluate the potential of virtual networks, a survey had been conducted to identify and describe the characteristics of some of the major social networks used in Brazil (LinkedIn, Orkut and Twitter. Even this phenomenon is not mature, the study identified the potential and benefits of social networks as informal structures that help in generation of knowledge and innovation diffusion, as a field to be explored and developed.

Distributed Data Networks That Support Public Health Information Needs.

Science.gov (United States)

Tabano, David C; Cole, Elizabeth; Holve, Erin; Davidson, Arthur J

Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

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

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.

Network of TAMCNS: Identifying Influence Regions Within the GCSS-MC Database

Science.gov (United States)

2017-06-01

friendship. You made spending many hours at NPS an enjoyable experience. In particular, I want to acknowledge Dan for his assistance with math and LaTeX...Hall, 2001. [7] K. H. Rosen, Discrete Mathematics and Its Applications, 7th ed. New York, NY: McGraw-Hill, 2012. [8] D. Ortiz-Arroyo, “Discovering sets...observations/whats-a-voxel-and-what-can-it-tell-us-a-primer-on-fmri/ [28] A. Beveridge and J. Shan, “Network of thrones,” Math Horizons, vol. 23, no. 4

Building clinical networks: a developmental evaluation framework.

Science.gov (United States)

Carswell, Peter; Manning, Benjamin; Long, Janet; Braithwaite, Jeffrey

2014-05-01

Clinical networks have been designed as a cross-organisational mechanism to plan and deliver health services. With recent concerns about the effectiveness of these structures, it is timely to consider an evidence-informed approach for how they can be developed and evaluated. To document an evaluation framework for clinical networks by drawing on the network evaluation literature and a 5-year study of clinical networks. We searched literature in three domains: network evaluation, factors that aid or inhibit network development, and on robust methods to measure network characteristics. This material was used to build a framework required for effective developmental evaluation. The framework's architecture identifies three stages of clinical network development; partner selection, network design and network management. Within each stage is evidence about factors that act as facilitators and barriers to network growth. These factors can be used to measure progress via appropriate methods and tools. The framework can provide for network growth and support informed decisions about progress. For the first time in one place a framework incorporating rigorous methods and tools can identify factors known to affect the development of clinical networks. The target user group is internal stakeholders who need to conduct developmental evaluation to inform key decisions along their network's developmental pathway.

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

Science.gov (United States)

Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

2010-01-01

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily

Activity-Driven Influence Maximization in Social Networks

DEFF Research Database (Denmark)

Kumar, Rohit; Saleem, Muhammad Aamir; Calders, Toon

2017-01-01

-driven approach based on the identification of influence propagation patterns. In the first work, we identify so-called information-channels to model potential pathways for information spread, while the second work exploits how users in a location-based social network check in to locations in order to identify...... influential locations. To make our algorithms scalable, approximate versions based on sketching techniques from the data streams domain have been developed. Experiments show that in this way it is possible to efficiently find good seed sets for influence propagation in social networks.......Interaction networks consist of a static graph with a timestamped list of edges over which interaction took place. Examples of interaction networks are social networks whose users interact with each other through messages or location-based social networks where people interact by checking...

Network-based identification of biomarkers coexpressed with multiple pathways.

Science.gov (United States)

Guo, Nancy Lan; Wan, Ying-Wooi

2014-01-01

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

Social Network: a Cytoscape app for visualizing co-authorship networks.

Science.gov (United States)

Kofia, Victor; Isserlin, Ruth; Buchan, Alison M J; Bader, Gary D

2015-01-01

Networks that represent connections between individuals can be valuable analytic tools. The Social Network Cytoscape app is capable of creating a visual summary of connected individuals automatically. It does this by representing relationships as networks where each node denotes an individual and an edge linking two individuals represents a connection. The app focuses on creating visual summaries of individuals connected by co-authorship links in academia, created from bibliographic databases like PubMed, Scopus and InCites. The resulting co-authorship networks can be visualized and analyzed to better understand collaborative research networks or to communicate the extent of collaboration and publication productivity among a group of researchers, like in a grant application or departmental review report. It can also be useful as a research tool to identify important research topics, researchers and papers in a subject area.

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.

Ecological network analysis for a virtual water network.

Science.gov (United States)

Fang, Delin; Chen, Bin

2015-06-02

The notions of virtual water flows provide important indicators to manifest the water consumption and allocation between different sectors via product transactions. However, the configuration of virtual water network (VWN) still needs further investigation to identify the water interdependency among different sectors as well as the network efficiency and stability in a socio-economic system. Ecological network analysis is chosen as a useful tool to examine the structure and function of VWN and the interactions among its sectors. A balance analysis of efficiency and redundancy is also conducted to describe the robustness (RVWN) of VWN. Then, network control analysis and network utility analysis are performed to investigate the dominant sectors and pathways for virtual water circulation and the mutual relationships between pairwise sectors. A case study of the Heihe River Basin in China shows that the balance between efficiency and redundancy is situated on the left side of the robustness curve with less efficiency and higher redundancy. The forestation, herding and fishing sectors and industrial sectors are found to be the main controllers. The network tends to be more mutualistic and synergic, though some competitive relationships that weaken the virtual water circulation still exist.

A Study of Scientometric Methods to Identify Emerging Technologies via Modeling of Milestones

Energy Technology Data Exchange (ETDEWEB)

Abercrombie, Robert K [ORNL; Udoeyop, Akaninyene W [ORNL; Schlicher, Bob G [ORNL

2012-01-01

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, patents, news archives, and online 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.

A network approach to leadership

DEFF Research Database (Denmark)

Lewis, Jenny; Ricard, Lykke Margot

Leaders’ ego-networks within an organization are pivotal as focal points that point to other organizational factors such as innovation capacity and leadership effectiveness. The aim of the paper is to provide a framework for exploring leaders’ ego-networks within the boundary of an organization. We...... a survey of senior administrators and politicians from Copenhagen municipality, we examine strategic information networks. Whole network analysis is used first to identify important individuals on the basis of centrality measures. The ego-networks of these individuals are then analysed to examine...

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…

A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology.

