"Us and them": a social network analysis of physicians' professional networks and their attitudes towards EBM.

PubMed

Mascia, Daniele; Cicchetti, Americo; Damiani, Gianfranco

2013-10-22

Extant research suggests that there is a strong social component to Evidence-Based Medicine (EBM) adoption since professional networks amongst physicians are strongly associated with their attitudes towards EBM. Despite this evidence, it is still unknown whether individual attitudes to use scientific evidence in clinical decision-making influence the position that physicians hold in their professional network. This paper explores how physicians' attitudes towards EBM is related to the network position they occupy within healthcare organizations. Data pertain to a sample of Italian physicians, whose professional network relationships, demographics and work-profile characteristics were collected. A social network analysis was performed to capture the structural importance of physicians in the collaboration network by the means of a core-periphery analysis and the computation of network centrality indicators. Then, regression analysis was used to test the association between the network position of individual clinicians and their attitudes towards EBM. Findings documented that the overall network structure is made up of a dense cohesive core of physicians and of less connected clinicians who occupy the periphery. A negative association between the physicians' attitudes towards EBM and the coreness they exhibited in the professional network was also found. Network centrality indicators confirmed these results documenting a negative association between physicians' propensity to use EBM and their structural importance in the professional network. Attitudes that physicians show towards EBM are related to the part (core or periphery) of the professional networks to which they belong as well as to their structural importance. By identifying virtuous attitudes and behaviors of professionals within their organizations, policymakers and executives may avoid marginalization and stimulate integration and continuity of care, both within and across the boundaries of healthcare providers.

Structural diversity effects of multilayer networks on the threshold of interacting epidemics

NASA Astrophysics Data System (ADS)

Wang, Weihong; Chen, MingMing; Min, Yong; Jin, Xiaogang

2016-02-01

Foodborne diseases always spread through multiple vectors (e.g. fresh vegetables and fruits) and reveal that multilayer network could spread fatal pathogen with complex interactions. In this paper, first, we use a "top-down analysis framework that depends on only two distributions to describe a random multilayer network with any number of layers. These two distributions are the overlaid degree distribution and the edge-type distribution of the multilayer network. Second, based on the two distributions, we adopt three indicators of multilayer network diversity to measure the correlation between network layers, including network richness, likeness, and evenness. The network richness is the number of layers forming the multilayer network. The network likeness is the degree of different layers sharing the same edge. The network evenness is the variance of the number of edges in every layer. Third, based on a simple epidemic model, we analyze the influence of network diversity on the threshold of interacting epidemics with the coexistence of collaboration and competition. Our work extends the "top-down" analysis framework to deal with the more complex epidemic situation and more diversity indicators and quantifies the trade-off between thresholds of inter-layer collaboration and intra-layer transmission.

Performance analysis of Aloha networks with power capture and near/far effect

NASA Astrophysics Data System (ADS)

McCartin, Joseph T.

1989-06-01

An analysis is presented for the throughput characteristics for several classes of Aloha packet networks. Specifically, the throughput for variable packet length Aloha utilizing multiple power levels to induce receiver capture is derived. The results are extended to an analysis of a selective-repeat ARQ Aloha network. Analytical results are presented which indicate a significant increase in throughput for a variable packet network implementing a random two power level capture scheme. Further research into the area of the near/far effect on Aloha networks is included. Improvements in throughput for mobile radio Aloha networks which are subject to the near/far effect are presented. Tactical Command, Control and Communications (C3) systems of the future will rely on Aloha ground mobile data networks. The incorporation of power capture and the near/far effect into future tactical networks will result in improved system analysis, design, and performance.

OSI Network-layer Abstraction: Analysis of Simulation Dynamics and Performance Indicators

NASA Astrophysics Data System (ADS)

Lawniczak, Anna T.; Gerisch, Alf; Di Stefano, Bruno

2005-06-01

The Open Systems Interconnection (OSI) reference model provides a conceptual framework for communication among computers in a data communication network. The Network Layer of this model is responsible for the routing and forwarding of packets of data. We investigate the OSI Network Layer and develop an abstraction suitable for the study of various network performance indicators, e.g. throughput, average packet delay, average packet speed, average packet path-length, etc. We investigate how the network dynamics and the network performance indicators are affected by various routing algorithms and by the addition of randomly generated links into a regular network connection topology of fixed size. We observe that the network dynamics is not simply the sum of effects resulting from adding individual links to the connection topology but rather is governed nonlinearly by the complex interactions caused by the existence of all randomly added and already existing links in the network. Data for our study was gathered using Netzwerk-1, a C++ simulation tool that we developed for our abstraction.

The relative efficiency of modular and non-modular networks of different size

PubMed Central

Tosh, Colin R.; McNally, Luke

2015-01-01

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity. PMID:25631996

Network analysis for the visualization and analysis of qualitative data.

PubMed

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks.

PubMed

Zhang, Jinfen; Teixeira, Ângelo P; Guedes Soares, C; Yan, Xinping; Liu, Kezhong

2016-06-01

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port. © 2016 Society for Risk Analysis.

Social Network Analysis: A New Methodology for Counseling Research.

ERIC Educational Resources Information Center

Koehly, Laura M.; Shivy, Victoria A.

1998-01-01

Social network analysis (SNA) uses indices of relatedness among individuals to produce representations of social structures and positions inherent in dyads or groups. SNA methods provide quantitative representations of ongoing transactional patterns in a given social environment. Methodological issues, applications and resources are discussed…

INTEGRATED ENVIRONMENTAL ASSESSMENT OF THE MID-ATLANTIC REGION WITH ANALYTICAL NETWORK PROCESS

EPA Science Inventory

A decision analysis method for integrating environmental indicators was developed. This was a combination of Principal Component Analysis (PCA) and the Analytic Network Process (ANP). Being able to take into account interdependency among variables, the method was capable of ran...

Decision support systems and methods for complex networks

DOEpatents

Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

2012-02-28

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

Traffic Profiling in Wireless Sensor Networks

DTIC Science & Technology

2006-12-01

components, that can be used for traffic profiling and monitoring of a wireless sensor network . The work demostrates how the IDS should capture and...observed and analyzed. Finally, initial indications from basic analysis of wireless sensor network traffic demonstrated a high degree of self-similarity.

Time Spent on Social Network Sites and Psychological Well-Being: A Meta-Analysis.

PubMed

Huang, Chiungjung

2017-06-01

This meta-analysis examines the relationship between time spent on social networking sites and psychological well-being factors, namely self-esteem, life satisfaction, loneliness, and depression. Sixty-one studies consisting of 67 independent samples involving 19,652 participants were identified. The mean correlation between time spent on social networking sites and psychological well-being was low at r = -0.07. The correlations between time spent on social networking sites and positive indicators (self-esteem and life satisfaction) were close to 0, whereas those between time spent on social networking sites and negative indicators (depression and loneliness) were weak. The effects of publication outlet, site on which users spent time, scale of time spent, and participant age and gender were not significant. As most included studies used student samples, future research should be conducted to examine this relationship for adults.

Diversity of social ties in scientific collaboration networks

NASA Astrophysics Data System (ADS)

Shi, Quan; Xu, Bo; Xu, Xiaomin; Xiao, Yanghua; Wang, Wei; Wang, Hengshan

2011-11-01

Diversity is one of the important perspectives to characterize behaviors of individuals in social networks. It is intuitively believed that diversity of social ties accounts for competition advantage and idea innovation. However, quantitative evidences in a real large social network can be rarely found in the previous research. Thanks to the availability of scientific publication records on WWW; now we can construct a large scientific collaboration network, which provides us a chance to gain insight into the diversity of relationships in a real social network through statistical analysis. In this article, we dedicate our efforts to perform empirical analysis on a scientific collaboration network extracted from DBLP, an online bibliographic database in computer science, in a systematical way, finding the following: distributions of diversity indices tend to decay in an exponential or Gaussian way; diversity indices are not trivially correlated to existing vertex importance measures; authors of diverse social ties tend to connect to each other and these authors are generally more competitive than others.

Novel indexes based on network structure to indicate financial market

NASA Astrophysics Data System (ADS)

Zhong, Tao; Peng, Qinke; Wang, Xiao; Zhang, Jing

2016-02-01

There have been various achievements to understand and to analyze the financial market by complex network model. However, current studies analyze the financial network model but seldom present quantified indexes to indicate or forecast the price action of market. In this paper, the stock market is modeled as a dynamic network, in which the vertices refer to listed companies and edges refer to their rank-based correlation based on price series. Characteristics of the network are analyzed and then novel indexes are introduced into market analysis, which are calculated from maximum and fully-connected subnets. The indexes are compared with existing ones and the results confirm that our indexes perform better to indicate the daily trend of market composite index in advance. Via investment simulation, the performance of our indexes is analyzed in detail. The results indicate that the dynamic complex network model could not only serve as a structural description of the financial market, but also work to predict the market and guide investment by indexes.

NEAT: an efficient network enrichment analysis test.

PubMed

Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

2016-09-05

Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

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

PubMed

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

2013-01-01

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

Networking K-12 Schools: Architecture Models and Evaluation of Costs and Benefits.

ERIC Educational Resources Information Center

Rothstein, Russell Isaac

This thesis examines the cost and benefits of communication networks in K-12 schools using cost analysis of five technology models with increasing levels of connectivity. Data indicate that the cost of the network hardware is only a small fraction of the overall networking costs. PC purchases, initial training, and retrofitting are the largest…

A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

PubMed

Mallik, Mrinmay Kumar

2018-02-07

Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that is indicative of their strong influence in the protein protein interaction network. Similarly the newly proposed GEADCA helped identify the transcription factors with high centrality values indicative of their key roles in transcriptional regulation. The enrichment studies provided a list of molecular functions, biological processes and biochemical pathways associated with the constructed network. The study shows how pathway based databases may be used to create and analyze a relevant protein interaction network in glioma cancer stem cells and identify the essential elements within it to gather insights into the molecular interactions that regulate the properties of glioma stem cells. How these insights may be utilized to help the development of future research towards formulation of new management strategies have been discussed from a theoretical standpoint. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks.

PubMed

Saad, E W; Prokhorov, D V; Wunsch, D C

1998-01-01

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

PubMed Central

Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

2016-01-01

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.

PubMed

Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

2016-01-01

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

Comparative analysis of quantitative efficiency evaluation methods for transportation networks

PubMed Central

He, Yuxin; Hong, Jian

2017-01-01

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess’s Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified. PMID:28399165

Comparative analysis of quantitative efficiency evaluation methods for transportation networks.

PubMed

He, Yuxin; Qin, Jin; Hong, Jian

2017-01-01

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess's Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified.

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

PubMed

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

The Earth Science Research Network as Seen Through Network Analysis of the AGU

NASA Astrophysics Data System (ADS)

Narock, T.; Hasnain, S.; Stephan, R.

2017-12-01

Scientometrics is the science of science. Scientometric research includes measurements of impact, mapping of scientific fields, and the production of indicators for use in policy and management. We have leveraged network analysis in a scientometric study of the American Geophysical Union (AGU). Data from the AGU's Linked Data Abstract Browser was used to create a visualization and analytics tools to explore the Earth science's research network. Our application applies network theory to look at network structure within the various AGU sections, identify key individuals and communities related to Earth science topics, and examine multi-disciplinary collaboration across sections. Opportunities to optimize Earth science output, as well as policy and outreach applications, are discussed.

The transmission of fluctuation among price indices based on Granger causality network

NASA Astrophysics Data System (ADS)

Sun, Qingru; Gao, Xiangyun; Wen, Shaobo; Chen, Zhihua; Hao, Xiaoqing

2018-09-01

In this paper, we provide a method of statistical physics to analyze the fluctuation of transmission by constructing Granger causality network among price indices (PIGCN) from a systematical perspective, using complex network theory combined with Granger causality method. In economic system, there are numerous price indices, of which the relationships are extreme complicated. Thus, time series data of 6 types of price indices of China, including 113 kinds of sub price indices, are selected as example of empirical study. Through the analysis of the structure of PIGCN, we identify important price indices with high transmission range, high intermediation capacity, high cohesion and the fluctuation transmission path of price indices, respectively. Furthermore, dynamic relationships among price indices are revealed. Based on these results, we provide several policy implications for monitoring the diffusion of risk of price fluctuation. Our method can also be used to study the price indices of other countries, which is generally applicable.

CD-Based Indices for Link Prediction in Complex Network.

PubMed

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

2016-01-01

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.

CD-Based Indices for Link Prediction in Complex Network

PubMed Central

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

2016-01-01

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks. PMID:26752405

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

PubMed Central

Schacter, Daniel L.

2012-01-01

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

Identifying gaps in conservation networks: of indicators and uncertainty in geographic-based analyses

Treesearch

Curtis H. Flather; Kenneth R. Wilson; Denis J. Dean; William C. McComb

1997-01-01

Mapping of biodiversity elements to expose gaps in. conservation networks has become a common strategy in nature-reserve design. We review a set of critical assumptions and issues that influence the interpretation and implementation of gap analysis, including: (1) the assumption that a subset of taxa can be used to indicate overall diversity patterns, and (2) the...

Efficiency disparities among community hospitals in Tennessee: do size, location, ownership, and network matter?

PubMed

Roh, Chul-Young; Moon, M Jae; Jung, Kwangho

2013-11-01

This study examined the impact of ownership, size, location, and network on the relative technical efficiency of community hospitals in Tennessee for the 2002-2006 period, by applying data envelopment analysis (DEA) to measure technical efficiency (decomposed into scale efficiency and pure technical efficiency). Data envelopment analysis results indicate that medium-size hospitals (126-250 beds) are more efficient than their counterparts. Interestingly, public hospitals are significantly more efficient than private and nonprofit hospitals in Tennessee, and rural hospitals are more efficient than urban hospitals. This is the first study to investigate whether hospital networks with other health care providers affect hospital efficiency. Results indicate that community hospitals with networks are more efficient than non-network hospitals. From a management and policy perspective, this study suggests that public policies should induce hospitals to downsize or upsize into optional size, and private hospitals and nonprofit hospitals should change their organizational objectives from profit-driven to quality-driven.

Social Network Type and Long-Term Condition Management Support: A Cross-Sectional Study in Six European Countries.

PubMed

Vassilev, Ivaylo; Rogers, Anne; Kennedy, Anne; Wensing, Michel; Koetsenruijter, Jan; Orlando, Rosanna; Portillo, Maria Carmen; Culliford, David

2016-01-01

Network types and characteristics have been linked to the capacity of inter-personal environments to mobilise and share resources. The aim of this paper is to examine personal network types in relation to long-term condition management in order to identify the properties of network types most likely to provide support for those with a long-term condition. A cross-sectional observational survey of people with type 2 diabetes using interviews and questionnaires was conducted between April and October 2013 in six European countries: Greece, Spain, Bulgaria, Norway, United Kingdom, and Netherlands. 1862 people with predominantly lower socio-economic status were recruited from each country. We used k-means clustering analysis to derive the network types, and one-way analysis of variance and multivariate logistic regression analysis to explore the relationship between network type socio-economic characteristics, self-management monitoring and skills, well-being, and network member work. Five network types of people with long-term conditions were identified: restricted, minimal family, family, weak ties, and diverse. Restricted network types represented those with the poorest self-management skills and were associated with limited support from social network members. Restricted networks were associated with poor indicators across self-management capacity, network support, and well-being. Diverse networks were associated with more enhanced self-management skills amongst those with a long-term condition and high level of emotional support. It was the three network types which had a large number of network members (diverse, weak ties, and family) where healthcare utilisation was most likely to correspond to existing health needs. Our findings suggest that type of increased social involvement is linked to greater self-management capacity and potentially lower formal health care costs indicating that diverse networks constitute the optimal network type as a policy in terms of the design of LTCM interventions and building support for people with LTCs.

Evaluation model of distribution network development based on ANP and grey correlation analysis

NASA Astrophysics Data System (ADS)

Ma, Kaiqiang; Zhan, Zhihong; Zhou, Ming; Wu, Qiang; Yan, Jun; Chen, Genyong

2018-06-01

The existing distribution network evaluation system cannot scientifically and comprehensively reflect the distribution network development status. Furthermore, the evaluation model is monotonous and it is not suitable for horizontal analysis of many regional power grids. For these reason, this paper constructs a set of universal adaptability evaluation index system and model of distribution network development. Firstly, distribution network evaluation system is set up by power supply capability, power grid structure, technical equipment, intelligent level, efficiency of the power grid and development benefit of power grid. Then the comprehensive weight of indices is calculated by combining the AHP with the grey correlation analysis. Finally, the index scoring function can be obtained by fitting the index evaluation criterion to the curve, and then using the multiply plus operator to get the result of sample evaluation. The example analysis shows that the model can reflect the development of distribution network and find out the advantages and disadvantages of distribution network development. Besides, the model provides suggestions for the development and construction of distribution network.

Systematic Review of Social Network Analysis in Adolescent Cigarette Smoking Behavior

ERIC Educational Resources Information Center

Seo, Dong-Chul; Huang, Yan

2012-01-01

Background: Social networks are important in adolescent smoking behavior. Previous research indicates that peer context is a major causal factor of adolescent smoking behavior. To date, however, little is known about the influence of peer group structure on adolescent smoking behavior. Methods: Studies that examined adolescent social networks with…

Research on International Student Flows from a Macro Perspective: A Network Analysis of 1985, 1989 and 1995.

ERIC Educational Resources Information Center

Chen, Tse-Mei; Barnett, George A.

2000-01-01

Analysis of 64 countries representing the largest number of international student exchanges examines student flows from a macro perspective. Findings indicate that the international student exchange network is relatively stable; the United States and Western industrialized nations are at the center; East European and Asian countries have become…

Global Electricity Trade Network: Structures and Implications

PubMed Central

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

Global Electricity Trade Network: Structures and Implications.

PubMed

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S F; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions.

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

PubMed

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

2016-10-01

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

Ecological network analysis for a virtual water network.

PubMed

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.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

PubMed

Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

2013-01-01

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models.

PubMed

Osama, Ahmed; Sayed, Tarek

2016-12-01

Many cities worldwide are recognizing the important role that cycling plays in creating green and livable communities. However, vulnerable road users such as cyclists are usually subjected to an elevated level of injury risk which discourages many road users to cycle. This paper studies cyclist-vehicle collisions at 134 traffic analysis zones in the city of Vancouver to assess the impact of bike network structure on cyclist safety. Several network indicators were developed using Graph theory and their effect on cyclist safety was investigated. The indicators included measures of connectivity, directness, and topography of the bike network. The study developed several macro-level (zonal) collision prediction models that explicitly incorporated bike network indicators as explanatory variables. As well, the models incorporated the actual cyclist exposure (bike kilometers travelled) as opposed to relying on proxies such as population or bike network length. The macro-level collision prediction models were developed using generalized linear regression and full Bayesian techniques, with and without spatial effects. The models showed that cyclist collisions were positively associated with bike and vehicle exposure. The exponents of the exposure variables were less than one which supports the "safety in numbers" hypothesis. Moreover, the models showed positive associations between cyclist collisions and the bike network connectivity and linearity indicators. In contrast, negative associations were found between cyclist collisions and the bike network continuity and topography indicators. The spatial effects were statistically significant in all of the developed models. Copyright © 2016 Elsevier Ltd. All rights reserved.

An Investigation of the Differences and Similarities between Generated Small-World Networks for Right- and Left-Hand Motor Imageries.

PubMed

Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen

2016-11-04

In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.

Analysis of linkage effects among industry sectors in China's stock market before and after the financial crisis

NASA Astrophysics Data System (ADS)

Yang, Rui; Li, Xiangyang; Zhang, Tong

2014-10-01

This paper uses two physics-derived techniques, the minimum spanning tree and the hierarchical tree, to investigate the networks formed by CITIC (China International Trust and Investment Corporation) industry indices in three periods from 2006 to 2013. The study demonstrates that obvious industry clustering effects exist in the networks, and Durable Consumer Goods, Industrial Products, Information Technology, Frequently Consumption and Financial Industry are the core nodes in the networks. We also use the rolling window technique to investigate the dynamic evolution of the networks' stability, by calculating the mean correlations and mean distances, as well as the variance of correlations and the distances of these indices. China's stock market is still immature and subject to administrative interventions. Therefore, through this analysis, regulators can focus on monitoring the core nodes to ensure the overall stability of the entire market, while investors can enhance their portfolio allocations or investment decision-making.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

PubMed Central

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A.; Zhang, Wenbo

2016-01-01

Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions. PMID:27740473

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

PubMed

Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A; Zhang, Wenbo; He, Bin

2016-12-01

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Structural Analysis of a Consumption-Based Stratification Indicator: Relational Proximity of Household Expenditures

ERIC Educational Resources Information Center

Katz-Gerro, Tally; Talmud, Ilan

2005-01-01

This paper proposes a new analysis of consumption inequality using relational methods, derived from network images of social structure. We combine structural analysis with theoretical concerns in consumer research to propose a relational theory of consumption space, to construct a stratification indicator, and to demonstrate its analytical…

Social network types and well-being among South Korean older adults.

PubMed

Park, Sojung; Smith, Jacqui; Dunkle, Ruth E

2014-01-01

The social networks of older individuals reflect personal life history and cultural factors. Despite these two sources of variation, four similar network types have been identified in Europe, North America, Japan, and China: namely 'restricted', 'family', 'friend', and 'diverse'. This study identified the social network types of Korean older adults and examined differential associations of the network types with well-being. The analysis used data from the 2008 wave of the Korean Longitudinal Study of Aging (KLoSA: N = 4251, age range 65-108). We used a two-step cluster analytical approach to identify network types from seven indicators of network structure and function. Regression models determined associations between network types and well-being outcomes, including life satisfaction and depressive symptomatology. Cluster analysis of indicators of network structure and function revealed four types, including the restricted, friend, and diverse types. Instead of a family type, we found a couple-focused type. The young-old (age 65-74) were more likely to be in the couple-focused type and more of the oldest old (age 85+) belonged to the restricted type. Compared with the restricted network, older adults in all other networks were more likely to report higher life satisfaction and lower depressive symptomatology. Life course and cohort-related factors contribute to similarities across societies in network types and their associations with well-being. Korean-specific life course and socio-historical factors, however, may contribute to our unique findings about network types.

Stream network analysis and geomorphic flood plain mapping from orbital and suborbital remote sensing imagery application to flood hazard studies in central Texas

NASA Technical Reports Server (NTRS)

Baker, V. R. (Principal Investigator); Holz, R. K.; Hulke, S. D.; Patton, P. C.; Penteado, M. M.

1975-01-01

The author has identified the following significant results. Development of a quantitative hydrogeomorphic approach to flood hazard evaluation was hindered by (1) problems of resolution and definition of the morphometric parameters which have hydrologic significance, and (2) mechanical difficulties in creating the necessary volume of data for meaningful analysis. Measures of network resolution such as drainage density and basin Shreve magnitude indicated that large scale topographic maps offered greater resolution than small scale suborbital imagery and orbital imagery. The disparity in network resolution capabilities between orbital and suborbital imagery formats depends on factors such as rock type, vegetation, and land use. The problem of morphometric data analysis was approached by developing a computer-assisted method for network analysis. The system allows rapid identification of network properties which can then be related to measures of flood response.

On the utility of the ionosonde Doppler-derived EXB drift during the daytime

NASA Astrophysics Data System (ADS)

Joshi, L. M.; Sripathi, S.

2016-03-01

Vertical EXB drift measured using the ionosonde Doppler sounding during the daytime suffers from an underestimation of the actual EXB drift because the reflection height of the ionosonde signals is also affected by the photochemistry of the ionosphere. Systematic investigations have indicated a fair/good correlation to exist between the C/NOFS and ionosonde Doppler-measured vertical EXB drift during the daytime over magnetic equator. A detailed analysis, however, indicated that the linear relation between the ionosonde Doppler drift and C/NOFS EXB drift varied with seasons. Thus, solar, seasonal, and also geomagnetic variables were included in the Doppler drift correction, using the artificial neural network-based approach. The RMS error in the neural network was found to be smaller than that in the linear regression analysis. Daytime EXB drift was derived using the neural network which was also used to model the ionospheric redistribution in the SAMI2 model. SAMI2 model reproduced strong (weak) equatorial ionization anomaly (EIA) for cases when neural network corrected daytime vertical EXB drift was high (low). Similar features were also observed in GIM TEC maps. Thus, the results indicate that the neural network can be utilized to derive the vertical EXB drift from its proxies, like the ionosonde Doppler drift. These results indicate that the daytime ionosonde measured vertical EXB drift can be relied upon, provided that adequate corrections are applied to it.

Ku-band signal design study. [space shuttle orbiter data processing network

NASA Technical Reports Server (NTRS)

Rubin, I.

1978-01-01

Analytical tools, methods and techniques for assessing the design and performance of the space shuttle orbiter data processing system (DPS) are provided. The computer data processing network is evaluated in the key areas of queueing behavior synchronization and network reliability. The structure of the data processing network is described as well as the system operation principles and the network configuration. The characteristics of the computer systems are indicated. System reliability measures are defined and studied. System and network invulnerability measures are computed. Communication path and network failure analysis techniques are included.

Ciência & Saúde Coletiva: scientific production analysis and collaborative research networks.

PubMed

Conner, Norma; Provedel, Attilio; Maciel, Ethel Leonor Noia

2017-03-01

The purpose of this metric and descriptive study was to identify the most productive authors and their collaborative research networks from articles published in Ciência & Saúde Coletiva between, 2005, and 2014. Authors meeting the cutoff criteria of at least 10 articles were considered the most productive authors. VOSviewer and Network Workbench technologies were applied for visual representations of collaborative research networks involving the most productive authors in the period. Initial analysis recovered 2511 distinct articles, with 8920 total authors with an average of 3.55 authors per article. Author analysis revealed 6288 distinct authors, 24 of these authors were identified as the most productive. These 24 authors generated 287 articles with an average of 4.31 authors per article, and represented 8 separate collaborative partnerships, the largest of which had 14 authors, indicating a significant degree of collaboration among these authors. This analysis provides a visual representation of networks of knowledge development in public health and demonstrates the usefulness of VOSviewer and Network Workbench technologies in future research.

Flory-Stockmayer analysis on reprocessable polymer networks

NASA Astrophysics Data System (ADS)

Li, Lingqiao; Chen, Xi; Jin, Kailong; Torkelson, John

Reprocessable polymer networks can undergo structure rearrangement through dynamic chemistries under proper conditions, making them a promising candidate for recyclable crosslinked materials, e.g. tires. This research field has been focusing on various chemistries. However, there has been lacking of an essential physical theory explaining the relationship between abundancy of dynamic linkages and reprocessability. Based on the classical Flory-Stockmayer analysis on network gelation, we developed a similar analysis on reprocessable polymer networks to quantitatively predict the critical condition for reprocessability. Our theory indicates that it is unnecessary for all bonds to be dynamic to make the resulting network reprocessable. As long as there is no percolated permanent network in the system, the material can fully rearrange. To experimentally validate our theory, we used a thiol-epoxy network model system with various dynamic linkage compositions. The stress relaxation behavior of resulting materials supports our theoretical prediction: only 50 % of linkages between crosslinks need to be dynamic for a tri-arm network to be reprocessable. Therefore, this analysis provides the first fundamental theoretical platform for designing and evaluating reprocessable polymer networks. We thank McCormick Research Catalyst Award Fund and ISEN cluster fellowship (L. L.) for funding support.

Principles of Biomimetic Vascular Network Design Applied to a Tissue-Engineered Liver Scaffold

PubMed Central

Hoganson, David M.; Pryor, Howard I.; Spool, Ira D.; Burns, Owen H.; Gilmore, J. Randall

2010-01-01

Branched vascular networks are a central component of scaffold architecture for solid organ tissue engineering. In this work, seven biomimetic principles were established as the major guiding technical design considerations of a branched vascular network for a tissue-engineered scaffold. These biomimetic design principles were applied to a branched radial architecture to develop a liver-specific vascular network. Iterative design changes and computational fluid dynamic analysis were used to optimize the network before mold manufacturing. The vascular network mold was created using a new mold technique that achieves a 1:1 aspect ratio for all channels. In vitro blood flow testing confirmed the physiologic hemodynamics of the network as predicted by computational fluid dynamic analysis. These results indicate that this biomimetic liver vascular network design will provide a foundation for developing complex vascular networks for solid organ tissue engineering that achieve physiologic blood flow. PMID:20001254

Principles of biomimetic vascular network design applied to a tissue-engineered liver scaffold.

PubMed

Hoganson, David M; Pryor, Howard I; Spool, Ira D; Burns, Owen H; Gilmore, J Randall; Vacanti, Joseph P

2010-05-01

Branched vascular networks are a central component of scaffold architecture for solid organ tissue engineering. In this work, seven biomimetic principles were established as the major guiding technical design considerations of a branched vascular network for a tissue-engineered scaffold. These biomimetic design principles were applied to a branched radial architecture to develop a liver-specific vascular network. Iterative design changes and computational fluid dynamic analysis were used to optimize the network before mold manufacturing. The vascular network mold was created using a new mold technique that achieves a 1:1 aspect ratio for all channels. In vitro blood flow testing confirmed the physiologic hemodynamics of the network as predicted by computational fluid dynamic analysis. These results indicate that this biomimetic liver vascular network design will provide a foundation for developing complex vascular networks for solid organ tissue engineering that achieve physiologic blood flow.

FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network

PubMed Central

Qin, Wei; Tian, Jie; Bai, Lijun; Pan, Xiaohong; Yang, Lin; Chen, Peng; Dai, Jianping; Ai, Lin; Zhao, Baixiao; Gong, Qiyong; Wang, Wei; von Deneen, Karen M; Liu, Yijun

2008-01-01

Background Recently, increasing evidence has indicated that the primary acupuncture effects are mediated by the central nervous system. However, specific brain networks underpinning these effects remain unclear. Results In the present study using fMRI, we employed a within-condition interregional covariance analysis method to investigate functional connectivity of brain networks involved in acupuncture. The fMRI experiment was performed before, during and after acupuncture manipulations on healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway for pain modulation. We first identified significant fMRI signal changes during acupuncture stimulation in the left amygdala, which was subsequently selected as a functional reference for connectivity analyses. Our results have demonstrated that there is a brain network associated with the amygdala during a resting condition. This network encompasses the brain structures that are implicated in both pain sensation and pain modulation. We also found that such a pain-related network could be modulated by both verum acupuncture and sham acupuncture. Furthermore, compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations among the amygdala-associated network. Conclusion Our findings indicate that acupuncture may change this amygdala-specific brain network into a functional state that underlies pain perception and pain modulation. PMID:19014532

Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics.

PubMed

Bruno, Jennifer Lynn; Hosseini, S M Hadi; Saggar, Manish; Quintin, Eve-Marie; Raman, Mira Michelle; Reiss, Allan L

2017-03-01

Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A universal indicator of critical state transitions in noisy complex networked systems

PubMed Central

Liang, Junhao; Hu, Yanqing; Chen, Guanrong; Zhou, Tianshou

2017-01-01

Critical transition, a phenomenon that a system shifts suddenly from one state to another, occurs in many real-world complex networks. We propose an analytical framework for exactly predicting the critical transition in a complex networked system subjected to noise effects. Our prediction is based on the characteristic return time of a simple one-dimensional system derived from the original higher-dimensional system. This characteristic time, which can be easily calculated using network data, allows us to systematically separate the respective roles of dynamics, noise and topology of the underlying networked system. We find that the noise can either prevent or enhance critical transitions, playing a key role in compensating the network structural defect which suffers from either internal failures or environmental changes, or both. Our analysis of realistic or artificial examples reveals that the characteristic return time is an effective indicator for forecasting the sudden deterioration of complex networks. PMID:28230166

Drug target identification using network analysis: Taking active components in Sini decoction as an example

NASA Astrophysics Data System (ADS)

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-04-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

PubMed Central

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-01-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound. PMID:27095146

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

Economic indicators selection for crime rates forecasting using cooperative feature selection

NASA Astrophysics Data System (ADS)

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

2013-04-01

Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

Cooperative spreading processes in multiplex networks.

PubMed

Wei, Xiang; Chen, Shihua; Wu, Xiaoqun; Ning, Di; Lu, Jun-An

2016-06-01

This study is concerned with the dynamic behaviors of epidemic spreading in multiplex networks. A model composed of two interacting complex networks is proposed to describe cooperative spreading processes, wherein the virus spreading in one layer can penetrate into the other to promote the spreading process. The global epidemic threshold of the model is smaller than the epidemic thresholds of the corresponding isolated networks. Thus, global epidemic onset arises in the interacting networks even though an epidemic onset does not arise in each isolated network. Simulations verify the analysis results and indicate that cooperative spreading processes in multiplex networks enhance the final infection fraction.

Assessing Interorganizational Networks as a Dimension of Community Capacity: Illustrations from a Community Intervention to Prevent Lead Poisoning

ERIC Educational Resources Information Center

Singer, Helen Harber; Kegler, Michelle Crozier

2004-01-01

Network analysis is often cited as a method for assessing collaboration among organizations as an indicator of community capacity. The purpose of this study was to (1) document patterns of collaboration in organizational networks related to lead poisoning prevention in a Native American community and (2) examine measurement issues in using…

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis.

PubMed

Richetin, Juliette; Preti, Emanuele; Costantini, Giulio; De Panfilis, Chiara

2017-01-01

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy.

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis

PubMed Central

Costantini, Giulio; De Panfilis, Chiara

2017-01-01

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy. PMID:29040324

Complex network description of the ionosphere

NASA Astrophysics Data System (ADS)

Lu, Shikun; Zhang, Hao; Li, Xihai; Li, Yihong; Niu, Chao; Yang, Xiaoyun; Liu, Daizhi

2018-03-01

Complex networks have emerged as an essential approach of geoscience to generate novel insights into the nature of geophysical systems. To investigate the dynamic processes in the ionosphere, a directed complex network is constructed, based on a probabilistic graph of the vertical total electron content (VTEC) from 2012. The results of the power-law hypothesis test show that both the out-degree and in-degree distribution of the ionospheric network are not scale-free. Thus, the distribution of the interactions in the ionosphere is homogenous. None of the geospatial positions play an eminently important role in the propagation of the dynamic ionospheric processes. The spatial analysis of the ionospheric network shows that the interconnections principally exist between adjacent geographical locations, indicating that the propagation of the dynamic processes primarily depends on the geospatial distance in the ionosphere. Moreover, the joint distribution of the edge distances with respect to longitude and latitude directions shows that the dynamic processes travel further along the longitude than along the latitude in the ionosphere. The analysis of small-world-ness indicates that the ionospheric network possesses the small-world property, which can make the ionosphere stable and efficient in the propagation of dynamic processes.

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

PubMed

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Neural network approach in multichannel auditory event-related potential analysis.

PubMed

Wu, F Y; Slater, J D; Ramsay, R E

1994-04-01

Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.

A reliability analysis tool for SpaceWire network

NASA Astrophysics Data System (ADS)

Zhou, Qiang; Zhu, Longjiang; Fei, Haidong; Wang, Xingyou

2017-04-01

A SpaceWire is a standard for on-board satellite networks as the basis for future data-handling architectures. It is becoming more and more popular in space applications due to its technical advantages, including reliability, low power and fault protection, etc. High reliability is the vital issue for spacecraft. Therefore, it is very important to analyze and improve the reliability performance of the SpaceWire network. This paper deals with the problem of reliability modeling and analysis with SpaceWire network. According to the function division of distributed network, a reliability analysis method based on a task is proposed, the reliability analysis of every task can lead to the system reliability matrix, the reliability result of the network system can be deduced by integrating these entire reliability indexes in the matrix. With the method, we develop a reliability analysis tool for SpaceWire Network based on VC, where the computation schemes for reliability matrix and the multi-path-task reliability are also implemented. By using this tool, we analyze several cases on typical architectures. And the analytic results indicate that redundancy architecture has better reliability performance than basic one. In practical, the dual redundancy scheme has been adopted for some key unit, to improve the reliability index of the system or task. Finally, this reliability analysis tool will has a directive influence on both task division and topology selection in the phase of SpaceWire network system design.

On the utility of the ionosonde Doppler derived EXB drift during the daytime

NASA Astrophysics Data System (ADS)

Mohan Joshi, Lalit; Sripathi, Samireddipelle

2016-07-01

Vertical EXB drift measured using the ionosonde Doppler sounding during the daytime suffers from an underestimation of the actual EXB drift. This is due to the photochemistry that determines the height of the F layer during the daytime, in addition to the zonal electric field. Systematic investigations have indicated a fair/good correlation to exist between the C/NOFS and ionosonde Doppler measured vertical EXB drift during the daytime over magnetic equator. A detailed analysis, however, indicated that the linear relation between the ionosonde Doppler drift and C/NOFS EXB drift varied with seasons. Thus, solar, seasonal and also geomagnetic variables were included in the Doppler drift correction, using the artificial neural network based approach. The RMS error in the neural network was found to be lesser than that in the linear regression analysis. Daytime EXB drift was derived using the neural network which was also used to model the ionospheic redistribution in the SAMI2 model. SAMI2 model reproduced strong (/weak) equatorial ionization anomaly (EIA) for cases when neural network corrected daytime vertical EXB drift was high (/low). Similar features were also observed in GIM TEC maps. Thus, the results indicate that the neural network can be utilized to derive the vertical EXB drift from its proxies, like the ionosonde Doppler drift. These results indicate that the daytime ionosonde measured vertical EXB drift can be relied upon, provided adequate corrections are applied to it.

Interdependent Multi-Layer Networks: Modeling and Survivability Analysis with Applications to Space-Based Networks

PubMed Central

Castet, Jean-Francois; Saleh, Joseph H.

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks. PMID:23599835

Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

PubMed

Castet, Jean-Francois; Saleh, Joseph H

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks.

An analysis of respondent-driven sampling with injecting drug users in a high HIV prevalent state of India.

PubMed

Phukan, Sanjib Kumar; Medhi, Gajendra Kumar; Mahanta, Jagadish; Adhikary, Rajatashuvra; Thongamba, Gay; Paranjape, Ramesh S; Akoijam, Brogen S

2017-07-03

Personal networks are significant social spaces to spread of HIV or other blood-borne infections among hard-to-reach population, viz., injecting drug users, female sex workers, etc. Sharing of infected needles or syringes among drug users is one of the major routes of HIV transmission in Manipur, a high HIV prevalence state in India. This study was carried out to describe the network characteristics and recruitment patterns of injecting drug users and to assess the association of personal network with injecting risky behaviors in Manipur. A total of 821 injecting drug users were recruited into the study using respondent-driven sampling (RDS) from Bishnupur and Churachandpur districts of Manipur; data on demographic characteristics, HIV risk behaviors, and network size were collected from them. Transition probability matrices and homophily indices were used to describe the network characteristics, and recruitment patterns of injecting drug users. Univariate and multivariate binary logistic regression models were performed to analyze the association between the personal networks and sharing of needles or syringes. The average network size was similar in both the districts. Recruitment analysis indicates injecting drug users were mostly engaged in mixed age group setting for injecting practice. Ever married and new injectors showed lack of in-group ties. Younger injecting drug users had mainly recruited older injecting drug users from their personal network. In logistic regression analysis, higher personal network was found to be significantly associated with increased likelihood of injecting risky behaviors. Because of mixed personal network of new injectors and higher network density associated with HIV exposure, older injecting drug users may act as a link for HIV transmission or other blood-borne infections to new injectors and also to their sexual partners. The information from this study may be useful to understanding the network pattern of injecting drug users for enriching the HIV prevention in this region.

Safety models incorporating graph theory based transit indicators.

PubMed

Quintero, Liliana; Sayed, Tarek; Wahba, Mohamed M

2013-01-01

There is a considerable need for tools to enable the evaluation of the safety of transit networks at the planning stage. One interesting approach for the planning of public transportation systems is the study of networks. Network techniques involve the analysis of systems by viewing them as a graph composed of a set of vertices (nodes) and edges (links). Once the transport system is visualized as a graph, various network properties can be evaluated based on the relationships between the network elements. Several indicators can be calculated including connectivity, coverage, directness and complexity, among others. The main objective of this study is to investigate the relationship between network-based transit indicators and safety. The study develops macro-level collision prediction models that explicitly incorporate transit physical and operational elements and transit network indicators as explanatory variables. Several macro-level (zonal) collision prediction models were developed using a generalized linear regression technique, assuming a negative binomial error structure. The models were grouped into four main themes: transit infrastructure, transit network topology, transit route design, and transit performance and operations. The safety models showed that collisions were significantly associated with transit network properties such as: connectivity, coverage, overlapping degree and the Local Index of Transit Availability. As well, the models showed a significant relationship between collisions and some transit physical and operational attributes such as the number of routes, frequency of routes, bus density, length of bus and 3+ priority lanes. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Multidimensional Analysis Tool for Visualizing Online Interactions

ERIC Educational Resources Information Center

Kim, Minjeong; Lee, Eunchul

2012-01-01

This study proposes and verifies the performance of an analysis tool for visualizing online interactions. A review of the most widely used methods for analyzing online interactions, including quantitative analysis, content analysis, and social network analysis methods, indicates these analysis methods have some limitations resulting from their…

The Conundrum of Functional Brain Networks: Small-World Efficiency or Fractal Modularity

PubMed Central

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

2012-01-01

The human brain has been studied at multiple scales, from neurons, circuits, areas with well-defined anatomical and functional boundaries, to large-scale functional networks which mediate coherent cognition. In a recent work, we addressed the problem of the hierarchical organization in the brain through network analysis. Our analysis identified functional brain modules of fractal structure that were inter-connected in a small-world topology. Here, we provide more details on the use of network science tools to elaborate on this behavior. We indicate the importance of using percolation theory to highlight the modular character of the functional brain network. These modules present a fractal, self-similar topology, identified through fractal network methods. When we lower the threshold of correlations to include weaker ties, the network as a whole assumes a small-world character. These weak ties are organized precisely as predicted by theory maximizing information transfer with minimal wiring costs. PMID:22586406

Time Series Analysis of the Bacillus subtilis Sporulation Network Reveals Low Dimensional Chaotic Dynamics.

PubMed

Lecca, Paola; Mura, Ivan; Re, Angela; Barker, Gary C; Ihekwaba, Adaoha E C

2016-01-01

Chaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.e., to be attributable to a small fraction of the total systems components. In this case, indeed, including the major drivers of chaos in a system into the modeling approach allows us to improve predictability of the systems dynamics. Here, we analyzed the numerical simulations of an accurate ordinary differential equation model of the gene network regulating sporulation initiation in Bacillus subtilis to explore whether the non-linearity underlying time series data is due to low-dimensional chaos. Low-dimensional chaos is expectedly common in systems with few degrees of freedom, but rare in systems with many degrees of freedom such as the B. subtilis sporulation network. The estimation of a number of indices, which reflect the chaotic nature of a system, indicates that the dynamics of this network is affected by deterministic chaos. The neat separation between the indices obtained from the time series simulated from the model and those obtained from time series generated by Gaussian white and colored noise confirmed that the B. subtilis sporulation network dynamics is affected by low dimensional chaos rather than by noise. Furthermore, our analysis identifies the principal driver of the networks chaotic dynamics to be sporulation initiation phosphotransferase B (Spo0B). We then analyzed the parameters and the phase space of the system to characterize the instability points of the network dynamics, and, in turn, to identify the ranges of values of Spo0B and of the other drivers of the chaotic dynamics, for which the whole system is highly sensitive to minimal perturbation. In summary, we described an unappreciated source of complexity in the B. subtilis sporulation network by gathering evidence for the chaotic behavior of the system, and by suggesting candidate molecules driving chaos in the system. The results of our chaos analysis can increase our understanding of the intricacies of the regulatory network under analysis, and suggest experimental work to refine our behavior of the mechanisms underlying B. subtilis sporulation initiation control.

Mass Spectrometry Analysis of Spatial Protein Networks by Colocalization Analysis (COLA).

PubMed

Mardakheh, Faraz K

2017-01-01

A major challenge in systems biology is comprehensive mapping of protein interaction networks. Crucially, such interactions are often dynamic in nature, necessitating methods that can rapidly mine the interactome across varied conditions and treatments to reveal change in the interaction networks. Recently, we described a fast mass spectrometry-based method to reveal functional interactions in mammalian cells on a global scale, by revealing spatial colocalizations between proteins (COLA) (Mardakheh et al., Mol Biosyst 13:92-105, 2017). As protein localization and function are inherently linked, significant colocalization between two proteins is a strong indication for their functional interaction. COLA uses rapid complete subcellular fractionation, coupled with quantitative proteomics to generate a subcellular localization profile for each protein quantified by the mass spectrometer. Robust clustering is then applied to reveal significant similarities in protein localization profiles, indicative of colocalization.

Introduction of A New Toolbox for Processing Digital Images From Multiple Camera Networks: FMIPROT

NASA Astrophysics Data System (ADS)

Melih Tanis, Cemal; Nadir Arslan, Ali

2017-04-01

Webcam networks intended for scientific monitoring of ecosystems is providing digital images and other environmental data for various studies. Also, other types of camera networks can also be used for scientific purposes, e.g. usage of traffic webcams for phenological studies, camera networks for ski tracks and avalanche monitoring over mountains for hydrological studies. To efficiently harness the potential of these camera networks, easy to use software which can obtain and handle images from different networks having different protocols and standards is necessary. For the analyses of the images from webcam networks, numerous software packages are freely available. These software packages have different strong features not only for analyzing but also post processing digital images. But specifically for the ease of use, applicability and scalability, a different set of features could be added. Thus, a more customized approach would be of high value, not only for analyzing images of comprehensive camera networks, but also considering the possibility to create operational data extraction and processing with an easy to use toolbox. At this paper, we introduce a new toolbox, entitled; Finnish Meteorological Institute Image PROcessing Tool (FMIPROT) which a customized approach is followed. FMIPROT has currently following features: • straightforward installation, • no software dependencies that require as extra installations, • communication with multiple camera networks, • automatic downloading and handling images, • user friendly and simple user interface, • data filtering, • visualizing results on customizable plots, • plugins; allows users to add their own algorithms. Current image analyses in FMIPROT include "Color Fraction Extraction" and "Vegetation Indices". The analysis of color fraction extraction is calculating the fractions of the colors in a region of interest, for red, green and blue colors along with brightness and luminance parameters. The analysis of vegetation indices is a collection of indices used in vegetation phenology and includes "Green Fraction" (green chromatic coordinate), "Green-Red Vegetation Index" and "Green Excess Index". "Snow cover fraction" analysis which detects snow covered pixels in the images and georeference them on a geospatial plane to calculate the snow cover fraction is being implemented at the moment. FMIPROT is being developed during the EU Life+ MONIMET project. Altogether we mounted 28 cameras at 14 different sites in Finland as MONIMET camera network. In this paper, we will present details of FMIPROT and analysis results from MONIMET camera network. We will also discuss on future planned developments of FMIPROT.

