Large-scale Cortical Network Properties Predict Future Sound-to-Word Learning Success

PubMed Central

Sheppard, John Patrick; Wang, Ji-Ping; Wong, Patrick C. M.

2013-01-01

The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants’ future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-to-word learning abilities among healthy, younger adults. PMID:22360625

Integration of Network Topological and Connectivity Properties for Neuroimaging Classification

PubMed Central

Jie, Biao; Gao, Wei; Wang, Qian; Wee, Chong-Yaw

2014-01-01

Rapid advances in neuroimaging techniques have provided an efficient and noninvasive way for exploring the structural and functional connectivity of the human brain. Quantitative measurement of abnormality of brain connectivity in patients with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD), have also been widely reported, especially at a group level. Recently, machine learning techniques have been applied to the study of AD and MCI, i.e., to identify the individuals with AD/MCI from the healthy controls (HCs). However, most existing methods focus on using only a single property of a connectivity network, although multiple network properties, such as local connectivity and global topological properties, can potentially be used. In this paper, by employing multikernel based approach, we propose a novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance. Specifically, two different types of kernels (i.e., vector-based kernel and graph kernel) are used to quantify two different yet complementary properties of the network, i.e., local connectivity and global topological properties. Then, multikernel learning (MKL) technique is adopted to fuse these heterogeneous kernels for neuroimaging classification. We test the performance of our proposed method on two different data sets. First, we test it on the functional connectivity networks of 12 MCI and 25 HC subjects. The results show that our method achieves significant performance improvement over those using only one type of network property. Specifically, our method achieves a classification accuracy of 91.9%, which is 10.8% better than those by single network-property-based methods. Then, we test our method for gender classification on a large set of functional connectivity networks with 133 infants scanned at birth, 1 year, and 2 years, also demonstrating very promising results. PMID:24108708

Changes in Brain Structural Networks and Cognitive Functions in Testicular Cancer Patients Receiving Cisplatin-Based Chemotherapy.

PubMed

Amidi, Ali; Hosseini, S M Hadi; Leemans, Alexander; Kesler, Shelli R; Agerbæk, Mads; Wu, Lisa M; Zachariae, Robert

2017-12-01

Cisplatin-based chemotherapy may have neurotoxic effects within the central nervous system. The aims of this study were 1) to longitudinally investigate the impact of cisplatin-based chemotherapy on whole-brain networks in testicular cancer patients undergoing treatment and 2) to explore whether possible changes are related to decline in cognitive functioning. Sixty-four newly orchiectomized TC patients underwent structural magnetic resonance imaging (T1-weighted and diffusion-weighted imaging) and cognitive testing at baseline prior to further treatment and again at a six-month follow-up. At follow-up, 22 participants had received cisplatin-based chemotherapy (CT) while 42 were in active surveillance (S). Brain structural networks were constructed for each participant, and network properties were investigated using graph theory and longitudinally compared across groups. Cognitive functioning was evaluated using standardized neuropsychological tests. All statistical tests were two-sided. Compared with the S group, the CT group demonstrated altered global and local brain network properties from baseline to follow-up as evidenced by decreases in important brain network properties such as small-worldness (P = .04), network clustering (P = .04), and local efficiency (P = .02). In the CT group, poorer overall cognitive performance was associated with decreased small-worldness (r = -0.46, P = .04) and local efficiency (r = -0.51, P = .02), and verbal fluency was associated with decreased local efficiency (r = -0.55, P = .008). Brain structural networks may be disrupted following treatment with cisplatin-based chemotherapy. Impaired brain networks may underlie poorer performance over time on both specific and nonspecific cognitive functions in patients undergoing chemotherapy. To the best of our knowledge, this is the first study to longitudinally investigate changes in structural brain networks in a cancer population, providing novel insights regarding the neurobiological mechanisms of cancer-related cognitive impairment.

Disrupted subject-specific gray matter network properties and cognitive dysfunction in type 1 diabetes patients with and without proliferative retinopathy.