Directory of Open Access Journals (Sweden)

Rutger Goekoop

Full Text Available INTRODUCTION: The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. AIM: To examine the ability of network community detection (NCD to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. METHODS: 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS. Principal component analysis (PCA was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS. An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS was optimized using a previously published technique. RESULTS: In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "Depression", "Mania", "Anxiety", "Psychosis", "Retardation", and "Behavioral Disorganization". Network metrics were used to quantify the continuities between the elementary syndromes. CONCLUSION: We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a 'Psychopathology Web'. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

How has climate change altered network connectivity in a mountain stream network?

Science.gov (United States)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.; Johnson, S.

2017-12-01

migration) and indirectly (e.g., stream temperature modeling). Additionally, our results inform management and regulatory needs such as estimating connectivity for entire river networks as a basis for regulation, and identifying the complexity of a shifting baseline in identifying a regulatory basis.

Exploring network organization in practice

DEFF Research Database (Denmark)

Hu, Yimei; Sørensen, Olav Jull

Constructing a network organization for global R&D is presented as a common sense practice in existing literature. However, there are still queries about the network organization, such as the persistence of hierarchies which make a network organization merely a “bureaucracy-lite” organization....... Furthermore, in practice, we rarely see radical organizational change towards a network organization that adopts an internal market. The co-existence of market, hierarchy and network triggered research interest. A multiple case study of three transnational corporations’ global R&D organization shows...... 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...

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.

Detecting Network Vulnerabilities Through Graph TheoreticalMethods

Energy Technology Data Exchange (ETDEWEB)

Cesarz, Patrick; Pomann, Gina-Maria; Torre, Luis de la; Villarosa, Greta; Flournoy, Tamara; Pinar, Ali; Meza Juan

2007-09-30

Identifying vulnerabilities in power networks is an important problem, as even a small number of vulnerable connections can cause billions of dollars in damage to a network. In this paper, we investigate a graph theoretical formulation for identifying vulnerabilities of a network. We first try to find the most critical components in a network by finding an optimal solution for each possible cutsize constraint for the relaxed version of the inhibiting bisection problem, which aims to find loosely coupled subgraphs with significant demand/supply mismatch. Then we investigate finding critical components by finding a flow assignment that minimizes the maximum among flow assignments on all edges. We also report experiments on IEEE 30, IEEE 118, and WSCC 179 benchmark power networks.

A Machine Learning Approach for Identifying and Classifying Faults in Wireless Sensor Networks

NARCIS (Netherlands)

Warriach, Ehsan Ullah; Aiello, Marco; Tei, Kenji

2012-01-01

Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data

Network Approach in Political Communication Studies

Directory of Open Access Journals (Sweden)

Нина Васильевна Опанасенко

2013-12-01

Full Text Available The article is devoted to issues of network approach application in political communication studies. The author considers communication in online and offline areas and gives the definition of rhizome, its characteristics, identifies links between rhizome and network approach. The author also analyses conditions and possibilities of the network approach in modern political communication. Both positive and negative features of the network approach are emphasized.

Bayesian Network Induction via Local Neighborhoods

National Research Council Canada - National Science Library

Margaritis, Dimitris

1999-01-01

.... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...

Genomic analyses identify molecular subtypes of pancreatic cancer.

Science.gov (United States)

Bailey, Peter; Chang, David K; Nones, Katia; Johns, Amber L; Patch, Ann-Marie; Gingras, Marie-Claude; Miller, David K; Christ, Angelika N; Bruxner, Tim J C; Quinn, Michael C; Nourse, Craig; Murtaugh, L Charles; Harliwong, Ivon; Idrisoglu, Senel; Manning, Suzanne; Nourbakhsh, Ehsan; Wani, Shivangi; Fink, Lynn; Holmes, Oliver; Chin, Venessa; Anderson, Matthew J; Kazakoff, Stephen; Leonard, Conrad; Newell, Felicity; Waddell, Nick; Wood, Scott; Xu, Qinying; Wilson, Peter J; Cloonan, Nicole; Kassahn, Karin S; Taylor, Darrin; Quek, Kelly; Robertson, Alan; Pantano, Lorena; Mincarelli, Laura; Sanchez, Luis N; Evers, Lisa; Wu, Jianmin; Pinese, Mark; Cowley, Mark J; Jones, Marc D; Colvin, Emily K; Nagrial, Adnan M; Humphrey, Emily S; Chantrill, Lorraine A; Mawson, Amanda; Humphris, Jeremy; Chou, Angela; Pajic, Marina; Scarlett, Christopher J; Pinho, Andreia V; Giry-Laterriere, Marc; Rooman, Ilse; Samra, Jaswinder S; Kench, James G; Lovell, Jessica A; Merrett, Neil D; Toon, Christopher W; Epari, Krishna; Nguyen, Nam Q; Barbour, Andrew; Zeps, Nikolajs; Moran-Jones, Kim; Jamieson, Nigel B; Graham, Janet S; Duthie, Fraser; Oien, Karin; Hair, Jane; Grützmann, Robert; Maitra, Anirban; Iacobuzio-Donahue, Christine A; Wolfgang, Christopher L; Morgan, Richard A; Lawlor, Rita T; Corbo, Vincenzo; Bassi, Claudio; Rusev, Borislav; Capelli, Paola; Salvia, Roberto; Tortora, Giampaolo; Mukhopadhyay, Debabrata; Petersen, Gloria M; Munzy, Donna M; Fisher, William E; Karim, Saadia A; Eshleman, James R; Hruban, Ralph H; Pilarsky, Christian; Morton, Jennifer P; Sansom, Owen J; Scarpa, Aldo; Musgrove, Elizabeth A; Bailey, Ulla-Maja Hagbo; Hofmann, Oliver; Sutherland, Robert L; Wheeler, David A; Gill, Anthony J; Gibbs, Richard A; Pearson, John V; Waddell, Nicola; Biankin, Andrew V; Grimmond, Sean M

2016-03-03

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

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

Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

Directory of Open Access Journals (Sweden)

Yunpeng Wang

2014-01-01

Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

Three neural network based sensor systems for environmental monitoring

International Nuclear Information System (INIS)

Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

1994-05-01

Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

Identifying gene coexpression networks underlying the dynamic regulation of wood-forming tissues in Populus under diverse environmental conditions.