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

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

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

Throughput Analysis on 3-Dimensional Underwater Acoustic Network with One-Hop Mobile Relay.

PubMed

Zhong, Xuefeng; Chen, Fangjiong; Fan, Jiasheng; Guan, Quansheng; Ji, Fei; Yu, Hua

2018-01-16

Underwater acoustic communication network (UACN) has been considered as an essential infrastructure for ocean exploitation. Performance analysis of UACN is important in underwater acoustic network deployment and management. In this paper, we analyze the network throughput of three-dimensional randomly deployed transmitter-receiver pairs. Due to the long delay of acoustic channels, complicated networking protocols with heavy signaling overhead may not be appropriate. In this paper, we consider only one-hop or two-hop transmission, to save the signaling cost. That is, we assume the transmitter sends the data packet to the receiver by one-hop direct transmission, or by two-hop transmission via mobile relays. We derive the closed-form formulation of packet delivery rate with respect to the transmission delay and the number of transmitter-receiver pairs. The correctness of the derivation results are verified by computer simulations. Our analysis indicates how to obtain a precise tradeoff between the delay constraint and the network capacity.

Throughput Analysis on 3-Dimensional Underwater Acoustic Network with One-Hop Mobile Relay

PubMed Central

Zhong, Xuefeng; Fan, Jiasheng; Guan, Quansheng; Ji, Fei; Yu, Hua

2018-01-01

Underwater acoustic communication network (UACN) has been considered as an essential infrastructure for ocean exploitation. Performance analysis of UACN is important in underwater acoustic network deployment and management. In this paper, we analyze the network throughput of three-dimensional randomly deployed transmitter–receiver pairs. Due to the long delay of acoustic channels, complicated networking protocols with heavy signaling overhead may not be appropriate. In this paper, we consider only one-hop or two-hop transmission, to save the signaling cost. That is, we assume the transmitter sends the data packet to the receiver by one-hop direct transmission, or by two-hop transmission via mobile relays. We derive the closed-form formulation of packet delivery rate with respect to the transmission delay and the number of transmitter–receiver pairs. The correctness of the derivation results are verified by computer simulations. Our analysis indicates how to obtain a precise tradeoff between the delay constraint and the network capacity. PMID:29337911

Advancing the application of systems thinking in health: analysing the contextual and social network factors influencing the use of sustainability indicators in a health system--a comparative study in Nepal and Somaliland.

PubMed

Blanchet, Karl; Palmer, Jennifer; Palanchowke, Raju; Boggs, Dorothy; Jama, Ali; Girois, Susan

2014-08-26

Health systems strengthening is becoming a key component of development agendas for low-income countries worldwide. Systems thinking emphasizes the role of diverse stakeholders in designing solutions to system problems, including sustainability. The objective of this paper is to compare the definition and use of sustainability indicators developed through the Sustainability Analysis Process in two rehabilitation sectors, one in Nepal and one in Somaliland, and analyse the contextual factors (including the characteristics of system stakeholder networks) influencing the use of sustainability data. Using the Sustainability Analysis Process, participants collectively clarified the boundaries of their respective systems, defined sustainability, and identified sustainability indicators. Baseline indicator data was gathered, where possible, and then researched again 2 years later. As part of the exercise, system stakeholder networks were mapped at baseline and at the 2-year follow-up. We compared stakeholder networks and interrelationships with baseline and 2-year progress toward self-defined sustainability goals. Using in-depth interviews and observations, additional contextual factors affecting the use of sustainability data were identified. Differences in the selection of sustainability indicators selected by local stakeholders from Nepal and Somaliland reflected differences in the governance and structure of the present rehabilitation system. At 2 years, differences in the structure of social networks were more marked. In Nepal, the system stakeholder network had become more dense and decentralized. Financial support by an international organization facilitated advancement toward self-identified sustainability goals. In Somaliland, the small, centralised stakeholder network suffered a critical rupture between the system's two main information brokers due to competing priorities and withdrawal of international support to one of these. Progress toward self-defined sustainability was nil. The structure of the rehabilitation system stakeholder network characteristics in Nepal and Somaliland evolved over time and helped understand the changing nature of relationships between actors and their capacity to work as a system rather than a sum of actors. Creating consensus on a common vision of sustainability requires additional system-level interventions such as identification of and support to stakeholders who promote systems thinking above individual interests.

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Gene network analysis: from heart development to cardiac therapy.

PubMed

Ferrazzi, Fulvia; Bellazzi, Riccardo; Engel, Felix B

2015-03-01

Networks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.

The importance of delineating networks by activity type in bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida.

PubMed

Gazda, Stefanie; Iyer, Swami; Killingback, Timothy; Connor, Richard; Brault, Solange

2015-03-01

Network analysis has proved to be a valuable tool for studying the behavioural patterns of complex social animals. Often such studies either do not distinguish between different behavioural states of the organisms or simply focus attention on a single behavioural state to the exclusion of all others. In either of these approaches it is impossible to ascertain how the behavioural patterns of individuals depend on the type of activity they are engaged in. Here we report on a network-based analysis of the behavioural associations in a population of bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida. We consider three distinct behavioural states-socializing, travelling and foraging-and analyse the association networks corresponding to each activity. Moreover, in constructing the different activity networks we do not simply record a spatial association between two individuals as being either present or absent, but rather quantify the degree of any association, thus allowing us to construct weighted networks describing each activity. The results of these weighted activity networks indicate that networks can reveal detailed patterns of bottlenose dolphins at the population level; dolphins socialize in large groups with preferential associations; travel in small groups with preferential associates; and spread out to forage in very small, weakly connected groups. There is some overlap in the socialize and travel networks but little overlap between the forage and other networks. This indicates that the social bonds maintained in other activities are less important as they forage on dispersed, solitary prey. The overall network, not sorted by activity, does not accurately represent any of these patterns.

The importance of delineating networks by activity type in bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida

PubMed Central

Gazda, Stefanie; Iyer, Swami; Killingback, Timothy; Connor, Richard; Brault, Solange

2015-01-01

Network analysis has proved to be a valuable tool for studying the behavioural patterns of complex social animals. Often such studies either do not distinguish between different behavioural states of the organisms or simply focus attention on a single behavioural state to the exclusion of all others. In either of these approaches it is impossible to ascertain how the behavioural patterns of individuals depend on the type of activity they are engaged in. Here we report on a network-based analysis of the behavioural associations in a population of bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida. We consider three distinct behavioural states—socializing, travelling and foraging—and analyse the association networks corresponding to each activity. Moreover, in constructing the different activity networks we do not simply record a spatial association between two individuals as being either present or absent, but rather quantify the degree of any association, thus allowing us to construct weighted networks describing each activity. The results of these weighted activity networks indicate that networks can reveal detailed patterns of bottlenose dolphins at the population level; dolphins socialize in large groups with preferential associations; travel in small groups with preferential associates; and spread out to forage in very small, weakly connected groups. There is some overlap in the socialize and travel networks but little overlap between the forage and other networks. This indicates that the social bonds maintained in other activities are less important as they forage on dispersed, solitary prey. The overall network, not sorted by activity, does not accurately represent any of these patterns. PMID:26064611

Artificial astrocytes improve neural network performance.

PubMed

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial Astrocytes Improve Neural Network Performance

PubMed Central

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

Recovery-related indicators of motor network plasticity according to impairment severity after stroke.

PubMed

Lee, J; Park, E; Lee, A; Chang, W H; Kim, D-S; Kim, Y-H

2017-10-01

Brain connectivity analysis has been widely used to investigate brain plasticity and recovery-related indicators of patients with stroke. However, results remain controversial because of interindividual variability of initial impairment and subsequent recovery of function. In this study, we aimed to investigate the differences in network plasticity and motor recovery-related indicators according to initial severity. We divided participants (16 males and 14 females, aged 54.2 ± 12.0 years) into groups of different severity by Fugl-Mayer Assessment score, i.e. moderate (50-84), severe (20-49) and extremely severe (<20) impairment groups. Longitudinal resting-state functional magnetic resonance imaging data were acquired at 2 weeks and 3 months after onset. The differences in network plasticity and recovery-related indicators between groups were investigated using network distance and graph measurements. As the level of impairment increased, the network balance was more disrupted. Network balance, interhemispheric connectivity and network efficiency were recovered at 3 months only in the moderate impairment group. However, this was not the case in the extremely severe impairment group. A single connection strength between the ipsilesional primary motor cortex and ventral premotor cortex was implicated in the recovery of motor function for the extremely severe impairment group. The connections of the ipsilesional primary motor cortex-ventral premotor cortex were positively associated with motor recovery as the patients were more severely impaired. Differences in plasticity and recovery-related indicators of motor networks were noted according to impairment severity. Our results may suggest meaningful implications for recovery prediction and treatment strategies in future stroke research. © 2017 EAN.

Efficient computation of aerodynamic influence coefficients for aeroelastic analysis on a transputer network

NASA Technical Reports Server (NTRS)

Janetzke, David C.; Murthy, Durbha V.

1991-01-01

Aeroelastic analysis is multi-disciplinary and computationally expensive. Hence, it can greatly benefit from parallel processing. As part of an effort to develop an aeroelastic capability on a distributed memory transputer network, a parallel algorithm for the computation of aerodynamic influence coefficients is implemented on a network of 32 transputers. The aerodynamic influence coefficients are calculated using a 3-D unsteady aerodynamic model and a parallel discretization. Efficiencies up to 85 percent were demonstrated using 32 processors. The effect of subtask ordering, problem size, and network topology are presented. A comparison to results on a shared memory computer indicates that higher speedup is achieved on the distributed memory system.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Multilayer motif analysis of brain networks

NASA Astrophysics Data System (ADS)

Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

2017-04-01

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

Quantitative Analysis of the Interdisciplinarity of Applied Mathematics.

PubMed

Xie, Zheng; Duan, Xiaojun; Ouyang, Zhenzheng; Zhang, Pengyuan

2015-01-01

The increasing use of mathematical techniques in scientific research leads to the interdisciplinarity of applied mathematics. This viewpoint is validated quantitatively here by statistical and network analysis on the corpus PNAS 1999-2013. A network describing the interdisciplinary relationships between disciplines in a panoramic view is built based on the corpus. Specific network indicators show the hub role of applied mathematics in interdisciplinary research. The statistical analysis on the corpus content finds that algorithms, a primary topic of applied mathematics, positively correlates, increasingly co-occurs, and has an equilibrium relationship in the long-run with certain typical research paradigms and methodologies. The finding can be understood as an intrinsic cause of the interdisciplinarity of applied mathematics.

[Clinical value evaluation of Chinese herbal formula in context of multi-omics network].

PubMed

Li, Bing; Han, Fei; Wang, Zhong; Wang, Yong-Yan

2017-03-01

Clinical value evaluation is the key issue to solve the problems such as high repetition rate, fuzzy clinical positioning, broad indications and unclear clinical values in Chinese herbal formula(Chinese patent medicine). By analyzing the challenges and opportunities of Chinese herbal formula in clinical value evaluation, this paper introduced a strategy of multi-omic network analysis. Through comparative analysis of three stroke treatment formulas, we suggested their different characteristic advantages for variant symptoms or phenotypes of stroke, which may provide reference for rational clinical choice. Such multi-omic network analysis strategy may open a unique angle of view for clinical evaluation and comparison of Chinese herbal formula. Copyright© by the Chinese Pharmaceutical Association.

From brain to earth and climate systems: small-world interaction networks or not?

PubMed

Bialonski, Stephan; Horstmann, Marie-Therese; Lehnertz, Klaus

2010-03-01

We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.

[Social Networks of Children with Mentally Ill Parents].

PubMed

Stiawa, Maja; Kilian, Reinhold

2017-10-01

Social Networks of Children with Mentally Ill Parents Mental illness of parents can be a load situation for children. Supporting social relations might be an important source in such a situation. Social relations can be shown by social network analysis. Studies about social networks and mental health indicate differences regarding structure and potential for support when compared with social networks of healthy individuals. If and how mental illness of parents has an impact on their children's network is widely unknown. This systematic review shows methods and results of studies about social networks of children with mentally ill parents. By systematic search in electronic databases as well as manual search, two studies were found who met the target criteria. Both studies were conducted in the USA. Results of studies indicate that parental mental illness affects the state of mental health and social networks of children. Symptomatology of children changed due to perceived social support of network contacts. Impact of social support and strong network contacts seems to depend on age of children and the family situation. That's why support offers should be adapt to children's age. Focusing on social networks as potential resource for support and needs of the family affected seems appropriate during treatment.

Comparison analysis on vulnerability of metro networks based on complex network

NASA Astrophysics Data System (ADS)

Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

2018-04-01

This paper analyzes the networked characteristics of three metro networks, and two malicious attacks are employed to investigate the vulnerability of metro networks based on connectivity vulnerability and functionality vulnerability. Meanwhile, the networked characteristics and vulnerability of three metro networks are compared with each other. The results show that Shanghai metro network has the largest transport capacity, Beijing metro network has the best local connectivity and Guangzhou metro network has the best global connectivity, moreover Beijing metro network has the best homogeneous degree distribution. Furthermore, we find that metro networks are very vulnerable subjected to malicious attacks, and Guangzhou metro network has the best topological structure and reliability among three metro networks. The results indicate that the proposed methodology is feasible and effective to investigate the vulnerability and to explore better topological structure of metro networks.

Latent Factors in Student-Teacher Interaction Factor Analysis

ERIC Educational Resources Information Center

Le, Thu; Bolt, Daniel; Camburn, Eric; Goff, Peter; Rohe, Karl

2017-01-01

Classroom interactions between students and teachers form a two-way or dyadic network. Measurements such as days absent, test scores, student ratings, or student grades can indicate the "quality" of the interaction. Together with the underlying bipartite graph, these values create a valued student-teacher dyadic interaction network. To…

Non-criticality of interaction network over system's crises: A percolation analysis.

PubMed

Shirazi, Amir Hossein; Saberi, Abbas Ali; Hosseiny, Ali; Amirzadeh, Ehsan; Toranj Simin, Pourya

2017-11-20

Extraction of interaction networks from multi-variate time-series is one of the topics of broad interest in complex systems. Although this method has a wide range of applications, most of the previous analyses have focused on the pairwise relations. Here we establish the potential of such a method to elicit aggregated behavior of the system by making a connection with the concepts from percolation theory. We study the dynamical interaction networks of a financial market extracted from the correlation network of indices, and build a weighted network. In correspondence with the percolation model, we find that away from financial crises the interaction network behaves like a critical random network of Erdős-Rényi, while close to a financial crisis, our model deviates from the critical random network and behaves differently at different size scales. We perform further analysis to clarify that our observation is not a simple consequence of the growth in correlations over the crises.

Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘Candidatus Liberibacter asiaticus’ infection

PubMed Central

2013-01-01

Background Huanglongbing (HLB) is arguably the most destructive disease for the citrus industry. HLB is caused by infection of the bacterium, Candidatus Liberibacter spp. Several citrus GeneChip studies have revealed thousands of genes that are up- or down-regulated by infection with Ca. Liberibacter asiaticus. However, whether and how these host genes act to protect against HLB remains poorly understood. Results As a first step towards a mechanistic view of citrus in response to the HLB bacterial infection, we performed a comparative transcriptome analysis and found that a total of 21 Probesets are commonly up-regulated by the HLB bacterial infection. In addition, a number of genes are likely regulated specifically at early, late or very late stages of the infection. Furthermore, using Pearson correlation coefficient-based gene coexpression analysis, we constructed a citrus HLB response network consisting of 3,507 Probesets and 56,287 interactions. Genes involved in carbohydrate and nitrogen metabolic processes, transport, defense, signaling and hormone response were overrepresented in the HLB response network and the subnetworks for these processes were constructed. Analysis of the defense and hormone response subnetworks indicates that hormone response is interconnected with defense response. In addition, mapping the commonly up-regulated HLB responsive genes into the HLB response network resulted in a core subnetwork where transport plays a key role in the citrus response to the HLB bacterial infection. Moreover, analysis of a phloem protein subnetwork indicates a role for this protein and zinc transporters or zinc-binding proteins in the citrus HLB defense response. Conclusion Through integrating transcriptome comparison and gene coexpression network analysis, we have provided for the first time a systems view of citrus in response to the Ca. Liberibacter spp. infection causing HLB. PMID:23324561

Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty.

PubMed

Fathollah Bayati, Mohsen; Sadjadi, Seyed Jafar

2017-01-01

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model.

Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty

PubMed Central

Sadjadi, Seyed Jafar

2017-01-01

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model. PMID:28953900

Controllability and observability analysis for vertex domination centrality in directed networks

NASA Astrophysics Data System (ADS)

Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

2014-06-01

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

Controllability and observability analysis for vertex domination centrality in directed networks

PubMed Central

Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

2014-01-01

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks. PMID:24954137

Psychophysiological whole-brain network clustering based on connectivity dynamics analysis in naturalistic conditions.

PubMed

Raz, Gal; Shpigelman, Lavi; Jacob, Yael; Gonen, Tal; Benjamini, Yoav; Hendler, Talma

2016-12-01

We introduce a novel method for delineating context-dependent functional brain networks whose connectivity dynamics are synchronized with the occurrence of a specific psychophysiological process of interest. In this method of context-related network dynamics analysis (CRNDA), a continuous psychophysiological index serves as a reference for clustering the whole-brain into functional networks. We applied CRNDA to fMRI data recorded during the viewing of a sadness-inducing film clip. The method reliably demarcated networks in which temporal patterns of connectivity related to the time series of reported emotional intensity. Our work successfully replicated the link between network connectivity and emotion rating in an independent sample group for seven of the networks. The demarcated networks have clear common functional denominators. Three of these networks overlap with distinct empathy-related networks, previously identified in distinct sets of studies. The other networks are related to sensorimotor processing, language, attention, and working memory. The results indicate that CRNDA, a data-driven method for network clustering that is sensitive to transient connectivity patterns, can productively and reliably demarcate networks that follow psychologically meaningful processes. Hum Brain Mapp 37:4654-4672, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells

USGS Publications Warehouse

Mishra, A.; Ray, C.; Kolpin, D.W.

2004-01-01

A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information.

Systematic review of social network analysis in adolescent cigarette smoking behavior.

PubMed

Seo, Dong-Chul; Huang, Yan

2012-01-01

Social networks are important in adolescent smoking behavior. Previous research indicates that peer context is a major causal factor of adolescent smoking behavior. To date, however, little is known about the influence of peer group structure on adolescent smoking behavior. Studies that examined adolescent social networks with regard to their cigarette smoking behavior were identified through online and manual literature searches. Ten social network analysis studies involving a total of 28,263 adolescents were included in the final review. Of the 10 reviewed studies, 6 identify clique members, liaisons, and isolates as contributing factors to adolescent cigarette smoking. Significantly higher rates of smoking are noted among isolates than clique members or liaisons in terms of peer network structure. Eight of the reviewed studies indicate that peer selection or influence precedes adolescents' smoking behavior and intent to smoke. Such peer selection or influence accounts for a large portion of similarities among smoking adolescents. Adolescents who are identified as isolates are more likely to smoke and engage in risk-taking behaviors than others in the peer network structure. Given that the vast majority of current adult smokers started their smoking habits during adolescence, adolescent smoking prevention efforts will likely benefit from incorporating social network analytic approaches and focusing the efforts on isolates and other vulnerable adolescents from a peer selection and influence perspective. © 2011, American School Health Association.

A Preliminary Examination of the Relationship Between Social Networking Interactions, Internet Use, and Thwarted Belongingness.

PubMed

Moberg, Fallon B; Anestis, Michael D

2015-01-01

Joiner's (2005) interpersonal-psychological theory of suicide hypothesizes that suicidal desire develops in response to the joint presence of thwarted belongingness and perceived burdensomeness. To consider the potential influence of online interactions and behaviors on these outcomes. To address this, we administered an online protocol assessing suicidal desire and online interactions in a sample of 305 undergraduates (83.6% female). We hypothesized negative interactions on social networking sites and a preference for online social interactions would be associated with thwarted belongingness. We also conducted an exploratory analysis examining the associations between Internet usage and perceived burdensomeness. Higher levels of negative interactions on social networking sites, but no other variables, significantly predicted thwarted belongingness. Our exploratory analysis showed that none of our predictors were associated with perceived burdensomeness after accounting for demographics, depression, and thwarted belongingness. Our findings indicate that a general tendency to have negative interactions on social networking sites could possibly impact suicidal desire and that these effects are significant above and beyond depression symptoms. Furthermore, no other aspect of problematic Internet use significantly predicted our outcomes in multivariate analyses, indicating that social networking in particular may have a robust effect on thwarted belongingness.

Top-down network analysis characterizes hidden termite-termite interactions.

PubMed

Campbell, Colin; Russo, Laura; Marins, Alessandra; DeSouza, Og; Schönrogge, Karsten; Mortensen, David; Tooker, John; Albert, Réka; Shea, Katriona

2016-09-01

The analysis of ecological networks is generally bottom-up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host-parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail-rich reference communities with known modes of interaction can inform our understanding of detail-sparse focal communities. With this top-down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant-pollinator and antagonistic host-parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant-pollinator communities than the antagonistic host-parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite-termite cohabitation networks may be overall mutualistic. More broadly, this work provides support for the argument that cryptic communities may be analyzed via comparison to well-characterized communities.

Egocentric Social Network Analysis of Pathological Gambling

PubMed Central

Meisel, Matthew K.; Clifton, Allan D.; MacKillop, James; Miller, Joshua D.; Campbell, W. Keith; Goodie, Adam S.

2012-01-01

Aims To apply social network analysis (SNA) to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family, and co-workers. is an innovative way to look at relationships among individuals; the current study was the first to our knowledge to apply SNA to gambling behaviors. Design Egocentric social network analysis was used to formally characterize the relationships between social network characteristics and gambling pathology. Setting Laboratory-based questionnaire and interview administration. Participants Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. Findings The SNA revealed significant social network compositional differences between the two groups: pathological gamblers (PGs) had more gamblers, smokers, and drinkers in their social networks than did nonpathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked, and drank with than those with who were NPG. Network ties were closer to individuals in their networks who gambled, smoked, and drank more frequently. Associations between gambling severity and structural network characteristics were not significant. Conclusions Pathological gambling is associated with compositional but not structural differences in social networks. Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers, and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention. PMID:23072641

Egocentric social network analysis of pathological gambling.

PubMed

Meisel, Matthew K; Clifton, Allan D; Mackillop, James; Miller, Joshua D; Campbell, W Keith; Goodie, Adam S

2013-03-01

To apply social network analysis (SNA) to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family and co-workers is an innovative way to look at relationships among individuals; the current study was the first, to our knowledge, to apply SNA to gambling behaviors. Egocentric social network analysis was used to characterize formally the relationships between social network characteristics and gambling pathology. Laboratory-based questionnaire and interview administration. Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. The SNA revealed significant social network compositional differences between the two groups: pathological gamblers (PGs) had more gamblers, smokers and drinkers in their social networks than did non-pathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked and drank than those with who were NPG. Network ties were closer to individuals in their networks who gambled, smoked and drank more frequently. Associations between gambling severity and structural network characteristics were not significant. Pathological gambling is associated with compositional but not structural differences in social networks. Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

Detection of Road Surface States from Tire Noise Using Neural Network Analysis

NASA Astrophysics Data System (ADS)

Kongrattanaprasert, Wuttiwat; Nomura, Hideyuki; Kamakura, Tomoo; Ueda, Koji

This report proposes a new processing method for automatically detecting the states of road surfaces from tire noises of passing vehicles. In addition to multiple indicators of the signal features in the frequency domain, we propose a few feature indicators in the time domain to successfully classify the road states into four categories: snowy, slushy, wet, and dry states. The method is based on artificial neural networks. The proposed classification is carried out in multiple neural networks using learning vector quantization. The outcomes of the networks are then integrated by the voting decision-making scheme. Experimental results obtained from recorded signals for ten days in the snowy season demonstrated that an accuracy of approximately 90% can be attained for predicting road surface states using only tire noise data.

A character network study of two Sci-Fi TV series

NASA Astrophysics Data System (ADS)

Tan, M. S. A.; Ujum, E. A.; Ratnavelu, K.

2014-03-01

This work is an analysis of the character networks in two science fiction television series: Stargate and Star Trek. These networks are constructed on the basis of scene co-occurrence between characters to indicate the presence of a connection. Global network structure measures such as the average path length, graph density, network diameter, average degree, median degree, maximum degree, and average clustering coefficient are computed as well as individual node centrality scores. The two fictional networks constructed are found to be quite similar in structure which is astonishing given that Stargate only ran for 18 years in comparison to the 48 years for Star Trek.

[Study based on ICA of "dorsal attention network" in patients with temporal lobe epilepsy].

PubMed

Yang, Zhigen; Wang, Huinan; Zhang, Zhiqiang; Zhong, Yuan; Chen, Zhili; Lu, Guangming

2010-02-01

Many functional magnetic resonance imaging (fMRI) studies have revealed the deactivation phenomenon of default mode network in the patients with epilepsy; however, nearly not any of the reports has focused on the dorsal attention network of epilepsy. In this paper, independent component analysis (ICA) was used to isolate the dorsal attention network of 16 patients with temporal lobe epilepsy (TLE) and of 20 healthy normals; and a goodness-of-fit analysis was applied at the individual subject level to choose the interesting component. Intra-group analysis and inter-group analysis were performed. The results indicated that the dorsal attention network included bilateral intraparietal sulcus, middle frontal gyrus, human frontal eye field, posterior lobe of right cerebellum, etc. The TLE group showed decreased functional connectivity in most of the dorsal attention regions with the predominance in the bilateral intraparietal sulcus, middle frontal gyrus, and posterior lobe of right cerebellum. These data suggested that the intrinsic organization of the brain function might be disrupted in TLE. In addition, the decrease of goodness-of-fit scores suggests that activity in the dorsal attention network may ultimately prove a sensitive biomarker for TLE.

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.

PubMed

Li, Lianshuo; Wang, Zicheng; He, Peng; Ma, Shining; Du, Jie; Jiang, Rui

2016-10-01

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D. Copyright © 2016. Production and hosting by Elsevier Ltd.

Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy.

PubMed

Wang, Junjing; Qiu, Shijun; Xu, Yong; Liu, Zhenyin; Wen, Xue; Hu, Xiangshu; Zhang, Ruibin; Li, Meng; Wang, Wensheng; Huang, Ruiwang

2014-09-01

Temporal lobe epilepsy (TLE) is one of the most common forms of drug-resistant epilepsy. Previous studies have indicated that the TLE-related impairments existed in extensive local functional networks. However, little is known about the alterations in the topological properties of whole brain functional networks. In this study, we acquired resting-state BOLD-fMRI (rsfMRI) data from 26 TLE patients and 25 healthy controls, constructed their whole brain functional networks, compared the differences in topological parameters between the TLE patients and the controls, and analyzed the correlation between the altered topological properties and the epilepsy duration. The TLE patients showed significant increases in clustering coefficient and characteristic path length, but significant decrease in global efficiency compared to the controls. We also found altered nodal parameters in several regions in the TLE patients, such as the bilateral angular gyri, left middle temporal gyrus, right hippocampus, triangular part of left inferior frontal gyrus, left inferior parietal but supramarginal and angular gyri, and left parahippocampus gyrus. Further correlation analysis showed that the local efficiency of the TLE patients correlated positively with the epilepsy duration. Our results indicated the disrupted topological properties of whole brain functional networks in TLE patients. Our findings indicated the TLE-related impairments in the whole brain functional networks, which may help us to understand the clinical symptoms of TLE patients and offer a clue for the diagnosis and treatment of the TLE patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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 framework for conservationists to analyze movement data. Functions created for the analyses are available within the R package moveNT. © 2018 by the Ecological Society of America.

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Discovering disease-associated genes in weighted protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

The QAP weighted network analysis method and its application in international services trade

NASA Astrophysics Data System (ADS)

Xu, Helian; Cheng, Long

2016-04-01

Based on QAP (Quadratic Assignment Procedure) correlation and complex network theory, this paper puts forward a new method named QAP Weighted Network Analysis Method. The core idea of the method is to analyze influences among relations in a social or economic group by building a QAP weighted network of networks of relations. In the QAP weighted network, a node depicts a relation and an undirect edge exists between any pair of nodes if there is significant correlation between relations. As an application of the QAP weighted network, we study international services trade by using the QAP weighted network, in which nodes depict 10 kinds of services trade relations. After the analysis of international services trade by QAP weighted network, and by using distance indicators, hierarchy tree and minimum spanning tree, the conclusion shows that: Firstly, significant correlation exists in all services trade, and the development of any one service trade will stimulate the other nine. Secondly, as the economic globalization goes deeper, correlations in all services trade have been strengthened continually, and clustering effects exist in those services trade. Thirdly, transportation services trade, computer and information services trade and communication services trade have the most influence and are at the core in all services trade.

Designing Industrial Networks Using Ecological Food Web Metrics.

PubMed

Layton, Astrid; Bras, Bert; Weissburg, Marc

2016-10-18

Biologically Inspired Design (biomimicry) and Industrial Ecology both look to natural systems to enhance the sustainability and performance of engineered products, systems and industries. Bioinspired design (BID) traditionally has focused on a unit operation and single product level. In contrast, this paper describes how principles of network organization derived from analysis of ecosystem properties can be applied to industrial system networks. Specifically, this paper examines the applicability of particular food web matrix properties as design rules for economically and biologically sustainable industrial networks, using an optimization model developed for a carpet recycling network. Carpet recycling network designs based on traditional cost and emissions based optimization are compared to designs obtained using optimizations based solely on ecological food web metrics. The analysis suggests that networks optimized using food web metrics also were superior from a traditional cost and emissions perspective; correlations between optimization using ecological metrics and traditional optimization ranged generally from 0.70 to 0.96, with flow-based metrics being superior to structural parameters. Four structural food parameters provided correlations nearly the same as that obtained using all structural parameters, but individual structural parameters provided much less satisfactory correlations. The analysis indicates that bioinspired design principles from ecosystems can lead to both environmentally and economically sustainable industrial resource networks, and represent guidelines for designing sustainable industry networks.

Sensitivity of the Positive and Negative Syndrome Scale (PANSS) in Detecting Treatment Effects via Network Analysis.

PubMed

Esfahlani, Farnaz Zamani; Sayama, Hiroki; Visser, Katherine Frost; Strauss, Gregory P

2017-12-01

Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with psychosis, suggesting that antipsychotics achieve their effect by enhancing a number of central symptoms, which then facilitate reduction of other highly coupled symptoms in a network-like fashion.

Complex networks in the Euclidean space of communicability distances

NASA Astrophysics Data System (ADS)

Estrada, Ernesto

2012-06-01

We study the properties of complex networks embedded in a Euclidean space of communicability distances. The communicability distance between two nodes is defined as the difference between the weighted sum of walks self-returning to the nodes and the weighted sum of walks going from one node to the other. We give some indications that the communicability distance identifies the least crowded routes in networks where simultaneous submission of packages is taking place. We define an index Q based on communicability and shortest path distances, which allows reinterpreting the “small-world” phenomenon as the region of minimum Q in the Watts-Strogatz model. It also allows the classification and analysis of networks with different efficiency of spatial uses. Consequently, the communicability distance displays unique features for the analysis of complex networks in different scenarios.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Network Indicators of the Social Ecology of Adolescents in Relative and Non-Relative Foster Households

PubMed Central

Kothari, Brianne H.; McBeath, Bowen; Sorenson, Paul; Bank, Lew

2016-01-01

Though the presence, composition, and quality of social relationships—particularly as found in family networks—has an important influence on adolescent well-being, little is known about the social ecology of youth in foster care. This study examined the social networks of foster youth participating in a large RCT of an intervention for siblings in foster care. Youth reported on the people they lived with and the relatives they were in contact with, which provided indicators of network size, composition, and relationship quality. Cluster analysis was used to identify five family network profiles for youth living in foster homes. Two identified subgroups reflected robust family networks where youth were living with relative caregiver(s) and related youth, and also reported multiple family ties outside the household, including with biological parents. The remaining three profiles reflected youth reports of fewer family connections within or beyond the foster household, with distinctions by whether they lived with siblings and/or reported having positive relationships with their mothers and/or fathers. The identified network profiles were validated using youth- and caregiver-reported measures of mental health functioning, with increased caregiver report of post-traumatic stress symptoms indicated for the three subgroups that were not characterized by a robust family network. PMID:28736465

Reciprocal Family, Friendship and Church Support Networks of African Americans: Findings from the National Survey of American Life.

PubMed

Taylor, Robert Joseph; Mouzon, Dawne M; Nguyen, Ann W; Chatters, Linda M

2016-12-01

This study examined reciprocal support networks involving extended family, friends and church members among African Americans. Our analysis examined specific patterns of reciprocal support (i.e., received only, gave only, both gave and received, neither gave or received), as well as network characteristics (i.e., contact and subjective closeness) as correlates of reciprocal support. The analysis is based on the African American sub-sample of the National Survey of American Life (NSAL). Overall, our findings indicate that African Americans are very involved in reciprocal support networks with their extended family, friends and church members. Respondents were most extensively involved in reciprocal supports with extended family members, followed closely by friends and church networks. Network characteristics (i.e., contact and subjective closeness) were significantly and consistently associated with involvement with reciprocal support exchanges for all three networks. These and other findings are discussed in detail. This study complements previous work on the complementary roles of family, friend and congregational support networks, as well as studies of racial differences in informal support networks.

Networks and the fiscal performance of rural hospitals in Oklahoma: are they associated?

PubMed

Broyles, R W; Brandt, E N; Biard-Holmes, D

1998-01-01

This paper uses regression analysis to explore the relation of network membership to the financial performance of rural hospitals in Oklahoma during fiscal year 1995. After adjusting for the scope of service, as measured by the number of facilities or services offered by the hospital, indicators of fiscal status are (1) the cash receipts derived from net patient revenue; (2) the cash disbursements related to operating costs, net of interest and depreciation expense, labor costs and nonlabor costs; and (3) net cash flow, defined as the difference between cash receipts and disbursements. Controlling for the effects of the hospital's structural attributes, operating characteristics and market conditions, the results indicate that members of a network reported lower net operating costs, labor costs and nonlabor expenses per service than nonmembers. Hence, the analysis seems to suggest that the membership of rural hospitals in a network is associated with lower cash disbursements and an improved net cash flow, outcomes that may preserve their fiscal viability and the access of the population at risk to service.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

Calibration of a Hall effect displacement measurement system for complex motion analysis using a neural network.

PubMed

Northey, G W; Oliver, M L; Rittenhouse, D M

2006-01-01

Biomechanics studies often require the analysis of position and orientation. Although a variety of transducer and camera systems can be utilized, a common inexpensive alternative is the Hall effect sensor. Hall effect sensors have been used extensively for one-dimensional position analysis but their non-linear behavior and cross-talk effects make them difficult to calibrate for effective and accurate two- and three-dimensional position and orientation analysis. The aim of this study was to develop and calibrate a displacement measurement system for a hydraulic-actuation joystick used for repetitive motion analysis of heavy equipment operators. The system utilizes an array of four Hall effect sensors that are all active during any joystick movement. This built-in redundancy allows the calibration to utilize fully connected feed forward neural networks in conjunction with a Microscribe 3D digitizer. A fully connected feed forward neural network with one hidden layer containing five neurons was developed. Results indicate that the ability of the neural network to accurately predict the x, y and z coordinates of the joystick handle was good with r(2) values of 0.98 and higher. The calibration technique was found to be equally as accurate when used on data collected 5 days after the initial calibration, indicating the system is robust and stable enough to not require calibration every time the joystick is used. This calibration system allowed an infinite number of joystick orientations and positions to be found within the range of joystick motion.

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

PubMed Central

Wu, Xia; Yu, Xinyu; Yao, Li; Li, Rui

2014-01-01

Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states. PMID:25309414

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

PubMed Central

Toppi, Jlenia; Astolfi, Laura; Risetti, Monica; Anzolin, Alessandra; Kober, Silvia E.; Wood, Guilherme; Mattia, Donatella

2018-01-01

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke. PMID:29379425

Association between suicidal symptoms and repeat suicidal behaviour within a sample of hospital-treated suicide attempters.

PubMed

de Beurs, Derek P; van Borkulo, Claudia D; O'Connor, Rory C

2017-05-01

Suicidal behaviour is the end result of the complex relation between many factors which are biological, psychological and environmental in nature. Network analysis is a novel method that may help us better understand the complex association between different factors. To examine the relationship between suicidal symptoms as assessed by the Beck Scale for Suicide Ideation and future suicidal behaviour in patients admitted to hospital following a suicide attempt, using network analysis. Secondary analysis was conducted on previously collected data from a sample of 366 patients who were admitted to a Scottish hospital following a suicide attempt. Network models were estimated to visualise and test the association between baseline symptom network structure and suicidal behaviour at 15-month follow-up. Network analysis showed that the desire for an active attempt was found to be the most central, strongly related suicide symptom. Of the 19 suicide symptoms that were assessed at baseline, 10 symptoms were directly related to repeat suicidal behaviour. When comparing baseline network structure of repeaters ( n =94) with the network of non-repeaters ( n =272), no significant differences were found. Network analysis can help us better understand suicidal behaviour by visualising the complex relation between relevant symptoms and by indicating which symptoms are most central within the network. These insights have theoretical implications as well as informing the assessment and treatment of suicidal behaviour. None. © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.

Use of Whatman-41 filters in air quality sampling networks (with applications to elemental analysis)

NASA Technical Reports Server (NTRS)

Neustadter, H. E.; Sidik, S. M.; King, R. B.; Fordyce, J. S.; Burr, J. C.

1974-01-01

The operation of a 16-site parallel high volume air sampling network with glass fiber filters on one unit and Whatman-41 filters on the other is reported. The network data and data from several other experiments indicate that (1) Sampler-to-sampler and filter-to-filter variabilities are small; (2) hygroscopic affinity of Whatman-41 filters need not introduce errors; and (3) suspended particulate samples from glass fiber filters averaged slightly, but not statistically significantly, higher than from Whatman-41-filters. The results obtained demonstrate the practicability of Whatman-41 filters for air quality monitoring and elemental analysis.

Definition of air quality measurements for monitoring space shuttle launches

NASA Technical Reports Server (NTRS)

Thorpe, R. D.

1978-01-01

A description of a recommended air quality monitoring network to characterize the impact on ambient air quality in the Kennedy Space Center (KSC) (area) of space shuttle launch operations is given. Analysis of ground cloud processes and prevalent meteorological conditions indicates that transient HCl depositions can be a cause for concern. The system designed to monitor HCl employs an extensive network of inexpensive detectors combined with a central analysis device. An acid rain network is also recommended. A quantitative measure of projected minimal long-term impact involves the limited monitoring of NOx and particulates. All recommended monitoring is confined ti KSC property.

Functional network connectivity analysis based on partial correlation in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Zhang, Nan; Guan, Xiaoting; Zhang, Yumei; Li, Jingjing; Chen, Hongyan; Chen, Kewei; Fleisher, Adam; Yao, Li; Wu, Xia

2009-02-01

Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences. Our results provide new evidences for the disconnection hypothesis in AD.

Network Analysis of Publications on Topological Indices from the Web of Science.

PubMed

Bodlaj, Jernej; Batagelj, Vladimir

2014-08-01

In this paper we analyze a collection of bibliographic networks, constructed from the data from the Web of Science on works (papers, books, etc.) on the topic of topological indices and on relating scientific fields. We present the general outlook and more specific findings about authors, works and journals, subtopics and keywords and also important relations between them based on scientometric approaches like the strongest and main citation paths, the main themes on citation path based on keywords, results of co-authorship analysis in form of the most prominent islands of citing authors, groups of collaborating authors, two-mode cores of authors and works. We investigate the nature of citing of authors, important journals and citing of works between them, journals preferred by authors and expose hierarchy of similar collaborating authors, based on keywords they use. We perform temporal analysis on one important journal as well. We give a comprehensive scientometric insight into the field of topological indices. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Right-side-stretched multifractal spectra indicate small-worldness in networks

NASA Astrophysics Data System (ADS)

Oświȩcimka, Paweł; Livi, Lorenzo; Drożdż, Stanisław

2018-04-01

Complex network formalism allows to explain the behavior of systems composed by interacting units. Several prototypical network models have been proposed thus far. The small-world model has been introduced to mimic two important features observed in real-world systems: i) local clustering and ii) the possibility to move across a network by means of long-range links that significantly reduce the characteristic path length. A natural question would be whether there exist several ;types; of small-world architectures, giving rise to a continuum of models with properties (partially) shared with other models belonging to different network families. Here, we take advantage of the interplay between network theory and time series analysis and propose to investigate small-world signatures in complex networks by analyzing multifractal characteristics of time series generated from such networks. In particular, we suggest that the degree of right-sided asymmetry of multifractal spectra is linked with the degree of small-worldness present in networks. This claim is supported by numerical simulations performed on several parametric models, including prototypical small-world networks, scale-free, fractal and also real-world networks describing protein molecules. Our results also indicate that right-sided asymmetry emerges with the presence of the following topological properties: low edge density, low average shortest path, and high clustering coefficient.

Integrating network ecology with applied conservation: a synthesis and guide to implementation.

PubMed

Kaiser-Bunbury, Christopher N; Blüthgen, Nico

2015-07-10

Ecological networks are a useful tool to study the complexity of biotic interactions at a community level. Advances in the understanding of network patterns encourage the application of a network approach in other disciplines than theoretical ecology, such as biodiversity conservation. So far, however, practical applications have been meagre. Here we present a framework for network analysis to be harnessed to advance conservation management by using plant-pollinator networks and islands as model systems. Conservation practitioners require indicators to monitor and assess management effectiveness and validate overall conservation goals. By distinguishing between two network attributes, the 'diversity' and 'distribution' of interactions, on three hierarchical levels (species, guild/group and network) we identify seven quantitative metrics to describe changes in network patterns that have implications for conservation. Diversity metrics are partner diversity, vulnerability/generality, interaction diversity and interaction evenness, and distribution metrics are the specialization indices d' and [Formula: see text] and modularity. Distribution metrics account for sampling bias and may therefore be suitable indicators to detect human-induced changes to plant-pollinator communities, thus indirectly assessing the structural and functional robustness and integrity of ecosystems. We propose an implementation pathway that outlines the stages that are required to successfully embed a network approach in biodiversity conservation. Most importantly, only if conservation action and study design are aligned by practitioners and ecologists through joint experiments, are the findings of a conservation network approach equally beneficial for advancing adaptive management and ecological network theory. We list potential obstacles to the framework, highlight the shortfall in empirical, mostly experimental, network data and discuss possible solutions. Published by Oxford University Press on behalf of the Annals of Botany Company.

Structural and functional social network attributes moderate the association of self-rated health with mental health in midlife and older adults.

PubMed

Windsor, Tim D; Rioseco, Pilar; Fiori, Katherine L; Curtis, Rachel G; Booth, Heather

2016-01-01

Social relationships are multifaceted, and different social network components can operate via different processes to influence well-being. This study examined associations of social network structure and relationship quality (positive and negative social exchanges) with mental health in midlife and older adults. The focus was on both direct associations of network structure and relationship quality with mental health, and whether these social network attributes moderated the association of self-rated health (SRH) with mental health. Analyses were based on survey data provided by 2001 (Mean age = 65, SD = 8.07) midlife and older adults. We used Latent Class Analysis (LCA) to classify participants into network types based on network structure (partner status, network size, contact frequency, and activity engagement), and used continuous measures of positive and negative social exchanges to operationalize relationship quality. Regression analysis was used to test moderation. LCA revealed network types generally consistent with those reported in previous studies. Participants in more diverse networks reported better mental health than those categorized into a restricted network type after adjustment for age, sex, education, and employment status. Analysis of moderation indicated that those with poorer SRH were less likely to report poorer mental health if they were classified into more diverse networks. A similar moderation effect was also evident for positive exchanges. The findings suggest that both quantity and quality of social relationships can play a role in buffering against the negative implications of physical health decline for mental health.

Information seeking for making evidence-informed decisions: a social network analysis on the staff of a public health department in Canada.

PubMed

Yousefi-Nooraie, Reza; Dobbins, Maureen; Brouwers, Melissa; Wakefield, Patricia

2012-05-16

Social network analysis is an approach to study the interactions and exchange of resources among people. It can help understanding the underlying structural and behavioral complexities that influence the process of capacity building towards evidence-informed decision making. A social network analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. The staff were invited to respond to an online questionnaire inquiring about information seeking behavior, identification of colleague expertise, and friendship status. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. The network analysis showed a low density and localized information-seeking network. Inter-personal connections were mainly clustered by organizational divisions; and people tended to limit information-seeking connections to a handful of peers in their division. However, recognition of expertise and friendship networks showed more cross-divisional connections. Members of the office of the Medical Officer of Health were located at the heart of the department, bridging across divisions. A small group of professional consultants and middle managers were the most-central staff in the network, also connecting their divisions to the center of the information-seeking network. In each division, there were some locally central staff, mainly practitioners, who connected their neighboring peers; but they were not necessarily connected to other experts or managers. The methods of social network analysis were useful in providing a systems approach to understand how knowledge might flow in an organization. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers.

Enabling Community Through Social Media

PubMed Central

Haythornthwaite, Caroline

2013-01-01

Background Social network analysis provides a perspective and method for inquiring into the structures that comprise online groups and communities. Traces from interaction via social media provide the opportunity for understanding how a community is formed and maintained online. Objective The paper aims to demonstrate how social network analysis provides a vocabulary and set of techniques for examining interaction patterns via social media. Using the case of the #hcsmca online discussion forum, this paper highlights what has been and can be gained by approaching online community from a social network perspective, as well as providing an inside look at the structure of the #hcsmca community. Methods Social network analysis was used to examine structures in a 1-month sample of Twitter messages with the hashtag #hcsmca (3871 tweets, 486 unique posters), which is the tag associated with the social media–supported group Health Care Social Media Canada. Network connections were considered present if the individual was mentioned, replied to, or had a post retweeted. Results Network analyses revealed patterns of interaction that characterized the community as comprising one component, with a set of core participants prominent in the network due to their connections with others. Analysis showed the social media health content providers were the most influential group based on in-degree centrality. However, there was no preferential attachment among people in the same professional group, indicating that the formation of connections among community members was not constrained by professional status. Conclusions Network analysis and visualizations provide techniques and a vocabulary for understanding online interaction, as well as insights that can help in understanding what, and who, comprises and sustains a network, and whether community emerges from a network of online interactions. PMID:24176835

The interplay between cognitive risk and resilience factors in remitted depression: A network analysis.