PubMed

van Duinkerken, Eelco; Ijzerman, Richard G; Klein, Martin; Moll, Annette C; Snoek, Frank J; Scheltens, Philip; Pouwels, Petra J W; Barkhof, Frederik; Diamant, Michaela; Tijms, Betty M

2016-03-01

Type 1 diabetes mellitus (T1DM) patients, especially with concomitant microvascular disease, such as proliferative retinopathy, have an increased risk of cognitive deficits. Local cortical gray matter volume reductions only partially explain these cognitive dysfunctions, possibly because volume reductions do not take into account the complex connectivity structure of the brain. This study aimed to identify gray matter network alterations in relation to cognition in T1DM. We investigated if subject-specific structural gray matter network properties, constructed from T1-weighted MRI scans, were different between T1DM patients with (n = 51) and without (n = 53) proliferative retinopathy versus controls (n = 49), and were associated to cognitive decrements and fractional anisotropy, as measured by voxel-based TBSS. Global normalized and local (45 bilateral anatomical regions) clustering coefficient and path length were assessed. These network properties measure how the organization of connections in a network differs from that of randomly connected networks. Global gray matter network topology was more randomly organized in both T1DM patient groups versus controls, with the largest effects seen in patients with proliferative retinopathy. Lower local path length values were widely distributed throughout the brain. Lower local clustering was observed in the middle frontal, postcentral, and occipital areas. Complex network topology explained up to 20% of the variance of cognitive decrements, beyond other predictors. Exploratory analyses showed that lower fractional anisotropy was associated with a more random gray matter network organization. T1DM and proliferative retinopathy affect cortical network organization that may consequently contribute to clinically relevant changes in cognitive functioning in these patients. © 2015 Wiley Periodicals, Inc.

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.

Quantifying randomness in real networks

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

A comparative study of theoretical graph models for characterizing structural networks of human brain.

PubMed

Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang

2013-01-01

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Towards Online Multiresolution Community Detection in Large-Scale Networks

PubMed Central

Huang, Jianbin; Sun, Heli; Liu, Yaguang; Song, Qinbao; Weninger, Tim

2011-01-01

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks. PMID:21887325

Biomimetic oral mucin from polymer micelle networks

NASA Astrophysics Data System (ADS)

Authimoolam, Sundar Prasanth

Mucin networks are formed by the complexation of bottlebrush-like mucin glycoprotein with other small molecule glycoproteins. These glycoproteins create nanoscale strands that then arrange into a nanoporous mesh. These networks play an important role in ensuring surface hydration, lubricity and barrier protection. In order to understand the functional behavior in mucin networks, it is important to decouple their chemical and physical effects responsible for generating the fundamental property-function relationship. To achieve this goal, we propose to develop a synthetic biomimetic mucin using a layer-by-layer (LBL) deposition approach. In this work, a hierarchical 3-dimensional structures resembling natural mucin networks was generated using affinity-based interactions on synthetic and biological surfaces. Unlike conventional polyelectrolyte-based LBL methods, pre-assembled biotin-functionalized filamentous (worm-like) micelles was utilized as the network building block, which from complementary additions of streptavidin generated synthetic networks of desired thickness. The biomimetic nature in those synthetic networks are studied by evaluating its structural and bio-functional properties. Structurally, synthetic networks formed a nanoporous mesh. The networks demonstrated excellent surface hydration property and were able capable of microbial capture. Those functional properties are akin to that of natural mucin networks. Further, the role of synthetic mucin as a drug delivery vehicle, capable of providing localized and tunable release was demonstrated. By incorporating antibacterial curcumin drug loading within synthetic networks, bacterial growth inhibition was also demonstrated. Thus, such bioactive interfaces can serve as a model for independently characterizing mucin network properties and through its role as a drug carrier vehicle it presents exciting future opportunities for localized drug delivery, in regenerative applications and as bio-functional implant coats. KEYWORDS: Biomimic, Bioapplication, Drug delivery, Filomicelle, Mucin, Polymer networks.

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators.

PubMed

Campbell, S; Wang, D

1996-01-01

A network of Wilson-Cowan (WC) oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a 2D matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piecewise linear approximation to the WC oscillator) synchronizes, and we present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections that preserve spatial relationships among components and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.

Enhanced collective influence: A paradigm to optimize network disruption

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-04-01

The function of complex networks typically relies on the integrity of underlying structure. Sometimes, practical applications need to attack networks' function, namely inactivate and fragment networks' underlying structure. To effectively dismantle complex networks and regulate the function of them, a centrality measure, named CI (Morone and Makse, 2015), was proposed for node ranking. We observe that the performance of CI centrality in network disruption problem may deteriorate when it is used in networks with different topology properties. Specifically, the structural features of local network topology are overlooked in CI centrality, even though the local network topology of the nodes with a fixed CI value may have very different organization. To improve the ranking accuracy of CI, this paper proposes a variant ECI to CI by considering loop density and degree diversity of local network topology. And the proposed ECI centrality would degenerate into CI centrality with the reduction of the loop density and the degree diversity level. By comparing ECI with CI and classical centrality measures in both synthetic and real networks, the experimental results suggest that ECI can largely improve the performance of CI for network disruption. Based on the results, we analyze the correlation between the improvement and the properties of the networks. We find that the performance of ECI is positively correlated with assortative coefficient and community modularity and negatively correlated with degree inequality of networks, which can be used as guidance for practical applications.