Science.gov (United States)

Zinkgraf, Matthew; Liu, Lijun; Groover, Andrew; Filkov, Vladimir

2017-06-01

Trees modify wood formation through integration of environmental and developmental signals in complex but poorly defined transcriptional networks, allowing trees to produce woody tissues appropriate to diverse environmental conditions. In order to identify relationships among genes expressed during wood formation, we integrated data from new and publically available datasets in Populus. These datasets were generated from woody tissue and include transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments. Coexpression modules were calculated, each of which contains genes showing similar expression patterns across experimental conditions, genotypes and treatments. Conserved gene coexpression modules (four modules totaling 8398 genes) were identified that were highly preserved across diverse environmental conditions and genetic backgrounds. Functional annotations as well as correlations with specific experimental treatments associated individual conserved modules with distinct biological processes underlying wood formation, such as cell-wall biosynthesis, meristem development and epigenetic pathways. Module genes were also enriched for DNase I hypersensitivity footprints and binding from four transcription factors associated with wood formation. The conserved modules are excellent candidates for modeling core developmental pathways common to wood formation in diverse environments and genotypes, and serve as testbeds for hypothesis generation and testing for future studies. No claim to original US government works. New Phytologist © 2017 New Phytologist Trust.

In Silico Functional Networks Identified in Fish Nucleated Red Blood Cells by Means of Transcriptomic and Proteomic Profiling

Directory of Open Access Journals (Sweden)

Sara Puente-Marin

2018-04-01

Full Text Available Nucleated red blood cells (RBCs of fish have, in the last decade, been implicated in several immune-related functions, such as antiviral response, phagocytosis or cytokine-mediated signaling. RNA-sequencing (RNA-seq and label-free shotgun proteomic analyses were carried out for in silico functional pathway profiling of rainbow trout RBCs. For RNA-seq, a de novo assembly was conducted, in order to create a transcriptome database for RBCs. For proteome profiling, we developed a proteomic method that combined: (a fractionation into cytosolic and membrane fractions, (b hemoglobin removal of the cytosolic fraction, (c protein digestion, and (d a novel step with pH reversed-phase peptide fractionation and final Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC ESI-MS/MS analysis of each fraction. Combined transcriptome- and proteome- sequencing data identified, in silico, novel and striking immune functional networks for rainbow trout nucleated RBCs, which are mainly linked to innate and adaptive immunity. Functional pathways related to regulation of hematopoietic cell differentiation, antigen presentation via major histocompatibility complex class II (MHCII, leukocyte differentiation and regulation of leukocyte activation were identified. These preliminary findings further implicate nucleated RBCs in immune function, such as antigen presentation and leukocyte activation.

A framework to find the logic backbone of a biological network.

Science.gov (United States)

Maheshwari, Parul; Albert, Réka

2017-12-06

Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network. In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures. The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Science.gov (United States)

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Reciprocity of weighted networks.

Science.gov (United States)

Squartini, Tiziano; Picciolo, Francesco; Ruzzenenti, Franco; Garlaschelli, Diego

2013-01-01

In directed networks, reciprocal links have dramatic effects on dynamical processes, network growth, and higher-order structures such as motifs and communities. While the reciprocity of binary networks has been extensively studied, that of weighted networks is still poorly understood, implying an ever-increasing gap between the availability of weighted network data and our understanding of their dyadic properties. Here we introduce a general approach to the reciprocity of weighted networks, and define quantities and null models that consistently capture empirical reciprocity patterns at different structural levels. We show that, counter-intuitively, previous reciprocity measures based on the similarity of mutual weights are uninformative. By contrast, our measures allow to consistently classify different weighted networks according to their reciprocity, track the evolution of a network's reciprocity over time, identify patterns at the level of dyads and vertices, and distinguish the effects of flux (im)balances or other (a)symmetries from a true tendency towards (anti-)reciprocation.

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....

Networking activities in technology-based entrepreneurial teams

DEFF Research Database (Denmark)

Neergaard, Helle

2005-01-01

Based on social network theoy, this article investigates the distribution of networking roles and responsibilities in entrepreneurial founding teams. Its focus is on the team as a collection of individuals, thus allowing the research to address differences in networking patterns. It identifies six...... central networking activities and shows that not all founding team members are equally active 'networkers'. The analyses show that team members prioritize different networking activities and that one member in particular has extensive networking activities whereas other memebrs of the team are more...

A distributed water level network in ephemeral river reaches to identify hydrological processes within anthropogenic catchments

Science.gov (United States)

Sarrazin, B.; Braud, I.; Lagouy, M.; Bailly, J. S.; Puech, C.; Ayroles, H.

2009-04-01

In order to study the impact of land use change on the water cycle, distributed hydrological models are more and more used, because they have the ability to take into account the land surface heterogeneity and its evolution due to anthropogenic pressure. These models provide continuous distributed simulations of streamflow, runoff, soil moisture, etc, which, ideally, should be evaluated against continuous distributed measurements, taken at various scales and located in nested sub-catchments. Distributed network of streamflow gauging stations are in general scarce and very expensive to maintain. Furthermore, they can hardly be installed in the upstream parts of the catchments where river beds are not well defined. In this paper, we present an alternative to these standard streamflow gauging stations network, based on self powered high resolution water level sensors using a capacitive water height data logger. One of their advantages is that they can be installed even in ephemeral reaches and from channel head locations to high order streams. Furthermore, these innovative and easily adaptable low cost sensors offer the possibility to develop in the near future, a wireless network application. Such a network, including 15 sensors has been set up on nested watersheds in small and intermittent streams of a 7 km² catchment, located in the mountainous "Mont du Lyonnais" area, close to the city of Lyon, France. The land use of this catchment is mostly pasture, crop and forest, but the catchment is significantly affected by human activities, through the existence of a dense roads and paths network and urbanized areas. The equipment provides water levels survey during precipitation events in the hydrological network with a very accurate time step (2 min). Water levels can be related to runoff production and catchment response as a function of scale. This response will depend, amongst other, on variable soil water storage capacity, physiographic data and characteristics of

Application of OLAM network in X-ray spectral analysis

International Nuclear Information System (INIS)

Liu Yinbing; Zhou Rongsheng

2001-01-01

The author describes a new approach to the automatic radioisotope identification problem based on the use of OLAM network. Different from the traditional methods, the OLAM network takes the spectrum as a whole comparing its shape with the patterns learned during the training period of the network. It is found that the OLAM network, once adequately trained, is quite suitable to identify a given isotope present in a mixture of elements as well as the relative proportions of each identified substance. Preliminary results are good enough to consider OLAM network as powerful and simple tools in the automatic spectrum analysis

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

International Nuclear Information System (INIS)

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

2008-01-01

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 avg .