PubMed

Hoorelbeke, Kristof; Marchetti, Igor; De Schryver, Maarten; Koster, Ernst H W

2016-05-01

Individuals in remission from depression are at increased risk for developing future depressive episodes. Several cognitive risk- and resilience factors have been suggested to account for this vulnerability. In the current study we explored how risk- and protective factors such as cognitive control, adaptive and maladaptive emotion regulation, residual symptomatology, and resilience relate to one another in a remitted depressed (RMD) sample. We examined the relationships between these constructs in a cross-sectional dataset of 69 RMD patients using network analyses in order to obtain a comprehensive, data-driven view on the interplay between these constructs. We subsequently present an association network, a concentration network, and a relative importance network. In all three networks resilience formed the central hub, connecting perceived cognitive control (i.e., working memory complaints), emotion regulation, and residual symptomatology. The contribution of the behavioral measure for cognitive control in the network was negligible. Moreover, the directed relative importance network indicates bidirectional influences between these constructs, with all indicators of centrality suggesting a key role of resilience in remission from depression. The presented findings are cross-sectional and networks are limited to a fixed set of key constructs in the literature pertaining cognitive vulnerability for depression. These findings indicate the importance of resilience to successfully cope with stressors following remission from depression. Further in-depth studies will be essential to identify the specific underlying resilience mechanisms that may be key to successful remission from depression. Copyright © 2016 Elsevier B.V. All rights reserved.

Characterisation of hydrogeological connections in a lowland karst network using time series analysis of water levels in ephemeral groundwater-fed lakes (turloughs)

NASA Astrophysics Data System (ADS)

Gill, L. W.; Naughton, O.; Johnston, P. M.; Basu, B.; Ghosh, B.

2013-08-01

This research has used continuous water level measurements five groundwater-fed lakes (or turloughs) in a linked lowland karst network of south Galway in Ireland over a 3 year period in order to elucidate the hydrogeological controls and conduit configurations forming the flooded karstic hydraulic system beneath the ground. The main spring outflow from this network discharges below mean sea level making it difficult to determine the hydraulic nature of the network using traditional rainfall-spring flow cross analysis, as has been done in many other studies on karst systems. However, the localised groundwater-surface water interactions (the turloughs) in this flooded lowland karst system can yield information about the nature of the hydraulic connections beneath the ground. Various different analytical techniques have been applied to the fluctuating turlough water level time series data in order to determine the nature of the linkage between them as well as hydraulic pipe configurations at key points in order to improve the conceptual model of the overall karst network. Initially, simple cross correlations between the different turlough water levels were carried out applying different time lags. Frequency analysis of the signals was then carried out using Fast Fourier transform analysis and then both discrete and continuous wavelet analyses have been applied to the data sets to characterise these inherently non-stationary time-series of fluctuating water levels. The analysis has indicated which turloughs are on the main line conduit system and which are somewhat off-line, the relative size of the main conduit in the network including evidence of localised constrictions, as well as clearly showing the tidal influence on the water levels in the three lower turloughs at shallow depths ∼8 km from the main spring outfall at the sea. It has also indicated that the timing of high rainfall events coincident with maximum spring tide levels may promote more consistent, long duration flooding of the turloughs throughout the winter.

How can we establish more successful knowledge networks in developing countries? Lessons learnt from knowledge networks in Iran.

PubMed

Yazdizadeh, Bahareh; Majdzadeh, Reza; Alami, Ali; Amrolalaei, Sima

2014-10-29

Formal knowledge networks are considered among the solutions for strengthening knowledge translation and one of the elements of innovative systems in developing and developed countries. In the year 2000, knowledge networks were established in Iran's health system to organize, lead, empower, and coordinate efforts made by health-related research centers in the country. Since the assessment of a knowledge network is one of the main requirements for its success, the current study was designed in two qualitative and quantitative sections to identify the strengths and weaknesses of the established knowledge networks and to assess their efficiency. In the qualitative section, semi-structured, in-depth interviews were held with network directors and secretaries. The interviews were analyzed through the framework approach. To analyze effectiveness, social network analysis approach was used. That is, by considering the networks' research council members as 'nodes', and the numbers of their joint articles--before and after the network establishments--as 'relations or ties', indices of density, clique, and centrality were calculated for each network. In the qualitative section, non-transparency of management, lack of goals, administrative problems were among the most prevalent issues observed. Currently, the most important challenges are the policies related to them and their management. In the quantitative section, we observed that density and clique indices had risen for some networks; however, the centrality index for the same networks was not as high. Consequently the attribution of density and clique indices to these networks was not possible. Therefore, consolidating and revising policies relevant to the networks and preparing a guide for establishing managing networks could prove helpful. To develop knowledge and technology in a country, networks need to solve the problems they face in management and governance. That is, the first step towards the realization of true knowledge networks in health system.

Fat fractal scaling of drainage networks from a random spatial network model

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1992-01-01

An alternative quantification of the scaling properties of river channel networks is explored using a spatial network model. Whereas scaling descriptions of drainage networks previously have been presented using a fractal analysis primarily of the channel lengths, we illustrate the scaling of the surface area of the channels defining the network pattern with an exponent which is independent of the fractal dimension but not of the fractal nature of the network. The methodology presented is a fat fractal analysis in which the drainage basin minus the channel area is considered the fat fractal. Random channel networks within a fixed basin area are generated on grids of different scales. The sample channel networks generated by the model have a common outlet of fixed width and a rule of upstream channel narrowing specified by a diameter branching exponent using hydraulic and geomorphologic principles. Scaling exponents are computed for each sample network on a given grid size and are regressed against network magnitude. Results indicate that the size of the exponents are related to magnitude of the networks and generally decrease as network magnitude increases. Cases showing differences in scaling exponents with like magnitudes suggest a direction of future work regarding other topologic basin characteristics as potential explanatory variables.

Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks.

PubMed

Takemoto, Kazuhiro; Kajihara, Kosuke

2016-01-01

Theoretical studies have indicated that nestedness and modularity-non-random structural patterns of ecological networks-influence the stability of ecosystems against perturbations; as such, climate change and human activity, as well as other sources of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks, and found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. Although our findings are preliminary owing to data-analysis limitations, they enhance our understanding of the effects of environmental change on ecological communities.

Network-based model of the growth of termite nests

NASA Astrophysics Data System (ADS)

Eom, Young-Ho; Perna, Andrea; Fortunato, Santo; Darrouzet, Eric; Theraulaz, Guy; Jost, Christian

2015-12-01

We present a model for the growth of the transportation network inside nests of the social insect subfamily Termitinae (Isoptera, termitidae). These nests consist of large chambers (nodes) connected by tunnels (edges). The model based on the empirical analysis of the real nest networks combined with pruning (edge removal, either random or weighted by betweenness centrality) and a memory effect (preferential growth from the latest added chambers) successfully predicts emergent nest properties (degree distribution, size of the largest connected component, average path lengths, backbone link ratios, and local graph redundancy). The two pruning alternatives can be associated with different genuses in the subfamily. A sensitivity analysis on the pruning and memory parameters indicates that Termitinae networks favor fast internal transportation over efficient defense strategies against ant predators. Our results provide an example of how complex network organization and efficient network properties can be generated from simple building rules based on local interactions and contribute to our understanding of the mechanisms that come into play for the formation of termite networks and of biological transportation networks in general.

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

PubMed Central

Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

2015-01-01

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

The evolution analysis of listed companies co-holding non-listed financial companies based on two-mode heterogeneous networks

NASA Astrophysics Data System (ADS)

An, Pengli; Li, Huajiao; Zhou, Jinsheng; Chen, Fan

2017-10-01

Complex network theory is a widely used tool in the empirical research of financial markets. Two-mode and multi-mode networks are new trends and represent new directions in that they can more accurately simulate relationships between entities. In this paper, we use data for Chinese listed companies holding non-listed financial companies over a ten-year period to construct two networks: a two-mode primitive network in which listed companies and non-listed financial companies are considered actors and events, respectively, and a one-mode network that is constructed based on the decreasing-mode method in which listed companies are considered nodes. We analyze the evolution of the listed company co-holding network from several perspectives, including that of the whole network, of information control ability, of implicit relationships, of community division and of small-world characteristics. The results of the analysis indicate that (1) China's developing stock market affects the share-holding condition of listed companies holding non-listed financial companies; (2) the information control ability of co-holding networks is focused on a few listed companies and the implicit relationship of investment preference between listed companies is determined by the co-holding behavior; (3) the community division of the co-holding network is increasingly obvious, as determined by the investment preferences among listed companies; and (4) the small-world characteristics of the co-holding network are increasingly obvious, resulting in reduced communication costs. In this paper, we conduct an evolution analysis and develop an understanding of the factors that influence the listed companies co-holding network. This study will help illuminate research on evolution analysis.

Data communication network at the ASRM facility

NASA Technical Reports Server (NTRS)

Moorhead, Robert J., III; Smith, Wayne D.; Nirgudkar, Ravi; Dement, James

1994-01-01

This three-year project (February 1991 to February 1994) has involved analyzing and helping to design the communication network for the Advanced Solid Rocket Motor (ASRM) facility at Yellow Creek, near Iuka, MS. The principal concerns in the analysis were the bandwidth (both on average and in the worst case) and the expandability of the network. As the communication network was designed and modified, a careful evaluation of the bandwidth of the network, the capabilities of the protocol, and the requirements of the controllers and computers on the network was required. The overall network, which was heterogeneous in protocol and bandwidth, needed to be modeled, analyzed, and simulated to obtain some degree of confidence in its performance capabilities and in its performance under nominal and heavy loads. The results of our analysis did have an impact on the design and operation of the ASRM facility. During 1993 we analyzed many configurations of this basic network structure. The analyses are described in detail in Section 2 and 3 herein. Section 2 reports on an analysis of the whole network. The preliminary results of that research indicated that the most likely bottleneck as the network traffic increased would be the hubs. Thus a study of Cabletron hubs was initiated. The results of that study are in Section 3. Section 4 herein reports on the final network configuration analyzed. When the ASRM facility was mothballed in December of 1993, this was basically the planned and partially installed network. A briefing was held at NASA/MSFC on December 7, 1993, at which time our final analysis and conclusions were disseminated. This report contains a written record of most of the information disseminated at that briefing.

Network meta-analyses performed by contracting companies and commissioned by industry.

PubMed

Schuit, Ewoud; Ioannidis, John Pa

2016-11-25

Industry commissions contracting companies to perform network meta-analysis for health technology assessment (HTA) and reimbursement submissions. Our objective was to estimate the number of network meta-analyses performed by consulting companies contracted by industry, to assess whether they were published, and to explore reasons for non-publication. We searched MEDLINE for network meta-analyses of randomized trials. Papers were included if they had authors affiliated with any contracting company. All identified contracting companies as well as additional ones from the list of the exhibitors at the International Society for Pharmacoeconomics and Outcomes Research, an annual meeting that representatives from many contracting companies attend and exhibit at, were surveyed regarding conduct and publication of network meta-analyses. In 162 of 822 (20%) network meta-analysis papers, authors were affiliated to 66 contracting companies. Another 36 contracting companies were identified by the exhibitors list. Three companies had no contact information and six merged with others, therefore 93 companies were contacted. Thirty seven out of ninety three (40%) companies responded, and 19 indicated that they had performed a total of 476 network meta-analyses, but only 102 (21%) papers were published. Thirteen companies that disclosed to have conducted 174 network meta-analyses (45 published) provided reasons for non-publication. Of the 129 still unpublished meta-analyses, for 40 there were plans for future publication, for 37 the sponsor did not allow publication, for 16 the contracting companies did not plan to publish the meta-analysis, for another 23 plans were unclear, and the remaining 13 were used as HTA submission. The protocol of the network meta-analysis was publically available from 11/162 (6.8%) network meta-analyses published by authors affiliated with contracting companies. There is a prolific sector of professional contracting companies that perform network meta-analyses. Industry commissions many network meta-analyses, but most are not registered before or published after analyses in the scientific literature. Mechanisms to improve publication rates of network meta-analysis commissioned by industry are warranted.

Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

PubMed

Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

2015-05-01

Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

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

PubMed

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

2005-01-01

To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and 'bed down'. Its primary "modus operand" was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not statistically significant. There was no difference in resource use. The Managed Clinical Network made a difference to ways of working, particularly in breaching traditional boundaries and involving the public, and made modest changes in patient care. However, it required a two-year "set-up" period. Managed clinical networks are complex initiatives with an increasing profile in health care policy. This study suggests that they require energetic leadership and improvements are likely to be slow and incremental.

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

PubMed Central

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

2005-01-01

Abstract Purpose To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. Methods This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Results Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and ‘bed down’. Its primary “modus operand” was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not statistically significant. There was no difference in resource use. Discussion and conclusions The Managed Clinical Network made a difference to ways of working, particularly in breaching traditional boundaries and involving the public, and made modest changes in patient care. However, it required a two-year “set-up” period. Managed clinical networks are complex initiatives with an increasing profile in health care policy. This study suggests that they require energetic leadership and improvements are likely to be slow and incremental. PMID:16773161

Ramp time synchronization. [for NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Hietzke, W.

1979-01-01

A new method of intercontinental clock synchronization has been developed and proposed for possible use by NASA's Deep Space Network (DSN), using a two-way/three-way radio link with a spacecraft. Analysis of preliminary data indicates that the real-time method has an uncertainty of 0.6 microsec, and it is very likely that further work will decrease the uncertainty. Also, the method is compatible with a variety of nonreal-time analysis techniques, which may reduce the uncertainty down to the tens of nanosecond range.

Interorganizational relationships within state tobacco control networks: a social network analysis.

PubMed

Krauss, Melissa; Mueller, Nancy; Luke, Douglas

2004-10-01

State tobacco control programs are implemented by networks of public and private agencies with a common goal to reduce tobacco use. The degree of a program's comprehensiveness depends on the scope of its activities and the variety of agencies involved in the network. Structural aspects of these networks could help describe the process of implementing a state's tobacco control program, but have not yet been examined. Social network analysis was used to examine the structure of five state tobacco control networks. Semi-structured interviews with key agencies collected quantitative and qualitative data on frequency of contact among network partners, money flow, relationship productivity, level of network effectiveness, and methods for improvement. Most states had hierarchical communication structures in which partner agencies had frequent contact with one or two central agencies. Lead agencies had the highest control over network communication. Networks with denser communication structures had denser productivity structures. Lead agencies had the highest financial influence within the networks, while statewide coalitions were financially influenced by others. Lead agencies had highly productive relationships with others, while agencies with narrow roles had fewer productive relationships. Statewide coalitions that received Robert Wood Johnson Foundation funding had more highly productive relationships than coalitions that did not receive the funding. Results suggest that frequent communication among network partners is related to more highly productive relationships. Results also highlight the importance of lead agencies and statewide coalitions in implementing a comprehensive state tobacco control program. Network analysis could be useful in developing process indicators for state tobacco control programs.

Evolution of the Max and Mlx networks in animals.

PubMed

McFerrin, Lisa G; Atchley, William R

2011-01-01

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

[Weighted gene co-expression network analysis in biomedicine research].

PubMed

Liu, Wei; Li, Li; Ye, Hua; Tu, Wei

2017-11-25

High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.

Research on the Spatial-Temporal Distribution Pattern of the Network Attention of Fog and Haze in China

NASA Astrophysics Data System (ADS)

Weng, Lingyan; Han, Xugao

2018-01-01

Understanding the spatial-temporal distribution pattern of fog and haze is the base to deal with them by adjusting measures to local conditions. Taking 31 provinces in China mainland as the research areas, this paper collected data from Baidu index on the network attention of fog and haze in relevant areas from 2011 to 2016, and conducted an analysis of their spatial-temporal distribution pattern by using autocorrelation analysis. The results show that the network attention of fog and haze has an overall spatial distribution pattern of “higher in the eastern and central, lower in the western China”. There are regional differences in different provinces in terms of network attention. Network attention of fog and haze indicates an obvious geographical agglomeration phenomenon, which is a gradual enlargement of the agglomeration area of higher value with a slight shrinking of those lower value agglomeration areas.

Complex Dynamics in Information Sharing Networks

NASA Astrophysics Data System (ADS)

Cronin, Bruce

This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.

Characterization of the Network of Protected Areas in Puerto Rico

Treesearch

J. Castro-Prieto; Maya Quinones; William Gould

2016-01-01

Our goal was to describe the biodiversity and associated landscape diversity and forest cover characteristics within the network of terrestrial protected areas in Puerto Rico. We conducted spatial analysis to quantify different indicators of diversity at these sites. We found that protected areas in Puerto Rico overlap the most species-rich regions on the island,...

Impact of Online Social Network on American College Students' Reading Practices

ERIC Educational Resources Information Center

Huang, SuHua; Capps, Matthew

2013-01-01

The purpose of this study was to investigate social networking sites (SNS) and ways college students spend their time on both conventional academic and recreational reading. A total of 1,265 (466 male and 799 female) college students voluntarily participated in the study by completing a self-report survey. Descriptive analysis indicated that the…

The Social Networks of Children With and Without Disabilities in Early Childhood Special Education Classrooms.

PubMed

Chen, Jing; Lin, Tzu-Jung; Justice, Laura; Sawyer, Brook

2017-09-01

Interaction with peers is an important contributor to young children's social and cognitive development. Yet, little is known about the nature of social networks within preschool inclusive classrooms. The current study applied a social network analysis to characterize children's peer interactions in inclusive classrooms and their relations with children's disability status. The participants were 485 preschoolers from 64 early childhood special education (ECSE) inclusive classrooms. Results from teachers' report of children's social networks showed that children with disabilities formed smaller play networks compared to their typically developing peers in the classroom, but no evidence indicated that children with disabilities engaged in more conflict networks than their counterparts. Children's play and conflict networks were segregated by children's disability status.

A systematic review protocol: social network analysis of tobacco use.

PubMed

Maddox, Raglan; Davey, Rachel; Lovett, Ray; van der Sterren, Anke; Corbett, Joan; Cochrane, Tom

2014-08-08

Tobacco use is the single most preventable cause of death in the world. Evidence indicates that behaviours such as tobacco use can influence social networks, and that social network structures can influence behaviours. Social network analysis provides a set of analytic tools to undertake methodical analysis of social networks. We will undertake a systematic review to provide a comprehensive synthesis of the literature regarding social network analysis and tobacco use. The review will answer the following research questions: among participants who use tobacco, does social network structure/position influence tobacco use? Does tobacco use influence peer selection? Does peer selection influence tobacco use? We will follow the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines and search the following databases for relevant articles: CINAHL (Cumulative Index to Nursing and Allied Health Literature); Informit Health Collection; PsycINFO; PubMed/MEDLINE; Scopus/Embase; Web of Science; and the Wiley Online Library. Keywords include tobacco; smoking; smokeless; cigarettes; cigar and 'social network' and reference lists of included articles will be hand searched. Studies will be included that provide descriptions of social network analysis of tobacco use.Qualitative, quantitative and mixed method data that meets the inclusion criteria for the review, including methodological rigour, credibility and quality standards, will be synthesized using narrative synthesis. Results will be presented using outcome statistics that address each of the research questions. This systematic review will provide a timely evidence base on the role of social network analysis of tobacco use, forming a basis for future research, policy and practice in this area. This systematic review will synthesise the evidence, supporting the hypothesis that social network structures can influence tobacco use. This will also include exploring the relationship between social network structure, social network position, peer selection, peer influence and tobacco use across all age groups, and across different demographics. The research will increase our understanding of social networks and their impact on tobacco use, informing policy and practice while highlighting gaps in the literature and areas for further research.

Data analysis of a dense GPS network operated during the ESCOMPTE campaign: first results

NASA Astrophysics Data System (ADS)

Walpersdorf, A.; Bock, O.; Doerflinger, E.; Masson, F.; van Baelen, J.; Somieski, A.; Bürki, B.

The experiment GPS/H 2O involving 17 GPS receivers has been operated for two weeks in June 2001 in a dense network around Marseille. This project was integrated into the ESCOMPTE campaign. This paper will focus on the GPS analysis in preparation of the tomographic inversion of GPS slant delays. As first results, GPS tropospheric parameters zenith delays and horizontal gradients have been extracted. For a first visualization of the humidity field overlying the network, zenith delays have been transformed into precipitable water. Successive humidity fields are presented for a period of sudden drop in humidity, indicating some spatial resolution in the small network. The time series of horizontal gradients evaluated at individual sites are compared to correlated zenith delay variations over the whole network (horizontal gradient of zenith delays), showing that in the small size network horizontal atmospheric structure is reflected by both types of parameters. To compare these two quantities, scaling of zenith delays due to different station altitudes was necessary. In this way, a GPS internal validation of the individual gradients by comparison with the horizontal gradient of zenith delays has been established. Differential features along transects across the network indicate a good spatial resolution of tropospheric phenomena, encouraging for the further tomographic exploitation of the data. Moreover, individual and zenith delay gradients weight differently atmospheric horizontal gradients occurring at different heights. This different sensitivity has been used for a first identification of a vertical atmospheric structure from GPS tropospheric delays, by observing an inclined frontal zone crossing the network.

The feasibility of measuring social networks among older adults in assisted living and dementia special care units.

PubMed

Abbott, Katherine M; Bettger, Janet Prvu; Hampton, Keith N; Kohler, Hans-Peter

2015-03-01

Studies indicate that social integration has a significant influence on physical and mental health. Older adults experience an increased risk of social isolation as their social networks decline with fewer traditional opportunities to add new social relationships. Deaths of similar aged friends, cognitive and functional impairments, and relocating to a nursing home (NH) or assisted-living (AL) facility contribute to difficulties in maintaining one's social network. Due to the paucity of research examining the social networks of people residing in AL and NH, this study was designed to develop and test the feasibility of using a combination of methodological approaches to capture social network data among older adults living in AL and a dementia special care unit NH. Social network analysis of both egocentric and sociocentric networks was conducted to visualize the social networks of 15 residents of an AL neighborhood and 12 residents of a dementia special care unit NH and to calculate measures network size, centrality, and reciprocity. The combined egocentric and sociocentric method was feasible and provided a robust indicator of resident social networks highlighting individuals who were socially integrated as well as isolated. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

The Analysis of a Diet for the Human Being and the Companion Animal using Big Data in 2016

PubMed Central

Kang, Hye Won

2017-01-01

The purpose of this study was to investigate the diet tendencies of human and companion animals using big data analysis. The keyword data of human diet and companion animals' diet were collected from the portal site Naver from January 1, 2016 until December 31, 2016 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. In terms of human, the word exercise had the highest frequency through simple frequency analysis, whereas diet menu most frequently appeared in the N-gram analysis. companion animals, the term dog had the highest frequency in simple frequency analysis, whereas diet method was most frequent through N-gram analysis. Keyword network analysis for human indicated 4 groups: diet group, exercise group, commercial diet food group, and commercial diet program group. However, the keyword network analysis for companion animals indicated 3 groups: diet group, exercise group, and professional medical help group. The analysis of seasonality showed that the interest in diet for both human and companion animals increased steadily since February of 2016 and reached its peak in July. In conclusion, diets of human and companion animals showed similar tendencies, particularly having higher preference for dietary control over other methods. The diets of companion animals are determined by the choice of their owners as effective diet method for owners are usually applied to the companion animals. Therefore, it is necessary to have empirical demonstration of whether correlation of obesity between human being and the companion animals exist. PMID:29124046

The Analysis of a Diet for the Human Being and the Companion Animal using Big Data in 2016.

PubMed

Jung, Eun-Jin; Kim, Young-Suk; Choi, Jung-Wa; Kang, Hye Won; Chang, Un-Jae

2017-10-01

The purpose of this study was to investigate the diet tendencies of human and companion animals using big data analysis. The keyword data of human diet and companion animals' diet were collected from the portal site Naver from January 1, 2016 until December 31, 2016 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. In terms of human, the word exercise had the highest frequency through simple frequency analysis, whereas diet menu most frequently appeared in the N-gram analysis. companion animals, the term dog had the highest frequency in simple frequency analysis, whereas diet method was most frequent through N-gram analysis. Keyword network analysis for human indicated 4 groups: diet group, exercise group, commercial diet food group, and commercial diet program group. However, the keyword network analysis for companion animals indicated 3 groups: diet group, exercise group, and professional medical help group. The analysis of seasonality showed that the interest in diet for both human and companion animals increased steadily since February of 2016 and reached its peak in July. In conclusion, diets of human and companion animals showed similar tendencies, particularly having higher preference for dietary control over other methods. The diets of companion animals are determined by the choice of their owners as effective diet method for owners are usually applied to the companion animals. Therefore, it is necessary to have empirical demonstration of whether correlation of obesity between human being and the companion animals exist.

Micropore analysis of polymer networks by gas sorption and 129Xe NMR spectroscopy: toward a better understanding of intrinsic microporosity.

PubMed

Weber, Jens; Schmidt, Johannes; Thomas, Arne; Böhlmann, Winfried

2010-10-05

The microporosity of two microporous polymer networks is investigated in detail. Both networks are based on a central spirobifluorene motif but have different linker groups, namely, imide and thiophene units. The microporosity of the networks is based on the "polymers of intrinsic microporosity (PIM)" design strategy. Nitrogen, argon, and carbon dioxide were used as sorbates in order to analyze the microporosity in greater detail. The gas sorption data was analyzed with respect to important parameters such as specific surface area, pore volume, and pore size (distribution). It is shown that the results can be strongly model dependent and swelling effects have to be regarded. (129)Xe NMR was used as an independent technique for the estimation of the average pore size of the polymer networks. The results indicate that both networks are mainly ultramicroporous (pore sizes < 0.8 nm) in the dry state, which was not expected based on the molecular design. Phase separation and network defects might influence the overall network morphology strongly. Finally, the observed swelling indicates that this "soft" microporous matter might have a different micropore size in the solvent swollen/filled state that in the dry state.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

Time irreversibility and intrinsics revealing of series with complex network approach

NASA Astrophysics Data System (ADS)

Xiong, Hui; Shang, Pengjian; Xia, Jianan; Wang, Jing

2018-06-01

In this work, we analyze time series on the basis of the visibility graph algorithm that maps the original series into a graph. By taking into account the all-round information carried by the signals, the time irreversibility and fractal behavior of series are evaluated from a complex network perspective, and considered signals are further classified from different aspects. The reliability of the proposed analysis is supported by numerical simulations on synthesized uncorrelated random noise, short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on daily closing prices of eleven worldwide stock indices. Obtained results suggest that finite size has a significant effect on the evaluation, and that there might be no direct relation between the time irreversibility and long-range correlation of series. Similarity and dissimilarity between stock indices are also indicated from respective regional and global perspectives, showing the existence of multiple features of underlying systems.

Evolving dynamics of trading behavior based on coordination game in complex networks

NASA Astrophysics Data System (ADS)

Bian, Yue-tang; Xu, Lu; Li, Jin-sheng

2016-05-01

This work concerns the modeling of evolvement of trading behavior in stock markets. Based on the assumption of the investors' limited rationality, the evolution mechanism of trading behavior is modeled according to the investment strategy of coordination game in network, that investors are prone to imitate their neighbors' activity through comprehensive analysis on the risk dominance degree of certain investment behavior, the network topology of their relationship and its heterogeneity. We investigate by mean-field analysis and extensive simulations the evolution of investors' trading behavior in various typical networks under different risk dominance degree of investment behavior. Our results indicate that the evolution of investors' behavior is affected by the network structure of stock market and the effect of risk dominance degree of investment behavior; the stability of equilibrium states of investors' behavior dynamics is directly related with the risk dominance degree of some behavior; connectivity and heterogeneity of the network plays an important role in the evolution of the investment behavior in stock market.

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

PubMed

Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Searching social networks for subgraph patterns

NASA Astrophysics Data System (ADS)

Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

2013-06-01

Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

Application of artificial neural networks for conformity analysis of fuel performed with an optical fiber sensor

NASA Astrophysics Data System (ADS)

Possetti, Gustavo Rafael Collere; Coradin, Francelli Klemba; Côcco, Lílian Cristina; Yamamoto, Carlos Itsuo; de Arruda, Lucia Valéria Ramos; Falate, Rosane; Muller, Marcia; Fabris, José Luís

2008-04-01

The liquid fuel quality control is an important issue that brings benefits for the State, for the consumers and for the environment. The conformity analysis, in special for gasoline, demands a rigorous sampling technique among gas stations and other economic agencies, followed by a series of standard physicochemical tests. Such procedures are commonly expensive and time demanding and, moreover, a specialist is often required to carry out the tasks. Such drawbacks make the development of alternative analysis tools an important research field. The fuel refractive index is an additional parameter to help the fuel conformity analysis, besides the prospective optical fiber sensors, which operate like transducers with singular properties. When this parameter is correlated with the sample density, it becomes possible to determine conformity zones that cannot be analytically defined. This work presents an application of artificial neural networks based on Radial Basis Function to determine these zones. A set of 45 gasoline samples, collected in several gas stations and previously analyzed according to the rules of Agência Nacional do Petróleo, Gás Natural e Biocombustíveis, a Brazilian regulatory agency, constituted the database to build two neural networks. The input variables of first network are the samples refractive indices, measured with an Abbe refractometer, and the density of the samples measured with a digital densimeter. For the second network the input variables included, besides the samples densities, the wavelength response of a long-period grating to the samples refractive indices. The used grating was written in an optical fiber using the point-to-point technique by submitting the fiber to consecutive electrical arcs from a splice machine. The output variables of both Radial Basis Function Networks are represented by the conformity status of each sample, according to report of tests carried out following the American Society for Testing and Materials and/or Brazilian Association of Technical Rules standards. A subset of 35 samples, randomly chosen from the database, was used to design and calibrate (train) both networks. The two networks topologies (numbers of Radial Basis Function neurons of the hidden layer and function radius) were built in order to minimize the root mean square error. The subset composed by the other 10 samples was used to validate the final networks architectures. The obtained results have demonstrated that both networks reach a good predictive capability.

Association between suicidal symptoms and repeat suicidal behaviour within a sample of hospital-treated suicide attempters

PubMed Central

van Borkulo, Claudia D.; O’Connor, Rory C.

2017-01-01

Background Suicidal behaviour is the end result of the complex relation between many factors which are biological, psychological and environmental in nature. Network analysis is a novel method that may help us better understand the complex association between different factors. Aims To examine the relationship between suicidal symptoms as assessed by the Beck Scale for Suicide Ideation and future suicidal behaviour in patients admitted to hospital following a suicide attempt, using network analysis. Method Secondary analysis was conducted on previously collected data from a sample of 366 patients who were admitted to a Scottish hospital following a suicide attempt. Network models were estimated to visualise and test the association between baseline symptom network structure and suicidal behaviour at 15-month follow-up. Results Network analysis showed that the desire for an active attempt was found to be the most central, strongly related suicide symptom. Of the 19 suicide symptoms that were assessed at baseline, 10 symptoms were directly related to repeat suicidal behaviour. When comparing baseline network structure of repeaters (n=94) with the network of non-repeaters (n=272), no significant differences were found. Conclusions Network analysis can help us better understand suicidal behaviour by visualising the complex relation between relevant symptoms and by indicating which symptoms are most central within the network. These insights have theoretical implications as well as informing the assessment and treatment of suicidal behaviour. Declaration of interest None. Copyright and usage © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license. PMID:28507771

PTP-ε HAS A CRITICAL ROLE IN SIGNALING TRANSDUCTION PATHWAYS AND PHOSPHOPROTEIN NETWORK TOPOLOGY IN RED CELLS

PubMed Central

De Franceschi, Lucia; Biondani, Andrea; Carta, Franco; Turrini, Franco; Laudanna, Carlo; Deana, Renzo; Brunati, Anna Maria; Turretta, Loris; Iolascon, Achille; Perrotta, Silverio; Elson, Ari; Bulato, Cristina; Brugnara, Carlo

2010-01-01

Protein tyrosine phosphatases (PTPs) are crucial components of cellular signal transduction pathways. We report here that red blood cells (RBCs) from mice lacking PTPε (Ptpre−/−) exhibit abnormal morphology and increased Ca2+-activated-K+ channel activity, which was partially blocked by the Src-Family-Kinases (SFKs) inhibitor PP1. In Ptpre−/− mouse RBCs, the activity of Fyn and Yes, two SFKs, were increased, suggesting a functional relationship between SFKs, PTPε and Ca2+-activated-K+-channel. The absence of PTPε markedly affected the RBC membrane tyrosine (Tyr-) phosphoproteome, indicating a perturbation of RBCs signal transduction pathways. Using signaling network computational analysis of the Tyr-phosphoproteomic data, we identified 7 topological clusters. We studied cluster 1, containing Syk-Tyr-kinase: Syk-kinase activity was higher in wild-type than in Ptpre−/− RBCs, validating the network computational analysis and indicating a novel signaling pathway, which involves Fyn and Syk in regulation of red cell morphology. PMID:18924107

Ecological Network Analysis for a Low-Carbon and High-Tech Industrial Park

PubMed Central

Lu, Yi; Su, Meirong; Liu, Gengyuan; Chen, Bin; Zhou, Shiyi; Jiang, Meiming

2012-01-01

Industrial sector is one of the indispensable contributors in global warming. Even if the occurrence of ecoindustrial parks (EIPs) seems to be a good improvement in saving ecological crises, there is still a lack of definitional clarity and in-depth researches on low-carbon industrial parks. In order to reveal the processes of carbon metabolism in a low-carbon high-tech industrial park, we selected Beijing Development Area (BDA) International Business Park in Beijing, China as case study, establishing a seven-compartment- model low-carbon metabolic network based on the methodology of Ecological Network Analysis (ENA). Integrating the Network Utility Analysis (NUA), Network Control Analysis (NCA), and system-wide indicators, we compartmentalized system sectors into ecological structure and analyzed dependence and control degree based on carbon metabolism. The results suggest that indirect flows reveal more mutuality and exploitation relation between system compartments and they are prone to positive sides for the stability of the whole system. The ecological structure develops well as an approximate pyramidal structure, and the carbon metabolism of BDA proves self-mutualistic and sustainable. Construction and waste management were found to be two active sectors impacting carbon metabolism, which was mainly regulated by internal and external environment. PMID:23365516

An algorithm of the wildfire classification by its acoustic emission spectrum using Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Khamukhin, A. A.; Demin, A. Y.; Sonkin, D. M.; Bertoldo, S.; Perona, G.; Kretova, V.

2017-01-01

Crown fires are extremely dangerous as the speed of their distribution is dozen times higher compared to surface fires. Therefore, it is important to classify the fire type as early as possible. A method for forest fires classification exploits their computed acoustic emission spectrum compared with a set of samples of the typical fire acoustic emission spectrum stored in the database. This method implies acquisition acoustic data using Wireless Sensors Networks (WSNs) and their analysis in a central processing and a control center. The paper deals with an algorithm which can be directly implemented on a sensor network node that will allow reducing considerably the network traffic and increasing its efficiency. It is hereby suggested to use the sum of the squares ratio, with regard to amplitudes of low and high frequencies of the wildfire acoustic emission spectrum, as the indicator of a forest fire type. It is shown that the value of the crown fires indicator is several times higher than that of the surface ones. This allows classifying the fire types (crown, surface) in a short time interval and transmitting a fire type indicator code alongside with an alarm signal through the network.

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis.

PubMed

Ripp, Isabelle; Zur Nieden, Anna-Nora; Blankenagel, Sonja; Franzmeier, Nicolai; Lundström, Johan N; Freiherr, Jessica

2018-05-07

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing. © 2018 Wiley Periodicals, Inc.

Applications of Social Network Analysis

NASA Astrophysics Data System (ADS)

Thilagam, P. Santhi

A social network [2] is a description of the social structure between actors, mostly persons, groups or organizations. It indicates the ways in which they are connected with each other by some relationship such as friendship, kinship, finance exchange etc. In a nutshell, when the person uses already known/unknown people to create new contacts, it forms social networking. The social network is not a new concept rather it can be formed when similar people interact with each other directly or indirectly to perform particular task. Examples of social networks include a friendship networks, collaboration networks, co-authorship networks, and co-employees networks which depict the direct interaction among the people. There are also other forms of social networks, such as entertainment networks, business Networks, citation networks, and hyperlink networks, in which interaction among the people is indirect. Generally, social networks operate on many levels, from families up to the level of nations and assists in improving interactive knowledge sharing, interoperability and collaboration.

Influence of dendrite network defects on channel segregate growth

NASA Technical Reports Server (NTRS)

Simpson, M.; Yerebakan, M.; Flemings, M. C.

1985-01-01

The solidifying ingot interdendritic flow analysis in which channel segregates are assumed to be produced by interdendritic fluid flow dissolving channels in the primary dendrite network is presently refined by examining the flow through a dendrite network possessing a small defect. Attention is given to the section of the mushy zone in a solidifying casting. Since defects such as that presently treated are unavoidable in a real casting, a more reliable indication may be furnished of the occurrence of channel segregates.

Retina Image Screening and Analysis Software Version 2.0

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

Tobin, Jr., Kenneth W.; Karnowski, Thomas P.; Aykac, Deniz

2009-04-01

The software allows physicians or researchers to ground-truth images of retinas, identifying key physiological features and lesions that are indicative of disease. The software features methods to automatically detect the physiological features and lesions. The software contains code to measure the quality of images received from a telemedicine network; create and populate a database for a telemedicine network; review and report the diagnosis of a set of images; and also contains components to transmit images from a Zeiss camera to the network through SFTP.

Simulation analysis of the effect of initial delay on flight delay diffusion

NASA Astrophysics Data System (ADS)

Que, Zufu; Yao, Hongguang; Yue, Wei

2018-01-01

The initial delay of the flight is an important factor affecting the spread of flight delays, so clarifying their relationship conduces to control flight delays in the aeronautical network. Through establishing a model of the chain aviation network and making simulation analysis of the effects of initial delay on the delay longitudinal diffusion, it’s found that the number of delayed airports in the air network, the total delay time and the average delay time of the delayed airport are generally positively correlated with the initial delay. This indicates that the occurrence of the initial delay should be avoided or reduced as much as possible to improve the punctuality of the flight.

Quantitative petri net model of gene regulated metabolic networks in the cell.

PubMed

Chen, Ming; Hofestädt, Ralf

2011-01-01

A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.

Social network correlates of risky sexual behavior among adolescents in Bahir Dar and Mecha Districts, North West Ethiopia: an institution-based study.

PubMed

Asrese, Kerebih; Mekonnen, Alemtsehay

2018-04-11

Behaviors established during adolescence such as risky sexual behaviors have negative effects on future health and well-being. Extant literature indicated that individual attributes such as peer pressure and substance use have impacts on healthy development of young peoples' sexual behavior. The patterns of relationships (social network structure) and the social network content (members' norm regarding sexual practice) established by adolescents' network on adolescents' risky sexual behaviors are not well investigated. This cross-sectional study assessed the roles of social networks on sexual behavior of high school adolescents in Bahir Dar and Mecha district, North West Ethiopia. Data were collected from 806 high school adolescents using a pretested anonymously self administered questionnaire. Hierarchical logistic regression model was used for analysis. The results indicated that more than 13% had risky sexual behavior. Taking social networks into account improved the explanation of risky sexual behavior over individual attributes. Adolescents embedded within increasing sexual practice approving norm (AOR 1.61; 95%CI: 1.04 - 2.50), increasing network tie strength (AOR 1.12; 95% CI: 1.06 - 1.19), and homogeneous networks (AOR 1.58; 95% CI: .98 - 2.55) were more likely to had risky sexual behavior. Engaging within increasing number of sexuality discussion networks was found protective of risky sexual behavior (AOR .84; 95% CI: .72 - .97). Social networks better predict adolescent's risky sexual behavior than individual attributes. The findings indicated the circumstances or contexts that social networks exert risks or protective effects on adolescents' sexual behavior. Programs designed to reduce school adolescents' sexual risk behavior should consider their patterns of social relationships.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

The neural basis of hand gesture comprehension: A meta-analysis of functional magnetic resonance imaging studies.

PubMed

Yang, Jie; Andric, Michael; Mathew, Mili M

2015-10-01

Gestures play an important role in face-to-face communication and have been increasingly studied via functional magnetic resonance imaging. Although a large amount of data has been provided to describe the neural substrates of gesture comprehension, these findings have never been quantitatively summarized and the conclusion is still unclear. This activation likelihood estimation meta-analysis investigated the brain networks underpinning gesture comprehension while considering the impact of gesture type (co-speech gestures vs. speech-independent gestures) and task demand (implicit vs. explicit) on the brain activation of gesture comprehension. The meta-analysis of 31 papers showed that as hand actions, gestures involve a perceptual-motor network important for action recognition. As meaningful symbols, gestures involve a semantic network for conceptual processing. Finally, during face-to-face interactions, gestures involve a network for social emotive processes. Our finding also indicated that gesture type and task demand influence the involvement of the brain networks during gesture comprehension. The results highlight the complexity of gesture comprehension, and suggest that future research is necessary to clarify the dynamic interactions among these networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma

PubMed Central

Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

2016-01-01

As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites. PMID:28261627

Network topology and resilience analysis of South Korean power grid

NASA Astrophysics Data System (ADS)

Kim, Dong Hwan; Eisenberg, Daniel A.; Chun, Yeong Han; Park, Jeryang

2017-01-01

In this work, we present topological and resilience analyses of the South Korean power grid (KPG) with a broad voltage level. While topological analysis of KPG only with high-voltage infrastructure shows an exponential degree distribution, providing another empirical evidence of power grid topology, the inclusion of low voltage components generates a distribution with a larger variance and a smaller average degree. This result suggests that the topology of a power grid may converge to a highly skewed degree distribution if more low-voltage data is considered. Moreover, when compared to ER random and BA scale-free networks, the KPG has a lower efficiency and a higher clustering coefficient, implying that highly clustered structure does not necessarily guarantee a functional efficiency of a network. Error and attack tolerance analysis, evaluated with efficiency, indicate that the KPG is more vulnerable to random or degree-based attacks than betweenness-based intentional attack. Cascading failure analysis with recovery mechanism demonstrates that resilience of the network depends on both tolerance capacity and recovery initiation time. Also, when the two factors are fixed, the KPG is most vulnerable among the three networks. Based on our analysis, we propose that the topology of power grids should be designed so the loads are homogeneously distributed, or functional hubs and their neighbors have high tolerance capacity to enhance resilience.

Revealing the hidden language of complex networks.

PubMed

Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

2014-04-01

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

Global value chains: Building blocks and network dynamics

NASA Astrophysics Data System (ADS)

Tsekeris, Theodore

2017-12-01

The paper employs measures and tools from complex network analysis to enhance the understanding and interpretation of structural characteristics pertaining to the Global Value Chains (GVCs) during the period 1995-2011. The analysis involves the country, sector and country-sector value chain networks to identify main drivers of structural change. The results indicate significant intertemporal changes, mirroring the increased globalization in terms of network size, strength and connectivity. They also demonstrate higher clustering and increased concentration of the most influential countries and country-sectors relative to all others in the GVC network, with the geographical dimension to prevail over the sectoral dimension in the formation of value chains. The regionalization and less hierarchical organization drive country-sector production sharing, while the sectoral value chain network has become more integrated and more competitive over time. The findings suggest that the impact of country-sector policies and/or shocks may vary with the own-group and network-wide influence of each country, take place in multiple geographical scales, as GVCs have a block structure, and involve time dynamics.

The social network of international health aid.

PubMed

Han, Lu; Koenig-Archibugi, Mathias; Opsahl, Tore

2018-06-01

International development assistance for health generates an emergent social network in which policy makers in recipient countries are connected to numerous bilateral and multilateral aid agencies and to other aid recipients. Ties in this global network are channels for the transmission of knowledge, norms and influence in addition to material resources, and policy makers in centrally situated governments receive information faster and are exposed to a more diverse range of sources and perspectives. Since diversity of perspectives improves problem-solving capacity, the structural position of aid-receiving governments in the health aid network can affect the health outcomes that those governments are able to attain. We apply a recently developed Social Network Analysis measure to health aid data for 1990-2010 to investigate the relationship between country centrality in the health aid network and improvements in child health. A generalized method of moments (GMM) analysis indicates that, controlling for the volume of health aid and other factors, higher centrality in the health aid network is associated with better child survival rates in a sample of 110 low and middle income countries. Copyright © 2018 Elsevier Ltd. All rights reserved.

Blacklist Ecosystem Analysis Update: 2014

DTIC Science & Technology

2014-12-01

example, we checked to see if any of the blacklisted IP addresses were known sinkhole IP addresses. This information would essentially invalidate the...indicator as an indicator of malicious activity, since sinkholes are operated by CERTCC-2014-82 4 Blacklist Ecosystem Analysis CERT/CC network defenders who...clean up and collect intelligence on threats. Only one list out of 67, LI_3, contained any sinkhole IP addresses and that list contained only 10. All

Cross-Cultural Validation of the Five-Factor Structure of Social Goals: A Filipino Investigation

ERIC Educational Resources Information Center

King, Ronnel B.; Watkins, David A.

2012-01-01

The aim of the present study was to test the cross-cultural validity of the five-factor structure of social goals that Dowson and McInerney proposed. Using both between-network and within-network approaches to construct validation, 1,147 Filipino high school students participated in the study. Confirmatory factor analysis indicated that the…

Allosteric mechanism of quinoline inhibitors for HIV RT-associated RNase with MD simulation and dynamics fluctuation network.

PubMed

Cai, Yi; Liu, Hao; Chen, Haifeng

2018-03-01

The human immunodeficiency virus (HIV) is a retrovirus which infects T lymphocyte of human body and causes immunodeficiency. Reverse transcriptase inhibitors (RTIs) can inhibit some functions of RT, preventing virus synthesis (double-stranded DNA), so that HIV virus replication can be reduced. Experimental results indicate a series of benzimidazole-based inhibitors which target HIV RT-associated RNase to inhibit the reverse transcription of HIV virus. However, the allosteric mechanism is still unclear. Here, molecular dynamics simulations and dynamics fluctuation network analysis were used to reveal the binding mode between the inhibitors and RT-associated RNase. The most active molecule has more hydrophobic and electrostatic interactions than the less active inhibitor. Dynamics correlation network analysis indicates that the most active inhibitor perturbs the network of RT-associated RNase and decreases the correlation of nodes. 3D-QSAR model suggests that two robust and reliable models were constructed and validated by independent test set. 3D-QSAR model also shows that bulky negatively charged or hydrophilic substituent is favorable to bioactivity. These results reveal the allosteric mechanism of quinoline inhibitors and help to improve the bioactivity. © 2017 John Wiley & Sons A/S.