Cross-Linking Molecules Modify Composite Actin Networks Independently

NASA Astrophysics Data System (ADS)

Schmoller, K. M.; Lieleg, O.; Bausch, A. R.

2008-09-01

While cells make use of many actin binding proteins (ABPs) simultaneously to tailor the mechanical properties of the cytoskeleton, the detailed interplay of different ABPs is not understood. By a combination of macrorheological measurements and confocal microscopy, we show that the ABPs fascin and filamin modify the structural and viscoelastic properties of composite in vitro actin networks independently. The outnumbering ABP dictates the local network structure and therefore also dominates the macromechanical network response.

The predictive power of local properties of financial networks

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2017-01-01

The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

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

PubMed Central

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

2013-01-01

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

Virtual network embedding in cross-domain network based on topology and resource attributes

NASA Astrophysics Data System (ADS)

Zhu, Lei; Zhang, Zhizhong; Feng, Linlin; Liu, Lilan

2018-03-01

Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.

Distributed rewiring model for complex networking: The effect of local rewiring rules on final structural properties.

PubMed

López Chavira, Magali Alexander; Marcelín-Jiménez, Ricardo

2017-01-01

The study of complex networks has become an important subject over the last decades. It has been shown that these structures have special features, such as their diameter, or their average path length, which in turn are the explanation of some functional properties in a system such as its fault tolerance, its fragility before attacks, or the ability to support routing procedures. In the present work, we study some of the forces that help a network to evolve to the point where structural properties are settled. Although our work is mainly focused on the possibility of applying our ideas to Information and Communication Technologies systems, we consider that our results may contribute to understanding different scenarios where complex networks have become an important modeling tool. Using a discrete event simulator, we get each node to discover the shortcuts that may connect it with regions away from its local environment. Based on this partial knowledge, each node can rewire some of its links, which allows modifying the topology of the entire underlying graph to achieve new structural properties. We proposed a distributed rewiring model that creates networks with features similar to those found in complex networks. Although each node acts in a distributed way and seeking to reduce only the trajectories of its packets, we observed a decrease of diameter and an increase in clustering coefficient in the global structure compared to the initial graph. Furthermore, we can find different final structures depending on slight changes in the local rewiring rules.

Local versus global knowledge in the Barabási-Albert scale-free network model.

PubMed

Gómez-Gardeñes, Jesús; Moreno, Yamir

2004-03-01

The scale-free model of Barabási and Albert (BA) gave rise to a burst of activity in the field of complex networks. In this paper, we revisit one of the main assumptions of the model, the preferential attachment (PA) rule. We study a model in which the PA rule is applied to a neighborhood of newly created nodes and thus no global knowledge of the network is assumed. We numerically show that global properties of the BA model such as the connectivity distribution and the average shortest path length are quite robust when there is some degree of local knowledge. In contrast, other properties such as the clustering coefficient and degree-degree correlations differ and approach the values measured for real-world networks.

Sexual networks: measuring sexual selection in structured, polyandrous populations.

PubMed

McDonald, Grant C; James, Richard; Krause, Jens; Pizzari, Tommaso

2013-03-05

Sexual selection is traditionally measured at the population level, assuming that populations lack structure. However, increasing evidence undermines this approach, indicating that intrasexual competition in natural populations often displays complex patterns of spatial and temporal structure. This complexity is due in part to the degree and mechanisms of polyandry within a population, which can influence the intensity and scale of both pre- and post-copulatory sexual competition. Attempts to measure selection at the local and global scale have been made through multi-level selection approaches. However, definitions of local scale are often based on physical proximity, providing a rather coarse measure of local competition, particularly in polyandrous populations where the local scale of pre- and post-copulatory competition may differ drastically from each other. These limitations can be solved by social network analysis, which allows us to define a unique sexual environment for each member of a population: 'local scale' competition, therefore, becomes an emergent property of a sexual network. Here, we first propose a novel quantitative approach to measure pre- and post-copulatory sexual selection, which integrates multi-level selection with information on local scale competition derived as an emergent property of networks of sexual interactions. We then use simple simulations to illustrate the ways in which polyandry can impact estimates of sexual selection. We show that for intermediate levels of polyandry, the proposed network-based approach provides substantially more accurate measures of sexual selection than the more traditional population-level approach. We argue that the increasing availability of fine-grained behavioural datasets provides exciting new opportunities to develop network approaches to study sexual selection in complex societies.