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

Directory of Open Access Journals (Sweden)

Jakubski J.

2013-06-01

Full Text Available Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. 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. The effect of changes in the training set quantity on the quality of the network is presented in this article. It has been shown that a small change in the data set would change the quality of the network, and may also make it necessary to change the type of network in order to obtain good results.

Network perturbation by recurrent regulatory variants in cancer.

Directory of Open Access Journals (Sweden)

Kiwon Jang

2017-03-01

Full Text Available Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

The relation between global migration and trade networks

Science.gov (United States)

Sgrignoli, Paolo; Metulini, Rodolfo; Schiavo, Stefano; Riccaboni, Massimo

2015-01-01

In this paper we develop a methodology to analyze and compare multiple global networks, focusing our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity, where to assure comparability across networks we apply a hypergeometric filter that lets us identify those links which intensity is significantly higher than expected. Next, proposing a new way to define country neighbors based on the most intense links in the trade network, we use spatial econometrics techniques to measure the effect of migration on international trade, while controlling for network interdependences. Overall, we find that migration significantly boosts trade across countries and we are able to identify product categories for which this effect is particularly strong.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

Directory of Open Access Journals (Sweden)

Min-Joo Kang

Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

Science.gov (United States)

Kang, Min-Joo; Kang, Je-Won

2016-01-01

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks.

Science.gov (United States)

Fischer, Martin; Grossmann, Patrick; Padi, Megha; DeCaprio, James A

2016-07-27

Cell cycle (CC) and TP53 regulatory networks are frequently deregulated in cancer. While numerous genome-wide studies of TP53 and CC-regulated genes have been performed, significant variation between studies has made it difficult to assess regulation of any given gene of interest. To overcome the limitation of individual studies, we developed a meta-analysis approach to identify high confidence target genes that reflect their frequency of identification in independent datasets. Gene regulatory networks were generated by comparing differential expression of TP53 and CC-regulated genes with chromatin immunoprecipitation studies for TP53, RB1, E2F, DREAM, B-MYB, FOXM1 and MuvB. RNA-seq data from p21-null cells revealed that gene downregulation by TP53 generally requires p21 (CDKN1A). Genes downregulated by TP53 were also identified as CC genes bound by the DREAM complex. The transcription factors RB, E2F1 and E2F7 bind to a subset of DREAM target genes that function in G1/S of the CC while B-MYB, FOXM1 and MuvB control G2/M gene expression. Our approach yields high confidence ranked target gene maps for TP53, DREAM, MMB-FOXM1 and RB-E2F and enables prediction and distinction of CC regulation. A web-based atlas at www.targetgenereg.org enables assessing the regulation of any human gene of interest. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Anomaly Detection in Dynamic Networks

Energy Technology Data Exchange (ETDEWEB)

Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2014-10-14

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the

Structure of Retail Services in the Brazilian Hosting Network

Directory of Open Access Journals (Sweden)

Claudio Zancan

2015-08-01

Full Text Available this research has identified Brazilian hosting networks through infrastructure services indicators that it was sold to tourists in organizations that form these networks. The theory consulted the discussion of structural techniques present in Social Network Analysis. The study has three stages: documental research, creation of Tourism database and interviews. The results identified three networks with the highest expression in Brazil formed by hotels, lodges, and resorts. Different char-acteristics of infrastructure and services were observed between hosting networks. Future studies suggest a comparative analysis of structural indicators present in other segments of tourism services, as well as the existing international influ-ence on the development of the Brazilian hosting networks.

A Network Disruption Modeling Tool

National Research Council Canada - National Science Library

Leinart, James

1998-01-01

Given that network disruption has been identified as a military objective and C2-attack has been identified as the mechanism to accomplish this objective, a target set must be acquired and priorities...

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

The network researchers' network: A social network analysis of the IMP Group 1985-2006

DEFF Research Database (Denmark)

Henneberg, Stephan C. M.; Ziang, Zhizhong; Naudé, Peter

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...

Cooperative and networking strategies in small business

CERN Document Server

Ferreira, João

2017-01-01

The book aims to collect the most recent research and best practices in the cooperative and networking small business field identifying new theoretical models and describing the relationship between cooperation and networks in the small business strategy context. It examines different concepts and analytical techniques better understand the links between cooperative strategies and networks in small business. It also studies the existing economic conditions of network and strategic implications to small business from the point of view of their internal and external consistency. Cooperation and networks is a fashionable topic. It is receiving increasing attention in popular management publications, as well as specialized academic journals. Cooperation between firms and industries is a means of leveraging and aggregating knowledge also generating direct benefits in terms of innovation, productivity and competitiveness. Various options and decisions made within the framework of strategic alliances may be identifi...

23 CFR 658.21 - Identification of National Network.

Science.gov (United States)

2010-04-01

... 23 Highways 1 2010-04-01 2010-04-01 false Identification of National Network. 658.21 Section 658... Identification of National Network. (a) To identify the National Network, a State may sign the routes or provide maps of lists of highways describing the National Network. (b) Exceptional local conditions on the...

High-throughput profiling of signaling networks identifies mechanism-based combination therapy to eliminate microenvironmental resistance in acute myeloid leukemia.