Using social network analysis to inform disease control interventions.

PubMed

Marquetoux, Nelly; Stevenson, Mark A; Wilson, Peter; Ridler, Anne; Heuer, Cord

2016-04-01

Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes. This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by "removing" a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics. This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.

PubMed

Li, Yongsheng; Sahni, Nidhi; Yi, Song

2016-11-29

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

Extracting topographic structure from digital elevation data for geographic information-system analysis

USGS Publications Warehouse

Jenson, Susan K.; Domingue, Julia O.

1988-01-01

The first phase of analysis is a conditioning phase that generates three data sets: the original OEM with depressions filled, a data set indicating the flow direction for each cell, and a flow accumulation data set in which each cell receives a value equal to the total number of cells that drain to it. The original OEM and these three derivative data sets can then be processed in a variety of ways to optionally delineate drainage networks, overland paths, watersheds for userspecified locations, sub-watersheds for the major tributaries of a drainage network, or pour point linkages between watersheds. The computer-generated drainage lines and watershed polygons and the pour point linkage information can be transferred to vector-based geographic information systems for futher analysis. Comparisons between these computergenerated features and their manually delineated counterparts generally show close agreement, indicating that these software tools will save analyst time spent in manual interpretation and digitizing.

Complex Network Analysis of CA3 Transcriptome Reveals Pathogenic and Compensatory Pathways in Refractory Temporal Lobe Epilepsy

PubMed Central

Bando, Silvia Yumi; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre V.; Pimentel-Silva, Luciana R.; Castro, Luiz HM.; Wen, Hung-Tzu; Amaro, Edson; Moreira-Filho, Carlos Alberto

2013-01-01

We previously described – studying transcriptional signatures of hippocampal CA3 explants – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks. PMID:24278214

Neuroendocrine and immune network re-modeling in chronic fatigue syndrome: an exploratory analysis.

PubMed

Fuite, Jim; Vernon, Suzanne D; Broderick, Gordon

2008-12-01

This work investigates the significance of changes in association patterns linking indicators of neuroendocrine and immune activity in patients with chronic fatigue syndrome (CFS). Gene sets preferentially expressed in specific immune cell isolates were integrated with neuroendocrine data from a large population-based study. Co-expression patterns linking immune cell activity with hypothalamic-pituitary-adrenal (HPA), thyroidal (HPT) and gonadal (HPG) axis status were computed using mutual information criteria. Networks in control and CFS subjects were compared globally in terms of a weighted graph edit distance. Local re-modeling of node connectivity was quantified by node degree and eigenvector centrality measures. Results indicate statistically significant differences between CFS and control networks determined mainly by re-modeling around pituitary and thyroid nodes as well as an emergent immune sub-network. Findings align with known mechanisms of chronic inflammation and support possible immune-mediated loss of thyroid function in CFS exacerbated by blunted HPA axis responsiveness.

Propagation and immunization of infection on general networks with both homogeneous and heterogeneous components

NASA Astrophysics Data System (ADS)

Liu, Zonghua; Lai, Ying-Cheng; Ye, Nong

2003-03-01

We consider the entire spectrum of architectures of general networks, ranging from being heterogeneous (scale-free) to homogeneous (random), and investigate the infection dynamics by using a three-state epidemiological model that does not involve the mechanism of self-recovery. This model is relevant to realistic situations such as the propagation of a flu virus or information over a social network. Our heuristic analysis and computations indicate that (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected and (2) heterogeneous networks are relatively more robust against spreads of infection as compared with homogeneous networks. We have also considered the problem of immunization for preventing wide spread of infection, with the result that targeted immunization is effective for heterogeneous networks.

Do motifs reflect evolved function?--No convergent evolution of genetic regulatory network subgraph topologies.

PubMed

Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

2008-01-01

Methods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. As the question is difficult to resolve with currently available biological data, we approach the issue using networks that abstractly model natural genetic regulatory networks (GRNs) which are evolved to show dynamical behaviors. Specifically one group of networks was evolved to be capable of exhibiting two different behaviors ("differentiation") in contrast to a group with a single target behavior. In both groups we find motif distribution differences within the groups to be larger than differences between them, indicating that evolutionary niches (target functions) do not necessarily mold network structure uniquely. These results show that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Moreover, analysis of motif functional relevance by lesioning did not suggest that motifs were of greater importance to the functioning of the network than arbitrary subgraph patterns. Only when drastically restricting network size, so that one motif corresponds to a whole functionally evolved network, was preference for particular connection patterns found. This suggests that in non-restricted, bigger networks, entanglement with the rest of the network hinders topological subgraph analysis.

Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA

NASA Astrophysics Data System (ADS)

Konapala, Goutam; Mishra, Ashok

2017-12-01

The quantification of spatio-temporal hydroclimatic extreme events is a key variable in water resources planning, disaster mitigation, and preparing climate resilient society. However, quantification of these extreme events has always been a great challenge, which is further compounded by climate variability and change. Recently complex network theory was applied in earth science community to investigate spatial connections among hydrologic fluxes (e.g., rainfall and streamflow) in water cycle. However, there are limited applications of complex network theory for investigating hydroclimatic extreme events. This article attempts to provide an overview of complex networks and extreme events, event synchronization method, construction of networks, their statistical significance and the associated network evaluation metrics. For illustration purpose, we apply the complex network approach to study the spatio-temporal evolution of droughts in Continental USA (CONUS). A different drought threshold leads to a new drought event as well as different socio-economic implications. Therefore, it would be interesting to explore the role of thresholds on spatio-temporal evolution of drought through network analysis. In this study, long term (1900-2016) Palmer drought severity index (PDSI) was selected for spatio-temporal drought analysis using three network-based metrics (i.e., strength, direction and distance). The results indicate that the drought events propagate differently at different thresholds associated with initiation of drought events. The direction metrics indicated that onset of mild drought events usually propagate in a more spatially clustered and uniform approach compared to onsets of moderate droughts. The distance metric shows that the drought events propagate for longer distance in western part compared to eastern part of CONUS. We believe that the network-aided metrics utilized in this study can be an important tool in advancing our knowledge on drought propagation as well as other hydroclimatic extreme events. Although the propagation of droughts is investigated using the network approach, however process (physics) based approaches is essential to further understand the dynamics of hydroclimatic extreme events.

Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks.

PubMed

Evans, Clare R; Onnela, Jukka-Pekka; Williams, David R; Subramanian, S V

2016-08-01

Adolescent health and behaviors are influenced by multiple contexts, including schools, neighborhoods, and social networks, yet these contexts are rarely considered simultaneously. In this study we combine social network community detection analysis and cross-classified multilevel modeling in order to compare the contributions of each of these three contexts to the total variation in adolescent body mass index (BMI). Wave 1 of the National Longitudinal Study of Adolescent to Adult Health is used, and for robustness we conduct the analysis in both the core sample (122 schools; N = 14,144) and a sub-set of the sample (16 schools; N = 3335), known as the saturated sample due to its completeness of neighborhood data. After adjusting for relevant covariates, we find that the school-level and neighborhood-level contributions to the variance are modest compared with the network community-level (σ(2)school = 0.069, σ(2)neighborhood = 0.144, σ(2)network = 0.463). These results are robust to two alternative algorithms for specifying network communities, and to analysis in the saturated sample. While this study does not determine whether network effects are attributable to social influence or selection, it does highlight the salience of adolescent social networks and indicates that they may be a promising context to address in the design of health promotion programs. Copyright © 2016 Elsevier Ltd. All rights reserved.

OSI in the NASA science internet: An analysis

NASA Technical Reports Server (NTRS)

Nitzan, Rebecca

1990-01-01

The Open Systems Interconnection (OSI) protocol suite is a result of a world-wide effort to develop international standards for networking. OSI is formalized through the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). The goal of OSI is to provide interoperability between network products without relying on one particular vendor, and to do so on a multinational basis. The National Institute for Standards and Technology (NIST) has developed a Government OSI Profile (GOSIP) that specified a subset of the OSI protocols as a Federal Information Processing Standard (FIPS 146). GOSIP compatibility has been adopted as the direction for all U.S. government networks. OSI is extremely diverse, and therefore adherence to a profile will facilitate interoperability within OSI networks. All major computer vendors have indicated current or future support of GOSIP-compliant OSI protocols in their products. The NASA Science Internet (NSI) is an operational network, serving user requirements under NASA's Office of Space Science and Applications. NSI consists of the Space Physics Analysis Network (SPAN) that uses the DECnet protocols and the NASA Science Network (NSN) that uses TCP/IP protocols. The NSI Project Office is currently working on an OSI integration analysis and strategy. A long-term goal is to integrate SPAN and NSN into one unified network service, using a full OSI protocol suite, which will support the OSSA user community.

Ecological Network Indicators of Ecosystem Status and Change in the Baltic Sea

PubMed Central

Tomczak, Maciej T.; Heymans, Johanna J.; Yletyinen, Johanna; Niiranen, Susa; Otto, Saskia A.; Blenckner, Thorsten

2013-01-01

Several marine ecosystems under anthropogenic pressure have experienced shifts from one ecological state to another. In the central Baltic Sea, the regime shift of the 1980s has been associated with food-web reorganization and redirection of energy flow pathways. These long-term dynamics from 1974 to 2006 have been simulated here using a food-web model forced by climate and fishing. Ecological network analysis was performed to calculate indices of ecosystem change. The model replicated the regime shift. The analyses of indicators suggested that the system’s resilience was higher prior to 1988 and lower thereafter. The ecosystem topology also changed from a web-like structure to a linearized food-web. PMID:24116045

A BEFORE AND AFTER TRIAL OF THE EFFECTIVENESS OF NETWORK ANALYSIS IN HEALTH OPERATIONS MANAGEMENT.

PubMed

Bhalwar, R; Srivastava, M; Verma, S S; Vaze, M; Tilak, V W

1996-10-01

An intervention trial using "before-and-after" approach was undertaken to address the question whether network analysis as a health managerial tool of control can favourably affect the delays that occur in planning and executing the antimalaria operations of a Station Health Organization in a large military station. Exposure variable of interest was intervention with a network diagram, by which the potential causes of delay along the various activities were assessed and remedial measures were introduced during the second year. Sample size was calculated using conventional alpha and beta error levels. The study indicated that there was a definite beneficial outcome in that the operations could be started as well as completed in time during the intervention year. There was reduction in time requirement in 5 out of the 9 activities, the exact 'p' value being 0.08, by both parametric and non-parametric tests. The use of network analysis in health care management has been recommended.

Localization of Epileptogenic Zone With the Correction of Pathological Networks.

PubMed

Yang, Chuanzuo; Luan, Guoming; Wang, Qian; Liu, Zhao; Zhai, Feng; Wang, Qingyun

2018-01-01

Patients with focal drug-resistant epilepsy are potential candidates for surgery. Stereo-electroencephalograph (SEEG) is often considered as the "gold standard" to identify the epileptogenic zone (EZ) that accounts for the onset and propagation of epileptiform discharges. However, visual analysis of SEEG still prevails in clinical practice. In addition, epilepsy is increasingly understood to be the result of network disorder, but the specific organization of the epileptic network is still unclear. Therefore, it is necessary to quantitatively localize the EZ and investigate the nature of epileptogenic networks. In this study, intracranial recordings from 10 patients were analyzed through adaptive directed transfer function, and the out-degree of effective network was selected as the principal indicator to localize the epileptogenic area. Furthermore, a coupled neuronal population model was used to qualitatively simulate electrical activity in the brain. By removing individual populations, virtual surgery adjusting the network organization could be performed. Results suggested that the accuracy and detection rate of the EZ localization were 82.86 and 85.29%, respectively. In addition, the same stage shared a relatively stable connectivity pattern, while the patterns changed with transition to different processes. Meanwhile, eight cases of simulations indicated that networks in the ictal stage were more likely to generate rhythmic spikes. This indicated the existence of epileptogenic networks, which could enhance local excitability and facilitate synchronization. The removal of the EZ could correct these pathological networks and reduce the amount of spikes by at least 75%. This might be one reason why accurate resection could reduce or even suppress seizures. This study provides novel insights into epilepsy and surgical treatments from the network perspective.

Methamphetamine Use among Homeless Former Foster Youth: The Mediating Role of Social Networks

PubMed Central

Yoshioka-Maxwell, Amanda; Rice, Eric; Rhoades, Harmony; Winetrobe, Hailey

2015-01-01

Objectives Social network analysis can provide added causal insight into otherwise confusing epidemiologic findings in public health research. Although foster care and homelessness are risk factors for methamphetamine use, current research has failed to explicate why homeless youth with foster care experience engage in methamphetamine use at higher rates than other homeless young adults. This study examined the mediating effect of network engagement and time spent homeless on the relationship between foster care experience and recent methamphetamine use among homeless youth in Los Angeles. Methods Egocentric network data from a cross-sectional community-based sample (n = 652) of homeless youth aged 13–25 were collected from drop-in centers in Los Angeles. Questions addressed foster care experience, time spent homeless, methamphetamine use, and perceived drug use in social networks. Path analysis was performed in SAS to examine mediation. Results Controlling for all other variables, results of path analysis regarding recent methamphetamine use indicated a direct effect between foster care experience and recent methamphetamine use (B = .269, t = 2.73, p < .01). However, this direct effect became statistically nonsignificant when time spent homeless and network methamphetamine use were added to the model, and indirect paths from time spent homeless and network methamphetamine use became statistically significant. Conclusions Foster care experience influenced recent methamphetamine use indirectly through time spent homeless and methamphetamine use by network members. Efforts to reduce methamphetamine use should focus on securing stable housing and addressing network interactions among homeless former foster youth. PMID:26146647

Social Network Type and Subjective Well-being in a National Sample of Older Americans

PubMed Central

Litwin, Howard; Shiovitz-Ezra, Sharon

2011-01-01

Purpose: The study considers the social networks of older Americans, a population for whom there have been few studies of social network type. It also examines associations between network types and well-being indicators: loneliness, anxiety, and happiness. Design and Methods: A subsample of persons aged 65 years and older from the first wave of the National Social Life, Health, and Aging Project was employed (N = 1,462). We applied K-means cluster analysis to derive social network types using 7 criterion variables. In the multivariate stage, the well-being outcomes were regressed on the network type construct and on background and health characteristics by means of logistic regression. Results: Five social network types were derived: “diverse,” “friend,” “congregant,” “family,” and “restricted.” Social network type was found to be associated with each of the well-being indicators after adjusting for demographic and health confounders. Respondents embedded in network types characterized by greater social capital tended to exhibit better well-being in terms of less loneliness, less anxiety, and greater happiness. Implications: Knowledge about differing network types should make gerontological practitioners more aware of the varying interpersonal milieus in which older people function. Adopting network type assessment as an integral part of intake procedures and tracing network shifts over time can serve as a basis for risk assessment as well as a means for determining the efficacy of interventions. PMID:21097553

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Development and psychometric testing of the clinical networks engagement tool

PubMed Central

Hecker, Kent G.; Rabatach, Leora; Noseworthy, Tom W.; White, Deborah E.

2017-01-01

Background Clinical networks are being used widely to facilitate large system transformation in healthcare, by engagement of stakeholders throughout the health system. However, there are no available instruments that measure engagement in these networks. Methods The study purpose was to develop and assess the measurement properties of a multiprofessional tool to measure engagement in clinical network initiatives. Based on components of the International Association of Public Participation Spectrum and expert panel review, we developed 40 items for testing. The draft instrument was distributed to 1,668 network stakeholders across different governance levels (leaders, members, support, frontline stakeholders) in 9 strategic clinical networks in Alberta (January to July 2014). With data from 424 completed surveys (25.4% response rate), descriptive statistics, exploratory and confirmatory factor analysis, Pearson correlations, linear regression, multivariate analysis, and Cronbach alpha were conducted to assess reliability and validity of the scores. Results Sixteen items were retained in the instrument. Exploratory factor analysis indicated a four-factor solution and accounted for 85.7% of the total variance in engagement with clinical network initiatives: global engagement, inform (provided with information), involve (worked together to address concerns), and empower (given final decision-making authority). All subscales demonstrated acceptable reliability (Cronbach alpha 0.87 to 0.99). Both the confirmatory factor analysis and regression analysis confirmed that inform, involve, and empower were all significant predictors of global engagement, with involve as the strongest predictor. Leaders had higher mean scores than frontline stakeholders, while members and support staff did not differ in mean scores. Conclusions This study provided foundational evidence for the use of this tool for assessing engagement in clinical networks. Further work is necessary to evaluate engagement in broader network functions and activities; to assess barriers and facilitators of engagement; and, to elucidate how the maturity of networks and other factors influence engagement. PMID:28350834

Information seeking for making evidence-informed decisions: a social network analysis on the staff of a public health department in Canada

PubMed Central

2012-01-01

Background Social network analysis is an approach to study the interactions and exchange of resources among people. It can help understanding the underlying structural and behavioral complexities that influence the process of capacity building towards evidence-informed decision making. A social network analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. Methods The staff were invited to respond to an online questionnaire inquiring about information seeking behavior, identification of colleague expertise, and friendship status. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. Results The network analysis showed a low density and localized information-seeking network. Inter-personal connections were mainly clustered by organizational divisions; and people tended to limit information-seeking connections to a handful of peers in their division. However, recognition of expertise and friendship networks showed more cross-divisional connections. Members of the office of the Medical Officer of Health were located at the heart of the department, bridging across divisions. A small group of professional consultants and middle managers were the most-central staff in the network, also connecting their divisions to the center of the information-seeking network. In each division, there were some locally central staff, mainly practitioners, who connected their neighboring peers; but they were not necessarily connected to other experts or managers. Conclusions The methods of social network analysis were useful in providing a systems approach to understand how knowledge might flow in an organization. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers. PMID:22591757

System Analysis for the Huntsville Operation Support Center, Distributed Computer System

NASA Technical Reports Server (NTRS)

Ingels, F. M.; Massey, D.

1985-01-01

HOSC as a distributed computing system, is responsible for data acquisition and analysis during Space Shuttle operations. HOSC also provides computing services for Marshall Space Flight Center's nonmission activities. As mission and nonmission activities change, so do the support functions of HOSC change, demonstrating the need for some method of simulating activity at HOSC in various configurations. The simulation developed in this work primarily models the HYPERchannel network. The model simulates the activity of a steady state network, reporting statistics such as, transmitted bits, collision statistics, frame sequences transmitted, and average message delay. These statistics are used to evaluate such performance indicators as throughout, utilization, and delay. Thus the overall performance of the network is evaluated, as well as predicting possible overload conditions.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

PubMed

Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

2015-12-01

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.

Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis.

PubMed

García Arroyo, Jose Luis; García Zapirain, Begoña

2014-01-01

By means of this study, a detection algorithm for the "pigment network" in dermoscopic images is presented, one of the most relevant indicators in the diagnosis of melanoma. The design of the algorithm consists of two blocks. In the first one, a machine learning process is carried out, allowing the generation of a set of rules which, when applied over the image, permit the construction of a mask with the pixels candidates to be part of the pigment network. In the second block, an analysis of the structures over this mask is carried out, searching for those corresponding to the pigment network and making the diagnosis, whether it has pigment network or not, and also generating the mask corresponding to this pattern, if any. The method was tested against a database of 220 images, obtaining 86% sensitivity and 81.67% specificity, which proves the reliability of the algorithm. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

NASA Astrophysics Data System (ADS)

de Andrés, Javier; Landajo, Manuel; Lorca, Pedro; Labra, Jose; Ordóñez, Patricia

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.

Emancipating traditional channel network types: quantification of topology and geometry, and relation to geologic boundary conditions

NASA Astrophysics Data System (ADS)

Temme, A.; Langston, A. L.

2017-12-01

Traditional classification of channel networks is helpful for qualitative geologic and geomorphic inference. For instance, a dendritic network indicates no strong lithological control on where channels flow. However, an approach where channel network structure is quantified, is required to be able to indicate for instance how increasing levels of lithological control lead, gradually or suddenly, to a trellis-type drainage network Our contribution aims to aid this transition to a quantitative analysis of channel networks. First, to establish the range of typically occurring channel network properties, we selected 30 examples of traditional drainage network types from around the world. For each of these, we calculated a set of topological and geometric properties, such as total drainage length, average length of a channel segment and the average angle of intersection of channel segments. A decision tree was used to formalize the relation between these newly quantified properties on the one hand, and traditional network types on the other hand. Then, to explore how variations in lithological and geomorphic boundary conditions affect channel network structure, we ran a set of experiments with landscape evolution model Landlab. For each simulated channel network, the same set of topological and geometric properties was calculated as for the 30 real-world channel networks. The latter were used for a first, visual evaluation to find out whether a simulated network that looked, for instance, rectangular, also had the same set of properties as real-world rectangular channel networks. Ultimately, the relation between these properties and the imposed lithological and geomorphic boundary conditions was explored using simple bivariate statistics.

Network analysis for science and technology management: Evidence from tuberculosis research in Fiocruz, Brazil

PubMed Central

da Silva, Marcus Vinicius Pereira; de Araújo, Kizi Mendonça; Sampaio, Ricardo Barros; Moraes, Milton Ozório

2017-01-01

Collaborative networks are of great value for science and technology (S&T) institutions as a way of sharing, generating and disseminating new knowledge that could ultimately lead to innovations. Driven by the need to assess the contribution and effectiveness of these networks in informing S&T management, we explored the evolution and dynamics of tuberculosis scientific networks involving the Oswaldo Cruz Foundation (Fiocruz), the major public health S&T Institution in Brazil. Social network analysis (SNA) was used to produce a 10-year (2005–2009, 2010–2014) retrospective longitudinal mapping of Brazilian tuberculosis research networks within the country and internationally, highlighting Fiocruz collaborations. Co-authorship analysis showed a significant expansion of collaboration in Brazil and the role of Fiocruz and other leading national institutions in maintaining connectivity, facilitating knowledge exchange and reducing network vulnerability. It also identified influential researchers that can act as information leaders and support strategic decisions. When we focused on networks inside the institution, the analysis showed a clear discontinuation between the clinical and the public health research areas, which needs specific internal policies to improve collaborations since outcomes in TB are expected to provide better diagnostic tools and more effective treatments. The approach provides evidence to support S&T management by pinpointing: key central institutions maintaining network connectivity; most influential researchers that can act as advisors/experts for investment and induction policies; key Fiocruz researchers that could improve information exchange, systems integration and innovation within the institution; opportunities for synergy between internal research groups working in complementary areas. In summary, we observed that SNA parameters proved to be a valuable tool that, along with other indicators, can strengthen knowledge platforms to support S&T management efforts. PMID:28792514

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

Coherence analysis of a class of weighted networks

NASA Astrophysics Data System (ADS)

Dai, Meifeng; He, Jiaojiao; Zong, Yue; Ju, Tingting; Sun, Yu; Su, Weiyi

2018-04-01

This paper investigates consensus dynamics in a dynamical system with additive stochastic disturbances that is characterized as network coherence by using the Laplacian spectrum. We introduce a class of weighted networks based on a complete graph and investigate the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. First, the recursive relationship of its eigenvalues at two successive generations of Laplacian matrix is deduced. Then, we compute the sum and square sum of reciprocal of all nonzero Laplacian eigenvalues. The obtained results show that the scalings of first- and second-order coherence with network size obey four and five laws, respectively, along with the range of the weight factor. Finally, it indicates that the scalings of our studied networks are smaller than other studied networks when 1/√{d }

An Analysis of Chemical Ingredients Network of Chinese Herbal Formulae for the Treatment of Coronary Heart Disease

PubMed Central

Ding, Fan; Zhang, Qianru; Ung, Carolina Oi Lam; Wang, Yitao; Han, Yifan; Hu, Yuanjia; Qi, Jin

2015-01-01

As a complex system, the complicated interactions between chemical ingredients, as well as the potential rules of interactive associations among chemical ingredients of traditional Chinese herbal formulae are not yet fully understood by modern science. On the other hand, network analysis is emerging as a powerful approach focusing on processing complex interactive data. By employing network approach in selected Chinese herbal formulae for the treatment of coronary heart disease (CHD), this article aims to construct and analyze chemical ingredients network of herbal formulae, and provide candidate herbs, chemical constituents, and ingredient groups for further investigation. As a result, chemical ingredients network composed of 1588 ingredients from 36 herbs used in 8 core formulae for the treatment of CHD was produced based on combination associations in herbal formulae. In this network, 9 communities with relative dense internal connections are significantly associated with 14 kinds of chemical structures with P<0.001. Moreover, chemical structural fingerprints of network communities were detected, while specific centralities of chemical ingredients indicating different levels of importance in the network were also measured. Finally, several distinct herbs, chemical ingredients, and ingredient groups with essential position in the network or high centrality value are recommended for further pharmacology study in the context of new drug development. PMID:25658855

Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

NASA Astrophysics Data System (ADS)

Xu, Chuan-Ming; Yan, Yan; Zhu, Xiao-Wu; Li, Xiao-Teng; Chen, Xiao-Song

2013-11-01

The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

Optimal Base Station Density of Dense Network: From the Viewpoint of Interference and Load.

PubMed

Feng, Jianyuan; Feng, Zhiyong

2017-09-11

Network densification is attracting increasing attention recently due to its ability to improve network capacity by spatial reuse and relieve congestion by offloading. However, excessive densification and aggressive offloading can also cause the degradation of network performance due to problems of interference and load. In this paper, with consideration of load issues, we study the optimal base station density that maximizes the throughput of the network. The expected link rate and the utilization ratio of the contention-based channel are derived as the functions of base station density using the Poisson Point Process (PPP) and Markov Chain. They reveal the rules of deployment. Based on these results, we obtain the throughput of the network and indicate the optimal deployment density under different network conditions. Extensive simulations are conducted to validate our analysis and show the substantial performance gain obtained by the proposed deployment scheme. These results can provide guidance for the network densification.

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Social network analysis of public health programs to measure partnership.

PubMed

Schoen, Martin W; Moreland-Russell, Sarah; Prewitt, Kim; Carothers, Bobbi J

2014-12-01

In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

FUNCTIONAL NETWORK ARCHITECTURE OF READING-RELATED REGIONS ACROSS DEVELOPMENT

PubMed Central

Vogel, Alecia C.; Church, Jessica A.; Power, Jonathan D.; Miezin, Fran M.; Petersen, Steven E.; Schlaggar, Bradley L.

2013-01-01

Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships between regions were defined as temporal correlations in spontaneous fMRI signal; i.e., resting state functional connectivity MRI (RSFC). Graph theory based network analysis defined the community structure of the “reading-related” regions. Regions sorted into previously defined communities, such as the fronto-parietal and cingulo-opercular control networks, and the default mode network. This structure was similar in children, and no apparent “reading” community was defined in any age group. These results argue against regions, or sets of regions, being specific or preferential for reading, instead indicating that regions used in reading are also used in a number of other tasks. PMID:23506969

Modeling Emergence in Neuroprotective Regulatory Networks

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

Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.

2013-01-05

The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatorymore » networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.« less

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules

PubMed Central

Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Castellani, Gastone; Milanesi, Luciano

2016-01-01

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD. PMID:27731320

A longitudinal social network analysis of the editorial boards of medical informatics and bioinformatics journals.

PubMed

Malin, Bradley; Carley, Kathleen

2007-01-01

The goal of this research is to learn how the editorial staffs of bioinformatics and medical informatics journals provide support for cross-community exposure. Models such as co-citation and co-author analysis measure the relationships between researchers; but they do not capture how environments that support knowledge transfer across communities are organized. In this paper, we propose a social network analysis model to study how editorial boards integrate researchers from disparate communities. We evaluate our model by building relational networks based on the editorial boards of approximately 40 journals that serve as research outlets in medical informatics and bioinformatics. We track the evolution of editorial relationships through a longitudinal investigation over the years 2000 through 2005. Our findings suggest that there are research journals that support the collocation of editorial board members from the bioinformatics and medical informatics communities. Network centrality metrics indicate that editorial board members are located in the intersection of the communities and that the number of individuals in the intersection is growing with time. Social network analysis methods provide insight into the relationships between the medical informatics and bioinformatics communities. The number of editorial board members facilitating the publication intersection of the communities has grown, but the intersection remains dependent on a small group of individuals and fragile.

Distinctive time-lagged resting-state networks revealed by simultaneous EEG-fMRI.

PubMed

Feige, Bernd; Spiegelhalder, Kai; Kiemen, Andrea; Bosch, Oliver G; Tebartz van Elst, Ludger; Hennig, Jürgen; Seifritz, Erich; Riemann, Dieter

2017-01-15

Functional activation as evidenced by blood oxygen level-dependent (BOLD) functional MRI changes or event-related EEG is known to closely follow patterns of stimulation or self-paced action. Any lags are compatible with axonal conduction velocities and neural integration times. The important analysis of resting state networks is generally based on the assumption that these principles also hold for spontaneous fluctuations in brain activity. Previous observations using simultaneous EEG and fMRI indicate that slower processes, with delays in the seconds range, determine at least part of the relationship between spontaneous EEG and fMRI. To assess this relationship systematically, we used deconvolution analysis of EEG-fMRI during the resting state, assessing the relationship between EEG frequency bands and fMRI BOLD across the whole brain while allowing for time lags of up to 10.5s. Cluster analysis, identifying similar BOLD time courses in relation to EEG band power peaks, showed a clear segregation of functional subsystems of the brain. Our analysis shows that fMRI BOLD increases commonly precede EEG power increases by seconds. Most zero-lag correlations, on the other hand, were negative. This indicates two main distinct neuromodulatory mechanisms: an "idling" mechanism of simultaneous electric and metabolic network anticorrelation and a "regulatory" mechanism in which metabolic network activity precedes increased EEG power by some seconds. This has to be taken into consideration in further studies which address the causal and functional relationship of metabolic and electric brain activity patterns. Copyright © 2016 Elsevier Inc. All rights reserved.

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue.

PubMed

Chunlin Zhao; Min Zhao; Yong Yang; Junfeng Gao; Nini Rao; Pan Lin

2017-05-01

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

Analyzing Collaborative Governance Through Social Network Analysis: A Case Study of River Management Along the Waal River in The Netherlands.

PubMed

Fliervoet, J M; Geerling, G W; Mostert, E; Smits, A J M

2016-02-01

Until recently, governmental organizations played a dominant and decisive role in natural resource management. However, an increasing number of studies indicate that this dominant role is developing towards a more facilitating role as equal partner to improve efficiency and create a leaner state. This approach is characterized by complex collaborative relationships between various actors and sectors on multiple levels. To understand this complexity in the field of environmental management, we conducted a social network analysis of floodplain management in the Dutch Rhine delta. We charted the current interorganizational relationships between 43 organizations involved in flood protection (blue network) and nature management (green network) and explored the consequences of abolishing the central actor in these networks. The discontinuation of this actor will decrease the connectedness of actors within the blue and green network and may therefore have a large impact on the exchange of ideas and decision-making processes. Furthermore, our research shows the dependence of non-governmental actors on the main governmental organizations. It seems that the Dutch governmental organizations still have a dominant and controlling role in floodplain management. This challenges the alleged shift from a dominant government towards collaborative governance and calls for detailed analysis of actual governance.

Analyzing Collaborative Governance Through Social Network Analysis: A Case Study of River Management Along the Waal River in The Netherlands

NASA Astrophysics Data System (ADS)

Fliervoet, J. M.; Geerling, G. W.; Mostert, E.; Smits, A. J. M.

2016-02-01

Until recently, governmental organizations played a dominant and decisive role in natural resource management. However, an increasing number of studies indicate that this dominant role is developing towards a more facilitating role as equal partner to improve efficiency and create a leaner state. This approach is characterized by complex collaborative relationships between various actors and sectors on multiple levels. To understand this complexity in the field of environmental management, we conducted a social network analysis of floodplain management in the Dutch Rhine delta. We charted the current interorganizational relationships between 43 organizations involved in flood protection (blue network) and nature management (green network) and explored the consequences of abolishing the central actor in these networks. The discontinuation of this actor will decrease the connectedness of actors within the blue and green network and may therefore have a large impact on the exchange of ideas and decision-making processes. Furthermore, our research shows the dependence of non-governmental actors on the main governmental organizations. It seems that the Dutch governmental organizations still have a dominant and controlling role in floodplain management. This challenges the alleged shift from a dominant government towards collaborative governance and calls for detailed analysis of actual governance.

Social networks of older adults living with HIV in Finland.

PubMed

Nobre, Nuno Ribeiro; Kylmä, Jari; Kirsi, Tapio; Pereira, Marco

2016-01-01

The aim of this study was to explore the social networks of older adults living with HIV. Interviews were conducted with nine individuals aged 50 or older living with HIV in Helsinki, Finland. Analysis of transcripts was analysed by inductive qualitative content analysis. Results indicated that these participants' networks tended to be large, including those both aware and unaware of the participants' health status. Analysis identified three main themes: large multifaceted social networks, importance of a support group, and downsizing of social networks. Support received appeared to be of great importance in coping with their health condition, especially since the time of diagnosis. Friends and family were the primary source of informal support. The majority of participants relied mostly on friends, some of whom were HIV-positive. Formal support came primarily from the HIV organisation's support group. In this study group, non-disclosure did not impact participants' well-being. In years to come, social networks of older adults living with HIV may shrink due to personal reasons other than HIV-disclosure. What is of primary importance is that healthcare professionals become knowledgeable about psychosocial issues of older adults living with HIV, identifying latent problems and developing adequate interventions in the early stages of the disease; this would help prevent social isolation and foster successful ageing with HIV.

Retail E-Commerce Security Status among Fortune 500 Corporations

ERIC Educational Resources Information Center

Zhao, Jensen J.; Zhao, Sherry Y.

2012-01-01

The authors assessed the "Fortune 500" corporations' retail e-commerce security to identify their strengths and weaknesses for improvement. They used online content analysis, information security auditing, and network security mapping for data collection and analysis. The findings indicate that most sites posted security policies; however, only…

Language Networks Associated with Computerized Semantic Indices

PubMed Central

Pakhomov, Serguei V. S.; Jones, David T.; Knopman, David S.

2014-01-01

Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. PMID:25315785

Evaluation of Coordination of Emergency Response Team through the Social Network Analysis. Case Study: Oil and Gas Refinery.

PubMed

Mohammadfam, Iraj; Bastani, Susan; Esaghi, Mahbobeh; Golmohamadi, Rostam; Saee, Ali

2015-03-01

The purpose of this study was to examine the cohesions status of the coordination within response teams in the emergency response team (ERT) in a refinery. For this study, cohesion indicators of social network analysis (SNA; density, degree centrality, reciprocity, and transitivity) were utilized to examine the coordination of the response teams as a whole network. The ERT of this research, which was a case study, included seven teams consisting of 152 members. The required data were collected through structured interviews and were analyzed using the UCINET 6.0 Social Network Analysis Program. The results reported a relatively low number of triple connections, poor coordination with key members, and a high level of mutual relations in the network with low density, all implying that there were low cohesions of coordination in the ERT. The results showed that SNA provided a quantitative and logical approach for the examination of the coordination status among response teams and it also provided a main opportunity for managers and planners to have a clear understanding of the presented status. The research concluded that fundamental efforts were needed to improve the presented situations.

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space.

PubMed

Ma, Chihua; Luciani, Timothy; Terebus, Anna; Liang, Jie; Marai, G Elisabeta

2017-02-15

Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

#LancerHealth: Using Twitter and Instagram as a tool in a campus wide health promotion initiative.

PubMed

Santarossa, Sara; Woodruff, Sarah J

2018-02-05

The present study aimed to explore using popular technology that people already have/use as a health promotion tool, in a campus wide social media health promotion initiative, entitled #LancerHealth . During a two-week period the university community was asked to share photos on Twitter and Instagram of What does being healthy on campus look like to you ?, while tagging the image with #LancerHealth . All publically tagged media was collected using the Netlytic software and analysed. Text analysis (N=234 records, Twitter; N=141 records, Instagram) revealed that the majority of the conversation was positive and focused on health and the university. Social network analysis, based on five network properties, showed a small network with little interaction. Lastly, photo coding analysis (N=71 unique image) indicated that the majority of the shared images were of physical activity (52%) and on campus (80%). Further research into this area is warranted.

Revealing the Hidden Language of Complex Networks

PubMed Central

Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

2014-01-01

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists. PMID:24686408

The interaction of social networks and child obesity prevention program effects: the pathways trial.

PubMed

Shin, Hee-Sung; Valente, Thomas W; Riggs, Nathaniel R; Huh, Jimi; Spruijt-Metz, Donna; Chou, Chih-Ping; Ann Pentz, Mary

2014-06-01

Social network analysis was used to examine whether peer influence from one's social networks moderates obesity prevention program effects on obesity-related behaviors: healthful and unhealthful. Participants included 557 children residing in Southern California. The survey assessed health-promoting behaviors (i.e., physical activity at school, physical activity outside of school, and fruit and vegetable intake), as well as unhealthful behaviors (high-calorie, low-nutrient intake and sedentary activity), and peer exposure calculated from social network nominations as indicators of peer influence. Multilevel models were conducted separately on outcomes predicted by program participation, peer exposure, and program participation by peer exposure. Results indicated that peer exposure was positively associated with one's own healthful and unhealthful behaviors. Program participation effects were moderated by peer influence, but only when unhealthful peer influence was present. Results suggest that peer influence can diminish or amplify prevention programs Future interventions should consider peer-led components to promote healthful influence of peers on healthful and unhealthful behaviors, and programs should be mindful that their effects are moderated by social networks. Copyright © 2014 The Obesity Society.

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

NASA Astrophysics Data System (ADS)

Kölzsch, A.; Blasius, B.

2011-12-01

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

The robustness of ecosystems to the species loss of community

NASA Astrophysics Data System (ADS)

Cai, Qing; Liu, Jiming

2016-10-01

To study the robustness of ecosystems is crucial to promote the sustainable development of human society. This paper aims to analyze the robustness of ecosystems from an interesting viewpoint of the species loss of community. Unlike the existing definitions, we first introduce the notion of a community as a population of species belonging to the same trophic level. We then put forward a novel multiobjective optimization model which can be utilized to discover community structures from arbitrary unipartite networks. Because an ecosystem is commonly represented as a multipartite network, we further introduce a mechanism of competition among species whereby a multipartite network is transformed into a unipartite signed network without loss of species interaction information. Finally, we examine three strategies to test the robustness of an ecosystem. Our experiments indicate that ecosystems are robust to random species loss of community but fragile to target ones. We also investigate the relationships between the robustness of an ecosystem and that of its community composed network both to species loss. Our experiments indicate that the robustness analysis of a large-scale ecosystem to species loss may be akin to that of its community composed network which is usually small in size.

Transnational Research Networks in Chinese Scientific Production. An Investigation on Health-Industry Related Sectors.

PubMed

Rubini, Lauretta; Pollio, Chiara; Di Tommaso, Marco R

2017-08-29

Transnational research networks (TRN) are becoming increasingly complex. Such complexity may have both positive and negative effects on the quality of research. Our work studies the evolution over time of Chinese TRN and the role of complexity on the quality of Chinese research, given the leading role this country has recently acquired in international science. We focus on the fields of geriatrics and gerontology. We build an original dataset of all scientific publications of China in these areas in 2009, 2012 and 2015, starting from the ISI Web of Knowledge (ISI WoK) database. Using Social Network Analysis (SNA), we analyze the change in scientific network structure across time. Second, we design indices to control for the different aspects of networks complexity (number of authors, country heterogeneity and institutional heterogeneity) and we perform negative binomial regressions to identify the main determinants of research quality. Our analysis shows that research networks in the field of geriatrics and gerontology have gradually become wider in terms of countries and have become more balanced. Furthermore, our results identify that different forms of complexity have different impacts on quality, including a reciprocal moderating effect. In particular, according to our analysis, research quality benefits from complex research networks both in terms of countries and of types of institutions involved, but that such networks should be "compact" in terms of number of authors. Eventually, we suggest that complexity should be carefully taken into account when designing policies aimed at enhancing the quality of research.

Functional network alterations and their structural substrate in drug-resistant epilepsy

PubMed Central

Caciagli, Lorenzo; Bernhardt, Boris C.; Hong, Seok-Jun; Bernasconi, Andrea; Bernasconi, Neda

2014-01-01

The advent of MRI has revolutionized the evaluation and management of drug-resistant epilepsy by allowing the detection of the lesion associated with the region that gives rise to seizures. Recent evidence indicates marked chronic alterations in the functional organization of lesional tissue and large-scale cortico-subcortical networks. In this review, we focus on recent methodological developments in functional MRI (fMRI) analysis techniques and their application to the two most common drug-resistant focal epilepsies, i.e., temporal lobe epilepsy related to mesial temporal sclerosis and extra-temporal lobe epilepsy related to focal cortical dysplasia. We put particular emphasis on methodological developments in the analysis of task-free or “resting-state” fMRI to probe the integrity of intrinsic networks on a regional, inter-regional, and connectome-wide level. In temporal lobe epilepsy, these techniques have revealed disrupted connectivity of the ipsilateral mesiotemporal lobe, together with contralateral compensatory reorganization and striking reconfigurations of large-scale networks. In cortical dysplasia, initial observations indicate functional alterations in lesional, peri-lesional, and remote neocortical regions. While future research is needed to critically evaluate the reliability, sensitivity, and specificity, fMRI mapping promises to lend distinct biomarkers for diagnosis, presurgical planning, and outcome prediction. PMID:25565942

Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data.

PubMed

Ye, Hao; Luo, Heng; Ng, Hui Wen; Meehan, Joe; Ge, Weigong; Tong, Weida; Hong, Huixiao

2016-01-01

ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals. Published by Elsevier Ltd.

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

PubMed Central

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 provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

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

PubMed

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 provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Analysis of changes in farm pond network connectivity in the peri-urban landscape of the Taoyuan area, Taiwan.

PubMed

Huang, Shu-Li; Lee, Ying-Chieh; Budd, William W; Yang, Min-Chia

2012-04-01

The farm pond system for irrigation is the most prominent feature in the Taoyuan area, Taiwan, giving the region a unique landscape and hydrological character. Although this area had more than 3,290 ponds in the 1970s, fewer than 1,800 now remain. This study analyzes changes in irrigation farm ponds and the canal network landscape in the Taoyuan area. The spatial and temporal changes to ponds and the canal network on the Taoyuan plain were examined graphically for each spatial unit (2,765 m × 2,525 m) using aerial photographs for 1979 and 2005. Landscape metrics were calculated to analyze landscape change associated with increased urbanization. Landscape indices of connectivity and circuitry were utilized to describe changes in the configuration of ponds and canal networks. The total length of canals and total number of ponds in the study area decreased significantly during 1979-2005. The average values of connectivity indices (γ- and α-index) also decreased during 1979-2005, reflecting degradation of canal networks due to urban sprawl. A multivariate technique was applied to portion the study area into three zones according to changes to land cover, ponds, and canal networks. The effects of urban sprawl on the spatial pattern of ponds and canal networks are discussed.

Variations in Social Network Type Membership Among Older African Americans, Caribbean Blacks, and Non-Hispanic Whites

PubMed Central

2017-01-01

Abstract Objectives: This study examined race differences in the probability of belonging to a specific social network typology of family, friends, and church members. Method: Samples of African Americans, Caribbean blacks, and non-Hispanic whites aged 55+ were drawn from the National Survey of American Life. Typology indicators related to social integration and negative interactions with family, friendship, and church networks were used. Latent class analysis was used to identify typologies, and latent class multinomial logistic regression was used to assess the influence of race, and interactions between race and age, and race and education on typology membership. Results: Four network typologies were identified: optimal (high social integration, low negative interaction), family-centered (high social integration within primarily the extended family network, low negative interaction), strained (low social integration, high negative interaction), and ambivalent (high social integration and high negative interaction). Findings for race and age and race and education interactions indicated that the effects of education and age on typology membership varied by race. Discussion: Overall, the findings demonstrate how race interacts with age and education to influence the probability of belonging to particular network types. A better understanding of the influence of race, education, and age on social network typologies will inform future research and theoretical developments in this area. PMID:28329871

The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders.

PubMed

Boschloo, Lynn; van Borkulo, Claudia D; Rhemtulla, Mijke; Keyes, Katherine M; Borsboom, Denny; Schoevers, Robert A

2015-01-01

Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.

Dynamic networks of PTSD symptoms during conflict.

PubMed

Greene, Talya; Gelkopf, Marc; Epskamp, Sacha; Fried, Eiko

2018-02-28

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict. Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network. Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms. This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes.

PubMed

Luo, Cheng; Yang, Fei; Deng, Jiayan; Zhang, Yaodan; Hou, Changyue; Huang, Yue; Cao, Weifang; Wang, Jianjun; Xiao, Ruhui; Zeng, Nanlin; Wang, Xiaoming; Yao, Dezhong

2016-06-01

There are 2 intrinsic networks in the human brain: the task positive network (TPN) and task negative network (alternately termed the default mode network, DMN) in which inverse correlations have been observed during resting state and event-related functional magnetic resonance imaging (fMRI). The antagonism between the 2 networks might indicate a dynamic interaction in the brain that is associated with development.To evaluate the alterations in the relations of the 2 networks in children with benign childhood epilepsy with centrotemporal spikes (BECTS), resting state fMRI was performed in 17 patients with BECTS and 17 healthy controls. The functional and effective connectivities of 29 nodes in the TPN and DMN were analyzed. Positive functional connectivity (FC) within the networks and negative FC between the 2 networks were observed in both groups.The patients exhibited increased FC within both networks, particularly in the frontoparietal nodes such as the left superior frontal cortex, and enhanced antagonism between the 2 networks, suggesting abnormal functional integration of the nodes of the 2 networks in the patients. Granger causality analysis revealed a significant difference in the degree of outflow to inflow in the left superior frontal cortex and the left ventral occipital lobe.The alterations observed in the combined functional and effective connectivity analyses might indicate an association of an abnormal ability to integrate information between the DMN and TPN and the epileptic neuropathology of BECTS and provide preliminary evidence supporting the occurrence of abnormal development in children with BECTS.

A quantitative meta-analysis and review of motor learning in the human brain

PubMed Central

Hardwick, Robert M.; Rottschy, Claudia; Miall, R. Chris; Eickhoff, Simon B.