Science.gov (United States)

Zeng, Zhihong; Liu, Wenbin; Tsao, Twee; Qiu, YiHua; Zhao, Yang; Samudio, Ismael; Sarbassov, Dos D; Kornblau, Steven M; Baggerly, Keith A; Kantarjian, Hagop M; Konopleva, Marina; Andreeff, Michael

2017-09-01

The bone marrow microenvironment is known to provide a survival advantage to residual acute myeloid leukemia cells, possibly contributing to disease recurrence. The mechanisms by which stroma in the microenvironment regulates leukemia survival remain largely unknown. Using reverse-phase protein array technology, we profiled 53 key protein molecules in 11 signaling pathways in 20 primary acute myeloid leukemia samples and two cell lines, aiming to understand stroma-mediated signaling modulation in response to the targeted agents temsirolimus (MTOR), ABT737 (BCL2/BCL-XL), and Nutlin-3a (MDM2), and to identify the effective combination therapy targeting acute myeloid leukemia in the context of the leukemia microenvironment. Stroma reprogrammed signaling networks and modified the sensitivity of acute myeloid leukemia samples to all three targeted inhibitors. Stroma activated AKT at Ser473 in the majority of samples treated with single-agent ABT737 or Nutlin-3a. This survival mechanism was partially abrogated by concomitant treatment with temsirolimus plus ABT737 or Nutlin-3a. Mapping the signaling networks revealed that combinations of two inhibitors increased the number of affected proteins in the targeted pathways and in multiple parallel signaling, translating into facilitated cell death. These results demonstrated that a mechanism-based selection of combined inhibitors can be used to guide clinical drug selection and tailor treatment regimens to eliminate microenvironment-mediated resistance in acute myeloid leukemia. Copyright© 2017 Ferrata Storti Foundation.

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...

Methodological aspects of network assets accounting

Directory of Open Access Journals (Sweden)

Yuhimenko-Nazaruk I.A.

2017-08-01

Full Text Available The necessity of using innovative tools of processing and representation of information about network assets is substantiated. The suggestions for displaying network assets in accounts are presented. The main reasons for the need to display the network assets in the financial statements of all members of the network structure (the economic essence of network assets as the object of accounting; the non-additional model for the formation of the value of network assets; the internetworking mechanism for the formation of the value of network assets are identified. The stages of accounting valuation of network assets are allocated and substantiated. The analytical table for estimating the value of network assets and additional network capital in accounting is developed. The order of additional network capital reflection in accounting is developed. The method of revaluation of network assets in accounting in the broad sense is revealed. The order of accounting of network assets with increasing or decreasing the number of participants in the network structure is determined.

Social network analysis applied to team sports analysis

CERN Document Server

Clemente, Filipe Manuel; Mendes, Rui Sousa

2016-01-01

Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

Community pharmacist participation in a practice-based research network: a report from the Medication Safety Research Network of Indiana (Rx-SafeNet).

Science.gov (United States)

Patel, Puja; Hemmeger, Heather; Kozak, Mary Ann; Gernant, Stephanie A; Snyder, Margie E

2015-01-01

To describe the experiences and opinions of pharmacists serving as site coordinators for the Medication Safety Research Network of Indiana (Rx-SafeNet). Retail chain, independent, and hospital/health system outpatient community pharmacies throughout Indiana, with a total of 127 pharmacy members represented by 26 site coordinators. Rx-SafeNet, a statewide practice-based research network (PBRN) formed in 2010 and administered by the Purdue University College of Pharmacy. Barriers and facilitators to participation in available research studies, confidence participating in research, and satisfaction with overall network communication. 22 of 26 site coordinators participated, resulting in an 85% response rate. Most (72.2%) of the respondents had received a doctor of pharmacy degree, and 13.6% had postgraduate year (PGY)1 residency training. The highest reported benefits of PBRN membership were an enhanced relationship with the Purdue University College of Pharmacy (81% agreed or strongly agreed) and enhanced professional development (80% agreed or strongly agreed). Time constraints were identified as the greatest potential barrier to network participation, reported by 62% of respondents. In addition, the majority (59%) of survey respondents identified no prior research experience. Last, respondents' confidence in performing research appeared to increase substantially after becoming network members, with 43% reporting a lack of confidence in engaging in research before joining the network compared with 90% reporting confidence after joining the network. In general, Rx-SafeNet site coordinators appeared to experience increased confidence in research engagement after joining the network. While respondents identified a number of benefits associated with network participation, concerns about potential time constraints remained a key barrier to participation. These findings will assist network leadership in identifying opportunities to positively increase member participation

Semaphore network encryption report

Science.gov (United States)

Johnson, Karen L.

1994-03-01

This paper documents the results of a preliminary assessment performed on the commercial off-the-shelf (COTS) Semaphore Communications Corporation (SCC) Network Security System (NSS). The Semaphore NSS is a family of products designed to address important network security concerns, such as network source address authentication and data privacy. The assessment was performed in the INFOSEC Core Integration Laboratory, and its scope was product usability focusing on interoperability and system performance in an existing operational network. Included in this paper are preliminary findings. Fundamental features and functionality of the Semaphore NSS are identified, followed by details of the assessment, including test descriptions and results. A summary of test results and future plans are also included. These findings will be useful to those investigating the use of commercially available solutions to network authentication and data privacy.

Eight challenges for network epidemic models

Directory of Open Access Journals (Sweden)

Lorenzo Pellis

2015-03-01

Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

Optimality principles in the regulation of metabolic networks.

Science.gov (United States)

Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas

2012-08-29

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

Directory of Open Access Journals (Sweden)

Xu Lei

Full Text Available EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs are extracted using spatial independent component analysis (ICA in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA. Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI. Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.

Network Physiology: How Organ Systems Dynamically Interact

Science.gov (United States)

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

2015-01-01

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

Network Physiology: How Organ Systems Dynamically Interact.

Science.gov (United States)

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

2015-01-01

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

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

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

Current-flow efficiency of networks

Science.gov (United States)

Liu, Kai; Yan, Xiaoyong

2018-02-01

Many real-world networks, from infrastructure networks to social and communication networks, can be formulated as flow networks. How to realistically measure the transport efficiency of these networks is of fundamental importance. The shortest-path-based efficiency measurement has limitations, as it assumes that flow travels only along those shortest paths. Here, we propose a new metric named current-flow efficiency, in which we calculate the average reciprocal effective resistance between all pairs of nodes in the network. This metric takes the multipath effect into consideration and is more suitable for measuring the efficiency of many real-world flow equilibrium networks. Moreover, this metric can handle a disconnected graph and can thus be used to identify critical nodes and edges from the efficiency-loss perspective. We further analyze how the topological structure affects the current-flow efficiency of networks based on some model and real-world networks. Our results enable a better understanding of flow networks and shed light on the design and improvement of such networks with higher transport efficiency.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Directory of Open Access Journals (Sweden)

Helmut Schmidt

2014-11-01

Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic

Advancing Health Professions Education Research by Creating a Network of Networks.

Science.gov (United States)

Carney, Patricia A; Brandt, Barbara; Dekhtyar, Michael; Holmboe, Eric S

2018-02-27

Producing the best evidence to show educational outcomes, such as competency achievement and credentialing effectiveness, across the health professions education continuum will require large multisite research projects and longitudinal studies. Current limitations that must be overcome to reach this goal include the prevalence of single-institution study designs, assessments of a single curricular component, and cross-sectional study designs that provide only a snapshot in time of a program or initiative rather than a longitudinal perspective.One solution to overcoming these limitations is to develop a network of networks that collaborates, using longitudinal approaches, across health professions and regions of the United States. Currently, individual networks are advancing educational innovation toward understanding the effectiveness of educational and credentialing programs. Examples of such networks include: (1) the American Medical Association's Accelerating Change in Medical Education initiative, (2) the National Center for Interprofessional Practice and Education, and (3) the Accreditation Council for Graduate Medical Education's Accreditation System. In this Invited Commentary, the authors briefly profile these existing networks, identify their progress and the challenges they have encountered, and propose a vigorous way forward toward creating a national network of networks designed to determine the effectiveness of health professions education and credentialing.

Encoding dependence in Bayesian causal networks

Science.gov (United States)

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Ranking Entities in Networks via Lefschetz Duality

DEFF Research Database (Denmark)

Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Poulsen, Bjarne

2014-01-01

then be ranked according to how essential their positions are in the network by considering the effect of their respective absences. Defining a ranking of a network which takes the individual position of each entity into account has the purpose of assigning different roles to the entities, e.g. agents......, in the network. In this paper it is shown that the topology of a given network induces a ranking of the entities in the network. Further, it is demonstrated how to calculate this ranking and thus how to identify weak sub-networks in any given network....

Network theory and its applications in economic systems

Science.gov (United States)

Huang, Xuqing

This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. 2) We analytically demonstrates that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the

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.

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.

Centrality in earthquake multiplex networks

Science.gov (United States)

Lotfi, Nastaran; Darooneh, Amir Hossein; Rodrigues, Francisco A.

2018-06-01

Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.

Analyzing negative ties in social networks

Directory of Open Access Journals (Sweden)

Mankirat Kaur

2016-03-01

Full Text Available Online social networks are a source of sharing information and maintaining personal contacts with other people through social interactions and thus forming virtual communities online. Social networks are crowded with positive and negative relations. Positive relations are formed by support, endorsement and friendship and thus, create a network of well-connected users whereas negative relations are a result of opposition, distrust and avoidance creating disconnected networks. Due to increase in illegal activities such as masquerading, conspiring and creating fake profiles on online social networks, exploring and analyzing these negative activities becomes the need of hour. Usually negative ties are treated in same way as positive ties in many theories such as balance theory and blockmodeling analysis. But the standard concepts of social network analysis do not yield same results in respect of each tie. This paper presents a survey on analyzing negative ties in social networks through various types of network analysis techniques that are used for examining ties such as status, centrality and power measures. Due to the difference in characteristics of flow in positive and negative tie networks some of these measures are not applicable on negative ties. This paper also discusses new methods that have been developed specifically for analyzing negative ties such as negative degree, and h∗ measure along with the measures based on mixture of positive and negative ties. The different types of social network analysis approaches have been reviewed and compared to determine the best approach that can appropriately identify the negative ties in online networks. It has been analyzed that only few measures such as Degree and PN centrality are applicable for identifying outsiders in network. For applicability in online networks, the performance of PN measure needs to be verified and further, new measures should be developed based upon negative clique concept.

A Network of Networks Perspective on Global Trade.

Science.gov (United States)

Maluck, Julian; Donner, Reik V

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990-2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector's role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network's substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed to

Coordinating knowledge transfer within manufacturing networks

DEFF Research Database (Denmark)

Yang, Cheng; Johansen, John; Boer, Harry

2008-01-01

Along with increasing globalization, the management of international manufacturing networks is becoming increasingly important for industrial companies. This paper mainly focuses on the coordination of knowledge transfer within manufacturing networks. In this context, we propose the time......-place matrix as a tool for mapping the distribution of knowledge within manufacturing networks. Using this tool, four important questions about the coordination of knowledge transfer within a manufacturing network are identified: know-where, know-what, know-when, know-how to transfer. The relationships among...

Omics analysis of human bone to identify genes and molecular networks regulating skeletal remodeling in health and disease.

Science.gov (United States)

Reppe, Sjur; Datta, Harish K; Gautvik, Kaare M

2017-08-01

The skeleton is a metabolically active organ throughout life where specific bone cell activity and paracrine/endocrine factors regulate its morphogenesis and remodeling. In recent years, an increasing number of reports have used multi-omics technologies to characterize subsets of bone biological molecular networks. The skeleton is affected by primary and secondary disease, lifestyle and many drugs. Therefore, to obtain relevant and reliable data from well characterized patient and control cohorts are vital. Here we provide a brief overview of omics studies performed on human bone, of which our own studies performed on trans-iliacal bone biopsies from postmenopausal women with osteoporosis (OP) and healthy controls are among the first and largest. Most other studies have been performed on smaller groups of patients, undergoing hip replacement for osteoarthritis (OA) or fracture, and without healthy controls. The major findings emerging from the combined studies are: 1. Unstressed and stressed bone show profoundly different gene expression reflecting differences in bone turnover and remodeling and 2. Omics analyses comparing healthy/OP and control/OA cohorts reveal characteristic changes in transcriptomics, epigenomics (DNA methylation), proteomics and metabolomics. These studies, together with genome-wide association studies, in vitro observations and transgenic animal models have identified a number of genes and gene products that act via Wnt and other signaling systems and are highly associated to bone density and fracture. Future challenge is to understand the functional interactions between bone-related molecular networks and their significance in OP and OA pathogenesis, and also how the genomic architecture is affected in health and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

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....