2013-01-01

Neuroimaging studies have improved our understanding of which brain structures are involved in motor learning. Despite this, questions remain regarding the areas that contribute consistently across paradigms with different task demands. For instance, sensorimotor tasks focus on learning novel movement kinematics and dynamics, while serial response time task (SRTT) variants focus on sequence learning. These differing task demands are likely to elicit quantifiably different patterns of neural activity on top of a potentially consistent core network. The current study identified consistent activations across 70 motor learning experiments using activation likelihood estimation (ALE) meta-analysis. A global analysis of all tasks revealed a bilateral cortical–subcortical network consistently underlying motor learning across tasks. Converging activations were revealed in the dorsal premotor cortex, supplementary motor cortex, primary motor cortex, primary somatosensory cortex, superior parietal lobule, thalamus, putamen and cerebellum. These activations were broadly consistent across task specific analyses that separated sensorimotor tasks and SRTT variants. Contrast analysis indicated that activity in the basal ganglia and cerebellum was significantly stronger for sensorimotor tasks, while activity in cortical structures and the thalamus was significantly stronger for SRTT variants. Additional conjunction analyses then indicated that the left dorsal premotor cortex was activated across all analyses considered, even when controlling for potential motor confounds. The highly consistent activation of the left dorsal premotor cortex suggests it is a critical node in the motor learning network. PMID:23194819

Chilling Affects Phytohormone and Post-Embryonic Development Pathways during Bud Break and Fruit Set in Apple (Malus domestica Borkh.)

PubMed Central

Kumar, Gulshan; Gupta, Khushboo; Pathania, Shivalika; Swarnkar, Mohit Kumar; Rattan, Usha Kumari; Singh, Gagandeep; Sharma, Ram Kumar; Singh, Anil Kumar

2017-01-01

The availability of sufficient chilling during bud dormancy plays an important role in the subsequent yield and quality of apple fruit, whereas, insufficient chilling availability negatively impacts the apple production. The transcriptome profiling during bud dormancy release and initial fruit set under low and high chill conditions was performed using RNA-seq. The comparative high number of differentially expressed genes during bud break and fruit set under high chill condition indicates that chilling availability was associated with transcriptional reorganization. The comparative analysis reveals the differential expression of genes involved in phytohormone metabolism, particularly for Abscisic acid, gibberellic acid, ethylene, auxin and cytokinin. The expression of Dormancy Associated MADS-box, Flowering Locus C-like, Flowering Locus T-like and Terminal Flower 1-like genes was found to be modulated under differential chilling. The co-expression network analysis indentified two high chill specific modules that were found to be enriched for “post-embryonic development” GO terms. The network analysis also identified hub genes including Early flowering 7, RAF10, ZEP4 and F-box, which may be involved in regulating chilling-mediated dormancy release and fruit set. The results of transcriptome and co-expression network analysis indicate that chilling availability majorly regulates phytohormone-related pathways and post-embryonic development during bud break. PMID:28198417

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

PubMed

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

2018-04-22

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

Differential reward responses during competition against in- and out-of-network others.

PubMed

Fareri, Dominic S; Delgado, Mauricio R

2014-04-01

Social interactions occur within a variety of different contexts--cooperative/competitive--and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against one's friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations.

Impact Factors and Risk Analysis of Tropical Cyclones on a Highway Network.

PubMed

Yang, Saini; Hu, Fuyu; Jaeger, Carlo

2016-02-01

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical-cyclone-induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the network's functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real-world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction. © 2015 Society for Risk Analysis.

Differential reward responses during competition against in- and out-of-network others

PubMed Central

Fareri, Dominic S.

2014-01-01

Social interactions occur within a variety of different contexts––cooperative/competitive––and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against one’s friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations. PMID:23314007

Heterogeneity of interactions of microbial communities in regions of Taihu Lake with different nutrient loadings: A network analysis.

PubMed

Cao, Xinyi; Zhao, Dayong; Xu, Huimin; Huang, Rui; Zeng, Jin; Yu, Zhongbo

2018-06-11

To investigate the differences in the interactions of microbial communities in two regions in Taihu Lake with different nutrient loadings [Meiliang Bay (MLB) and Xukou Bay (XKB)], water samples were collected and both intra- and inter-kingdom microbial community interactions were examined with network analysis. It is demonstrated that all of the bacterioplankton, microeukaryotes and inter-kingdom communities networks in Taihu Lake were non-random. For the networks of bacterioplankton and inter-kingdom community in XKB, higher clustering coefficient and average degree but lower average path length indexes were observed, indicating the nodes in XKB were more clustered and closely connected with plenty edges than those of MLB. The bacterioplankton and inter-kingdom networks were considerably larger and more complex with more module hubs and connectors in XKB compared with those of MLB, whereas the microeukaryotes networks were comparable and had no module hubs or connectors in the two lake zones. The phyla of Acidobacteria, Cyanobacteria and Planctomycetes maintained greater cooperation with other phyla in XKB, rather than competition. The relationships between microbial communities and environmental factors in MLB were weaker. Compared with the microbial community networks of XKB, less modules in networks of MLB were significantly correlated with total phosphorous and total nitrogen.

Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position, Economic Wealth, Pollution and Mortality.

PubMed

Prell, Christina; Sun, Laixiang; Feng, Kuishuang; Myroniuk, Tyler W

2015-01-01

In this paper we investigate how structural patterns of international trade give rise to emissions inequalities across countries, and how such inequality in turn impact countries' mortality rates. We employ Multi-regional Input-Output analysis to distinguish between sulfur-dioxide (SO2) emissions produced within a country's boarders (production-based emissions) and emissions triggered by consumption in other countries (consumption-based emissions). We use social network analysis to capture countries' level of integration within the global trade network. We then apply the Prais-Winsten panel estimation technique to a panel data set across 172 countries over 20 years (1990-2010) to estimate the relationships between countries' level of integration and SO2 emissions, and the impact of trade integration and SO2 emission on mortality rates. Our findings suggest a positive, (log-) linear relationship between a country's level of integration and both kinds of emissions. In addition, although more integrated countries are mainly responsible for both forms of emissions, our findings indicate that they also tend to experience lower mortality rates. Our approach offers a unique combination of social network analysis with multiregional input-output analysis, which better operationalizes intuitive concepts about global trade and trade structure.

A recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure.

PubMed

Liao, Fuyuan; Jan, Yih-Kuen

2012-06-01

This paper presents a recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure. Recurrence is a fundamental property of many dynamical systems, which can be explored in phase spaces constructed from observational time series. A visualization tool of recurrence analysis called recurrence plot (RP) has been proved to be highly effective to detect transitions in the dynamics of the system. However, it was found that delay embedding can produce spurious structures in RPs. Network-based concepts have been applied for the analysis of nonlinear time series recently. We demonstrate that time series with different types of dynamics exhibit distinct global clustering coefficients and distributions of local clustering coefficients and that the global clustering coefficient is robust to the embedding parameters. We applied the approach to study skin blood flow oscillations (BFO) response to loading pressure. The results showed that global clustering coefficients of BFO significantly decreased in response to loading pressure (p<0.01). Moreover, surrogate tests indicated that such a decrease was associated with a loss of nonlinearity of BFO. Our results suggest that the recurrence network approach can practically quantify the nonlinear dynamics of BFO.

Default network activation during episodic and semantic memory retrieval: A selective meta-analytic comparison.

PubMed

Kim, Hongkeun

2016-01-08

It remains unclear whether and to what extent the default network subregions involved in episodic memory (EM) and semantic memory (SM) processes overlap or are separated from one another. This study addresses this issue through a controlled meta-analysis of functional neuroimaging studies involving healthy participants. Various EM and SM task paradigms differ widely in the extent of default network involvement. Therefore, the issue at hand cannot be properly addressed without some control for this factor. In this regard, this study employs a two-stage analysis: a preliminary meta-analysis to select EM and SM task paradigms that recruit relatively extensive default network regions and a main analysis to compare the selected task paradigms. Based on a within-EM comparison, the default network contributed more to recollection/familiarity effects than to old/new effects, and based on a within-SM comparison, it contributed more to word/pseudoword effects than to semantic/phonological effects. According to a direct comparison of recollection/familiarity and word/pseudoword effects, each involving a range of default network regions, there were more overlaps than separations in default network subregions involved in these two effects. More specifically, overlaps included the bilateral posterior cingulate/retrosplenial cortex, left inferior parietal lobule, and left anteromedial prefrontal regions, whereas separations included only the hippocampal formation and the parahippocampal cortex region, which was unique to recollection/familiarity effects. These results indicate that EM and SM retrieval processes involving strong memory signals recruit extensive and largely overlapping default network regions and differ mainly in distinct contributions of hippocampus and parahippocampal regions to EM retrieval. Copyright © 2015 Elsevier Ltd. All rights reserved.

Agricultural science in the wild: a social network analysis of farmer knowledge exchange.

PubMed

Wood, Brennon A; Blair, Hugh T; Gray, David I; Kemp, Peter D; Kenyon, Paul R; Morris, Steve T; Sewell, Alison M

2014-01-01

Responding to demands for transformed farming practices requires new forms of knowledge. Given their scale and complexity, agricultural problems can no longer be solved by linear transfers in which technology developed by specialists passes to farmers by way of extension intermediaries. Recent research on alternative approaches has focused on the innovation systems formed by interactions between heterogeneous actors. Rather than linear transfer, systems theory highlights network facilitation as a specialized function. This paper contributes to our understanding of such facilitation by investigating the networks in which farmers discuss science. We report findings based on the study of a pastoral farming experiment collaboratively undertaken by a group of 17 farmers and five scientists. Analysis of prior contact and alter sharing between the group's members indicates strongly tied and decentralized networks. Farmer knowledge exchanges about the experiment have been investigated using a mix of quantitative and qualitative methods. Network surveys identified who the farmers contacted for knowledge before the study began and who they had talked to about the experiment by 18 months later. Open-ended interviews collected farmer statements about their most valuable contacts and these statements have been thematically analysed. The network analysis shows that farmers talked about the experiment with 192 people, most of whom were fellow farmers. Farmers with densely tied and occupationally homogeneous contacts grew their networks more than did farmers with contacts that are loosely tied and diverse. Thematic analysis reveals three general principles: farmers value knowledge delivered by persons rather than roles, privilege farming experience, and develop knowledge with empiricist rather than rationalist techniques. Taken together, these findings suggest that farmers deliberate about science in intensive and durable networks that have significant implications for theorizing agricultural innovation. The paper thus concludes by considering the findings' significance for current efforts to rethink agricultural extension.

Dengue research networks: building evidence for policy and planning in Brazil.

PubMed

de Paula Fonseca E Fonseca, Bruna; Zicker, Fabio

2016-11-08

The analysis of scientific networks has been applied in health research to map and measure relationships between researchers and institutions, describing collaboration structures, individual roles, and research outputs, and helping the identification of knowledge gaps and cooperation opportunities. Driven by dengue continued expansion in Brazil, we explore the contribution, dynamics and consolidation of dengue scientific networks that could ultimately inform the prioritisation of research, financial investments and health policy. Social network analysis (SNA) was used to produce a 20-year (1995-2014) retrospective longitudinal evaluation of dengue research networks within Brazil and with its partners abroad, with special interest in describing institutional collaboration and their research outputs. The analysis of institutional co-authorship showed a significant expansion of collaboration over the years, increased international involvement, and ensured a shift from public health research toward vector control and basic biomedical research, probably as a reflection of the expansion of transmission, high burden and increasing research funds from the Brazilian government. The analysis identified leading national organisations that maintained the research network connectivity, facilitated knowledge exchange and reduced network vulnerability. SNA proved to be a valuable tool that, along with other indicators, can strengthen a knowledge platform to inform future policy, planning and funding decisions. The paper provides relevant information to policy and planning for dengue research as it reveals: (1) the effectiveness of the research network in knowledge generation, sharing and diffusion; (2) the near-absence of collaboration with the private sector; and (3) the key central organisations that can support strategic decisions on investments, development and implementation of innovations. In addition, the increase in research activities and collaboration has not yet significantly affected dengue transmission, suggesting a limited translation of research efforts into public health solutions.

Identifying critical transitions and their leading biomolecular networks in complex diseases.

PubMed

Liu, Rui; Li, Meiyi; Liu, Zhi-Ping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

2012-01-01

Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.

Scaling of global input-output networks

NASA Astrophysics Data System (ADS)

Liang, Sai; Qi, Zhengling; Qu, Shen; Zhu, Ji; Chiu, Anthony S. F.; Jia, Xiaoping; Xu, Ming

2016-06-01

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input-output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input-output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input-output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input-output networks to support economic policymaking.

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

PubMed Central

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 genes identified are known to be up-regulated in response to osmotic stress in pine and other plant species and encode proteins involved in both signal transduction and stress tolerance. Gene expression levels returned to control values within a 48-hour recovery period in all but 76 transcripts. Correlation network analysis indicates a scale-free network topology for the pine root transcriptome and identifies central nodes that may serve as drivers of drought-responsive transcriptome dynamics in the roots of loblolly pine. PMID:21609476

On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

PubMed Central

Boscolo Galazzo, Ilaria; Brusini, Lorenza; Obertino, Silvia; Zucchelli, Mauro; Granziera, Cristina; Menegaz, Gloria

2018-01-01

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology. PMID:29515362

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

The Analysis of Search Results for the Clarification and Identification of Technology Emergence (AR-CITE) computer code 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 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, world patents, news archives, and on-line mapping networks) are assembled to become one collective networkmore » (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 subject domain to be clarified and identified.« less

Link prediction based on nonequilibrium cooperation effect

NASA Astrophysics Data System (ADS)

Li, Lanxi; Zhu, Xuzhen; Tian, Hui

2018-04-01

Link prediction in complex networks has become a common focus of many researchers. But most existing methods concentrate on neighbors, and rarely consider degree heterogeneity of two endpoints. Node degree represents the importance or status of endpoints. We describe the large-degree heterogeneity as the nonequilibrium between nodes. This nonequilibrium facilitates a stable cooperation between endpoints, so that two endpoints with large-degree heterogeneity tend to connect stably. We name such a phenomenon as the nonequilibrium cooperation effect. Therefore, this paper proposes a link prediction method based on the nonequilibrium cooperation effect to improve accuracy. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in 12 real-world networks to compare the mainstream methods with our indices in the network through numerical analysis.

Establishment of apoptotic regulatory network for genetic markers of colorectal cancer.

PubMed

Hao, Yibin; Shan, Guoyong; Nan, Kejun

2017-03-01

Our purpose is to screen out genetic markers applicable to early diagnosis for colorectal cancer and to establish apoptotic regulatory network model for colorectal cancer, thereby providing theoretical evidence and targeted therapy for early diagnosis of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers applied to early diagnosis of colorectal cancer were searched to perform comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to establish apoptotic regulatory network model based on screened genetic markers, and then verification experiment was conducted. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, among which DCC shows highest diagnostic efficiency. GO analysis of genetic markers found that six genetic markers played role in biological process, molecular function and cellular component. It was indicated in apoptotic regulatory network built by KEGG analysis and verification experiment that WWOX could promote tumor cell apoptotic in colorectal cancer and elevate expression level of p53. The apoptotic regulatory model of colorectal cancer established in this study provides clinically theoretical evidence and targeted therapy for early diagnosis of colorectal cancer.

Methods for evaluating temporal groundwater quality data and results of decadal-scale changes in chloride, dissolved solids, and nitrate concentrations in groundwater in the United States, 1988-2010

USGS Publications Warehouse

Lindsey, Bruce D.; Rupert, Michael G.

2012-01-01

Decadal-scale changes in groundwater quality were evaluated by the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program. Samples of groundwater collected from wells during 1988-2000 - a first sampling event representing the decade ending the 20th century - were compared on a pair-wise basis to samples from the same wells collected during 2001-2010 - a second sampling event representing the decade beginning the 21st century. The data set consists of samples from 1,236 wells in 56 well networks, representing major aquifers and urban and agricultural land-use areas, with analytical results for chloride, dissolved solids, and nitrate. Statistical analysis was done on a network basis rather than by individual wells. Although spanning slightly more or less than a 10-year period, the two-sample comparison between the first and second sampling events is referred to as an analysis of decadal-scale change based on a step-trend analysis. The 22 principal aquifers represented by these 56 networks account for nearly 80 percent of the estimated withdrawals of groundwater used for drinking-water supply in the Nation. Well networks where decadal-scale changes in concentrations were statistically significant were identified using the Wilcoxon-Pratt signed-rank test. For the statistical analysis of chloride, dissolved solids, and nitrate concentrations at the network level, more than half revealed no statistically significant change over the decadal period. However, for networks that had statistically significant changes, increased concentrations outnumbered decreased concentrations by a large margin. Statistically significant increases of chloride concentrations were identified for 43 percent of 56 networks. Dissolved solids concentrations increased significantly in 41 percent of the 54 networks with dissolved solids data, and nitrate concentrations increased significantly in 23 percent of 56 networks. At least one of the three - chloride, dissolved solids, or nitrate - had a statistically significant increase in concentration in 66 percent of the networks. Statistically significant decreases in concentrations were identified in 4 percent of the networks for chloride, 2 percent of the networks for dissolved solids, and 9 percent of the networks for nitrate. A larger percentage of urban land-use networks had statistically significant increases in chloride, dissolved solids, and nitrate concentrations than agricultural land-use networks. In order to assess the magnitude of statistically significant changes, the median of the differences between constituent concentrations from the first full-network sampling event and those from the second full-network sampling event was calculated using the Turnbull method. The largest median decadal increases in chloride concentrations were in networks in the Upper Illinois River Basin (67 mg/L) and in the New England Coastal Basins (34 mg/L), whereas the largest median decadal decrease in chloride concentrations was in the Upper Snake River Basin (1 mg/L). The largest median decadal increases in dissolved solids concentrations were in networks in the Rio Grande Valley (260 mg/L) and the Upper Illinois River Basin (160 mg/L). The largest median decadal decrease in dissolved solids concentrations was in the Apalachicola-Chattahoochee-Flint River Basin (6.0 mg/L). The largest median decadal increases in nitrate as nitrogen (N) concentrations were in networks in the South Platte River Basin (2.0 mg/L as N) and the San Joaquin-Tulare Basins (1.0 mg/L as N). The largest median decadal decrease in nitrate concentrations was in the Santee River Basin and Coastal Drainages (0.63 mg/L). The magnitude of change in networks with statistically significant increases typically was much larger than the magnitude of change in networks with statistically significant decreases. The magnitude of change was greatest for chloride in the urban land-use networks and greatest for dissolved solids and nitrate in the agricultural land-use networks. Analysis of data from all networks combined indicated statistically significant increases for chloride, dissolved solids, and nitrate. Although chloride, dissolved solids, and nitrate concentrations were typically less than the drinking-water standards and guidelines, a statistical test was used to determine whether or not the proportion of samples exceeding the drinking-water standard or guideline changed significantly between the first and second full-network sampling events. The proportion of samples exceeding the U.S. Environmental Protection Agency (USEPA) Secondary Maximum Contaminant Level for dissolved solids (500 milligrams per liter) increased significantly between the first and second full-network sampling events when evaluating all networks combined at the national level. Also, for all networks combined, the proportion of samples exceeding the USEPA Maximum Contaminant Level (MCL) of 10 mg/L as N for nitrate increased significantly. One network in the Delmarva Peninsula had a significant increase in the proportion of samples exceeding the MCL for nitrate. A subset of 261 wells was sampled every other year (biennially) to evaluate decadal-scale changes using a time-series analysis. The analysis of the biennial data set showed that changes were generally similar to the findings from the analysis of decadal-scale change that was based on a step-trend analysis. Because of the small number of wells in a network with biennial data (typically 4-5 wells), the time-series analysis is more useful for understanding water-quality responses to changes in site-specific conditions rather than as an indicator of the change for the entire network.

The study on serum and urine of renal interstitial fibrosis rats induced by unilateral ureteral obstruction based on metabonomics and network analysis methods.

PubMed

Xiang, Zheng; Sun, Hao; Cai, Xiaojun; Chen, Dahui

2016-04-01

Transmission of biological information is a biochemical process of multistep cascade from genes/proteins to metabolites. However, because most metabolites reflect the terminal information of the biochemical process, it is difficult to describe the transmission process of disease information in terms of the metabolomics strategy. In this paper, by incorporating network and metabolomics methods, an integrated approach was proposed to systematically investigate and explain the molecular mechanism of renal interstitial fibrosis. Through analysis of the network, the cascade transmission process of disease information starting from genes/proteins to metabolites was putatively identified and uncovered. The results indicated that renal fibrosis was involved in metabolic pathways of glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids and arachidonic acid metabolism, riboflavin metabolism, tyrosine metabolism, and sphingolipid metabolism. These pathways involve kidney disease genes such as TGF-β1 and P2RX7. Our results showed that combining metabolomics and network analysis can provide new strategies and ideas for the interpretation of pathogenesis of disease with full consideration of "gene-protein-metabolite."

Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis

NASA Astrophysics Data System (ADS)

Vouterakos, P. A.; Moustris, K. P.; Bartzokas, A.; Ziomas, I. C.; Nastos, P. T.; Paliatsos, A. G.

2012-12-01

In this work, artificial neural networks (ANNs) were developed and applied in order to forecast the discomfort levels due to the combination of high temperature and air humidity, during the hot season of the year, in eight different regions within the Greater Athens area (GAA), Greece. For the selection of the best type and architecture of ANNs-forecasting models, the multiple criteria analysis (MCA) technique was applied. Three different types of ANNs were developed and tested with the MCA method. Concretely, the multilayer perceptron, the generalized feed forward networks (GFFN), and the time-lag recurrent networks were developed and tested. Results showed that the best ANNs type performance was achieved by using the GFFN model for the prediction of discomfort levels due to high temperature and air humidity within GAA. For the evaluation of the constructed ANNs, appropriate statistical indices were used. The analysis proved that the forecasting ability of the developed ANNs models is very satisfactory at a significant statistical level of p < 0.01.

Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

NASA Astrophysics Data System (ADS)

Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid

2016-08-01

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Topological and Historical Considerations for Infectious Disease Transmission among Injecting Drug Users in Bushwick, Brooklyn (USA)

PubMed Central

Dombrowski, Kirk; Curtis, Richard; Friedman, Samuel; Khan, Bilal

2014-01-01

Recent interest by physicists in social networks and disease transmission factors has prompted debate over the topology of degree distributions in sexual networks. Social network researchers have been critical of “scale-free” Barabasi-Albert approaches, and largely rejected the preferential attachment, “rich-get-richer” assumptions that underlie that model. Instead, research on sexual networks has pointed to the importance of homophily and local sexual norms in dictating degree distributions, and thus disease transmission thresholds. Injecting Drug User (IDU) network topologies may differ from the emerging models of sexual networks, however. Degree distribution analysis of a Brooklyn, NY, IDU network indicates a different topology than the spanning tree configurations discussed for sexual networks, instead featuring comparatively short cycles and high concurrency. Our findings suggest that IDU networks do in some ways conform to a “scale-free” topology, and thus may represent “reservoirs” of potential infection despite seemingly low transmission thresholds. PMID:24672745

CS_TOTR: A new vertex centrality method for directed signed networks based on status theory

NASA Astrophysics Data System (ADS)

Ma, Yue; Liu, Min; Zhang, Peng; Qi, Xingqin

Measuring the importance (or centrality) of vertices in a network is a significant topic in complex network analysis, which has significant applications in diverse domains, for example, disease control, spread of rumors, viral marketing and so on. Existing studies mainly focus on social networks with only positive (or friendship) relations, while signed networks with also negative (or enemy) relations are seldom studied. Various signed networks commonly exist in real world, e.g. a network indicating friendship/enmity, love/hate or trust/mistrust relationships. In this paper, we propose a new centrality method named CS_TOTR to give a ranking of vertices in directed signed networks. To design this new method, we use the “status theory” for signed networks, and also adopt the vertex ranking algorithm for a tournament and the topological sorting algorithm for a general directed graph. We apply this new centrality method on the famous Sampson Monastery dataset and obtain a convincing result which shows its validity.

Community Structure in Online Collegiate Social Networks

NASA Astrophysics Data System (ADS)

Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

2009-03-01

Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

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

NASA Astrophysics Data System (ADS)

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.

Network analysis applications in hydrology

NASA Astrophysics Data System (ADS)

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.

An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

PubMed

Botía, Juan A; Vandrovcova, Jana; Forabosco, Paola; Guelfi, Sebastian; D'Sa, Karishma; Hardy, John; Lewis, Cathryn M; Ryten, Mina; Weale, Michael E

2017-04-12

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

Constraints and entropy in a model of network evolution

NASA Astrophysics Data System (ADS)

Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.

2017-11-01

Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

Next-Generation WDM Network Design and Routing

NASA Astrophysics Data System (ADS)

Tsang, Danny H. K.; Bensaou, Brahim

2003-08-01

Call for Papers The Editors of JON are soliciting papers on WDM Network Design and Routing. The aim in this focus issue is to publish original research on topics including - but not limited to - the following: - WDM network architectures and protocols - GMPLS network architectures - Wavelength converter placement in WDM networks - Routing and wavelength assignment (RWA) in WDM networks - Protection and restoration strategies and algorithms in WDM networks - Traffic grooming in WDM networks - Dynamic routing strategies and algorithms - Optical Burst Switching - Support of Multicast - Protection and restoration in WDM networks - Performance analysis and optimization in WDM networks Manuscript Submission To submit to this special issue, follow the normal procedure for submission to JON, indicating "WDM Network Design" in the "Comments" field of the online submission form. For all other questions relating to this focus issue, please send an e-mail to jon@osa.org, subject line "WDM Network Design." Additional information can be found on the JON website: http://www.osa-jon.org/submission/. Schedule Paper Submission Deadline: November 1, 2003 Notification to Authors: January 15, 2004 Final Manuscripts to Publisher: February 15, 2004 Publication of Focus Issue: February/March 2004

Next-Generation WDM Network Design and Routing

NASA Astrophysics Data System (ADS)

Tsang, Danny H. K.; Bensaou, Brahim

2003-10-01

Call for Papers The Editors of JON are soliciting papers on WDM Network Design and Routing. The aim in this focus issue is to publish original research on topics including - but not limited to - the following: - WDM network architectures and protocols - GMPLS network architectures - Wavelength converter placement in WDM networks - Routing and wavelength assignment (RWA) in WDM networks - Protection and restoration strategies and algorithms in WDM networks - Traffic grooming in WDM networks - Dynamic routing strategies and algorithms - Optical burst switching - Support of multicast - Protection and restoration in WDM networks - Performance analysis and optimization in WDM networks Manuscript Submission To submit to this special issue, follow the normal procedure for submission to JON, indicating "WDM Network Design" in the "Comments" field of the online submission form. For all other questions relating to this focus issue, please send an e-mail to jon@osa.org, subject line "WDM Network Design." Additional information can be found on the JON website: http://www.osa-jon.org/submission/. Schedule - Paper Submission Deadline: November 1, 2003 - Notification to Authors: January 15, 2004 - Final Manuscripts to Publisher: February 15, 2004 - Publication of Focus Issue: February/March 2004

Next-Generation WDM Network Design and Routing

NASA Astrophysics Data System (ADS)

Tsang, Danny H. K.; Bensaou, Brahim

2003-09-01

Call for Papers The Editors of JON are soliciting papers on WDM Network Design and Routing. The aim in this focus issue is to publish original research on topics including - but not limited to - the following: - WDM network architectures and protocols - GMPLS network architectures - Wavelength converter placement in WDM networks - Routing and wavelength assignment (RWA) in WDM networks - Protection and restoration strategies and algorithms in WDM networks - Traffic grooming in WDM networks - Dynamic routing strategies and algorithms - Optical burst switching - Support of multicast - Protection and restoration in WDM networks - Performance analysis and optimization in WDM networks Manuscript Submission To submit to this special issue, follow the normal procedure for submission to JON, indicating "WDM Network Design" in the "Comments" field of the online submission form. For all other questions relating to this focus issue, please send an e-mail to jon@osa.org, subject line "WDM Network Design." Additional information can be found on the JON website: http://www.osa-jon.org/submission/. Schedule - Paper Submission Deadline: November 1, 2003 - Notification to Authors: January 15, 2004 - Final Manuscripts to Publisher: February 15, 2004 - Publication of Focus Issue: February/March 2004

[Development of an index system for the comprehensive evaluation on public health emergency events surveillance system in China].

PubMed

Hong, Zhiheng; Ni, Daxin; Cao, Yang; Meng, Ling; Tu, Wenxiao; Li, Leilei; Li, Qun; Jin, Lianmei

2015-06-01

To establish a comprehensive evaluation index system for the China Public Health Emergency Events Surveillance System (CPHEESS). A draft index system was built through literature review and under the consideration of the characteristics on CPHEESS. Delphi method was adapted to determine the final index system. The index system was divided into primary, secondary and tertiary levels. There were 4 primary indicators: System structure, Network platform, Surveillance implementation reports with Data analysis and utilization. There were 16 secondary and 70 tertiary indicators being set, with System structure including 14 tertiary indicators (accounted for 20.00%), 21 Network platforms (accounted for 30.00%). Twenty-four Surveillance implementation reports (accounted for 34.29%), 11 Data analysis and utilization (accounted for 15.71%). The average score of importance of each indicators was 4.29 (3.77-4.94), with an average coefficient variation as 0.14 (0.12-0.16). The mean Chronbach's α index was 0.84 (0.81-0.89). The adaptability of each related facilities indicator was specified. The primary indicators were set in accordance with the characteristics and goals of the surveillance systems. Secondary indicators provided key elements in the management and control of the system while the tertiary indicators were available and operative. The agreement rate of experts was high with good validity and reliability. This index system could be used for CPHEESS in future.

The structure of gallery networks in the nests of termite Cubitermes spp. revealed by X-ray tomography

NASA Astrophysics Data System (ADS)

Perna, Andrea; Jost, Christian; Couturier, Etienne; Valverde, Sergi; Douady, Stéphane; Theraulaz, Guy

2008-09-01

Recent studies have introduced computer tomography (CT) as a tool for the visualisation and characterisation of insect architectures. Here, we use CT to map the three-dimensional networks of galleries inside Cubitermes nests in order to analyse them with tools from graph theory. The structure of these networks indicates that connections inside the nest are rearranged during the whole nest life. The functional analysis reveals that the final network topology represents an excellent compromise between efficient connectivity inside the nest and defence against attacking predators. We further discuss and illustrate the usefulness of CT to disentangle environmental and specific influences on nest architecture.

Higher Education Research in Asia: A Publication and Co-Publication Analysis

ERIC Educational Resources Information Center

Jung, Jisun; Horta, Hugo

2013-01-01

This study explores higher education research in Asia. Drawing on scientometrics, the mapping of science and social network analysis, this paper examines the publications of 38 specialised journals on higher education over the past three decades. The findings indicate a growing number of higher education research publications but the proportion of…

Friendship, Status, and Repayment as Factors in Seeking Help.

ERIC Educational Resources Information Center

Shapiro, E. Gary

Three variables, derived from Blau's (1955) analysis of consultation networks among federal agents and Homans' (1961) social exchange analysis, were manipulated to observe their effects on help seeking. Subjects were presented with a hypothetical situation where help was needed. Subjects indicated that they would be more likely to seek help from a…

US adolescents’ friendship networks and health risk behaviors: a systematic review of studies using social network analysis and Add Health data

PubMed Central

Goodson, Patricia

2015-01-01

Background. Documented trends in health-related risk behaviors among US adolescents have remained high over time. Studies indicate relationships among mutual friends are a major influence on adolescents’ risky behaviors. Social Network Analysis (SNA) can help understand friendship ties affecting individual adolescents’ engagement in these behaviors. Moreover, a systematic literature review can synthesize findings from a range of studies using SNA, as well as assess these studies’ methodological quality. Review findings also can help health educators and promoters develop more effective programs. Objective. This review systematically examined studies of the influence of friendship networks on adolescents’ risk behaviors, which utilized SNA and the Add Health data (a nationally representative sample). Methods. We employed the Matrix Method to synthesize and evaluate 15 published studies that met our inclusion and exclusion criteria, retrieved from the Add Health website and 3 major databases (Medline, Eric, and PsycINFO). Moreover, we assigned each study a methodological quality score (MQS). Results. In all studies, friendship networks among adolescents promoted their risky behaviors, including drinking alcohol, smoking, sexual intercourse, and marijuana use. The average MQS was 4.6, an indicator of methodological rigor (scale: 1–9). Conclusion. Better understanding of risky behaviors influenced by friends can be useful for health educators and promoters, as programs targeting friendships might be more effective. Additionally, the overall MQ of these reviewed studies was good, as average scores fell above the scale’s mid-point. PMID:26157622

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

PubMed

Mazaya, Maulida; Trinh, Hung-Cuong; Kwon, Yung-Keun

2017-12-21

Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships. Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.

New methodologies for multi-scale time-variant reliability analysis of complex lifeline networks

NASA Astrophysics Data System (ADS)

Kurtz, Nolan Scot

The cost of maintaining existing civil infrastructure is enormous. Since the livelihood of the public depends on such infrastructure, its state must be managed appropriately using quantitative approaches. Practitioners must consider not only which components are most fragile to hazard, e.g. seismicity, storm surge, hurricane winds, etc., but also how they participate on a network level using network analysis. Focusing on particularly damaged components does not necessarily increase network functionality, which is most important to the people that depend on such infrastructure. Several network analyses, e.g. S-RDA, LP-bounds, and crude-MCS, and performance metrics, e.g. disconnection bounds and component importance, are available for such purposes. Since these networks are existing, the time state is also important. If networks are close to chloride sources, deterioration may be a major issue. Information from field inspections may also have large impacts on quantitative models. To address such issues, hazard risk analysis methodologies for deteriorating networks subjected to seismicity, i.e. earthquakes, have been created from analytics. A bridge component model has been constructed for these methodologies. The bridge fragilities, which were constructed from data, required a deeper level of analysis as these were relevant for specific structures. Furthermore, chloride-induced deterioration network effects were investigated. Depending on how mathematical models incorporate new information, many approaches are available, such as Bayesian model updating. To make such procedures more flexible, an adaptive importance sampling scheme was created for structural reliability problems. Additionally, such a method handles many kinds of system and component problems with singular or multiple important regions of the limit state function. These and previously developed analysis methodologies were found to be strongly sensitive to the network size. Special network topologies may be more or less computationally difficult, while the resolution of the network also has large affects. To take advantage of some types of topologies, network hierarchical structures with super-link representation have been used in the literature to increase the computational efficiency by analyzing smaller, densely connected networks; however, such structures were based on user input and subjective at times. To address this, algorithms must be automated and reliable. These hierarchical structures may indicate the structure of the network itself. This risk analysis methodology has been expanded to larger networks using such automated hierarchical structures. Component importance is the most important objective from such network analysis; however, this may only provide the information of which bridges to inspect/repair earliest and little else. High correlations influence such component importance measures in a negative manner. Additionally, a regional approach is not appropriately modelled. To investigate a more regional view, group importance measures based on hierarchical structures have been created. Such structures may also be used to create regional inspection/repair approaches. Using these analytical, quantitative risk approaches, the next generation of decision makers may make both component and regional-based optimal decisions using information from both network function and further effects of infrastructure deterioration.

Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia

NASA Astrophysics Data System (ADS)

Chang, K. L.; Petropavlovskikh, I. V.; Cooper, O. R.; Schultz, M.; Wang, T.

2017-12-01

Surface ozone is a greenhouse gas and pollutant detrimental to human health and crop and ecosystem productivity. The Tropospheric Ozone Assessment Report (TOAR) is designed to provide the research community with an up-to-date observation-based overview of tropospheric ozone's global distribution and trends. The TOAR Surface Ozone Database contains ozone metrics at thousands of monitoring sites around the world, densely clustered across mid-latitude North America, western Europe and East Asia. Calculating regional ozone trends across these locations is challenging due to the uneven spacing of the monitoring sites across urban and rural areas. To meet this challenge we conducted a spatial and temporal trend analysis of several TOAR ozone metrics across these three regions for summertime (April-September) 2000-2014, using the generalized additive mixed model (GAMM). Our analysis indicates that East Asia has the greatest human and plant exposure to ozone pollution among investigating regions, with increasing ozone levels through 2014. The results also show that ozone mixing ratios continue to decline significantly over eastern North America and Europe, however, there is less evidence for decreases of daytime average ozone at urban sites. The present-day spatial coverage of ozone monitors in East Asia (South Korea and Japan) and eastern North America is adequate for estimating regional trends by simply taking the average of the individual trends at each site. However the European network is more sparsely populated across its northern and eastern regions and therefore a simple average of the individual trends at each site does not yield an accurate regional trend. This analysis demonstrates that the GAMM technique can be used to assess the regional representativeness of existing monitoring networks, indicating those networks for which a regional trend can be obtained by simply averaging the trends of all individual sites and those networks that require a more sophisticated statistical approach.

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Ecological network analysis on global virtual water trade.

PubMed

Yang, Zhifeng; Mao, Xufeng; Zhao, Xu; Chen, Bin

2012-02-07

Global water interdependencies are likely to increase with growing virtual water trade. To address the issues of the indirect effects of water trade through the global economic circulation, we use ecological network analysis (ENA) to shed insight into the complicated system interactions. A global model of virtual water flow among agriculture and livestock production trade in 1995-1999 is also built as the basis for network analysis. Control analysis is used to identify the quantitative control or dependency relations. The utility analysis provides more indicators for describing the mutual relationship between two regions/countries by imitating the interactions in the ecosystem and distinguishes the beneficiary and the contributor of virtual water trade system. Results show control and utility relations can well depict the mutual relation in trade system, and direct observable relations differ from integral ones with indirect interactions considered. This paper offers a new way to depict the interrelations between trade components and can serve as a meaningful start as we continue to use ENA in providing more valuable implications for freshwater study on a global scale.

HubAlign: an accurate and efficient method for global alignment of protein-protein interaction networks.

PubMed

Hashemifar, Somaye; Xu, Jinbo

2014-09-01

High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency. This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins. HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zip. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Analysis of the enzyme network involved in cattle milk production using graph theory.

PubMed

Ghorbani, Sholeh; Tahmoorespur, Mojtaba; Masoudi Nejad, Ali; Nasiri, Mohammad; Asgari, Yazdan

2015-06-01

Understanding cattle metabolism and its relationship with milk products is important in bovine breeding. A systemic view could lead to consequences that will result in a better understanding of existing concepts. Topological indices and quantitative characterizations mostly result from the application of graph theory on biological data. In the present work, the enzyme network involved in cattle milk production was reconstructed and analyzed based on available bovine genome information using several public datasets (NCBI, Uniprot, KEGG, and Brenda). The reconstructed network consisted of 3605 reactions named by KEGG compound numbers and 646 enzymes that catalyzed the corresponding reactions. The characteristics of the directed and undirected network were analyzed using Graph Theory. The mean path length was calculated to be4.39 and 5.41 for directed and undirected networks, respectively. The top 11 hub enzymes whose abnormality could harm bovine health and reduce milk production were determined. Therefore, the aim of constructing the enzyme centric network was twofold; first to find out whether such network followed the same properties of other biological networks, and second, to find the key enzymes. The results of the present study can improve our understanding of milk production in cattle. Also, analysis of the enzyme network can help improve the modeling and simulation of biological systems and help design desired phenotypes to increase milk production quality or quantity.

Network evaluation: principles, structures and outcomes of the German working group of Health Promoting Universities.

PubMed

Stock, Christiane; Milz, Simone; Meier, Sabine

2010-03-01

With more than 60 participating universities, the German working group of Health Promoting Universities (German HPU network) is the largest and most active network of universities as healthy settings. This study aims at evaluating processes and effects of the German HPU network and at supporting the future development of the network. The evaluation was based on the multi faceted network assessment instrument developed by Broesskamp-Stone (7). We used a document analysis, two expert interviews and a survey among members (n = 33) to collect relevant data for the assessment. The analysis showed that the visions of the network can be regarded as fulfilled in most aspects. The members of the network received network support through trustful and mutual relationships. The network ranked high on general network principles like implementation of mutual relationships, sharing of information, risks and resources, equal access to resources, responsibility and consensus orientation. However, a high degree of centralization was found as a negative indicator. Other critical aspects of the network's structures and processes have been the regional predominance of universities from the northern and middle part of Germany, the low representation of students in the network, and the low proportion of members that could successfully implement health promotion into the guiding principles of their university. Overall, the evaluation has shown that the network has worked effectively, has developed meaningful processes and structures and has formulated practical guidelines. Since its 12 years of existence the German HPU network has been able to adapt and to adequately respond to changing contextual conditions regarding health promotion at universities in Germany. The network should develop strategies to counteract the critical aspects and detected imbalances in order to further increase its impact on universities as healthy settings.

Molecular ecological network analyses.

PubMed

Deng, Ye; Jiang, Yi-Huei; Yang, Yunfeng; He, Zhili; Luo, Feng; Zhou, Jizhong

2012-05-30

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Exploring the networking behaviors of hospital organizations.

PubMed

Di Vincenzo, Fausto

2018-05-08

Despite an extensive body of knowledge exists on network outcomes and on how hospital network structures may contribute to the creation of outcomes at different levels of analysis, less attention has been paid to understanding how and why hospital organizational networks evolve and change. The aim of this paper is to study the dynamics of networking behaviors of hospital organizations. Stochastic actor-based model for network dynamics was used to quantitatively examine data covering six-years of patient transfer relations among 35 hospital organizations. Specifically, the study investigated about determinants of patient transfer evolution modeling partner selection choice as a combination of multiple organizational attributes and endogenous network-based processes. The results indicate that having overlapping specialties and treating patients with the same case-mix decrease the likelihood of observing network ties between hospitals. Also, results revealed as geographical proximity and membership of the same LHA have a positive impact on the networking behavior of hospitals organizations, there is a propensity in the network to choose larger hospitals as partners, and to transfer patients between hospitals facing similar levels of operational uncertainty. Organizational attributes (overlapping specialties and case-mix), institutional factors (LHA), and geographical proximity matter in the formation and shaping of hospital networks over time. Managers can benefit from the use of these findings by clearly identifying the role and strategic positioning of their hospital with respect to the entire network. Social network analysis can yield novel information and also aid policy makers in the formation of interventions, encouraging alliances among providers as well as planning health system restructuring.

Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information; with a section on theory and application of generalized least squares

USGS Publications Warehouse

Medina, K.D.; Tasker, Gary D.

1987-01-01

This report documents the results of an analysis of the surface-water data network in Kansas for its effectiveness in providing regional streamflow information. The network was analyzed using generalized least squares regression. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-, low-, and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow-gaging-station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations, and (or) adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and for discontinued stations for which unregulated flow characteristics, as well as physical and climatic characteristics, were available. The State was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for the three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean-square error for each cost level could be obtained by adding new stations and discontinuing some current network stations. Large reductions in sampling mean-square error for low-flow information could be achieved in all three network areas, the reduction in western Kansas being the most dramatic. The addition of new stations would be most beneficial for mean-flow information in western Kansas. The reduction of sampling mean-square error for high-flow information would benefit most from the addition of new stations in western Kansas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas and establishing more new stations in western Kansas.

Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

PubMed Central

Toubiana, David; Semel, Yaniv; Tohge, Takayuki; Beleggia, Romina; Cattivelli, Luigi; Rosental, Leah; Nikoloski, Zoran; Zamir, Dani; Fernie, Alisdair R.; Fait, Aaron

2012-01-01

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping. PMID:22479206

Ecological network analysis for economic systems: growth and development and implications for sustainable development.

PubMed

Huang, Jiali; Ulanowicz, Robert E

2014-01-01

The quantification of growth and development is an important issue in economics, because these phenomena are closely related to sustainability. We address growth and development from a network perspective in which economic systems are represented as flow networks and analyzed using ecological network analysis (ENA). The Beijing economic system is used as a case study and 11 input-output (I-O) tables for 1985-2010 are converted into currency networks. ENA is used to calculate system-level indices to quantify the growth and development of Beijing. The contributions of each direct flow toward growth and development in 2010 are calculated and their implications for sustainable development are discussed. The results show that during 1985-2010, growth was the main attribute of the Beijing economic system. Although the system grew exponentially, its development fluctuated within only a small range. The results suggest that system ascendency should be increased in order to favor more sustainable development. Ascendency can be augmented in two ways: (1) strengthen those pathways with positive contributions to increasing ascendency and (2) weaken those with negative effects.

Ecological Network Analysis for Economic Systems: Growth and Development and Implications for Sustainable Development

PubMed Central

Huang, Jiali; Ulanowicz, Robert E.

2014-01-01

The quantification of growth and development is an important issue in economics, because these phenomena are closely related to sustainability. We address growth and development from a network perspective in which economic systems are represented as flow networks and analyzed using ecological network analysis (ENA). The Beijing economic system is used as a case study and 11 input–output (I-O) tables for 1985–2010 are converted into currency networks. ENA is used to calculate system-level indices to quantify the growth and development of Beijing. The contributions of each direct flow toward growth and development in 2010 are calculated and their implications for sustainable development are discussed. The results show that during 1985–2010, growth was the main attribute of the Beijing economic system. Although the system grew exponentially, its development fluctuated within only a small range. The results suggest that system ascendency should be increased in order to favor more sustainable development. Ascendency can be augmented in two ways: (1) strengthen those pathways with positive contributions to increasing ascendency and (2) weaken those with negative effects. PMID:24979465

The Impact of Drug Use in Social Networks of Patients with Substance Use and Bipolar Disorders

PubMed Central

McDonald, Leah J.; Griffin, Margaret L.; Kolodziej, Monika E.; Fitzmaurice, Garrett M.; Weiss, Roger D.

2011-01-01

In this exploratory analysis, we assessed the effect of drug use among social network members on recovery from drug dependence in patients with co-occurring bipolar disorder. Patients (n=57) enrolled in a group therapy study completed assessments over 15 months. Patients with 0–1 drug users in their social networks at intake had few days of drug use during treatment and follow-up, whereas those with ≥ 2 drug users had significantly more days of drug use. Multivariate analysis showed that patients who consistently named multiple drug users in their social networks had a marked increase in drug use over 15 months, while those who never or occasionally named multiple drug users had a small decline in drug use over time. Multiple drug users in social networks of treatment-seeking drug dependent patients with co-occurring bipolar disorder may indicate poor drug use outcomes; efforts to reduce the association with drug users may be useful. This clinical trial has been registered in a public trials registry at clinicaltrials.gov (identifier is NCT00227838). PMID:21314751

Operational problems of Haniwa net as a form of social capital: interdependence between human networks of physicians and information networks.