Detecting Hidden Hierarchy of Non Hierarchical Terrorist Networks

DEFF Research Database (Denmark)

Memon, Nasrullah

measures (and combinations of them) to identify key players (important nodes) in terrorist networks. Our recently introduced techniques and algorithms (which are also implemented in the investigative data mining toolkit known as iMiner) will be particularly useful for law enforcement agencies that need...... to analyze terrorist networks and prioritize their targets. Applying recently introduced mathematical methods for constructing the hidden hierarchy of "nonhierarchical" terrorist networks; we present case studies of the terrorist attacks occurred / planned in the past, in order to identify hidden hierarchy...

Tips for a Successful Job Search. PEPNet Tipsheet

Science.gov (United States)

PEPNet-Northeast, 2009

2009-01-01

Looking for a job can be a challenging experience. It helps to have a positive attitude and to be well prepared for every aspect of the job search. This tipsheet uses information from the NTID (National Technical Institute for the Deaf) Center on Employment (NCE) at Rochester Institute of Technology/National Technical Institute for the Deaf and…

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…

Persistent Identifiers for Dutch cultural heritage institutions

Science.gov (United States)

Ras, Marcel; Kruithof, Gijsbert

2016-04-01

Over the past years, more and more collections belonging to archives, libraries, media, museums, and knowledge institutes are being digitised and made available online. These are exciting times for ALM institutions. They are realising that, in the information society, their collections are goldmines. Unfortunately most heritage institutions in the Netherlands do not yet meet the basic preconditions for long-term availability of their collections. The digital objects often have no long lasting fixed reference yet. URL's and web addresses change. Some digital objects that were referenced in Europeana and other portals can no longer be found. References in scientific articles have a very short life span, which is damaging for scholarly research. In 2015, the Dutch Digital Heritage Network (NDE) has started a two-year work program to co-ordinate existing initiatives in order to improve the (long-term) accessibility of the Dutch digital heritage for a wide range of users, anytime, anyplace. The Digital Heritage Network is a partnership established on the initiative of the Ministry of Education, Culture and Science. The members of the NDE are large, national institutions that strive to professionally preserve and manage digital data, e.g. the National Library, The Netherlands Institute for Sound and Vision, the Netherlands Cultural Heritage Agency, the Royal Netherlands Academy of Arts and Sciences, the National Archive of the Netherlands and the DEN Foundation, and a growing number of associations and individuals both within and outside the heritage sector. By means of three work programmes the goals of the Network should be accomplished and improve the visibility, the usability and the sustainability of digital heritage. Each programme contains of a set of projects. Within the sustainability program a project on creating a model for persistent identifiers is taking place. The main goals of the project are (1) raise awareness among cultural heritage institutions on the

Cooperative Team Networks

Science.gov (United States)

2016-06-01

team processes, such as identifying motifs of dynamic communication exchanges which goes well beyond simple dyadic and triadic configurations; as well...new metrics and ways to formulate team processes, such as identifying motifs of dynamic communication exchanges which goes well beyond simple dyadic ...sensing, communication , information, and decision networks - Darryl Ahner (AFIT: Air Force Inst Tech) Panel Session: Mathematical Models of

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.

The hierarchical brain network for face recognition.

Science.gov (United States)

Zhen, Zonglei; Fang, Huizhen; Liu, Jia

2013-01-01

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

Measuring Health Information Dissemination and Identifying Target Interest Communities on Twitter: Methods Development and Case Study of the @SafetyMD Network.

Science.gov (United States)

Kandadai, Venk; Yang, Haodong; Jiang, Ling; Yang, Christopher C; Fleisher, Linda; Winston, Flaura Koplin

2016-05-05

Little is known about the ability of individual stakeholder groups to achieve health information dissemination goals through Twitter. This study aimed to develop and apply methods for the systematic evaluation and optimization of health information dissemination by stakeholders through Twitter. Tweet content from 1790 followers of @SafetyMD (July-November 2012) was examined. User emphasis, a new indicator of Twitter information dissemination, was defined and applied to retweets across two levels of retweeters originating from @SafetyMD. User interest clusters were identified based on principal component analysis (PCA) and hierarchical cluster analysis (HCA) of a random sample of 170 followers. User emphasis of keywords remained across levels but decreased by 9.5 percentage points. PCA and HCA identified 12 statistically unique clusters of followers within the @SafetyMD Twitter network. This study is one of the first to develop methods for use by stakeholders to evaluate and optimize their use of Twitter to disseminate health information. Our new methods provide preliminary evidence that individual stakeholders can evaluate the effectiveness of health information dissemination and create content-specific clusters for more specific targeted messaging.

Business socialising: women’s social networking perceptions

OpenAIRE

13104802 - Bogaards, Marlene; Mostert, Karina; 10868445 - De Klerk, Saskia

2012-01-01

The primary research objective of the study was to investigate the perceptions of the social networking practices of businesswomen. A non-probability purposive voluntary sample, followed by snowball sampling, was used to select businesswomen (n = 31) living and working in the Gauteng province for in-depth interviews. Various perceptions of businesswomen of social networking practices were identified. A number of networking challenges that businesswomen experience in their social networking ef...

Effects of multi-state links in network community detection

International Nuclear Information System (INIS)

Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

2017-01-01

A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

Network propagation in the cytoscape cyberinfrastructure.

Science.gov (United States)

Carlin, Daniel E; Demchak, Barry; Pratt, Dexter; Sage, Eric; Ideker, Trey

2017-10-01

Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.