PubMed

Maeda, Minoru; Araki, Sanae; Suzuki, Muneou; Umemoto, Katsuhiro; Kai, Yukiko; Araki, Kenji

2012-10-01

In August 2009, Miyazaki Health and Welfare Network (Haniwa Net, hereafter referred to as "the Net"), centrally led by University of Miyazaki Hospital (UMH), adopted a center hospital-based system offering a unilateral linkage that enables the viewing of UMH's medical records through a web-based browser (electronic medical records (EMR)). By the end of December 2010, the network had developed into a system of 79 collaborating physicians from within the prefecture. Beginning in August 2010, physicians in 12 medical institutions were visited and asked to speak freely on the operational issues concerning the Net. Recordings and written accounts were coded using the text analysis software MAXQDA 10 to understand the actual state of operations. Analysis of calculations of Kendall's rank correlation confirmed that the interdependency between human networks and information networks is significant. At the same time, while the negative opinions concerning the functions of the Net were somewhat conspicuous, the results showed a correlation between requests and proposals for operational improvements of the Net, clearly indicating the need for a more user-friendly system and a better viewer.

Information networks in the stock market based on the distance of the multi-attribute dimensions between listed companies

NASA Astrophysics Data System (ADS)

Liu, Qian; Li, Huajiao; Liu, Xueyong; Jiang, Meihui

2018-04-01

In the stock market, there are widespread information connections between economic agents. Listed companies can obtain mutual information about investment decisions from common shareholders, and the extent of sharing information often determines the relationships between listed companies. Because different shareholder compositions and investment shares lead to different formations of the company's governance mechanisms, we map the investment relationships between shareholders to the multi-attribute dimensional spaces of the listed companies (each shareholder investment in a company is a company dimension). Then, we construct the listed company's information network based on co-shareholder relationships. The weights for the edges in the information network are measured with the Euclidean distance between the listed companies in the multi-attribute dimension space. We define two indices to analyze the information network's features. We conduct an empirical study that analyzes Chinese listed companies' information networks. The results from the analysis show that with the diversification and decentralization of shareholder investments, almost all Chinese listed companies exchanged information through common shareholder relationships, and there is a gradual reduction in information sharing capacity between listed companies that have common shareholders. This network analysis has benefits for risk management and portfolio investments.

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

Integrating Entropy and Closed Frequent Pattern Mining for Social Network Modelling and Analysis

NASA Astrophysics Data System (ADS)

Adnan, Muhaimenul; Alhajj, Reda; Rokne, Jon

The recent increase in the explicitly available social networks has attracted the attention of the research community to investigate how it would be possible to benefit from such a powerful model in producing effective solutions for problems in other domains where the social network is implicit; we argue that social networks do exist around us but the key issue is how to realize and analyze them. This chapter presents a novel approach for constructing a social network model by an integrated framework that first preparing the data to be analyzed and then applies entropy and frequent closed patterns mining for network construction. For a given problem, we first prepare the data by identifying items and transactions, which arc the basic ingredients for frequent closed patterns mining. Items arc main objects in the problem and a transaction is a set of items that could exist together at one time (e.g., items purchased in one visit to the supermarket). Transactions could be analyzed to discover frequent closed patterns using any of the well-known techniques. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader set of domains. Entropies of the frequent closed patterns arc used to keep the dimensionality of the feature vectors to a reasonable size; it is a kind of feature reduction process. Finally, we analyze the dynamic behavior of the constructed social network. Experiments were conducted on a synthetic dataset and on the Enron corpus email dataset. The results presented in the chapter show that social networks extracted from a feature set as frequent closed patterns successfully carry the community structure information. Moreover, for the Enron email dataset, we present an analysis to dynamically indicate the deviations from each user's individual and community profile. These indications of deviations can be very useful to identify unusual events.

Social networks and links to isolation and loneliness among elderly HCBS clients.

PubMed

Medvene, Louis J; Nilsen, Kari M; Smith, Rachel; Ofei-Dodoo, Samuel; DiLollo, Anthony; Webster, Noah; Graham, Annette; Nance, Anita

2016-01-01

The purpose of this study was to explore the network types of HCBS clients based on the structural characteristics of their social networks. We also examined how the network types were associated with social isolation, relationship quality and loneliness. Forty personal interviews were carried out with HCBS clients to assess the structure of their social networks as indicated by frequency of contact with children, friends, family and participation in religious and community organizations. Hierarchical cluster analysis was conducted to identify network types. Four network types were found including: family (n = 16), diverse (n = 8), restricted (n = 8) and religious (n = 7). Family members comprised almost half of participants' social networks, and friends comprised less than one-third. Clients embedded in family, diverse and religious networks had significantly more positive relationships than clients embedded in restricted networks. Clients embedded in restricted networks had significantly higher social isolation scores and were lonelier than clients in diverse and family networks. The findings suggest that HCBS clients' isolation and loneliness are linked to the types of social networks in which they are embedded. The findings also suggest that clients embedded in restricted networks are at high risk for negative outcomes.

How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2018-02-06

Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

Breast cancer publication network: profile of co-authorship and co-organization.

PubMed

Biglu, Mohammad-Hossein; Abotalebi, Parvaneh; Ghavami, Mostafa

2016-01-01

Introduction: Breast cancer is one of the highest reasons of deaths for people in the world. The objective of current study is to analyze and visualize the trend of global scientific activities in the field of breast cancer during a period of 10 years through 2006-2015. Methods: The current study was performed by utilizing the scientometrics analysis and mapping the co-authorship and co-organization networks. The Web of Science Core Collection (WoS-CC)database was used to extract all papers indexed as a topic of breast cancer through 2006 to 2015. Research productivity was measured through analysis several parameters, including: the number and time course of publications, the journal and language of publications, the frequency and type of publications, as well as top 20 active sub-categories together with country contribution. The extracted data were transferred into the Excel charts and plotted as diagrams. The Science of Science (Sci2) and CiteSpace softwares were used as tools for mapping the co-authorship and co-organization networks of the published papers. Results: Analysis of data indicated that the number of publications in the field of breast cancer has linearly increased and correlated with the time-course of the study. The number of publication indexed in WoS-CC in 2015 was two times greater than that of 2006, which reached from 15 229 documents in 2006 to 30 667 documents in 2015. English Language accounted for 98% of total publications as the most dominant language. The vast majority of publications' type was in the form of original journal articles (64.7%). Based on Bradford scatterings law, the journal of "Cancer Research" was the most productive journal among the core journals, while the USA, China, and England were the most prolific countries in the field. The co-organization network indicated the dominant role of Harvard University in the field. Conclusion: The integrity of network indicated that scientists in the field of breast cancer working collaboratively to tackle the number one threat in women health.

Network-Based Comparative Analysis of Arabidopsis Immune Responses to Golovinomyces orontii and Botrytis cinerea Infections.

PubMed

Jiang, Zhenhong; Dong, Xiaobao; Zhang, Ziding

2016-01-11

A comprehensive exploration of common and specific plant responses to biotrophs and necrotrophs is necessary for a better understanding of plant immunity. Here, we compared the Arabidopsis defense responses evoked by the biotrophic fungus Golovinomyces orontii and the necrotrophic fungus Botrytis cinerea through integrative network analysis. Two time-course transcriptional datasets were integrated with an Arabidopsis protein-protein interaction (PPI) network to construct a G. orontii conditional PPI sub-network (gCPIN) and a B. cinerea conditional PPI sub-network (bCPIN). We found that hubs in gCPIN and bCPIN played important roles in disease resistance. Hubs in bCPIN evolved faster than hubs in gCPIN, indicating the different selection pressures imposed on plants by different pathogens. By analyzing the common network from gCPIN and bCPIN, we identified two network components in which the genes were heavily involved in defense and development, respectively. The co-expression relationships between interacting proteins connecting the two components were different under G. orontii and B. cinerea infection conditions. Closer inspection revealed that auxin-related genes were overrepresented in the interactions connecting these two components, suggesting a critical role of auxin signaling in regulating the different co-expression relationships. Our work may provide new insights into plant defense responses against pathogens with different lifestyles.

The use of social network analysis to examine the transmission of Salmonella spp. within a vertically integrated broiler enterprise.

PubMed

Crabb, Helen Kathleen; Allen, Joanne Lee; Devlin, Joanne Maree; Firestone, Simon Matthew; Stevenson, Mark Anthony; Gilkerson, James Rudkin

2018-05-01

To better understand factors influencing infectious agent dispersal within a livestock population information is needed on the nature and frequency of contacts between farm enterprises. This study uses social network analysis to describe the contact network within a vertically integrated broiler poultry enterprise to identify the potential horizontal and vertical transmission pathways for Salmonella spp. Nodes (farms, sheds, production facilities) were identified and the daily movement of commodities (eggs, birds, feed, litter) and people between nodes were extracted from routinely kept farm records. Three time periods were examined in detail, 1- and 8- and 17-weeks of the production cycle and contact networks were described for all movements, and by commodity and production type. All nodes were linked by at least one movement during the study period but network density was low indicating that all potential pathways between nodes did not exist. Salmonella spp. transmission via vertical or horizontal pathways can only occur along directed pathways when those pathways are present. Only two locations (breeder or feed nodes) were identified where the transmission of a single Salmonella spp. clone could theoretically percolate through the network to the broiler or processing nodes. Only the feed transmission pathway directly connected all parts of the network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network.

PubMed

Li, S; Zhang, Z Q; Wu, L J; Zhang, X G; Li, Y D; Wang, Y Y

2007-01-01

Traditional Chinese medicine uses ZHENG as the key pathological principle to understand the human homeostasis and guide the applications of Chinese herbs. Here, a systems biology approach with the combination of computational analysis and animal experiment is used to investigate this complex issue, ZHENG, in the context of the neuro-endocrine-immune (NEI) system. By using the methods of literature mining, network analysis and topological comparison, it is found that hormones are predominant in the Cold ZHENG network, immune factors are predominant in the Hot ZHENG network, and these two networks are connected by neuro-transmitters. In addition, genes related to Hot ZHENG-related diseases are mainly present in the cytokine-cytokine receptor interaction pathway, whereas genes related to both the Cold-related and Hot-related diseases are linked to the neuroactive ligand-receptor interaction pathway. These computational findings were subsequently verified by experiments on a rat model of collagen-induced arthritis, which indicate that the Cold ZHENG-oriented herbs tend to affect the hub nodes in the Cold ZHENG network, and the Hot ZHENG-oriented herbs tend to affect the hub nodes in the Hot ZHENG network. These investigations demonstrate that the thousand-year-old concept of ZHENG may have a molecular basis with NEI as background.

[Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale].

PubMed

Li, Su-Yi; Ji, Yan-Ju; Liu, Wei-Yu; Wang, Zhi-Hong

2013-04-01

In the present study, an innovative method is proposed, employing both wavelet transform and neural network, to analyze the near-infrared spectrum data in oil shale survey. The method entails using db8 wavelet at 3 levels decomposition to process raw data, using the transformed data as the input matrix, and creating the model through neural network. To verify the validity of the method, this study analyzes 30 synthesized oil shale samples, in which 20 samples are randomly selected for network training, the other 10 for model prediction, and uses the full spectrum and the wavelet transformed spectrum to carry out 10 network models, respectively. Results show that the mean speed of the full spectrum neural network modeling is 570.33 seconds, and the predicted residual sum of squares (PRESS) and correlation coefficient of prediction are 0.006 012 and 0.843 75, respectively. In contrast, the mean speed of the wavelet network modeling method is 3.15 seconds, and the mean PRESS and correlation coefficient of prediction are 0.002 048 and 0.953 19, respectively. These results demonstrate that the wavelet neural network modeling method is significantly superior to the full spectrum neural network modeling method. This study not only provides a new method for more efficient and accurate detection of the oil content of oil shale, but also indicates the potential for applying wavelet transform and neutral network in broad near-infrared spectrum analysis.

Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

2015-06-01

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation

PubMed Central

Kwak, Doyeon

2017-01-01

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks. PMID:28542367

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

PubMed

Kwak, Doyeon; Kim, Wonjoon

2017-01-01

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.

[Analysis of Spanish research collaboration in emergency medicine: 2010-2014].

PubMed

Burbano Santos, Pablo; Fernández-Guerrero, Inés María; Martín-Sánchez, Francisco Javier; Burillo, Guillermo; Miró, Òscar

2017-10-01

To describe the structure of the Spanish emergency medicine research network or networks, researchers' roles, and patterns of collaboration between hospitals. The search for publications was carried out in the SCOPUS database for the 5-year period of 2010 to 2014. We used network analysis software to map ties between researchers and hospitals that had established at least 5 and 10 relationships, respectively, during the period under study. We calculated indicators of degree of centrality for individual scientists and hospitals and tabulated data for the main authors and centers. We identified 2626 articles with 12 372 different authors at 1134 hospitals in 75 countries. The largest number of international relations were with France, the United States, and the United Kingdom. Authors had established 93 687 connections that allowed us to identify 23 collaborating groups, the largest of which was comprised of 30 individuals. We also found 12 855 connections between hospitals that identified a single subnetwork of collaboration comprised of 19 hospitals, 1 of which was in Switzerland. Measures of centrality, intermediation, and proximity led to classification of the most important members of author and hospital networks. This analysis of research networks in emergency medicine has afforded the first details describing the relationships maintained by Spanish scientists and hospitals.

Analyzing Crisis in Global Financial Indices

NASA Astrophysics Data System (ADS)

Kumar, Sunil; Deo, Nivedita

We apply the Random Matrix Theory and complex network techniques to 20 global financial indices and study the correlation and network properties before and during the financial crisis of 2008 respectively. We find that the largest eigenvalue deviate significantly from the upper bound which shows a strong correlation between financial indices. By using a sliding window of 25 days we find that largest eigenvalue represent the collective information about the correlation between global financial indices and its trend indicate the market conditions. It is confirmed that eigenvectors corresponding to second largest eigenvalue gives useful information about the sector formation in the global financial indices. We find that these clusters are formed on the basis of the geographical location. The correlation network is constructed using threshold method for different values of threshold θ in the range 0 to 0.9, at θ=0.2 the network is fully connected. At θ=0.6, the Americas, Europe and Asia/Pacific form different clusters before the crisis but during the crisis Americas and Europe are strongly linked. If we further increase the threshold to 0.9 we find that European countries France, Germany and UK consistently constitute the most tightly linked markets before and during the crisis. We find that the structure of Minimum Spanning Tree before the crisis is more star like whereas during the crisis it changes to be more chain like. Using the multifractal analysis, we find that Hurst exponents of financial indices increases during the period of crisis as compared to the period before the crisis. The empirical results verify the validity of measures, and this has led to a better understanding of complex financial markets.

Social network analysis provides insights into African swine fever epidemiology.

PubMed

Lichoti, Jacqueline Kasiiti; Davies, Jocelyn; Kitala, Philip M; Githigia, Samuel M; Okoth, Edward; Maru, Yiheyis; Bukachi, Salome A; Bishop, Richard P

2016-04-01

Pig movements play a significant role in the spread of economically important infectious diseases such as the African swine fever. Characterization of movement networks between pig farms and through other types of farm and household enterprises that are involved in pig value chains can provide useful information on the role that different participants in the networks play in pathogen transmission. Analysis of social networks that underpin these pig movements can reveal pathways that are important in the transmission of disease, trade in commodities, the dissemination of information and the influence of behavioural norms. We assessed pig movements among pig keeping households within West Kenya and East Uganda and across the shared Kenya-Uganda border in the study region, to gain insight into within-country and trans-boundary pig movements. Villages were sampled using a randomized cluster design. Data were collected through interviews in 2012 and 2013 from 683 smallholder pig-keeping households in 34 villages. NodeXL software was used to describe pig movement networks at village level. The pig movement and trade networks were localized and based on close social networks involving family ties, friendships and relationships with neighbours. Pig movement network modularity ranged from 0.2 to 0.5 and exhibited good community structure within the network implying an easy flow of knowledge and adoption of new attitudes and beliefs, but also promoting an enhanced rate of disease transmission. The average path length of 5 defined using NodeXL, indicated that disease could easily reach every node in a cluster. Cross-border boar service between Uganda and Kenya was also recorded. Unmonitored trade in both directions was prevalent. While most pig transactions in the absence of disease, were at a small scale (<5km) and characterized by regular agistment, most pig sales during ASF outbreaks were to traders or other farmers from outside the sellers' village at a range of >10km. The close social relationships between actors in pig movement networks indicate the potential for possible interventions to develop shared norms and mutually accepted protocols amongst smallholder pig keepers to better manage the risk of ASF introduction and transmission. Copyright © 2016 Elsevier B.V. All rights reserved.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

Structural aspects of calcium iron phosphate glass containing neodymium oxide

NASA Astrophysics Data System (ADS)

Li, Haijian; Liang, Xiaofeng; Wang, Cuiling; Yu, Huijun; Li, Zhen; Yang, Shiyuan

2014-06-01

Homogeneous glasses of the xNd2O3sbnd (100 - x)(12CaOsbnd 20Fe2O3sbnd 68P2O5) system were obtained within the 0 ⩽ x ⩽ 10 mol% composition range. The density and molar volume measurements helped to understand the structural changes occurring in these glasses. Vickers-hardness results showed that addition of Nd2O3 strengthened the crosslinking of the glass network. Spectra analysis indicated that Nd2O3 enters in the structure of the phosphate glasses as a network modifier. The depolymerization of the glass network by the addition of Nd2O3 is characterized by the increase in the concentration of pyrophosphate. The decrease of the Q1 terminal oxygen with increasing Nd2O3 content indicated that Psbnd Osbnd Nd bonds participated in the pyrophosphate glass structure, determined from the Raman spectra.

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

PubMed

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-01-01

This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.

Friends, Depressive Symptoms, and Life Satisfaction Among Older Korean Americans.

PubMed

Roh, Soonhee; Lee, Yeon-Shim; Lee, Kyoung Hag; Shibusawa, Tazuko; Yoo, Grace J

2015-08-01

This study examined the interactive effects of social network support and depressive symptoms on life satisfaction among older Korean Americans (KAs). Using data from a sample of 200 elders in a large metropolitan area (M age = 72.50, SD = 5.15), hierarchical regression analysis was used to examine the interaction between social network support and depressive symptoms on life satisfaction among older KAs. After controlling for demographic variables, both social network support and depressive symptoms were identified as predictors for life satisfaction. Interaction effects indicated strong associations between higher social network support specifically from friends and lower depressive symptoms with higher levels of life satisfaction. Findings highlight the important role that friends play in terms of social network support for the mental health of older KAs, and the need for geriatric practitioners to monitor and assess the quality of social network support-including friendships-when working with older KAs.

Fractal Analysis of Drainage Basins on Mars

NASA Technical Reports Server (NTRS)

Stepinski, T. F.; Marinova, M. M.; McGovern, P. J.; Clifford, S. M.

2002-01-01

We used statistical properties of drainage networks on Mars as a measure of martian landscape morphology and an indicator of landscape evolution processes. We utilize the Mars Orbiter Laser Altimeter (MOLA) data to construct digital elevation maps (DEMs) of several, mostly ancient, martian terrains. Drainage basins and channel networks are computationally extracted from DEMs and their structures are analyzed and compared to drainage networks extracted from terrestrial and lunar DEMs. We show that martian networks are self-affine statistical fractals with planar properties similar to terrestrial networks, but vertical properties similar to lunar networks. The uniformity of martian drainage density is between those for terrestrial and lunar landscapes. Our results are consistent with the roughening of ancient martian terrains by combination of rainfall-fed erosion and impacts, although roughening by other fluvial processes cannot be excluded. The notion of sustained rainfall in recent Mars history is inconsistent with our findings.

The large-scale organization of metabolic networks

NASA Astrophysics Data System (ADS)

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

2000-10-01

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

[Research on brain white matter network in cerebral palsy infant].

PubMed

Li, Jun; Yang, Cheng; Wang, Yuanjun; Nie, Shengdong

2017-10-01

Present study used diffusion tensor image and tractography to construct brain white matter networks of 15 cerebral palsy infants and 30 healthy infants that matched for age and gender. After white matter network analysis, we found that both cerebral palsy and healthy infants had a small-world topology in white matter network, but cerebral palsy infants exhibited abnormal topological organization: increased shortest path length but decreased normalize clustering coefficient, global efficiency and local efficiency. Furthermore, we also found that white matter network hub regions were located in the left cuneus, precuneus, and left posterior cingulate gyrus. However, some abnormal nodes existed in the frontal, temporal, occipital and parietal lobes of cerebral palsy infants. These results indicated that the white matter networks for cerebral palsy infants were disrupted, which was consistent with previous studies about the abnormal brain white matter areas. This work could help us further study the pathogenesis of cerebral palsy infants.

Enhanced Security and Pairing-free Handover Authentication Scheme for Mobile Wireless Networks

NASA Astrophysics Data System (ADS)

Chen, Rui; Shu, Guangqiang; Chen, Peng; Zhang, Lijun

2017-10-01

With the widely deployment of mobile wireless networks, we aim to propose a secure and seamless handover authentication scheme that allows users to roam freely in wireless networks without worrying about security and privacy issues. Given the open characteristic of wireless networks, safety and efficiency should be considered seriously. Several previous protocols are designed based on a bilinear pairing mapping, which is time-consuming and inefficient work, as well as unsuitable for practical situations. To address these issues, we designed a new pairing-free handover authentication scheme for mobile wireless networks. This scheme is an effective improvement of the protocol by Xu et al., which is suffer from the mobile node impersonation attack. Security analysis and simulation experiment indicate that the proposed protocol has many excellent security properties when compared with other recent similar handover schemes, such as mutual authentication and resistance to known network threats, as well as requiring lower computation and communication cost.

From detection monitoring to evaluation monitoring - a case study involving crown dieback in northern white-cedar

Treesearch

KaDonna Randolph; William Bechtold; Randall Morin; Stanley Zarnoch

2009-01-01

The Forest Inventory and Analysis (FIA) Phase 3 plot network is a crucial part of the U.S. Forest Health Monitoring program's detection monitoring system, where select indicators are monitored for signals that may indicate deteriorating forest health. When a negative signal is identified, evaluation monitoring provides a mechanism whereby a potential problem can...

The power of FIA Phase 3 Crown-Indicator variables to detect change

Treesearch

William Bechtold; KaDonna Randolph; Stanley Zarnoch

2009-01-01

The goal of Phase 3 Detection Monitoring as implemented by the Forest Inventory and Analysis Program is to identify forest ecosystems where conditions might be deteriorating in subtle ways over large areas. At the relatively sparse sampling intensity of the Phase 3 plot network, a rough measure of success for the forest health indicators developed for this purpose is...

The analysis of social network data: an exciting frontier for statisticians.

PubMed

O'Malley, A James

2013-02-20

The catalyst for this paper is the recent interest in the relationship between social networks and an individual's health, which has arisen following a series of papers by Nicholas Christakis and James Fowler on person- to-person spread of health behaviors. In this issue, they provide a detailed explanation of their methods that offers insights, justifications, and responses to criticisms. In this paper, we introduce some of the key statistical methods used in social network analysis and indicate where those used by Christakis and Fowler (CF) fit into the general framework. The intent is to provide the background necessary for readers to be able to make their own evaluation of the work by CF and understand the challenges of research involving social networks. We entertain possible solutions to some of the difficulties encountered in accounting for confounding effects in analyses of peer effects and provide comments on the contributions of CF. Copyright © 2012 John Wiley & Sons, Ltd.

Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa

PubMed Central

Gehan, Malia A; Mockler, Todd C; Weinig, Cynthia; Ewers, Brent E

2017-01-01

The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought. PMID:28826479

A multilevel path analysis of contact frequency between social network members

NASA Astrophysics Data System (ADS)

van den Berg, Pauline; Arentze, Theo; Timmermans, Harry

2012-04-01

Recently, there has been an increasing interest in the role of social networks in spatial-choice and travel behavior. It has been acknowledged that social activities and the travel for these activities can emerge from individuals' social networks and that social activities are responsible for an important portion of travel demand. The influence of information and communication technologies (ICT's) is also important in this respect. The purpose of the paper is to examine the effects of characteristics of egos and ego-alter relationships on the frequency of social interaction by different communication modes, using multilevel path analysis. The analyses are based on social network data collected in 2008 in the Eindhoven region in the Netherlands among 116 respondents. The results indicate a complementary relationship between contact frequencies by different modes. The contact frequencies of the different modes, especially face-to-face and telephone, can also be largely explained by the ego's personal characteristics and the type of relationship and the distance between ego and alter.

Complex Network Analysis for Characterizing Global Value Chains in Equipment Manufacturing.

PubMed

Xiao, Hao; Sun, Tianyang; Meng, Bo; Cheng, Lihong

2017-01-01

The rise of global value chains (GVCs) characterized by the so-called "outsourcing", "fragmentation production", and "trade in tasks" has been considered one of the most important phenomena for the 21st century trade. GVCs also can play a decisive role in trade policy making. However, due to the increasing complexity and sophistication of international production networks, especially in the equipment manufacturing industry, conventional trade statistics and the corresponding trade indicators may give us a distorted picture of trade. This paper applies various network analysis tools to the new GVC accounting system proposed by Koopman et al. (2014) and Wang et al. (2013) in which gross exports can be decomposed into value-added terms through various routes along GVCs. This helps to divide the equipment manufacturing-related GVCs into some sub-networks with clear visualization. The empirical results of this paper significantly improve our understanding of the topology of equipment manufacturing-related GVCs as well as the interdependency of countries in these GVCs that is generally invisible from the traditional trade statistics.

Analyzing comprehensive QoS with security constraints for services composition applications in wireless sensor networks.

PubMed

Xiong, Naixue; Wu, Zhao; Huang, Yannong; Xu, Degang

2014-12-01

Services composition is fundamental to software development in multi-service wireless sensor networks (WSNs). The quality of service (QoS) of services composition applications (SCAs) are confronted with severe challenges due to the open, dynamic, and complex natures of WSNs. Most previous research separated various QoS indices into different fields and studied them individually due to the computational complexity. This approach ignores the mutual influence between these QoS indices, and leads to a non-comprehensive and inaccurate analysis result. The universal generating function (UGF) shows the speediness and precision in QoS analysis. However, only one QoS index at a time can be analyzed by the classic UGF. In order to efficiently analyze the comprehensive QoS of SCAs, this paper proposes an improved UGF technique-vector universal generating function (VUGF)-which considers the relationship between multiple QoS indices, including security, and can simultaneously analyze multiple QoS indices. The numerical examples demonstrate that it can be used for the evaluation of the comprehensive QoS of SCAs subjected to the security constraint in WSNs. Therefore, it can be effectively applied to the optimal design of multi-service WSNs.

Analyzing Comprehensive QoS with Security Constraints for Services Composition Applications in Wireless Sensor Networks

PubMed Central

Xiong, Naixue; Wu, Zhao; Huang, Yannong; Xu, Degang

2014-01-01

Services composition is fundamental to software development in multi-service wireless sensor networks (WSNs). The quality of service (QoS) of services composition applications (SCAs) are confronted with severe challenges due to the open, dynamic, and complex natures of WSNs. Most previous research separated various QoS indices into different fields and studied them individually due to the computational complexity. This approach ignores the mutual influence between these QoS indices, and leads to a non-comprehensive and inaccurate analysis result. The universal generating function (UGF) shows the speediness and precision in QoS analysis. However, only one QoS index at a time can be analyzed by the classic UGF. In order to efficiently analyze the comprehensive QoS of SCAs, this paper proposes an improved UGF technique—vector universal generating function (VUGF)—which considers the relationship between multiple QoS indices, including security, and can simultaneously analyze multiple QoS indices. The numerical examples demonstrate that it can be used for the evaluation of the comprehensive QoS of SCAs subjected to the security constraint in WSNs. Therefore, it can be effectively applied to the optimal design of multi-service WSNs. PMID:25470488

U.S. Geological Survey external quality-assurance project report for the National Atmospheric Deposition Program / National Trends Network and Mercury Deposition Network, 2011-2012

USGS Publications Warehouse

Wetherbee, Gregory A.; Martin, RoseAnn

2014-01-01

The U.S. Geological Survey operated six distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program (NADP) / National Trends Network (NTN) and Mercury Deposition Network (MDN) during 2011–2012. The field-audit program assessed the effects of onsite exposure, sample handling, and shipping on the chemistry of NTN samples; a system-blank program assessed the same effects for MDN. Two interlaboratory-comparison programs assessed the bias and variability of the chemical analysis data from the Central Analytical Laboratory and Mercury Analytical Laboratory (HAL). A blind-audit program was implemented for the MDN during 2011 to evaluate analytical bias in HAL total mercury concentration data. The co-located–sampler program was used to identify and quantify potential shifts in NADP data resulting from the replacement of original network instrumentation with new electronic recording rain gages and precipitation collectors that use optical precipitation sensors. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends of chemical constituents in wet deposition across the United States. Co-located rain gage results indicate -3.7 to +6.5 percent bias in NADP precipitation-depth measurements. Co-located collector results suggest that the retrofit of the NADP networks with the new precipitation collectors could cause +10 to +36 percent shifts in NADP annual deposition values for ammonium, nitrate, and sulfate; -7.5 to +41 percent shifts for hydrogen-ion deposition; and larger shifts (-51 to +52 percent) for calcium, magnesium, sodium, potassium, and chloride. The prototype N-CON Systems bucket collector typically catches more precipitation than the NADP-approved Aerochem Metrics Model 301 collector.

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

PubMed Central

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-01-01

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. PMID:27754380

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

PubMed

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors

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

Coles, Garill A.; Brothers, Alan J.; Olson, Jarrod

A Bayesian network (BN) model of social factors can support proliferation assessments by estimating the likelihood that a state will pursue a nuclear weapon. Social factors including political, economic, nuclear capability, security, and national identity and psychology factors may play as important a role in whether a State pursues nuclear weapons as more physical factors. This paper will show how using Bayesian reasoning on a generic case of a would-be proliferator State can be used to combine evidence that supports proliferation assessment. Theories and analysis by political scientists can be leveraged in a quantitative and transparent way to indicate proliferationmore » risk. BN models facilitate diagnosis and inference in a probabilistic environment by using a network of nodes and acyclic directed arcs between the nodes whose connections, or absence of, indicate probabilistic relevance, or independence. We propose a BN model that would use information from both traditional safeguards and the strengthened safeguards associated with the Additional Protocol to indicate countries with a high risk of proliferating nuclear weapons. This model could be used in a variety of applications such a prioritization tool and as a component of state safeguards evaluations. This paper will discuss the benefits of BN reasoning, the development of Pacific Northwest National Laboratory’s (PNNL) BN state proliferation model and how it could be employed as an analytical tool.« less

A network pharmacology approach to determine the synergetic mechanisms of herb couple for treating rheumatic arthritis.

PubMed

Xu, Xi-Xi; Bi, Jian-Ping; Ping, Li; Li, Ping; Li, Fei

2018-01-01

The purpose of this study was to investigate the therapeutic mechanism(s) of Clematis chinensis Osbeck/ Notopterygium incisum K.C. Ting ex H.T (CN). A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used. This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system. The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects.

Understanding Process in Group-Based Intervention Delivery: Social Network Analysis and Intra-entity Variability Methods as Windows into the "Black Box".

PubMed

Molloy Elreda, Lauren; Coatsworth, J Douglas; Gest, Scott D; Ram, Nilam; Bamberger, Katharine

2016-11-01

Although the majority of evidence-based programs are designed for group delivery, group process and its role in participant outcomes have received little empirical attention. Data were collected from 20 groups of participants (94 early adolescents, 120 parents) enrolled in an efficacy trial of a mindfulness-based adaptation of the Strengthening Families Program (MSFP). Following each weekly session, participants reported on their relations to group members. Social network analysis and methods sensitive to intraindividual variability were integrated to examine weekly covariation between group process and participant progress, and to predict post-intervention outcomes from levels and changes in group process. Results demonstrate hypothesized links between network indices of group process and intervention outcomes and highlight the value of this unique analytic approach to studying intervention group process.

Classification and recognition of texture collagen obtaining by multiphoton microscope with neural network analysis

NASA Astrophysics Data System (ADS)

Wu, Shulian; Peng, Yuanyuan; Hu, Liangjun; Zhang, Xiaoman; Li, Hui

2016-01-01

Second harmonic generation microscopy (SHGM) was used to monitor the process of chronological aging skin in vivo. The collagen structures of mice model with different ages were obtained using SHGM. Then, texture feature with contrast, correlation and entropy were extracted and analysed using the grey level co-occurrence matrix. At last, the neural network tool of Matlab was applied to train the texture of collagen in different statues during the aging process. And the simulation of mice collagen texture was carried out. The results indicated that the classification accuracy reach 85%. Results demonstrated that the proposed approach effectively detected the target object in the collagen texture image during the chronological aging process and the analysis tool based on neural network applied the skin of classification and feature extraction method is feasible.

#LancerHealth: Using Twitter and Instagram as a tool in a campus wide health promotion initiative

PubMed Central

Santarossa, Sara; Woodruff, Sarah J.

2018-01-01

The present study aimed to explore using popular technology that people already have/use as a health promotion tool, in a campus wide social media health promotion initiative, entitled #LancerHealth. During a two-week period the university community was asked to share photos on Twitter and Instagram of What does being healthy on campus look like to you?, while tagging the image with #LancerHealth. All publically tagged media was collected using the Netlytic software and analysed. Text analysis (N=234 records, Twitter; N=141 records, Instagram) revealed that the majority of the conversation was positive and focused on health and the university. Social network analysis, based on five network properties, showed a small network with little interaction. Lastly, photo coding analysis (N=71 unique image) indicated that the majority of the shared images were of physical activity (52%) and on campus (80%). Further research into this area is warranted. Significance for public healthAs digital media continues to become a popular tool among both public health organizations and those in academia, it is important to understand how, why, and which platforms individuals are using in regards to their health. This campus wide, social media health promotion initiative found that people will use popular social networking sites like Twitter and Instagram to share their healthy behaviours. Online social networks, created through social networking sites, can play a role in social diffusion of public health information and health behaviours. In this study, however, social network analysis revealed that there needs to be influential and highly connected individuals sharing information to generate social diffusion. This study can help guide future public health research in the area of social media and its potential influence on health promotion. PMID:29780763

Language networks associated with computerized semantic indices.

PubMed

Pakhomov, Serguei V S; Jones, David T; Knopman, David S

2015-01-01

Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. Copyright © 2014 Elsevier Inc. All rights reserved.

Becoming popular: interpersonal emotion regulation predicts relationship formation in real life social networks.

PubMed

Niven, Karen; Garcia, David; van der Löwe, Ilmo; Holman, David; Mansell, Warren

2015-01-01

Building relationships is crucial for satisfaction and success, especially when entering new social contexts. In the present paper, we investigate whether attempting to improve others' feelings helps people to make connections in new networks. In Study 1, a social network study following new networks of people for a 12-week period indicated that use of interpersonal emotion regulation (IER) strategies predicted growth in popularity, as indicated by other network members' reports of spending time with the person, in work and non-work interactions. In Study 2, linguistic analysis of the tweets from over 8000 Twitter users from formation of their accounts revealed that use of IER predicted greater popularity in terms of the number of followers gained. However, not all types of IER had positive effects. Behavioral IER strategies (which use behavior to reassure or comfort in order to regulate affect) were associated with greater popularity, while cognitive strategies (which change a person's thoughts about his or her situation or feelings in order to regulate affect) were negatively associated with popularity. Our findings have implications for our understanding of how new relationships are formed, highlighting the important the role played by intentional emotion regulatory processes.

Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain

PubMed Central

2017-01-01

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization. PMID:28873432

Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain.

PubMed

Xing, Lizhi

2017-01-01

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization.

Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market.

PubMed

Long, Haiming; Zhang, Ji; Tang, Nengyu

2017-01-01

This study considers the effect of an industry's network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry's conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry's systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust.

Becoming popular: interpersonal emotion regulation predicts relationship formation in real life social networks

PubMed Central

Niven, Karen; Garcia, David; van der Löwe, Ilmo; Holman, David; Mansell, Warren

2015-01-01

Building relationships is crucial for satisfaction and success, especially when entering new social contexts. In the present paper, we investigate whether attempting to improve others’ feelings helps people to make connections in new networks. In Study 1, a social network study following new networks of people for a 12-week period indicated that use of interpersonal emotion regulation (IER) strategies predicted growth in popularity, as indicated by other network members’ reports of spending time with the person, in work and non-work interactions. In Study 2, linguistic analysis of the tweets from over 8000 Twitter users from formation of their accounts revealed that use of IER predicted greater popularity in terms of the number of followers gained. However, not all types of IER had positive effects. Behavioral IER strategies (which use behavior to reassure or comfort in order to regulate affect) were associated with greater popularity, while cognitive strategies (which change a person’s thoughts about his or her situation or feelings in order to regulate affect) were negatively associated with popularity. Our findings have implications for our understanding of how new relationships are formed, highlighting the important the role played by intentional emotion regulatory processes. PMID:26483718

Interconnected network motifs control podocyte morphology and kidney function.

PubMed

Azeloglu, Evren U; Hardy, Simon V; Eungdamrong, Narat John; Chen, Yibang; Jayaraman, Gomathi; Chuang, Peter Y; Fang, Wei; Xiong, Huabao; Neves, Susana R; Jain, Mohit R; Li, Hong; Ma'ayan, Avi; Gordon, Ronald E; He, John Cijiang; Iyengar, Ravi

2014-02-04

Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3',5'-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element-binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a β-adrenergic receptor-driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease.

Interconnected Network Motifs Control Podocyte Morphology and Kidney Function

PubMed Central

Azeloglu, Evren U.; Hardy, Simon V.; Eungdamrong, Narat John; Chen, Yibang; Jayaraman, Gomathi; Chuang, Peter Y.; Fang, Wei; Xiong, Huabao; Neves, Susana R.; Jain, Mohit R.; Li, Hong; Ma’ayan, Avi; Gordon, Ronald E.; He, John Cijiang; Iyengar, Ravi

2014-01-01

Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3′,5′-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element–binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a β-adrenergic receptor–driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease. PMID:24497609

Symptoms of posttraumatic stress disorder in a clinical sample of refugees: a network analysis

PubMed Central

Spiller, Tobias R.; Schick, Matthis; Schnyder, Ulrich; Bryant, Richard A.; Nickerson, Angela; Morina, Naser

2017-01-01

ABSTRACT Background: Network analysis is an emerging methodology for investigating psychopathological symptoms. Given the unprecedented number of refugees and the increased prevalence of mental disorders such as posttraumatic stress disorder (PTSD) in this population, new methodologies that help us better to understand psychopathology in refugees are crucial. Objective: The objective of this study was to explore the network structure and centrality indices of DSM-5 PTSD symptoms in a cross-sectional clinical sample of 151 severely traumatized refugees with and without a formal PTSD diagnosis. Method: The R-packages qgraph and bootnet were used to estimate the structure of a PTSD symptom network and its centrality indices. In addition, robustness and significance analyses for the edges weights and the order of centrality were performed. Results: Three pairs of symptoms showed significantly stronger connections than at least half of the other connections: hypervigilance and exaggerated startle response, intrusion and difficulties falling asleep, and irritability or outbursts of anger and self-destructive or reckless behaviour. Emotional cue reactivity had the highest centrality and trauma-related amnesia the lowest. Conclusion: Although only 51.0% of participants fulfilled criteria for a probable PTSD diagnosis, emotional cue reactivity showed the highest centrality, emphasizing the importance of emotional trauma reminders in severely traumatized refugees attending an outpatient clinic. However, due to the small sample size, the results should be interpreted with care. PMID:29038688

Variations in solar Lyman alpha irradiance on short time scales

NASA Astrophysics Data System (ADS)

Pap, J. M.

1992-10-01

Variations in solar UV irradiance at Lyman alpha are studied on short time scales (from days to months) after removing the long-term changes over the solar cycle. The SME/Lyman alpha irradiance is estimated from various solar indices using linear regression analysis. In order to study the nonlinear effects, Lyman alpha irradiance is modeled with a 5th-degree polynomial as well. It is shown that the full-disk equivalent width of the He line at 1083 nm, which is used as a proxy for the plages and active network, can best reproduce the changes observed in Lyman alpha. Approximately 72 percent of the solar-activity-related changes in Lyman alpha irradiance arise from plages and the network. The network contribution is estimated by the correlation analysis to be about 19 percent. It is shown that significant variability remains in Lyman alpha irradiance, with periods around 300, 27, and 13.5d, which is not explained by the solar activity indices. It is shown that the nonlinear effects cannot account for a significant part of the unexplained variation in Lyman alpha irradiance. Therefore, additional events (e.g., large-scale motions and/or a systematic difference in the area and intensity of the plages and network observed in the lines of Ca-K, He 1083, and Lyman alpha) may explain the discrepancies found between the observed and estimated irradiance values.

Variations in solar Lyman alpha irradiance on short time scales

NASA Technical Reports Server (NTRS)

Pap, J. M.

1992-01-01

Variations in solar UV irradiance at Lyman alpha are studied on short time scales (from days to months) after removing the long-term changes over the solar cycle. The SME/Lyman alpha irradiance is estimated from various solar indices using linear regression analysis. In order to study the nonlinear effects, Lyman alpha irradiance is modeled with a 5th-degree polynomial as well. It is shown that the full-disk equivalent width of the He line at 1083 nm, which is used as a proxy for the plages and active network, can best reproduce the changes observed in Lyman alpha. Approximately 72 percent of the solar-activity-related changes in Lyman alpha irradiance arise from plages and the network. The network contribution is estimated by the correlation analysis to be about 19 percent. It is shown that significant variability remains in Lyman alpha irradiance, with periods around 300, 27, and 13.5d, which is not explained by the solar activity indices. It is shown that the nonlinear effects cannot account for a significant part of the unexplained variation in Lyman alpha irradiance. Therefore, additional events (e.g., large-scale motions and/or a systematic difference in the area and intensity of the plages and network observed in the lines of Ca-K, He 1083, and Lyman alpha) may explain the discrepancies found between the observed and estimated irradiance values.

A social network analysis of the goal scoring passing networks of the 2016 European Football Championships.

PubMed

Mclean, Scott; Salmon, Paul M; Gorman, Adam D; Stevens, Nicholas J; Solomon, Colin

2018-02-01

In the current study, social network analysis (SNA) and notational analysis (NA) methods were applied to examine the goal scoring passing networks (GSPN) for all goals scored at the 2016 European Football Championships. The aim of the study was to determine the GSPN characteristics for the overall tournament, between the group and knock out stages, and for the successful and unsuccessful teams. The study also used degree centrality (DC) metrics as a novel method to determine the relative contributions of the pitch locations involved in the GSPN. To determine changes in GSPN characteristics as a function of changing score line, the analysis considered the match status of the game when goals were scored. There were significant differences for SNA metrics as a function of match status, and for the DC metrics in the comparison of the different pitch locations. There were no differences in the SNA metrics for the GSPN between teams in the group and knock out stages, or between the successful and unsuccessful teams. The results indicate that the GSPN had low values for network density, cohesion, connections, and duration. The networks were direct in terms of pitch zones utilised, where 85% of the GSPN included passes that were played within zones or progressed through the zones towards the goal. SNA and NA metrics were significantly different as a function of changing match status. The current study adds to the previous research on goal scoring in football, and demonstrates a novel method to determine the prominent pitch zones involved in the GSPN. These results have implications for match analysis and the coaching process. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-07-01

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P < .05). Using Ingenuity pathway analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis. © 2017 Wiley Periodicals, Inc.

Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton.

PubMed

Zhao, Dayong; Shen, Feng; Zeng, Jin; Huang, Rui; Yu, Zhongbo; Wu, Qinglong L

2016-12-15

Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks. Copyright © 2016 Elsevier B.V. All rights reserved.

Google Correlations: New approaches to collecting data for statistical network analysis

NASA Astrophysics Data System (ADS)

Mahdavi, Paasha

This thesis introduces a new method for data collection on political elite networks using non-obtrusive web-based techniques. One possible indicator of elite connectivity is the frequency with which individuals appear at the same political events. Using a Google search scraping algorithm (Lee 2010) to capture how often pairs of individuals appear in the same news articles reporting on these events, I construct network matrices for a given list of individuals that I identify as elites using a variety of criteria. To assess cross-validity and conceptual accuracy, I compare data from this method to previously collected data on the network connectedness of three separate populations. I then supply an application of the Google method to collect network data on the Nigerian oil elite in 2012. Conducting a network analysis, I show that appointments to the Nigerian National Petroleum Corporation board of directors are made on the basis of political connectivity and not necessarily on technical experience or merit. These findings lend support to hypotheses that leaders use patronage appointments to lucrative bureaucratic positions in order to satisfy political elites. Given that many political theories on elite behavior aim to understand individual- and group-level interactions, the potential applicability of network data using the proposed technique is very large, especially in situations where collecting network data intrusively is costly or prohibitive.

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

PubMed

Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

2011-08-15

Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary data are available at Bioinformatics online. dario.floreano@epfl.ch.

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

Spreading dynamics of an e-commerce preferential information model on scale-free networks

NASA Astrophysics Data System (ADS)

Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding

2017-02-01

In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.

A Journal-Level Analysis of Progress in Transplantation.

PubMed

Feeley, Thomas; Lee, Seyoung; Moon, Shin-Il

2018-03-01

Citations to articles published in academic journals represent a proxy for influence in bibliometrics. To measure the journal impact factor for Progress in Transplantation over time and to also identify related journals indexed in transplantation and surgery. Data from Journal Citation Reports (ISI web of science) were used to rank Progress in Transplantation compared to peer journals using journal impact and journal relatedness measures. Social network analysis was used to measure relationships between pairs of journals in Progress in Transplantation's relatedness network. Journal impact factor and journal relatedness. Data from 2010 through 2015 indicate the average journal article in PIT was cited 0.87 times (standard deviation [SD] = 0.12) and this estimate was stable over time. Progress in Transplantation most often cited American Journal of Transplantation, Transplantation, American Journal of Kidney Diseases, and Liver Transplantation. In terms of cited data, the journal was most often referenced by Clinical Transplantation, Transplant International, and Current Opinion in Organ Transplantation. The journal is listed both in surgery and transplantation categories of Journal Citation Reports and its impact factors over time fare better with surgery journals than with transplant journals. Network data using betweenness centrality indicate Progress in Transplantation links transplantation-focused journals and journals indexed in health sciences categories.

Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder.

PubMed

Xing, Mengqi; Tadayonnejad, Reza; MacNamara, Annmarie; Ajilore, Olusola; DiGangi, Julia; Phan, K Luan; Leow, Alex; Klumpp, Heide

2017-01-01

Functional magnetic resonance imaging (fMRI) resting-state studies show generalized social anxiety disorder (gSAD) is associated with disturbances in networks involved in emotion regulation, emotion processing, and perceptual functions, suggesting a network framework is integral to elucidating the pathophysiology of gSAD. However, fMRI does not measure the fast dynamic interconnections of functional networks. Therefore, we examined whole-brain functional connectomics with electroencephalogram (EEG) during resting-state. Resting-state EEG data was recorded for 32 patients with gSAD and 32 demographically-matched healthy controls (HC). Sensor-level connectivity analysis was applied on EEG data by using Weighted Phase Lag Index (WPLI) and graph analysis based on WPLI was used to determine clustering coefficient and characteristic path length to estimate local integration and global segregation of networks. WPLI results showed increased oscillatory midline coherence in the theta frequency band indicating higher connectivity in the gSAD relative to HC group during rest. Additionally, WPLI values positively correlated with state anxiety levels within the gSAD group but not the HC group. Our graph theory based connectomics analysis demonstrated increased clustering coefficient and decreased characteristic path length in theta-based whole brain functional organization in subjects with gSAD compared to HC. Theta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls). Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network) in gSAD.

An intelligent algorithm for optimizing emergency department job and patient satisfaction.

PubMed

Azadeh, Ali; Yazdanparast, Reza; Abdolhossein Zadeh, Saeed; Keramati, Abbas

2018-06-11

Purpose Resilience engineering, job satisfaction and patient satisfaction were evaluated and analyzed in one Tehran emergency department (ED) to determine ED strengths, weaknesses and opportunities to improve safety, performance, staff and patient satisfaction. The paper aims to discuss these issues. Design/methodology/approach The algorithm included data envelopment analysis (DEA), two artificial neural networks: multilayer perceptron and radial basis function. Data were based on integrated resilience engineering (IRE) and satisfaction indicators. IRE indicators are considered inputs and job and patient satisfaction indicators are considered output variables. Methods were based on mean absolute percentage error analysis. Subsequently, the algorithm was employed for measuring staff and patient satisfaction separately. Each indicator is also identified through sensitivity analysis. Findings The results showed that salary, wage, patient admission and discharge are the crucial factors influencing job and patient satisfaction. The results obtained by the algorithm were validated by comparing them with DEA. Practical implications The approach is a decision-making tool that helps health managers to assess and improve performance and take corrective action. Originality/value This study presents an IRE and intelligent algorithm for analyzing ED job and patient satisfaction - the first study to present an integrated IRE, neural network and mathematical programming approach for optimizing job and patient satisfaction, which simultaneously optimizes job and patient satisfaction, and IRE. The results are validated by DEA through statistical methods.

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine.

PubMed

Adouni, Amel; Chariag, Dhia; Diallo, Demba; Ben Hamed, Mouna; Sbita, Lassaâd

2016-09-01

As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

[Study on Abnormal Topological Properties of Structural Brain Networks of Patients with Depression Comorbid with Anxiety].

PubMed

Wu, Xiuyong; Wu, Xiaoming; Peng, Hongjun; Ning, Yuping; Wu, Kai

2016-06-01

This paper is aimed to analyze the topological properties of structural brain networks in depressive patients with and without anxiety and to explore the neuropath logical mechanisms of depression comorbid with anxiety.Diffusion tensor imaging and deterministic tractography were applied to map the white matter structural networks.We collected 20 depressive patients with anxiety(DPA),18 depressive patients without anxiety(DP),and 28 normal controls(NC)as comparative groups.The global and nodal properties of the structural brain networks in the three groups were analyzed with graph theoretical methods.The result showed that1 the structural brain networks in three groups showed small-world properties and highly connected global hubs predominately from association cortices;2DP group showed lower local efficiency and global efficiency compared to NC group,whereas DPA group showed higher local efficiency and global efficiency compared to NC group;3significant differences of network properties(clustering coefficient,characteristic path lengths,local efficiency,global efficiency)were found between DPA and DP groups;4DP group showed significant changes of nodal efficiency in the brain areas primarily in the temporal lobe and bilateral frontal gyrus,compared to DPA and NC groups.The analysis indicated that the DP and DPA groups showed nodal properties of the structural brain networks,compared to NC group.Moreover,the two diseased groups indicated an opposite trend in the network properties.The results of this study may provide a new imaging index for clinical diagnosis for depression comorbid with anxiety.

Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position, Economic Wealth, Pollution and Mortality

PubMed Central

Prell, Christina; Sun, Laixiang; Feng, Kuishuang; Myroniuk, Tyler W.

2015-01-01

In this paper we investigate how structural patterns of international trade give rise to emissions inequalities across countries, and how such inequality in turn impact countries’ mortality rates. We employ Multi-regional Input-Output analysis to distinguish between sulfur-dioxide (SO2) emissions produced within a country’s boarders (production-based emissions) and emissions triggered by consumption in other countries (consumption-based emissions). We use social network analysis to capture countries’ level of integration within the global trade network. We then apply the Prais-Winsten panel estimation technique to a panel data set across 172 countries over 20 years (1990–2010) to estimate the relationships between countries’ level of integration and SO2 emissions, and the impact of trade integration and SO2 emission on mortality rates. Our findings suggest a positive, (log-) linear relationship between a country’s level of integration and both kinds of emissions. In addition, although more integrated countries are mainly responsible for both forms of emissions, our findings indicate that they also tend to experience lower mortality rates. Our approach offers a unique combination of social network analysis with multiregional input-output analysis, which better operationalizes intuitive concepts about global trade and trade structure. PMID:26642202

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

NASA Astrophysics Data System (ADS)

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.

Comprehensive evaluation of impacts of distributed generation integration in distribution network

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: a diffusion tensor imaging study.

PubMed

Xu, Y; Qiu, S; Wang, J; Liu, Z; Zhang, R; Li, S; Cheng, L; Liu, Z; Wang, W; Huang, R

2014-10-24

Mesial temporal lobe epilepsy (mTLE) is the most common drug-refractory focal epilepsy in adults. Although previous functional and morphological studies have revealed abnormalities in the brain networks of mTLE, the topological organization of the brain white matter (WM) networks in mTLE patients is still ambiguous. In this study, we constructed brain WM networks for 14 left mTLE patients and 22 age- and gender-matched normal controls using diffusion tensor tractography and estimated the alterations of network properties in the mTLE brain networks using graph theoretical analysis. We found that networks for both the mTLE patients and the controls exhibited prominent small-world properties, suggesting a balanced topology of integration and segregation. However, the brain WM networks of mTLE patients showed a significant increased characteristic path length but significant decreased global efficiency, which indicate a disruption in the organization of the brain WM networks in mTLE patients. Moreover, we found significant between-group differences in the nodal properties in several brain regions, such as the left superior temporal gyrus, left hippocampus, the right occipital and right temporal cortices. The robustness analysis showed that the results were likely to be consistent for the networks constructed with different definitions of node and edge weight. Taken together, our findings may suggest an adverse effect of epileptic seizures on the organization of large-scale brain WM networks in mTLE patients. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Examination of China’s performance and thematic evolution in quantum cryptography research using quantitative and computational techniques

PubMed Central

2018-01-01

This study performed two phases of analysis to shed light on the performance and thematic evolution of China’s quantum cryptography (QC) research. First, large-scale research publication metadata derived from QC research published from 2001–2017 was used to examine the research performance of China relative to that of global peers using established quantitative and qualitative measures. Second, this study identified the thematic evolution of China’s QC research using co-word cluster network analysis, a computational science mapping technique. The results from the first phase indicate that over the past 17 years, China’s performance has evolved dramatically, placing it in a leading position. Among the most significant findings is the exponential rate at which all of China’s performance indicators (i.e., Publication Frequency, citation score, H-index) are growing. China’s H-index (a normalized indicator) has surpassed all other countries’ over the last several years. The second phase of analysis shows how China’s main research focus has shifted among several QC themes, including quantum-key-distribution, photon-optical communication, network protocols, and quantum entanglement with an emphasis on applied research. Several themes were observed across time periods (e.g., photons, quantum-key-distribution, secret-messages, quantum-optics, quantum-signatures); some themes disappeared over time (e.g., computer-networks, attack-strategies, bell-state, polarization-state), while others emerged more recently (e.g., quantum-entanglement, decoy-state, unitary-operation). Findings from the first phase of analysis provide empirical evidence that China has emerged as the global driving force in QC. Considering China is the premier driving force in global QC research, findings from the second phase of analysis provide an understanding of China’s QC research themes, which can provide clarity into how QC technologies might take shape. QC and science and technology policy researchers can also use these findings to trace previous research directions and plan future lines of research. PMID:29385151

Examination of China's performance and thematic evolution in quantum cryptography research using quantitative and computational techniques.

PubMed

Olijnyk, Nicholas V

2018-01-01

This study performed two phases of analysis to shed light on the performance and thematic evolution of China's quantum cryptography (QC) research. First, large-scale research publication metadata derived from QC research published from 2001-2017 was used to examine the research performance of China relative to that of global peers using established quantitative and qualitative measures. Second, this study identified the thematic evolution of China's QC research using co-word cluster network analysis, a computational science mapping technique. The results from the first phase indicate that over the past 17 years, China's performance has evolved dramatically, placing it in a leading position. Among the most significant findings is the exponential rate at which all of China's performance indicators (i.e., Publication Frequency, citation score, H-index) are growing. China's H-index (a normalized indicator) has surpassed all other countries' over the last several years. The second phase of analysis shows how China's main research focus has shifted among several QC themes, including quantum-key-distribution, photon-optical communication, network protocols, and quantum entanglement with an emphasis on applied research. Several themes were observed across time periods (e.g., photons, quantum-key-distribution, secret-messages, quantum-optics, quantum-signatures); some themes disappeared over time (e.g., computer-networks, attack-strategies, bell-state, polarization-state), while others emerged more recently (e.g., quantum-entanglement, decoy-state, unitary-operation). Findings from the first phase of analysis provide empirical evidence that China has emerged as the global driving force in QC. Considering China is the premier driving force in global QC research, findings from the second phase of analysis provide an understanding of China's QC research themes, which can provide clarity into how QC technologies might take shape. QC and science and technology policy researchers can also use these findings to trace previous research directions and plan future lines of research.

Visibility graph network analysis of natural gas price: The case of North American market

NASA Astrophysics Data System (ADS)

Sun, Mei; Wang, Yaqi; Gao, Cuixia

2016-11-01

Fluctuations in prices of natural gas significantly affect global economy. Therefore, the research on the characteristics of natural gas price fluctuations, turning points and its influencing cycle on the subsequent price series is of great significance. Global natural gas trade concentrates on three regional markets: the North American market, the European market and the Asia-Pacific market, with North America having the most developed natural gas financial market. In addition, perfect legal supervision and coordinated regulations make the North American market more open and more competitive. This paper focuses on the North American natural gas market specifically. The Henry Hub natural gas spot price time series is converted to a visibility graph network which provides a new direction for macro analysis of time series, and several indicators are investigated: degree and degree distribution, the average shortest path length and community structure. The internal mechanisms underlying price fluctuations are explored through the indicators. The results show that the natural gas prices visibility graph network (NGP-VGN) is of small-world and scale-free properties simultaneously. After random rearrangement of original price time series, the degree distribution of network becomes exponential distribution, different from the original ones. This means that, the original price time series is of long-range negative correlation fractal characteristic. In addition, nodes with large degree correspond to significant geopolitical or economic events. Communities correspond to time cycles in visibility graph network. The cycles of time series and the impact scope of hubs can be found by community structure partition.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

Effective Young's modulus of bacterial and microfibrillated cellulose fibrils in fibrous networks.

PubMed

Tanpichai, Supachok; Quero, Franck; Nogi, Masaya; Yano, Hiroyuki; Young, Robert J; Lindström, Tom; Sampson, William W; Eichhorn, Stephen J

2012-05-14

The deformation micromechanics of bacterial cellulose (BC) and microfibrillated cellulose (MFC) networks have been investigated using Raman spectroscopy. The Raman spectra of both BC and MFC networks exhibit a band initially located at ≈ 1095 cm(-1). We have used the intensity of this band as a function of rotation angle of the specimens to study the cellulose fibril orientation in BC and MFC networks. We have also used the change in this peak's wavenumber position with applied tensile deformation to probe the stress-transfer behavior of these cellulosic materials. The intensity of this Raman band did not change significantly with rotation angle, indicating an in-plane 2D network of fibrils with uniform random orientation; conversely, a highly oriented flax fiber exhibited a marked change in intensity with rotation angle. Experimental data and theoretical analysis shows that the Raman band shift rate arising from deformation of networks under tension is dependent on the angles between the axis of fibrils, the strain axis, the incident laser polarization direction, and the back scattered polarization configurations. From this analysis, the effective moduli of single fibrils of BC and MFC in the networks were estimated to be in the ranges of 79-88 and 29-36 GPa, respectively. It is shown also that for the model to fit the data it is necessary to use a negative Poisson's ratio for MFC networks and BC networks. Discussion of this in-plane "auxetic" behavior is given.

Closer to Guns: the Role of Street Gangs in Facilitating Access to Illegal Firearms.

PubMed

Roberto, Elizabeth; Braga, Anthony A; Papachristos, Andrew V

2018-05-09

Criminal offenders often turn to social networks to gain access to firearms, yet we know little about how networks facilitate access to firearms. This study conducts a network analysis of a co-offending network for the City of Chicago to determine how close any offender may be to a firearm. We use arrest data to recreate the co-offending network of all individuals who were arrested with at least one other person over an eight-year period. We then use data on guns recovered by the police to measure potential network pathways of any individual to known firearms. We test the hypothesis that gangs facilitate access to firearms and the extent to which such access relates to gunshot injury among gang members. Findings reveal that gang membership reduces the potential network distance (how close someone is) to known firearms by 20% or more, and regression results indicate that the closer gang members are to guns, the greater their risk of gunshot victimization.

Financial networks based on Granger causality: A case study

NASA Astrophysics Data System (ADS)

Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-09-01

Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to specify the direction of the interrelationships among the international stock indexes and portray the links of the resulting networks, by the presence of direct couplings between variables exploiting all available information. However, their differences are assessed due to the presence of nonlinearity. The weighted networks formed with respect to the causality measures are transformed to binary ones using a significance test. The financial networks are formed on sliding windows in order to examine the network characteristics and trace changes in the connectivity structure. Subsequently, two statistical network quantities are calculated; the average degree and the average shortest path length. The empirical findings reveal interesting time-varying properties of the constructed network, which are clearly dependent on the nature of the financial cycle.

Evolutionary dynamics on networks of selectively neutral genotypes: effects of topology and sequence stability.

PubMed

Aguirre, Jacobo; Buldú, Javier M; Manrubia, Susanna C

2009-12-01

Networks of selectively neutral genotypes underlie the evolution of populations of replicators in constant environments. Previous theoretical analysis predicted that such populations will evolve toward highly connected regions of the genome space. We first study the evolution of populations of replicators on simple networks and quantify how the transient time to equilibrium depends on the initial distribution of sequences on the neutral network, on the topological properties of the latter, and on the mutation rate. Second, network neutrality is broken through the introduction of an energy for each sequence. This allows to study the competition between two features (neutrality and energetic stability) relevant for survival and subjected to different selective pressures. In cases where the two features are negatively correlated, the population experiences sudden migrations in the genome space for values of the relevant parameters that we calculate. The numerical study of larger networks indicates that the qualitative behavior to be expected in more realistic cases is already seen in representative examples of small networks.

Social networks and environmental outcomes.

PubMed

Barnes, Michele L; Lynham, John; Kalberg, Kolter; Leung, PingSun

2016-06-07

Social networks can profoundly affect human behavior, which is the primary force driving environmental change. However, empirical evidence linking microlevel social interactions to large-scale environmental outcomes has remained scarce. Here, we leverage comprehensive data on information-sharing networks among large-scale commercial tuna fishers to examine how social networks relate to shark bycatch, a global environmental issue. We demonstrate that the tendency for fishers to primarily share information within their ethnic group creates segregated networks that are strongly correlated with shark bycatch. However, some fishers share information across ethnic lines, and examinations of their bycatch rates show that network contacts are more strongly related to fishing behaviors than ethnicity. Our findings indicate that social networks are tied to actions that can directly impact marine ecosystems, and that biases toward within-group ties may impede the diffusion of sustainable behaviors. Importantly, our analysis suggests that enhanced communication channels across segregated fisher groups could have prevented the incidental catch of over 46,000 sharks between 2008 and 2012 in a single commercial fishery.

Evolutionary dynamics on networks of selectively neutral genotypes: Effects of topology and sequence stability

NASA Astrophysics Data System (ADS)

Aguirre, Jacobo; Buldú, Javier M.; Manrubia, Susanna C.

2009-12-01

Networks of selectively neutral genotypes underlie the evolution of populations of replicators in constant environments. Previous theoretical analysis predicted that such populations will evolve toward highly connected regions of the genome space. We first study the evolution of populations of replicators on simple networks and quantify how the transient time to equilibrium depends on the initial distribution of sequences on the neutral network, on the topological properties of the latter, and on the mutation rate. Second, network neutrality is broken through the introduction of an energy for each sequence. This allows to study the competition between two features (neutrality and energetic stability) relevant for survival and subjected to different selective pressures. In cases where the two features are negatively correlated, the population experiences sudden migrations in the genome space for values of the relevant parameters that we calculate. The numerical study of larger networks indicates that the qualitative behavior to be expected in more realistic cases is already seen in representative examples of small networks.

Differential Impact of Types of Social Support in the Mental Health of Formerly Incarcerated Latino Men

PubMed Central

Muñoz-Laboy, Miguel; Severson, Nicolette; Perry, Ashley; Guilamo-Ramos, Vincent

2015-01-01

The role of social support in the mental health of formerly incarcerated Latino men (FILM) is an issue overlooked in public health prevention efforts. The objectives of this analysis were to (a) describe the levels of social support perceived and received by FILM; (b) identify the associations, if any, between levels of social support and mental health indicators such as depression and anxiety; and (c) explore the impact of familism and hypermasculinity on the receptivity of social support and the potential role of these factors in mediating associations between social support and mental health indicators. To accomplish the objectives, we conducted a cross-sectional survey with FILM (n = 259), ages 18 to 59, in New York City, and one nominated member of their social network (n = 130 dyads). In this analysis, we examined four dimensions of social support (instrumental, informational, appraisal, and emotional) from two perspectives: provided (as reported by members of the social networks) and perceived (as reported by FILM). The major outcome variables for this analysis were the presence/absence of major anxiety and depressive symptoms. Our logistic regression analyses suggest that perceived emotional support was inversely associated with both anxiety and depression. Our findings suggest that familism mediated the association between perceived emotional support and anxiety/depression. Therefore, we must consider designing network enhancement interventions that focus on both FILM and their social support systems. PMID:24323767

Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis.

PubMed

Banks, Victoria A; Stanton, Neville A

2015-01-01

Automated assistance in driving emergencies aims to improve the safety of our roads by avoiding or mitigating the effects of accidents. However, the behavioural implications of such systems remain unknown. This paper introduces the driver decision-making in emergencies (DDMiEs) framework to investigate how the level and type of automation may affect driver decision-making and subsequent responses to critical braking events using network analysis to interrogate retrospective verbalisations. Four DDMiE models were constructed to represent different levels of automation within the driving task and its effects on driver decision-making. Findings suggest that whilst automation does not alter the decision-making pathway (e.g. the processes between hazard detection and response remain similar), it does appear to significantly weaken the links between information-processing nodes. This reflects an unintended yet emergent property within the task network that could mean that we may not be improving safety in the way we expect. This paper contrasts models of driver decision-making in emergencies at varying levels of automation using the Southampton University Driving Simulator. Network analysis of retrospective verbalisations indicates that increasing the level of automation in driving emergencies weakens the link between information-processing nodes essential for effective decision-making.

Multilayer network decoding versatility and trust

NASA Astrophysics Data System (ADS)

Sarkar, Camellia; Yadav, Alok; Jalan, Sarika

2016-01-01

In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real-world systems. We analyze massive time-varying social data drawn from the largest film industry of the world under a multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher-order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. The analysis of weak ties of the industry uncovers nodes from a lower-degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real-world systems.

An analysis of respondent driven sampling with Injection Drug Users (IDU) in Albania and the Russian Federation.

PubMed

Stormer, Ame; Tun, Waimar; Guli, Lisa; Harxhi, Arjan; Bodanovskaia, Zinaida; Yakovleva, Anna; Rusakova, Maia; Levina, Olga; Bani, Roland; Rjepaj, Klodian; Bino, Silva

2006-11-01

Injection drug users in Tirana, Albania and St. Petersburg, Russia were recruited into a study assessing HIV-related behaviors and HIV serostatus using Respondent Driven Sampling (RDS), a peer-driven recruitment sampling strategy that results in a probability sample. (Salganik M, Heckathorn DD. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Method. 2004;34:193-239). This paper presents a comparison of RDS implementation, findings on network and recruitment characteristics, and lessons learned. Initiated with 13 to 15 seeds, approximately 200 IDUs were recruited within 8 weeks. Information resulting from RDS indicates that social network patterns from the two studies differ greatly. Female IDUs in Tirana had smaller network sizes than male IDUs, unlike in St. Petersburg where female IDUs had larger network sizes than male IDUs. Recruitment patterns in each country also differed by demographic categories. Recruitment analyses indicate that IDUs form socially distinct groups by sex in Tirana, whereas there was a greater degree of gender mixing patterns in St. Petersburg. RDS proved to be an effective means of surveying these hard-to-reach populations.

Analysis of inter-country input-output table based on bibliographic coupling network: How industrial sectors on the GVC compete for production resources

NASA Astrophysics Data System (ADS)

Guan, Jun; Xu, Xiaoyu; Xing, Lizhi

2018-03-01

The input-output table is comprehensive and detailed in describing national economic systems with abundance of economic relationships depicting information of supply and demand among industrial sectors. This paper focuses on how to quantify the degree of competition on the global value chain (GVC) from the perspective of econophysics. Global Industrial Strongest Relevant Network models are established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output (ICIO) tables and then have them transformed into Global Industrial Resource Competition Network models to analyze the competitive relationships based on bibliographic coupling approach. Three indicators well suited for the weighted and undirected networks with self-loops are introduced here, including unit weight for competitive power, disparity in the weight for competitive amplitude and weighted clustering coefficient for competitive intensity. Finally, these models and indicators were further applied empirically to analyze the function of industrial sectors on the basis of the latest World Input-Output Database (WIOD) in order to reveal inter-sector competitive status during the economic globalization.

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine

PubMed Central

Lee, Ji-Hyun; Park, Kyoung Mii; Han, Dong-Jin; Bang, Nam Young; Kim, Do-Hee; Na, Hyeongjin; Lim, Semi; Kim, Tae Bum; Kim, Dae Gyu; Kim, Hyun-Jung; Chung, Yeonseok; Sung, Sang Hyun; Surh, Young-Joon; Kim, Sunghoon; Han, Byung Woo

2015-01-01

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org. PMID:26555441

A lithology identification method for continental shale oil reservoir based on BP neural network

NASA Astrophysics Data System (ADS)

Han, Luo; Fuqiang, Lai; Zheng, Dong; Weixu, Xia

2018-06-01

The Dongying Depression and Jiyang Depression of the Bohai Bay Basin consist of continental sedimentary facies with a variable sedimentary environment and the shale layer system has a variety of lithologies and strong heterogeneity. It is difficult to accurately identify the lithologies with traditional lithology identification methods. The back propagation (BP) neural network was used to predict the lithology of continental shale oil reservoirs. Based on the rock slice identification, x-ray diffraction bulk rock mineral analysis, scanning electron microscope analysis, and the data of well logging and logging, the lithology was divided with carbonate, clay and felsic as end-member minerals. According to the core-electrical relationship, the frequency histogram was then used to calculate the logging response range of each lithology. The lithology-sensitive curves selected from 23 logging curves (GR, AC, CNL, DEN, etc) were chosen as the input variables. Finally, the BP neural network training model was established to predict the lithology. The lithology in the study area can be divided into four types: mudstone, lime mudstone, lime oil-mudstone, and lime argillaceous oil-shale. The logging responses of lithology were complicated and characterized by the low values of four indicators and medium values of two indicators. By comparing the number of hidden nodes and the number of training times, we found that the number of 15 hidden nodes and 1000 times of training yielded the best training results. The optimal neural network training model was established based on the above results. The lithology prediction results of BP neural network of well XX-1 showed that the accuracy rate was over 80%, indicating that the method was suitable for lithology identification of continental shale stratigraphy. The study provided the basis for the reservoir quality and oily evaluation of continental shale reservoirs and was of great significance to shale oil and gas exploration.

Networks of Professional Supervision

ERIC Educational Resources Information Center

Annan, Jean; Ryba, Ken

2013-01-01

An ecological analysis of the supervisory activity of 31 New Zealand school psychologists examined simultaneously the theories of school psychology, supervision practices, and the contextual qualities that mediated participants' supervisory actions. The findings indicated that the school psychologists worked to achieve the supervision goals of…

Roles of Thermophiles and Fungi in Bitumen Degradation in Mostly Cold Oil Sands Outcrops

PubMed Central

Wong, Man-Ling; An, Dongshan; Caffrey, Sean M.; Soh, Jung; Dong, Xiaoli; Sensen, Christoph W.; Oldenburg, Thomas B. P.; Larter, Steve R.

2015-01-01

Oil sands are surface exposed in river valley outcrops in northeastern Alberta, where flat slabs (tablets) of weathered, bitumen-saturated sandstone can be retrieved from outcrop cliffs or from riverbeds. Although the average yearly surface temperature of this region is low (0.7°C), we found that the temperatures of the exposed surfaces of outcrop cliffs reached 55 to 60°C on sunny summer days, with daily maxima being 27 to 31°C. Analysis of the cooccurrence of taxa derived from pyrosequencing of 16S/18S rRNA genes indicated that an aerobic microbial network of fungi and hydrocarbon-, methane-, or acetate-oxidizing heterotrophic bacteria was present in all cliff tablets. Metagenomic analyses indicated an elevated presence of fungal cytochrome P450 monooxygenases in these samples. This network was distinct from the heterotrophic community found in riverbeds, which included fewer fungi. A subset of cliff tablets had a network of anaerobic and/or thermophilic taxa, including methanogens, Firmicutes, and Thermotogae, in the center. Long-term aerobic incubation of outcrop samples at 55°C gave a thermophilic microbial community. Analysis of residual bitumen with a Fourier transform ion cyclotron resonance mass spectrometer indicated that aerobic degradation proceeded at 55°C but not at 4°C. Little anaerobic degradation was observed. These results indicate that bitumen degradation on outcrop surfaces is a largely aerobic process with a minor anaerobic contribution and is catalyzed by a consortium of bacteria and fungi. Bitumen degradation is stimulated by periodic high temperatures on outcrop cliffs, which cause significant decreases in bitumen viscosity. PMID:26209669

Co-occurrence correlations of heavy metals in sediments revealed using network analysis.

PubMed

Liu, Lili; Wang, Zhiping; Ju, Feng; Zhang, Tong

2015-01-01

In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Reveal genes functionally associated with ACADS by a network study.

PubMed

Chen, Yulong; Su, Zhiguang

2015-09-15

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

Effect of households' social networks on lice infestation among vulnerable Mexican children: a qualitative comparative analysis.

PubMed

Ortega-Marín, Lydia; Márquez-Serrano, Margarita; Lara-López, Luz M; Moncada, Ligia I; Idrovo, Alvaro J

2013-10-01

The prevalence of pediculosis is high among elementary and secondary school children, which favors the belief that infestation occurs more often in schools than in homes. This study explored the role of households' social networks in the transmission of head lice. Seventeen school children and their social networks (n = 22) from Acatlipa (Morelos, Mexico) participated in a prospective observational study during school vacation. The hair of all the school children was washed with shampoo containing permethrin at the beginning of the study and the incidence of pediculosis (O) was evaluated at the beginning of the school term (follow-up at 1.5 months). The sets included in the qualitative comparative analysis were sex (S), length of hair (H), baseline diagnostic of pediculosis (I) and degree (D) and infestation index (N) obtained through the analysis of social networks. The prevalence of pediculosis was the same at the beginning and the end of follow-up (17.6%). The degree of the school children's networks ranged between 2 and 14. There were 8 configurations, the most frequent being F*i*d*n*h. The most parsimonious configuration associated with the incidence of pediculosis was F*I*d*H (female, previous infestation, low degree and long hair), with a coverage of 0.344 and a consistency of 0.941. Indicators of social networks made it possible to identify the role of households' social networks in the transmission of lice. Individual actions such as the use of shampoo containing insecticides are temporary and, therefore, structural actions should be favored.

Alveolar ridge preservation after tooth extraction: a Bayesian Network meta-analysis of grafting materials efficacy on prevention of bone height and width reduction.

PubMed

Iocca, Oreste; Farcomeni, Alessio; Pardiñas Lopez, Simon; Talib, Huzefa S

2017-01-01

To conduct a traditional meta-analysis and a Bayesian Network meta-analysis to synthesize the information coming from randomized controlled trials on different socket grafting materials and combine the resulting indirect evidence in order to make inferences on treatments that have not been compared directly. RCTs were identified for inclusion in the systematic review and subsequent statistical analysis. Bone height and width remodelling were selected as the chosen summary measures for comparison. First, a series of pairwise meta-analyses were performed and overall mean difference (MD) in mm with 95% CI was calculated between grafted versus non-grafted sockets. Then, a Bayesian Network meta-analysis was performed to draw indirect conclusions on which grafting materials can be considered most likely the best compared to the others. From the six included studies, seven comparisons were obtained. Traditional meta-analysis showed statistically significant results in favour of grafting the socket compared to no-graft both for height (MD 1.02, 95% CI 0.44-1.59, p value < 0.001) than for width (MD 1.52 95% CI 1.18-1.86, p value <0.000001) remodelling. Bayesian Network meta-analysis allowed to obtain a rank of intervention efficacy. On the basis of the results of the present analysis, socket grafting seems to be more favourable than unassisted socket healing. Moreover, Bayesian Network meta-analysis indicates that freeze-dried bone graft plus membrane is the most likely effective in the reduction of bone height remodelling. Autologous bone marrow resulted the most likely effective when width remodelling was considered. Studies with larger samples and less risk of bias should be conducted in the future in order to further strengthen the results of this analysis. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Functional disorganization of small-world brain networks in mild Alzheimer's Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE).

PubMed

Frantzidis, Christos A; Vivas, Ana B; Tsolaki, Anthoula; Klados, Manousos A; Tsolaki, Magda; Bamidis, Panagiotis D

2014-01-01

Previous neuroscientific findings have linked Alzheimer's Disease (AD) with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI) remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD). Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG) data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT), and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N = 500, 600, 700, 800 edges) across all participants and groups (fixed density values). All groups exhibited a small-world (SW) brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant's generic cognitive status. The deterioration of the network's organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation.

The role of interpersonal communication in the process of knowledge mobilization within a community-based organization: a network analysis.

PubMed

Gainforth, Heather L; Latimer-Cheung, Amy E; Athanasopoulos, Peter; Moore, Spencer; Ginis, Kathleen A Martin

2014-05-22

Diffusion of innovations theory has been widely used to explain knowledge mobilization of research findings. This theory posits that individuals who are more interpersonally connected within an organization may be more likely to adopt an innovation (e.g., research evidence) than individuals who are less interconnected. Research examining this tenet of diffusion of innovations theory in the knowledge mobilization literature is limited. The purpose of the present study was to use network analysis to examine the role of interpersonal communication in the adoption and mobilization of the physical activity guidelines for people with spinal cord injury (SCI) among staff in a community-based organization (CBO). The study used a cross-sectional, whole-network design. In total, 56 staff completed the network survey. Adoption of the guidelines was assessed using Rogers' innovation-decision process and interpersonal communication was assessed using an online network instrument. The patterns of densities observed within the network were indicative of a core-periphery structure revealing that interpersonal communication was greater within the core than between the core and periphery and within the periphery. Membership in the core, as opposed to membership in the periphery, was associated with greater knowledge of the evidence-based physical activity resources available and engagement in physical activity promotion behaviours (ps < 0.05). Greater in-degree centrality was associated with adoption of evidence-based behaviours (p < 0.05). Findings suggest that interpersonal communication is associated with knowledge mobilization and highlight how the network structure could be improved for further dissemination efforts. diffusion of innovations; network analysis; community-based organization; knowledge mobilization; knowledge translation, interpersonal communication.

A simplified method of performance indicators development for epidemiological surveillance networks--application to the RESAPATH surveillance network.

PubMed

Sorbe, A; Chazel, M; Gay, E; Haenni, M; Madec, J-Y; Hendrikx, P

2011-06-01

Develop and calculate performance indicators allows to continuously follow the operation of an epidemiological surveillance network. This is an internal evaluation method, implemented by the coordinators in collaboration with all the actors of the network. Its purpose is to detect weak points in order to optimize management. A method for the development of performance indicators of epidemiological surveillance networks was developed in 2004 and was applied to several networks. Its implementation requires a thorough description of the network environment and all its activities to define priority indicators. Since this method is considered to be complex, our objective consisted in developing a simplified approach and applying it to an epidemiological surveillance network. We applied the initial method to a theoretical network model to obtain a list of generic indicators that can be adapted to any surveillance network. We obtained a list of 25 generic performance indicators, intended to be reformulated and described according to the specificities of each network. It was used to develop performance indicators for RESAPATH, an epidemiological surveillance network of antimicrobial resistance in pathogenic bacteria of animal origin in France. This application allowed us to validate the simplified method, its value in terms of practical implementation, and its level of user acceptance. Its ease of use and speed of application compared to the initial method argue in favor of its use on broader scale. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Dynamic robustness of knowledge collaboration network of open source product development community

NASA Astrophysics Data System (ADS)

Zhou, Hong-Li; Zhang, Xiao-Dong

2018-01-01

As an emergent innovative design style, open source product development communities are characterized by a self-organizing, mass collaborative, networked structure. The robustness of the community is critical to its performance. Using the complex network modeling method, the knowledge collaboration network of the community is formulated, and the robustness of the network is systematically and dynamically studied. The characteristics of the network along the development period determine that its robustness should be studied from three time stages: the start-up, development and mature stages of the network. Five kinds of user-loss pattern are designed, to assess the network's robustness under different situations in each of these three time stages. Two indexes - the largest connected component and the network efficiency - are used to evaluate the robustness of the community. The proposed approach is applied in an existing open source car design community. The results indicate that the knowledge collaboration networks show different levels of robustness in different stages and different user loss patterns. Such analysis can be applied to provide protection strategies for the key users involved in knowledge dissemination and knowledge contribution at different stages of the network, thereby promoting the sustainable and stable development of the open source community.

Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

PubMed

He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

2012-06-25

Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Sociometric Indicators of Leadership: An Exploratory Analysis

DTIC Science & Technology

2018-01-01

streamline existing observational protocols and assessment methods . This research provides an initial test of sociometric badges in the context of the U.S...understand, the requirements of the mission. Traditional research and assessment methods focusing on leader and follower interactions require direct...based methods of social network analysis. Novel Measures of Leadership Building on these findings and earlier research , it is apparent that

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

Vulnerability analysis methods for road networks

NASA Astrophysics Data System (ADS)

Bíl, Michal; Vodák, Rostislav; Kubeček, Jan; Rebok, Tomáš; Svoboda, Tomáš

2014-05-01

Road networks rank among the most important lifelines of modern society. They can be damaged by either random or intentional events. Roads are also often affected by natural hazards, the impacts of which are both direct and indirect. Whereas direct impacts (e.g. roads damaged by a landslide or due to flooding) are localized in close proximity to the natural hazard occurrence, the indirect impacts can entail widespread service disabilities and considerable travel delays. The change in flows in the network may affect the population living far from the places originally impacted by the natural disaster. These effects are primarily possible due to the intrinsic nature of this system. The consequences and extent of the indirect costs also depend on the set of road links which were damaged, because the road links differ in terms of their importance. The more robust (interconnected) the road network is, the less time is usually needed to secure the serviceability of an area hit by a disaster. These kinds of networks also demonstrate a higher degree of resilience. Evaluating road network structures is therefore essential in any type of vulnerability and resilience analysis. There are a range of approaches used for evaluation of the vulnerability of a network and for identification of the weakest road links. Only few of them are, however, capable of simulating the impacts of the simultaneous closure of numerous links, which often occurs during a disaster. The primary problem is that in the case of a disaster, which usually has a large regional extent, the road network may remain disconnected. The majority of the commonly used indices use direct computation of the shortest paths or time between OD (origin - destination) pairs and therefore cannot be applied when the network breaks up into two or more components. Since extensive break-ups often occur in cases of major disasters, it is important to study the network vulnerability in these cases as well, so that appropriate steps can be taken in order to make it more resilient. Performing such an analysis of network break-ups requires consideration of the network as a whole, ideally identifying all the cases generated by simultaneous closure of multiple links and evaluating them using various criteria. The spatial distribution of settlements, important companies and the overall population in the nodes of the network are several factors, apart from the topology of the network which could be taken into account when computing vulnerability indices and identifying the weakest links and/or weakest link combinations. However, even for small networks (i.e., hundreds of nodes and links), the problem of break-up identification becomes extremely difficult to resolve. The naive approaches of the brute force examination consequently fail and more elaborated algorithms have to be applied. We address the problem of evaluating the vulnerability of road networks in our work by simulating the impacts of the simultaneous closure of multiple roads/links. We present an ongoing work on a sophisticated algorithm focused on the identification of network break-ups and evaluating them by various criteria.

Agricultural Science in the Wild: A Social Network Analysis of Farmer Knowledge Exchange

PubMed Central

Wood, Brennon A.; Blair, Hugh T.; Gray, David I.; Kemp, Peter D.; Kenyon, Paul R.; Morris, Steve T.; Sewell, Alison M.

2014-01-01

Responding to demands for transformed farming practices requires new forms of knowledge. Given their scale and complexity, agricultural problems can no longer be solved by linear transfers in which technology developed by specialists passes to farmers by way of extension intermediaries. Recent research on alternative approaches has focused on the innovation systems formed by interactions between heterogeneous actors. Rather than linear transfer, systems theory highlights network facilitation as a specialized function. This paper contributes to our understanding of such facilitation by investigating the networks in which farmers discuss science. We report findings based on the study of a pastoral farming experiment collaboratively undertaken by a group of 17 farmers and five scientists. Analysis of prior contact and alter sharing between the group’s members indicates strongly tied and decentralized networks. Farmer knowledge exchanges about the experiment have been investigated using a mix of quantitative and qualitative methods. Network surveys identified who the farmers contacted for knowledge before the study began and who they had talked to about the experiment by 18 months later. Open-ended interviews collected farmer statements about their most valuable contacts and these statements have been thematically analysed. The network analysis shows that farmers talked about the experiment with 192 people, most of whom were fellow farmers. Farmers with densely tied and occupationally homogeneous contacts grew their networks more than did farmers with contacts that are loosely tied and diverse. Thematic analysis reveals three general principles: farmers value knowledge delivered by persons rather than roles, privilege farming experience, and develop knowledge with empiricist rather than rationalist techniques. Taken together, these findings suggest that farmers deliberate about science in intensive and durable networks that have significant implications for theorizing agricultural innovation. The paper thus concludes by considering the findings’ significance for current efforts to rethink agricultural extension. PMID:25121487

Structural study in ceramic multiferroic Co{sub 3}TeO{sub 6} and analysis of possible Co-Co networks

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

Singh, Harishchandra; Sinha, A. K., E-mail: anil@rrcat.gov.in; Ghosh, Haranath

2015-06-24

We show that there exist four networks (Co1-Co4, Co2-Co3-Co5, Co1-Co5 and Co2-Co3-Co4) in contrast to earlier observations of two networks (Co1-Co4 and Co2-Co3-Co5) in Co{sub 3}TeO{sub 6} (CTO) multiferroic [Phys. Rev. B 88, 184427 (2013)]. Due to five crystallographically different sites of Co ions coordinated by [IV], [V] and [VI] oxygen atoms, the coordination polyhedra exhibit strong distortions from their respective ideal polyhedra, and thus potentially allow to resolve low-symmetry crystal field splittings of d-d electronic transitions. Our structural analysis using Rietveld refinements on the room temperature Synchrotron X-ray Diffraction data indicates possible magnetic order, and may provide a basismore » for the complex and multiple magnetic transitions of CTO at low temperature.« less

Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

NASA Astrophysics Data System (ADS)

Wu, Xiao-Yu; Gu, Ren-Tao; Ji, Yue-Feng

2016-11-01

QoS (quality of service) fluctuations caused by Internet bursty flows influence the user experience in the Internet, such as the increment of packet loss and transmission time. In this paper, we establish a mathematical model to study the influence propagation behavior of the bursty flow, which is helpful for developing a deep understanding of the network dynamics in the Internet complex system. To intuitively reflect the propagation process, a data flow interaction network with a hierarchical structure is constructed, where the neighbor order is proposed to indicate the neighborhood relationship between the bursty flow and other flows. The influence spreads from the bursty flow to each order of neighbors through flow interactions. As the influence spreads, the bursty flow has negative effects on the odd order neighbors and positive effects on the even order neighbors. The influence intensity of bursty flow decreases sharply between two adjacent orders and the decreasing degree can reach up to dozens of times in the experimental simulation. Moreover, the influence intensity increases significantly when network congestion situation becomes serious, especially for the 1st order neighbors. Network structural factors are considered to make a further study. Simulation results show that the physical network scale expansion can reduce the influence intensity of bursty flow by decreasing the flow distribution density. Furthermore, with the same network scale, the influence intensity in WS small-world networks is 38.18% and 18.40% lower than that in ER random networks and BA scale-free networks, respectively, due to a lower interaction probability between flows. These results indicate that the macro-structural changes such as network scales and styles will affect the inner propagation behaviors of the bursty flow.

Self-Organized Link State Aware Routing for Multiple Mobile Agents in Wireless Network

NASA Astrophysics Data System (ADS)

Oda, Akihiro; Nishi, Hiroaki

Recently, the importance of data sharing structures in autonomous distributed networks has been increasing. A wireless sensor network is used for managing distributed data. This type of distributed network requires effective information exchanging methods for data sharing. To reduce the traffic of broadcasted messages, reduction of the amount of redundant information is indispensable. In order to reduce packet loss in mobile ad-hoc networks, QoS-sensitive routing algorithm have been frequently discussed. The topology of a wireless network is likely to change frequently according to the movement of mobile nodes, radio disturbance, or fading due to the continuous changes in the environment. Therefore, a packet routing algorithm should guarantee QoS by using some quality indicators of the wireless network. In this paper, a novel information exchanging algorithm developed using a hash function and a Boolean operation is proposed. This algorithm achieves efficient information exchanges by reducing the overhead of broadcasting messages, and it can guarantee QoS in a wireless network environment. It can be applied to a routing algorithm in a mobile ad-hoc network. In the proposed routing algorithm, a routing table is constructed by using the received signal strength indicator (RSSI), and the neighborhood information is periodically broadcasted depending on this table. The proposed hash-based routing entry management by using an extended MAC address can eliminate the overhead of message flooding. An analysis of the collision of hash values contributes to the determination of the length of the hash values, which is minimally required. Based on the verification of a mathematical theory, an optimum hash function for determining the length of hash values can be given. Simulations are carried out to evaluate the effectiveness of the proposed algorithm and to validate the theory in a general wireless network routing algorithm.

Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis.

PubMed

Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula

2017-01-01

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

The human factor: re-organisations in public health policy.

PubMed

Oliver, Kathryn; Everett, Martin; Verma, Arpana; de Vocht, Frank

2012-06-01

Public health policy-making activities are currently split between local authority and NHS organisations. Despite an increasing body of research on evidence-based policy (EBP), few studies explore the process of policy-making. Little is known about how policies are made in a local context, or how (scientific) evidence is used. Previous research has ignored the 'human element' in EBP. Social network analysis (SNA) techniques are becoming increasingly important in health policy. This paper describes an innovative study giving a fresh perspective on policy-making processes in public health. A social network analysis of public health policy making networks in Greater Manchester based on publicly available data (documents, websites and meeting papers) and an electronic survey, asking actors to nominate those who influenced their own views, those who were powerful, and those who were a source of evidence or information. Policy-making networks are described. Formal executive roles are loosely related to perceived influence and power. Evidence-seeking networks are less coherent, with key organisations not represented. These data indicate the importance of collaboration and good relationships between researchers and policy-makers, but few academic researchers with a direct impact on health policy were identified within the networks. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

Drivers and Pattern of Social Vulnerability to Flood in Metropolitan Lagos, Nigeria

NASA Astrophysics Data System (ADS)

Fasona, M.

2016-12-01

Lagos is Africa's second largest city and a city-state in southwest Nigeria. Population and economic activities in the city are concentrated in the greater Lagos metropolitan area - a group of barrier islands less than a thousand square kilometer. Several physical factors and critical human-environmental conditions contribute to high flood vulnerability across the city. Flood impact is highly denominated and the poor tend to suffer more due to higher risk of exposure and poor adaptive capacity. In this study we present the pattern of social vulnerability to flooding across the Lagos metropolis and argued that the pattern substantially reflects the pattern and severity of flooding impact on people across the metropolis. Twenty nine social indicators and experiences including poverty profile, housing conditions, education, population and demography, social network, and communication, among others, were considered. The data were collated through field survey and subjected to principal component analysis. The results were processed into raster surfaces using GIS for social vulnerability characterization at neighborhood levels. The results suggest the social status indicators, neighborhood standing and social networks indictors, the indicators of emergency responses and security, and the neighborhood conditions, in that order, are the most important determinants of social vulnerability. Six of the 16 LGAs in metropolitan Lagos have high social vulnerability. Neighborhoods that combine poor social status indicators and poor neighborhood standing and social networks are found to have high social vulnerability whereas other poor neighborhoods with strong social networks performed better. We conclude that improved human living condition and social network and communication in poor urban neighborhoods are important to reducing social vulnerability to flooding in the metropolis.

Comparison of the Efficacy of Different Drugs on Non-Motor Symptoms of Parkinson's Disease: a Network Meta-Analysis.