Network Analysis in Community Psychology: Looking Back, Looking Forward

OpenAIRE

Neal, Zachary P.; Neal, Jennifer Watling

2017-01-01

Highlights Network analysis is ideally suited for community psychology research because it focuses on context. Use of network analysis in community psychology is growing. Network analysis in community psychology has employed some potentially problematic practices. Recommended practices are identified to improve network analysis in community psychology.

Modeling the interdependent network based on two-mode networks

Science.gov (United States)

An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

2017-10-01

Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

Optimality Principles in the Regulation of Metabolic Networks

Directory of Open Access Journals (Sweden)

Jan Berkhout

2012-08-01

Full Text Available One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

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.

Animal welfare: a social networks perspective.

Science.gov (United States)

Kleinhappel, Tanja K; John, Elizabeth A; Pike, Thomas W; Wilkinson, Anna; Burman, Oliver H P

2016-01-01

Social network theory provides a useful tool to study complex social relationships in animals. The possibility to look beyond dyadic interactions by considering whole networks of social relationships allows researchers the opportunity to study social groups in more natural ways. As such, network-based analyses provide an informative way to investigate the factors influencing the social environment of group-living animals, and so has direct application to animal welfare. For example, animal groups in captivity are frequently disrupted by separations, reintroductions and/or mixing with unfamiliar individuals and this can lead to social stress and associated aggression. Social network analysis ofanimal groups can help identify the underlying causes of these socially-derived animal welfare concerns. In this review we discuss how this approach can be applied, and how it could be used to identify potential interventions and solutions in the area of animal welfare.

Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

Science.gov (United States)

Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

2018-03-01

In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

Seeded Bayesian Networks: Constructing genetic networks from microarray data

Directory of Open Access Journals (Sweden)

Quackenbush John

2008-07-01

Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

Modified Small Business Network Security

OpenAIRE

Md. Belayet Ali; Oveget Das; Md. Shamim Hossain

2012-01-01

This paper covers some likely threats and effectivesteps for a secure small business. It also involves a flowchart tocomprehend the overall small business network security easilyand we identify a set of security issues and applyappropriate techniques to satisfy the correspondingsecurity requirements. In respect of all, this document isstrong enough for any small business network security.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

International Nuclear Information System (INIS)

Xu Yuhua; Zhou Wuneng; Fang Jian'an; Lu Hongqian

2009-01-01

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

Energy Technology Data Exchange (ETDEWEB)

Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

2009-12-28

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Bitcoin network simulator data explotation

OpenAIRE

Berini Sarrias, Martí

2015-01-01

This project starts with a brief introduction to the concepts of Bitcoin and blockchain, followed by the description of the di erent known attacks to the Bitcoin network. Once reached this point, the basic structure of the Bitcoin network simulator is presented. The main objective of this project is to help in the security assessment of the Bitcoin network. To accomplish that, we try to identify useful metrics, explain them and implement them in the corresponding simulator modules, aiming to ...

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

Buffer sizing for multi-hop networks

KAUST Repository

Shihada, Basem

2014-01-28

A cumulative buffer may be defined for an interference domain in a wireless mesh network and distributed among nodes in the network to maintain or improve capacity utilization of network resources in the interference domain without increasing packet queuing delay times. When an interference domain having communications links sharing resources in a network is identified, a cumulative buffer size is calculated. The cumulative buffer may be distributed among buffers in each node of the interference domain according to a simple division or according to a cost function taking into account a distance of the communications link from the source and destination. The network may be monitored and the cumulative buffer size recalculated and redistributed when the network conditions change.

Decreased triple network connectivity in patients with post-traumatic stress disorder

Science.gov (United States)

Liu, Yang; Li, Liang; Li, Baojuan; Zhang, Xi; Lu, Hongbing

2017-03-01

The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With arterial spin labeling sequence, three networks were identified using independent component analysis in 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus was identified to associate with clinical severity. These results indicated the decreased triple network connectivity, which not only supported the proposal of the triple network model, but also prompted possible neurobiology mechanism of cognitive dysfunction for this kind of PTSD.

Syphilis Networks in Louisiana: An Analysis of Network Configuration and Disease Transmission

Science.gov (United States)

Desmarais, Catherine Theresa

structures were dendritic in nature. A total of 121 cases did not report any partners and an additional 15.9% reported only being involved in dyads. Several large connected components were also observed in syphilis transmission networks in Louisiana. The average age of persons in these large components was greater than in the regional network. A total of 27.3% of male partnerships were with other males in Louisiana, and 4.0% of female pairings were with other females. Blacks practiced assortative mixing, with 93.1% of contacts with a reported race/ethnicity also being black, while 58.9% of contacts reported by whites were also white. Visualization of networks at the regional level illustrated different patterns, with some regions having large disconnected networks while others had more highly connected components. Visualization also uncovered a high concentration of males that had sex with males and females within Region 2 that were note detected during descriptive analyses. Graphs also highlighted highly connected persons within networks that did not have reactive syphilis tests. Upon geocoding the addresses of network members, it was found that most persons lived adjacent to major highways and in major urban areas. Cluster analyses detected a large number of geographic clusters of study subjects throughout the state. Several different patterns were identified; regions with many clusters in a small geographic area, regions with many clusters over a wide geographic area, regions with few clusters, and regions with no small clusters. Most regions had geographically small clusters of a size that could benefit from targeted interventions. Conclusion: This study provides a more in-depth understanding of syphilis spread in the state of Louisiana and demonstrates the feasibility of using network and geospatial methods in future state surveillance and prevention activities. By tying these two approaches together with the addition of basic demographic information, regional patterns

Interdisciplinary and physics challenges of network theory

Science.gov (United States)

Bianconi, Ginestra

2015-09-01

Network theory has unveiled the underlying structure of complex systems such as the Internet or the biological networks in the cell. It has identified universal properties of complex networks, and the interplay between their structure and dynamics. After almost twenty years of the field, new challenges lie ahead. These challenges concern the multilayer structure of most of the networks, the formulation of a network geometry and topology, and the development of a quantum theory of networks. Making progress on these aspects of network theory can open new venues to address interdisciplinary and physics challenges including progress on brain dynamics, new insights into quantum technologies, and quantum gravity.

Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

Directory of Open Access Journals (Sweden)

Liu Heng

Full Text Available This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method.