PubMed

Li, Bao-Dong; Cui, Jing-Jun; Song, Jia; Qi, Ce; Ma, Pei-Feng; Wang, Ya-Rong; Bai, Jing

2018-01-01

A network meta-analysis is used to compare the efficacy of ropinirole, rasagiline, rotigotine, entacapone, apomorphine, pramipexole, sumanirole, bromocriptine, piribedil and levodopa, with placebo as a control, for non-motor symptoms in Parkinson's disease (PD). PubMed, Embase and the Cochrane Library were searched from their establishment dates up to January 2017 for randomized controlled trials (RCTs) investigating the efficacy of the above ten drugs on the non-motor symptoms of PD. A network meta-analysis combined the evidence from direct comparisons and indirect comparisons and evaluated the pooled weighted mean difference (WMD) values and surfaces under the cumulative ranking curves (SUCRA). The network meta-analysis included 21 RCTs. The analysis results indicated that, using the United Parkinson's Disease Rating Scale (UPDRS) III, the efficacies of placebo, ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole and levodopa in treating PD were lower than that of apomorphine (WMD = -10.90, 95% CI = -16.12∼-5.48; WMD = -11.85, 95% CI = -17.31∼-6.16; WMD = -11.15, 95% CI = -16.64∼-5.04; WMD = -11.70, 95% CI = -16.98∼-5.60; WMD = -11.04, 95% CI = -16.97∼-5.34; WMD = -13.27, 95% CI = -19.22∼-7.40; WMD = -10.25, 95% CI = -15.66∼-4.32; and WMD = -11.60, 95% CI = -17.89∼-5.57, respectively). Treatment with ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole, bromocriptine, piribedil or levodopa, with placebo as a control, on PD exhibited no significant differences on PD symptoms when the UPDRS II was used for evaluation. Moreover, using the UPDRS III, the SUCRA values indicated that a pomorphine had the best efficacy on the non-motor symptoms of PD (99.0%). Using the UPDRS II, the SUCRA values for ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole, bromocriptine, piribedil and levodopa treatments, with placebo as a control, indicated that bromocriptine showed the best efficacy on the non-motor symptoms of PD (75.6%). Among ropinirole, rasagiline, rotigotine, entacapone, apomorphine, pramipexole, sumanirole, bromocriptine, piribedil and levodopa, with placebo as a control, apomorphine may be the most efficacious drug for therapy in treating the non-motor symptoms of PD. © 2018 The Author(s). Published by S. Karger AG, Basel.

Healthy versus Unhealthy Suppliers in Food Desert Neighborhoods: A Network Analysis of Corner Stores' Food Supplier Networks.

PubMed

Mui, Yeeli; Lee, Bruce Y; Adam, Atif; Kharmats, Anna Y; Budd, Nadine; Nau, Claudia; Gittelsohn, Joel

2015-11-30

Products in corner stores may be affected by the network of suppliers from which storeowners procure food and beverages. To date, this supplier network has not been well characterized. Using network analysis, we examined the connections between corner stores (n = 24) in food deserts of Baltimore City (MD, USA) and their food/beverage suppliers (n = 42), to determine how different store and supplier characteristics correlated. Food and beverage suppliers fell into two categories: Those providing primarily healthy foods/beverages (n = 15) in the healthy supplier network (HSN) and those providing primarily unhealthy food/beverages (n = 41) in the unhealthy supplier network (UHSN). Corner store connections to suppliers in the UHSN were nearly two times greater (t = 5.23, p < 0.001), and key suppliers in the UHSN core were more diverse, compared to the HSN. The UHSN was significantly more cohesive and densely connected, with corner stores sharing a greater number of the same unhealthy suppliers, compared to HSN, which was less cohesive and sparsely connected (t = 5.82; p < 0.001). Compared to African Americans, Asian and Hispanic corner storeowners had on average -1.53 (p < 0.001) fewer connections to suppliers in the HSN (p < 0.001). Our findings indicate clear differences between corner stores' HSN and UHSN. Addressing ethnic/cultural differences of storeowners may also be important to consider.

Healthy versus Unhealthy Suppliers in Food Desert Neighborhoods: A Network Analysis of Corner Stores’ Food Supplier Networks

PubMed Central

Mui, Yeeli; Lee, Bruce Y.; Adam, Atif; Kharmats, Anna Y.; Budd, Nadine; Nau, Claudia; Gittelsohn, Joel

2015-01-01

Background: Products in corner stores may be affected by the network of suppliers from which storeowners procure food and beverages. To date, this supplier network has not been well characterized. Methods: Using network analysis, we examined the connections between corner stores (n = 24) in food deserts of Baltimore City (MD, USA) and their food/beverage suppliers (n = 42), to determine how different store and supplier characteristics correlated. Results: Food and beverage suppliers fell into two categories: Those providing primarily healthy foods/beverages (n = 15) in the healthy supplier network (HSN) and those providing primarily unhealthy food/beverages (n = 41) in the unhealthy supplier network (UHSN). Corner store connections to suppliers in the UHSN were nearly two times greater (t = 5.23, p < 0.001), and key suppliers in the UHSN core were more diverse, compared to the HSN. The UHSN was significantly more cohesive and densely connected, with corner stores sharing a greater number of the same unhealthy suppliers, compared to HSN, which was less cohesive and sparsely connected (t = 5.82; p < 0.001). Compared to African Americans, Asian and Hispanic corner storeowners had on average −1.53 (p < 0.001) fewer connections to suppliers in the HSN (p < 0.001). Conclusions: Our findings indicate clear differences between corner stores’ HSN and UHSN. Addressing ethnic/cultural differences of storeowners may also be important to consider. PMID:26633434

The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach.

PubMed

Guo, Qiang; Xu, Pengpeng; Pei, Xin; Wong, S C; Yao, Danya

2017-02-01

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies.

PubMed

Emch, Michael; Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2012-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies

PubMed Central

Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2013-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how. PMID:24163443

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

PubMed

Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

2015-04-15

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic Channel Network Extraction from Satellite Imagery of the Jamuna River

NASA Astrophysics Data System (ADS)

Addink, E. A.; Marra, W. A.; Kleinhans, M. G.

2010-12-01

Evolution of the largest rivers on Earth is poorly understood while their response to global change is dramatic, such as severe drought and flooding problems. Rivers with high annual dynamics, like the Jamuna, allow us to study their response to changing conditions. Most remote-sensing work so far focused only on pixel-based analysis of channels and change detection or manual digitisation of channels, which is far from urgently needed quantifiers of pattern and pattern change. Using a series of Landsat TM images taken at irregular intervals showing inter- and intra-annual variation, we demonstrate that braided rivers can be represented as nearly chain-like directional networks. These can be studied with novel methods gleaned from neurology. These networks provide an integral spatial description of the network and should not be confused with hierarchical hydrological stream network descriptions developed in the ’60s to describe drainage basins. The images were first classified into water, bare sediment and vegetation. The contiguous water body of the river was then selected and translated into a network description with bifurcations and confluences at the nodes, and interconnecting channels. Along the entire river the well-known braiding indices were derived from the network. The channel width is a crucial attribute of the channel network as this allows the calculation of bifurcation asymmetry. The width was also used with channel length as weights to all the elements in the network in the calculation of more advanced measures for the nature and evolution of the channel network. The key step here is to describe river network evolution by identifying the same node in multiple subsequent images as well as new and abandoned nodes, in order to distinguish migration of bifurcations from avulsion processes. Once identified through time, the changes in node position and the changes in the connected channels can be quantified. These changes can potentially be linked to channel migration and vegetation cover along the channels. A network evolves in time by adding or removing channels and their bifurcation- and confluence couples. Using the network topology, we quantified network properties such as `centrality’, which provides a measure for the overall importance of individual channels in a network. This is a novel and robust indicator to assess the effect of a change or engineering measure in a channel on the entire network. The physical basis for downstream propagation of information through a fluvial network is the flood conveyance and sediment transport, and for upstream propagation it is the backwater effect. Using the dynamic network description we can start quantifying the effects of local changes in the network on the entire upstream and downstream network. We conclude that the developed workflow allows the use of novel and useful measures borrowed from other sciences in river network analysis, and provides, e.g., the assessment of the importance of individual branches in a large complicated network.

Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis.

PubMed

Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel Dajun; Zhou, Xingshe; Leischow, Scott James; Chung, Wingyan

2015-06-19

Social media is becoming a new battlefield for tobacco "wars". Evaluating the current situation is very crucial for the advocacy of tobacco control in the age of social media. To reveal the impact of tobacco-related user-generated content, this paper characterizes user interaction and social influence utilizing social network analysis and information theoretic approaches. Our empirical studies demonstrate that the exploding pro-tobacco content has long-lasting effects with more active users and broader influence, and reveal the shortage of social media resources in global tobacco control. It is found that the user interaction in the pro-tobacco group is more active, and user-generated content for tobacco promotion is more successful in obtaining user attention. Furthermore, we construct three tobacco-related social networks and investigate the topological patterns of these tobacco-related social networks. We find that the size of the pro-tobacco network overwhelms the others, which suggests a huge number of users are exposed to the pro-tobacco content. These results indicate that the gap between tobacco promotion and tobacco control is widening and tobacco control may be losing ground to tobacco promotion in social media.

Localization-Free Detection of Replica Node Attacks in Wireless Sensor Networks Using Similarity Estimation with Group Deployment Knowledge

PubMed Central

Ding, Chao; Yang, Lijun; Wu, Meng

2017-01-01

Due to the unattended nature and poor security guarantee of the wireless sensor networks (WSNs), adversaries can easily make replicas of compromised nodes, and place them throughout the network to launch various types of attacks. Such an attack is dangerous because it enables the adversaries to control large numbers of nodes and extend the damage of attacks to most of the network with quite limited cost. To stop the node replica attack, we propose a location similarity-based detection scheme using deployment knowledge. Compared with prior solutions, our scheme provides extra functionalities that prevent replicas from generating false location claims without deploying resource-consuming localization techniques on the resource-constraint sensor nodes. We evaluate the security performance of our proposal under different attack strategies through heuristic analysis, and show that our scheme achieves secure and robust replica detection by increasing the cost of node replication. Additionally, we evaluate the impact of network environment on the proposed scheme through theoretic analysis and simulation experiments, and indicate that our scheme achieves effectiveness and efficiency with substantially lower communication, computational, and storage overhead than prior works under different situations and attack strategies. PMID:28098846

Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis

PubMed Central

Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel Dajun; Zhou, Xingshe; Leischow, Scott James; Chung, Wingyan

2015-01-01

Social media is becoming a new battlefield for tobacco “wars”. Evaluating the current situation is very crucial for the advocacy of tobacco control in the age of social media. To reveal the impact of tobacco-related user-generated content, this paper characterizes user interaction and social influence utilizing social network analysis and information theoretic approaches. Our empirical studies demonstrate that the exploding pro-tobacco content has long-lasting effects with more active users and broader influence, and reveal the shortage of social media resources in global tobacco control. It is found that the user interaction in the pro-tobacco group is more active, and user-generated content for tobacco promotion is more successful in obtaining user attention. Furthermore, we construct three tobacco-related social networks and investigate the topological patterns of these tobacco-related social networks. We find that the size of the pro-tobacco network overwhelms the others, which suggests a huge number of users are exposed to the pro-tobacco content. These results indicate that the gap between tobacco promotion and tobacco control is widening and tobacco control may be losing ground to tobacco promotion in social media. PMID:26091553

Localization-Free Detection of Replica Node Attacks in Wireless Sensor Networks Using Similarity Estimation with Group Deployment Knowledge.

PubMed

Ding, Chao; Yang, Lijun; Wu, Meng

2017-01-15

Due to the unattended nature and poor security guarantee of the wireless sensor networks (WSNs), adversaries can easily make replicas of compromised nodes, and place them throughout the network to launch various types of attacks. Such an attack is dangerous because it enables the adversaries to control large numbers of nodes and extend the damage of attacks to most of the network with quite limited cost. To stop the node replica attack, we propose a location similarity-based detection scheme using deployment knowledge. Compared with prior solutions, our scheme provides extra functionalities that prevent replicas from generating false location claims without deploying resource-consuming localization techniques on the resource-constraint sensor nodes. We evaluate the security performance of our proposal under different attack strategies through heuristic analysis, and show that our scheme achieves secure and robust replica detection by increasing the cost of node replication. Additionally, we evaluate the impact of network environment on the proposed scheme through theoretic analysis and simulation experiments, and indicate that our scheme achieves effectiveness and efficiency with substantially lower communication, computational, and storage overhead than prior works under different situations and attack strategies.

Specific CA3 neurons decode neural information of dentate granule cells evoked by paired-pulse stimulation in co-cultured networks.

PubMed

Poli, Daniele; DeMarse, Thomas B; Wheeler, Bruce C; Brewer, Gregory J

2017-07-01

CA3 and dentate gyrus (DG) neurons are cultured in two-chamber devices on multi-electrode arrays (MEAs) and connected via micro-tunnels. In order to evoke time-locked activity, paired-pulse stimulation is applied to 22 different sites and repeated 25 times in each well in 5 MEA co-cultures and results compared to CA3-CA3 and DG-DG networks homologous controls. In these hippocampal sub-regions, we focus on the mechanisms underpinning a network's ability to decode the identity of site specific stimulation from analysis of evoked network responses using a support vector machine classifier. Our results indicate that a pool of CA3 neurons is able to reliably decode the identity of DG stimulation site information.

Lineage-specific enhancers activate self-renewal genes in macrophages and embryonic stem cells

PubMed Central

Soucie, Erinn L.; Weng, Ziming; Geirsdóttir, Laufey; Molawi, Kaaweh; Maurizio, Julien; Fenouil, Romain; Mossadegh-Keller, Noushine; Gimenez, Gregory; VanHille, Laurent; Beniazza, Meryam; Favret, Jeremy; Berruyer, Carole; Perrin, Pierre; Hacohen, Nir; Andrau, J.-C.; Ferrier, Pierre; Dubreuil, Patrice; Sidow, Arend; Sieweke, Michael H.

2016-01-01

Differentiated macrophages can self-renew in tissues and expand long-term in culture, but the gene regulatory mechanisms that accomplish self-renewal in the differentiated state have remained unknown. Here we show that in mice, the transcription factors MafB and c-Maf repress a macrophage-specific enhancer repertoire associated with a gene network controlling self-renewal. Single cell analysis revealed that, in vivo, proliferating resident macrophages can access this network by transient down-regulation of Maf transcription factors. The network also controls embryonic stem cell self-renewal but is associated with distinct embryonic stem cell-specific enhancers. This indicates that distinct lineage-specific enhancer platforms regulate a shared network of genes that control self-renewal potential in both stem and mature cells. PMID:26797145

Analysis of 100Mb/s Ethernet for the Whitney Commodity Computing Testbed

NASA Technical Reports Server (NTRS)

Fineberg, Samuel A.; Pedretti, Kevin T.; Kutler, Paul (Technical Monitor)

1997-01-01

We evaluate the performance of a Fast Ethernet network configured with a single large switch, a single hub, and a 4x4 2D torus topology in a testbed cluster of "commodity" Pentium Pro PCs. We also evaluated a mixed network composed of ethernet hubs and switches. An MPI collective communication benchmark, and the NAS Parallel Benchmarks version 2.2 (NPB2) show that the torus network performs best for all sizes that we were able to test (up to 16 nodes). For larger networks the ethernet switch outperforms the hub, though its performance is far less than peak. The hub/switch combination tests indicate that the NAS parallel benchmarks are relatively insensitive to hub densities of less than 7 nodes per hub.

Applications of CCSDS recommendations to Integrated Ground Data Systems (IGDS)

NASA Technical Reports Server (NTRS)

Mizuta, Hiroshi; Martin, Daniel; Kato, Hatsuhiko; Ihara, Hirokazu

1993-01-01

This paper describes an application of the CCSDS Principle Network (CPH) service model to communications network elements of a postulated Integrated Ground Data System (IGDS). Functions are drawn principally from COSMICS (Cosmic Information and Control System), an integrated space control infrastructure, and the Earth Observing System Data and Information System (EOSDIS) Core System (ECS). From functional requirements, this paper derives a set of five communications network partitions which, taken together, support proposed space control infrastructures and data distribution systems. Our functional analysis indicates that the five network partitions derived in this paper should effectively interconnect the users, centers, processors, and other architectural elements of an IGDS. This paper illustrates a useful application of the CCSDS (Consultive Committee for Space Data Systems) Recommendations to ground data system development.

Exploratory Network Meta Regression Analysis of Stroke Prevention in Atrial Fibrillation Fails to Identify Any Interactions with Treatment Effect.

PubMed

Batson, Sarah; Sutton, Alex; Abrams, Keith

2016-01-01

Patients with atrial fibrillation are at a greater risk of stroke and therefore the main goal for treatment of patients with atrial fibrillation is to prevent stroke from occurring. There are a number of different stroke prevention treatments available to include warfarin and novel oral anticoagulants. Previous network meta-analyses of novel oral anticoagulants for stroke prevention in atrial fibrillation acknowledge the limitation of heterogeneity across the included trials but have not explored the impact of potentially important treatment modifying covariates. To explore potentially important treatment modifying covariates using network meta-regression analyses for stroke prevention in atrial fibrillation. We performed a network meta-analysis for the outcome of ischaemic stroke and conducted an exploratory regression analysis considering potentially important treatment modifying covariates. These covariates included the proportion of patients with a previous stroke, proportion of males, mean age, the duration of study follow-up and the patients underlying risk of ischaemic stroke. None of the covariates explored impacted relative treatment effects relative to placebo. Notably, the exploration of 'study follow-up' as a covariate supported the assumption that difference in trial durations is unimportant in this indication despite the variation across trials in the network. This study is limited by the quantity of data available. Further investigation is warranted, and, as justifying further trials may be difficult, it would be desirable to obtain individual patient level data (IPD) to facilitate an effort to relate treatment effects to IPD covariates in order to investigate heterogeneity. Observational data could also be examined to establish if there are potential trends elsewhere. The approach and methods presented have potentially wide applications within any indication as to highlight the potential benefit of extending decision problems to include additional comparators outside of those of primary interest to allow for the exploration of heterogeneity.

Characterization of biomarkers in stroke based on ego-networks and pathways.

PubMed

Li, Haixia; Guo, Qianqian

2017-12-01

To explore potential biomarkers in stroke based on ego-networks and pathways. EgoNet method was applied to search for the underlying biomarkers in stroke using transcription profiling of E-GEOD-58294 and protein-protein interaction (PPI) data. Eight ego-genes were identified from PPI network according to the degree characteristics at the criteria of top 5% ranked z-sore and degree >1. Eight candidate ego-networks with classification accuracy ≥0.9 were selected. After performed randomization test, seven significant ego-networks with adjusted p value < 0.05 were identified. Pathway enrichment analysis was then conducted with these ego-networks to search for the significant pathways. Finally, two significant pathways were identified, and six of seven ego-networks were enriched to "3'-UTR-mediated translational regulation" pathway, indicating that this pathway performs an important role in the development of stroke. Seven ego-networks were constructed using EgoNet and two significant enriched by pathways were identified. These may provide new insights into the potential biomarkers for the development of stroke.

Potential Theory for Directed Networks

PubMed Central

Zhang, Qian-Ming; Lü, Linyuan; Wang, Wen-Qiang; Zhou, Tao

2013-01-01

Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation. PMID:23408979

Reducing a cortical network to a Potts model yields storage capacity estimates

NASA Astrophysics Data System (ADS)

Naim, Michelangelo; Boboeva, Vezha; Kang, Chol Jun; Treves, Alessandro

2018-04-01

An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e. in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback w, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss the implications for semantic memory in humans.

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

PubMed

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

2017-03-01

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

PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory.

PubMed

Niknam, Niloofar; Khakzad, Hamed; Arab, Seyed Shahriar; Naderi-Manesh, Hossein

2016-05-01

The integrative and cooperative nature of protein structure involves the assessment of topological and global features of constituent parts. Network concept takes complete advantage of both of these properties in the analysis concomitantly. High compatibility to structural concepts or physicochemical properties in addition to exploiting a remarkable simplification in the system has made network an ideal tool to explore biological systems. There are numerous examples in which different protein structural and functional characteristics have been clarified by the network approach. Here, we present an interactive and user-friendly Matlab-based toolbox, PDB2Graph, devoted to protein structure network construction, visualization, and analysis. Moreover, PDB2Graph is an appropriate tool for identifying critical nodes involved in protein structural robustness and function based on centrality indices. It maps critical amino acids in protein networks and can greatly aid structural biologists in selecting proper amino acid candidates for manipulating protein structures in a more reasonable and rational manner. To introduce the capability and efficiency of PDB2Graph in detail, the structural modification of Calmodulin through allosteric binding of Ca(2+) is considered. In addition, a mutational analysis for three well-identified model proteins including Phage T4 lysozyme, Barnase and Ribonuclease HI, was performed to inspect the influence of mutating important central residues on protein activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterization of contact structures for the spread of infectious diseases in a pork supply chain in northern Germany by dynamic network analysis of yearly and monthly networks.

PubMed

Büttner, K; Krieter, J; Traulsen, I

2015-04-01

A major risk factor in the spread of diseases between holdings is the transport of live animals. This study analysed the animal movements of the pork supply chain of a producer group in Northern Germany. The parameters in-degree and out-degree, ingoing and outgoing infection chain, betweenness and ingoing and outgoing closeness were measured using dynamic network analysis to identify holdings with central positions in the network and to characterize the overall network topology. The potential maximum epidemic size was also estimated. All parameters were calculated for three time periods: the 3-yearly network, the yearly and the monthly networks. The yearly and the monthly networks were more fragmented than the 3-yearly network. On average, one-third of the holdings were isolated in the yearly networks and almost three quarters in the monthly networks. This represented an immense reduction in the number of holdings participating in the trade of the monthly networks. The overall network topology showed right-skewed distributions for all calculated centrality parameters indicating that network resilience was high concerning the random removal of holdings. However, for a targeted removal of holdings according to their centrality, a rapid fragmentation of the trade network could be expected. Furthermore, to capture the real importance of holdings for disease transmission, indirect trade contacts (infection chain) should be considered. In contrast to the parameters regarding direct trade contacts (degree), the infection chain parameter did not underestimate the potential risk of disease transmission. This became more obvious, the longer the observed time period was. For all three time periods, the results for the estimation of the potential maximum epidemic size illustrated that the outgoing infection chain should be chosen. It considers the chronological order and the directed nature of the contacts and has no restrictions such as the strongly connected components of a cyclic network. © 2013 Blackwell Verlag GmbH.

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

PubMed

Ma, Hong-Wu; Zeng, An-Ping

2003-07-22

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

Emerging late adolescent friendship networks and Big Five personality traits: a social network approach.

PubMed

Selfhout, Maarten; Burk, William; Branje, Susan; Denissen, Jaap; van Aken, Marcel; Meeus, Wim

2010-04-01

The current study focuses on the emergence of friendship networks among just-acquainted individuals, investigating the effects of Big Five personality traits on friendship selection processes. Sociometric nominations and self-ratings on personality traits were gathered from 205 late adolescents (mean age=19 years) at 5 time points during the first year of university. SIENA, a novel multilevel statistical procedure for social network analysis, was used to examine effects of Big Five traits on friendship selection. Results indicated that friendship networks between just-acquainted individuals became increasingly more cohesive within the first 3 months and then stabilized. Whereas individuals high on Extraversion tended to select more friends than those low on this trait, individuals high on Agreeableness tended to be selected more as friends. In addition, individuals tended to select friends with similar levels of Agreeableness, Extraversion, and Openness.

Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based Models

PubMed Central

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

2015-01-01

Although stochastic actor based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models. PMID:25745276

Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based Models.

PubMed

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

2015-05-01

Although stochastic actor based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models.

Transformational leadership and group interaction as climate antecedents: a social network analysis.

PubMed

Zohar, Dov; Tenne-Gazit, Orly

2008-07-01

In order to test the social mechanisms through which organizational climate emerges, this article introduces a model that combines transformational leadership and social interaction as antecedents of climate strength (i.e., the degree of within-unit agreement about climate perceptions). Despite their longstanding status as primary variables, both antecedents have received limited empirical research. The sample consisted of 45 platoons of infantry soldiers from 5 different brigades, using safety climate as the exemplar. Results indicate a partially mediated model between transformational leadership and climate strength, with density of group communication network as the mediating variable. In addition, the results showed independent effects for group centralization of the communication and friendship networks, which exerted incremental effects on climate strength over transformational leadership. Whereas centralization of the communication network was found to be negatively related to climate strength, centralization of the friendship network was positively related to it. Theoretical and practical implications are discussed.

Locating influential nodes in complex networks

PubMed Central

Malliaros, Fragkiskos D.; Rossi, Maria-Evgenia G.; Vazirgiannis, Michalis

2016-01-01

Understanding and controlling spreading processes in networks is an important topic with many diverse applications, including information dissemination, disease propagation and viral marketing. It is of crucial importance to identify which entities act as influential spreaders that can propagate information to a large portion of the network, in order to ensure efficient information diffusion, optimize available resources or even control the spreading. In this work, we capitalize on the properties of the K-truss decomposition, a triangle-based extension of the core decomposition of graphs, to locate individual influential nodes. Our analysis on real networks indicates that the nodes belonging to the maximal K-truss subgraph show better spreading behavior compared to previously used importance criteria, including node degree and k-core index, leading to faster and wider epidemic spreading. We further show that nodes belonging to such dense subgraphs, dominate the small set of nodes that achieve the optimal spreading in the network. PMID:26776455

Using food-web theory to conserve ecosystems

PubMed Central

McDonald-Madden, E.; Sabbadin, R.; Game, E. T.; Baxter, P. W. J.; Chadès, I.; Possingham, H. P.

2016-01-01

Food-web theory can be a powerful guide to the management of complex ecosystems. However, we show that indices of species importance common in food-web and network theory can be a poor guide to ecosystem management, resulting in significantly more extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial Optimization to find optimal management strategies for a wide range of real and hypothetical food webs. This Artificial Intelligence approach provides the ability to test the performance of any index for prioritizing species management in a network. While no single network theory index provides an appropriate guide to management for all food webs, a modified version of the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes. Our analysis shows that by prioritizing ecosystem management based on the network-wide impact of species protection rather than species loss, we can substantially improve conservation outcomes. PMID:26776253

Using new edges for anomaly detection in computer networks

DOEpatents

Neil, Joshua Charles

2017-07-04

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

Using new edges for anomaly detection in computer networks

DOEpatents

Neil, Joshua Charles

2015-05-19

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

PubMed Central

Hasmi, Laila; Drukker, Marjan; Guloksuz, Sinan; Menne-Lothmann, Claudia; Decoster, Jeroen; van Winkel, Ruud; Collip, Dina; Delespaul, Philippe; De Hert, Marc; Derom, Catherine; Thiery, Evert; Jacobs, Nele; Rutten, Bart P. F.; Wichers, Marieke; van Os, Jim

2017-01-01

Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing. Conclusions: The present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL) and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence. PMID:29163289

Network Meta-Analysis of the Efficacy of Acupuncture, Alpha-blockers and Antibiotics on Chronic Prostatitis/Chronic Pelvic Pain Syndrome

PubMed Central

Qin, Zongshi; Wu, Jiani; Tian, Jinhui; Zhou, Jing; Liu, Yali; Liu, Zhishun

2016-01-01

Alpha-blockers and antibiotics are most commonly used to treat chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) in clinical practice. Currently, increasing evidence also suggests acupuncture as an effective strategy. This network meta-analysis intended to assess the comparative efficacy and safety of acupuncture, alpha-blockers and antibiotics for CP/CPPS. Twelve trials involving 1203 participants were included. Based on decreases in the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) score, a network meta-analysis indicated that electro-acupuncture (standard mean difference [SMD]: 4.29; 95% credible interval [CrI], 1.96–6.65), acupuncture (SMD: 3.69; 95% CrI, 0.27–7.17), alpha-blockers (SMD: 1.85; 95% CrI, 1.07–2.64), antibiotics (SMD: 2.66; 95% CrI, 1.57–3.76), and dual therapy (SMD: 3.20; 95% CrI, 1.95–4.42) are superior to placebo in decreasing this score. Additionally, electro-acupuncture (SMD: 2.44; 95% CrI, 0.08–4.83) and dual therapy (SMD: 1.35; 95% CrI, 0.07–2.62) were more effective than alpha-blockers in decreasing the total NIH-CPSI total score. Other network meta-analyses did not show significant differences between interventions other placebo. The incidence of adverse events of acupuncture was relatively rare (5.4%) compared with placebo (17.1%), alpha-blockers (24.9%), antibiotics (31%) and dual therapy (48.6%). Overall, rank tests and safety analyses indicate that electro-acupuncture/acupuncture may be recommended for the treatment of CP/CPPS. PMID:27759111

Genome-Wide Identification and Expression Analysis of the Mitogen-Activated Protein Kinase Gene Family in Cassava

PubMed Central

Yan, Yan; Wang, Lianzhe; Ding, Zehong; Tie, Weiwei; Ding, Xupo; Zeng, Changying; Wei, Yunxie; Zhao, Hongliang; Peng, Ming; Hu, Wei

2016-01-01

Mitogen-activated protein kinases (MAPKs) play central roles in plant developmental processes, hormone signaling transduction, and responses to abiotic stress. However, no data are currently available about the MAPK family in cassava, an important tropical crop. Herein, 21 MeMAPK genes were identified from cassava. Phylogenetic analysis indicated that MeMAPKs could be classified into four subfamilies. Gene structure analysis demonstrated that the number of introns in MeMAPK genes ranged from 1 to 10, suggesting large variation among cassava MAPK genes. Conserved motif analysis indicated that all MeMAPKs had typical protein kinase domains. Transcriptomic analysis suggested that MeMAPK genes showed differential expression patterns in distinct tissues and in response to drought stress between wild subspecies and cultivated varieties. Interaction networks and co-expression analyses revealed that crucial pathways controlled by MeMAPK networks may be involved in the differential response to drought stress in different accessions of cassava. Expression of nine selected MAPK genes showed that these genes could comprehensively respond to osmotic, salt, cold, oxidative stressors, and abscisic acid (ABA) signaling. These findings yield new insights into the transcriptional control of MAPK gene expression, provide an improved understanding of abiotic stress responses and signaling transduction in cassava, and lead to potential applications in the genetic improvement of cassava cultivars. PMID:27625666

Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data.

PubMed

Deshpande, Gopikrishna; Santhanam, Priya; Hu, Xiaoping

2011-01-15

Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network. Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries. Copyright © 2010 Elsevier Inc. All rights reserved.

Variations in Social Network Type Membership Among Older African Americans, Caribbean Blacks, and Non-Hispanic Whites.

PubMed

Nguyen, Ann W

2017-07-01

This study examined race differences in the probability of belonging to a specific social network typology of family, friends, and church members. Samples of African Americans, Caribbean blacks, and non-Hispanic whites aged 55+ were drawn from the National Survey of American Life. Typology indicators related to social integration and negative interactions with family, friendship, and church networks were used. Latent class analysis was used to identify typologies, and latent class multinomial logistic regression was used to assess the influence of race, and interactions between race and age, and race and education on typology membership. Four network typologies were identified: optimal (high social integration, low negative interaction), family-centered (high social integration within primarily the extended family network, low negative interaction), strained (low social integration, high negative interaction), and ambivalent (high social integration and high negative interaction). Findings for race and age and race and education interactions indicated that the effects of education and age on typology membership varied by race. Overall, the findings demonstrate how race interacts with age and education to influence the probability of belonging to particular network types. A better understanding of the influence of race, education, and age on social network typologies will inform future research and theoretical developments in this area. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Bridging the gap between motor imagery and motor execution with a brain-robot interface.

PubMed

Bauer, Robert; Fels, Meike; Vukelić, Mathias; Ziemann, Ulf; Gharabaghi, Alireza

2015-03-01

According to electrophysiological studies motor imagery and motor execution are associated with perturbations of brain oscillations over spatially similar cortical areas. By contrast, neuroimaging and lesion studies suggest that at least partially distinct cortical networks are involved in motor imagery and execution. We sought to further disentangle this relationship by studying the role of brain-robot interfaces in the context of motor imagery and motor execution networks. Twenty right-handed subjects performed several behavioral tasks as indicators for imagery and execution of movements of the left hand, i.e. kinesthetic imagery, visual imagery, visuomotor integration and tonic contraction. In addition, subjects performed motor imagery supported by haptic/proprioceptive feedback from a brain-robot-interface. Principal component analysis was applied to assess the relationship of these indicators. The respective cortical resting state networks in the α-range were investigated by electroencephalography using the phase slope index. We detected two distinct abilities and cortical networks underlying motor control: a motor imagery network connecting the left parietal and motor areas with the right prefrontal cortex and a motor execution network characterized by transmission from the left to right motor areas. We found that a brain-robot-interface might offer a way to bridge the gap between these networks, opening thereby a backdoor to the motor execution system. This knowledge might promote patient screening and may lead to novel treatment strategies, e.g. for the rehabilitation of hemiparesis after stroke. Copyright © 2014 Elsevier Inc. All rights reserved.

Measuring Networking as an Outcome Variable in Undergraduate Research Experiences

PubMed Central

Hanauer, David I.; Hatfull, Graham

2015-01-01

The aim of this paper is to propose, present, and validate a simple survey instrument to measure student conversational networking. The tool consists of five items that cover personal and professional social networks, and its basic principle is the self-reporting of degrees of conversation, with a range of specific discussion partners. The networking instrument was validated in three studies. The basic psychometric characteristics of the scales were established by conducting a factor analysis and evaluating internal consistency using Cronbach’s alpha. The second study used a known-groups comparison and involved comparing outcomes for networking scales between two different undergraduate laboratory courses (one involving a specific effort to enhance networking). The final study looked at potential relationships between specific networking items and the established psychosocial variable of project ownership through a series of binary logistic regressions. Overall, the data from the three studies indicate that the networking scales have high internal consistency (α = 0.88), consist of a unitary dimension, can significantly differentiate between research experiences with low and high networking designs, and are related to project ownership scales. The ramifications of the networking instrument for student retention, the enhancement of public scientific literacy, and the differentiation of laboratory courses are discussed. PMID:26538387

"Geo-statistics methods and neural networks in geophysical applications: A case study"

NASA Astrophysics Data System (ADS)

Rodriguez Sandoval, R.; Urrutia Fucugauchi, J.; Ramirez Cruz, L. C.

2008-12-01

The study is focus in the Ebano-Panuco basin of northeastern Mexico, which is being explored for hydrocarbon reservoirs. These reservoirs are in limestones and there is interest in determining porosity and permeability in the carbonate sequences. The porosity maps presented in this study are estimated from application of multiattribute and neural networks techniques, which combine geophysics logs and 3-D seismic data by means of statistical relationships. The multiattribute analysis is a process to predict a volume of any underground petrophysical measurement from well-log and seismic data. The data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs are neutron porosity logs. From the 3-D seismic volume a series of sample attributes is calculated. The objective of this study is to derive a set of attributes and the target log values. The selected set is determined by a process of forward stepwise regression. The analysis can be linear or nonlinear. In the linear mode the method consists of a series of weights derived by least-square minimization. In the nonlinear mode, a neural network is trained using the select attributes as inputs. In this case we used a probabilistic neural network PNN. The method is applied to a real data set from PEMEX. For better reservoir characterization the porosity distribution was estimated using both techniques. The case shown a continues improvement in the prediction of the porosity from the multiattribute to the neural network analysis. The improvement is in the training and the validation, which are important indicators of the reliability of the results. The neural network showed an improvement in resolution over the multiattribute analysis. The final maps provide more realistic results of the porosity distribution.

Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China

PubMed Central

Fan, Yaxin; Zhu, Xinyan; Guo, Wei; Guo, Tao

2018-01-01

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions. PMID:29672551

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California

PubMed Central

Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

2017-01-01

Background Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. Conclusions The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. PMID:28676471

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

PubMed Central

Schwarz, Roland; Musch, Patrick; von Kamp, Axel; Engels, Bernd; Schirmer, Heiner; Schuster, Stefan; Dandekar, Thomas

2005-01-01

Background A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. Results YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at . Conclusion A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts. PMID:15929789

Identification of GRB2 and GAB1 Coexpression as an Unfavorable Prognostic Factor for Hepatocellular Carcinoma by a Combination of Expression Profile and Network Analysis

PubMed Central

Yang, Mei; Wang, Danhua; Yu, Lingxiang; Guo, Chaonan; Guo, Xiaodong; Lin, Na

2013-01-01

Aim To screen novel markers for hepatocellular carcinoma (HCC) by a combination of expression profile, interaction network analysis and clinical validation. Methods HCC significant molecules which are differentially expressed or had genetic variations in HCC tissues were obtained from five existing HCC related databases (OncoDB.HCC, HCC.net, dbHCCvar, EHCO and Liverome). Then, the protein-protein interaction (PPI) network of these molecules was constructed. Three topological features of the network ('Degree', 'Betweenness', and 'Closeness') and the k-core algorithm were used to screen candidate HCC markers which play crucial roles in tumorigenesis of HCC. Furthermore, the clinical significance of two candidate HCC markers growth factor receptor-bound 2 (GRB2) and GRB2-associated-binding protein 1 (GAB1) was validated. Results In total, 6179 HCC significant genes and 977 HCC significant proteins were collected from existing HCC related databases. After network analysis, 331 candidate HCC markers were identified. Especially, GAB1 has the highest k-coreness suggesting its central localization in HCC related network, and the interaction between GRB2 and GAB1 has the largest edge-betweenness implying it may be biologically important to the function of HCC related network. As the results of clinical validation, the expression levels of both GRB2 and GAB1 proteins were significantly higher in HCC tissues than those in their adjacent nonneoplastic tissues. More importantly, the combined GRB2 and GAB1 protein expression was significantly associated with aggressive tumor progression and poor prognosis in patients with HCC. Conclusion This study provided an integrative analysis by combining expression profile and interaction network analysis to identify a list of biologically significant HCC related markers and pathways. Further experimental validation indicated that the aberrant expression of GRB2 and GAB1 proteins may be strongly related to tumor progression and prognosis in patients with HCC. The overexpression of GRB2 in combination with upregulation of GAB1 may be an unfavorable prognostic factor for HCC. PMID:24391994

Health policy and systems research collaboration pathways: lessons from a network science analysis.

PubMed

English, Krista M; Pourbohloul, Babak

2017-08-28

The 2004 Mexico Declaration, and subsequent World Health Assembly resolutions, proposed a concerted support for the global development of health policy and systems research (HPSR). This included coordination across partners and advocates for the field of HPSR to monitor the development of the field, while promoting decision-making power and implementing responsibilities in low- and middle-income countries (LMICs). We used a network science approach to examine the structural properties of the HPSR co-authorship network across country economic groups in the PubMed citation database from 1990 to 2015. This analysis summarises the evolution of the publication, co-authorship and citation networks within HPSR. This method allows identification of several features otherwise not apparent. The co-authorship network has evolved steadily from 1990 to 2015 in terms of number of publications, but more importantly, in terms of co-authorship network connectedness. Our analysis suggests that, despite growth in the contribution from low-income countries to HPSR literature, co-authorship remains highly localised. Lower middle-income countries have made progress toward global connectivity through diversified collaboration with various institutions and regions. Global connectivity of the upper middle-income countries (UpperMICs) are almost on par with high-income countries (HICs), indicating the transition of this group of countries toward becoming major contributors to the field. Network analysis allows examination of the connectedness among the HSPR community. Initially (early 1990s), research groups operated almost exclusively independently and, despite the topic being specifically on health policy in LMICs, HICs provided lead authorship. Since the early 1990s, the network has evolved significantly. In the full set analysis (1990-2015), for the first time in HPSR history, more than half of the authors are connected and lead authorship from UpperMICs is on par with that of HICs. This demonstrates the shift in participation and influence toward regions which HPSR primarily serves. Understanding these interactions can highlight the current strengths and future opportunities for identifying new strategies to enhance collaboration and support capacity-building efforts for HPSR.

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa

PubMed Central

Geisler, Daniel; Borchardt, Viola; Lord, Anton R.; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A.; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Background Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. Methods To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Results Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. Limitations The present results may be limited to the methods applied during preprocessing and network construction. Conclusion We demonstrated anorexia nervosa–related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger. PMID:26252451

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa.

PubMed

Geisler, Daniel; Borchardt, Viola; Lord, Anton R; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. The present results may be limited to the methods applied during preprocessing and network construction. We demonstrated anorexia nervosa-related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger.

An event- and network-level analysis of college students' maximum drinking day.

PubMed

Meisel, Matthew K; DiBello, Angelo M; Balestrieri, Sara G; Ott, Miles Q; DiGuiseppi, Graham T; Clark, Melissa A; Barnett, Nancy P

2018-04-01

Heavy episodic drinking is common among college students and remains a serious public health issue. Previous event-level research among college students has examined behaviors and individual-level characteristics that drive consumption and related consequences but often ignores the social network of people with whom these heavy drinking episodes occur. The main aim of the current study was to investigate the network of social connections between drinkers on their heaviest drinking occasions. Sociocentric network methods were used to collect information from individuals in the first-year class (N=1342) at one university. Past-month drinkers (N=972) reported on the characteristics of their heaviest drinking occasion in the past month and indicated who else among their network connections was present during this occasion. Average max drinking day indegree, or the total number of times a participant was nominated as being present on another students' heaviest drinking occasion, was 2.50 (SD=2.05). Network autocorrelation models indicated that max drinking day indegree (e.g., popularity on heaviest drinking occassions) and peers' number of drinks on their own maximum drinking occasions were significantly associated with participant maximum number of drinks, after controlling for demographic variables, pregaming, and global network indegree (e.g., popularity in the entire first-year class). Being present at other peers' heaviest drinking occasions is associated with greater drinking quantities on one's own heaviest drinking occasion. These findings suggest the potential for interventions that target peer influences within close social networks of drinkers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Changes in task-based effective connectivity in language networks following rehabilitation in post-stroke patients with aphasia

PubMed Central

Kiran, Swathi; Meier, Erin L.; Kapse, Kushal J.; Glynn, Peter A.

2015-01-01

In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, LSFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and that this similarity is accentuated by rehabilitation. PMID:26106314

Baseline results from the Lichen Community Indicator Program in the Pacific Northwest: Air quality patterns and evidence of a nitrogen pollution problem

Treesearch

Sarah Jovan

2009-01-01

Why Are Epiphytic Lichen Communities Important? Lichens are one of the bioindicators used by the Forest Inventory and Analysis (FIA) Program to monitor forest health. To obtain data for use in its Lichen Community Indicator Program, FIA samples a regular network of permanent field plots to determine the composition of epiphytic, i.e., tree dwelling, lichen communities...

Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market

PubMed Central

Long, Haiming; Tang, Nengyu

2017-01-01

This study considers the effect of an industry’s network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry’s conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry’s systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust. PMID:28683130

Automation of route identification and optimisation based on data-mining and chemical intuition.

PubMed

Lapkin, A A; Heer, P K; Jacob, P-M; Hutchby, M; Cunningham, W; Bull, S D; Davidson, M G

2017-09-21

Data-mining of Reaxys and network analysis of the combined literature and in-house reactions set were used to generate multiple possible reaction routes to convert a bio-waste feedstock, limonene, into a pharmaceutical API, paracetamol. The network analysis of data provides a rich knowledge-base for generation of the initial reaction screening and development programme. Based on the literature and the in-house data, an overall flowsheet for the conversion of limonene to paracetamol was proposed. Each individual reaction-separation step in the sequence was simulated as a combination of the continuous flow and batch steps. The linear model generation methodology allowed us to identify the reaction steps requiring further chemical optimisation. The generated model can be used for global optimisation and generation of environmental and other performance indicators, such as cost indicators. However, the identified further challenge is to automate model generation to evolve optimal multi-step chemical routes and optimal process configurations.

Genome-Wide Analysis of the Complex Transcriptional Networks of Rice Developing Seeds

PubMed Central

Xue, Liang-Jiao; Zhang, Jing-Jing; Xue, Hong-Wei

2012-01-01

Background The development of rice (Oryza sativa) seed is closely associated with assimilates storage and plant yield, and is fine controlled by complex regulatory networks. Exhaustive transcriptome analysis of developing rice embryo and endosperm will help to characterize the genes possibly involved in the regulation of seed development and provide clues of yield and quality improvement. Principal Findings Our analysis showed that genes involved in metabolism regulation, hormone response and cellular organization processes are predominantly expressed during rice development. Interestingly, 191 transcription factor (TF)-encoding genes are predominantly expressed in seed and 59 TFs are regulated during seed development, some of which are homologs of seed-specific TFs or regulators of Arabidopsis seed development. Gene co-expression network analysis showed these TFs associated with multiple cellular and metabolism pathways, indicating a complex regulation of rice seed development. Further, by employing a cold-resistant cultivar Hanfeng (HF), genome-wide analyses of seed transcriptome at normal and low temperature reveal that rice seed is sensitive to low temperature at early stage and many genes associated with seed development are down-regulated by low temperature, indicating that the delayed development of rice seed by low temperature is mainly caused by the inhibition of the development-related genes. The transcriptional response of seed and seedling to low temperature is different, and the differential expressions of genes in signaling and metabolism pathways may contribute to the chilling tolerance of HF during seed development. Conclusions These results provide informative clues and will significantly improve the understanding of rice seed development regulation and the mechanism of cold response in rice seed. PMID:22363552

Parenclitic Network Analysis of Methylation Data for Cancer Identification

PubMed Central

Karsakov, Alexander; Bartlett, Thomas; Ryblov, Artem; Meyerov, Iosif; Ivanchenko, Mikhail; Zaikin, Alexey

2017-01-01

We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as 93−99% is achieved with only 12 network topology indices, in a dramatic reduction of complexity from the original 15295 gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer. PMID:28107365

The effects of friendship network popularity on depressive symptoms during early adolescence: moderation by fear of negative evaluation and gender.

PubMed

Kornienko, Olga; Santos, Carlos E

2014-04-01

We integrated a social network analysis and developmental perspectives to examine the effects of friendship network popularity on depressive symptoms during early adolescence. We explored whether the association between social status processes (i.e., friendship network popularity) and depressive symptoms was moderated by socio-cognitive aspects of peer relations (i.e., a fear of negative evaluation by peers) and gender. This longitudinal study was conducted with a sample of 367 adolescents (48.5 % female; M age = 11.9 years; 9 % European American, 19 % African American, 7 % Native American, 60 % Latino(a), 5 % other) attending sixth and seventh grades at Time 1. Results indicated that, for males with high levels of fear of negative evaluation, friendship network popularity was associated negatively with increases in depressive symptoms. Conversely, for females with high levels of fear of negative evaluation, friendship network popularity was associated positively with increases in depressive symptoms. Theoretical and clinical implications are discussed.

Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

PubMed

Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

2015-08-01

Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes. Copyright © 2015 Elsevier Inc. All rights reserved.