Robustness and structure of complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks are much more vulnerable to localized attack compared with random attack. In the second part, we extend the tree-like generating function method to incorporating clustering structure in complex networks. We study the robustness of a complex network system, especially a network of networks (NON) with clustering structure in each network. We find that the system becomes less robust as we increase the clustering coefficient of each network. For a partially dependent network system, we also find that the influence of the clustering coefficient on network robustness decreases as we decrease the coupling strength, and the critical coupling strength qc, at which the first-order phase transition changes to second-order, increases as we increase the clustering coefficient.

Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state.

PubMed

Cao, Hengyi; Plichta, Michael M; Schäfer, Axel; Haddad, Leila; Grimm, Oliver; Schneider, Michael; Esslinger, Christine; Kirsch, Peter; Meyer-Lindenberg, Andreas; Tost, Heike

2014-01-01

The investigation of the brain connectome with functional magnetic resonance imaging (fMRI) and graph theory analyses has recently gained much popularity, but little is known about the robustness of these properties, in particular those derived from active fMRI tasks. Here, we studied the test-retest reliability of brain graphs calculated from 26 healthy participants with three established fMRI experiments (n-back working memory, emotional face-matching, resting state) and two parcellation schemes for node definition (AAL atlas, functional atlas proposed by Power et al.). We compared the intra-class correlation coefficients (ICCs) of five different data processing strategies and demonstrated a superior reliability of task-regression methods with condition-specific regressors. The between-task comparison revealed significantly higher ICCs for resting state relative to the active tasks, and a superiority of the n-back task relative to the face-matching task for global and local network properties. While the mean ICCs were typically lower for the active tasks, overall fair to good reliabilities were detected for global and local connectivity properties, and for the n-back task with both atlases, smallworldness. For all three tasks and atlases, low mean ICCs were seen for the local network properties. However, node-specific good reliabilities were detected for node degree in regions known to be critical for the challenged functions (resting-state: default-mode network nodes, n-back: fronto-parietal nodes, face-matching: limbic nodes). Between-atlas comparison demonstrated significantly higher reliabilities for the functional parcellations for global and local network properties. Our findings can inform the choice of processing strategies, brain atlases and outcome properties for fMRI studies using active tasks, graph theory methods, and within-subject designs, in particular future pharmaco-fMRI studies. © 2013 Elsevier Inc. All rights reserved.

Effects of local and global network connectivity on synergistic epidemics

NASA Astrophysics Data System (ADS)

Broder-Rodgers, David; Pérez-Reche, Francisco J.; Taraskin, Sergei N.

2015-12-01

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Effects of local and global network connectivity on synergistic epidemics.

PubMed

Broder-Rodgers, David; Pérez-Reche, Francisco J; Taraskin, Sergei N

2015-12-01

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Network models provide insights into how oriens–lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations

PubMed Central

Ferguson, Katie A.; Huh, Carey Y. L.; Amilhon, Bénédicte; Manseau, Frédéric; Williams, Sylvain; Skinner, Frances K.

2015-01-01

Hippocampal theta is a 4–12 Hz rhythm associated with episodic memory, and although it has been studied extensively, the cellular mechanisms underlying its generation are unclear. The complex interactions between different interneuron types, such as those between oriens–lacunosum-moleculare (OLM) interneurons and bistratified cells (BiCs), make their contribution to network rhythms difficult to determine experimentally. We created network models that are tied to experimental work at both cellular and network levels to explore how these interneuron interactions affect the power of local oscillations. Our cellular models were constrained with properties from patch clamp recordings in the CA1 region of an intact hippocampus preparation in vitro. Our network models are composed of three different types of interneurons: parvalbumin-positive (PV+) basket and axo-axonic cells (BC/AACs), PV+ BiCs, and somatostatin-positive OLM cells. Also included is a spatially extended pyramidal cell model to allow for a simplified local field potential representation, as well as experimentally-constrained, theta frequency synaptic inputs to the interneurons. The network size, connectivity, and synaptic properties were constrained with experimental data. To determine how the interactions between OLM cells and BiCs could affect local theta power, we explored how the number of OLM-BiC connections and connection strength affected local theta power. We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not. Inactivation of OLM cells could result in no change or even an increase in theta power. We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations. Overall, our network models reveal a dynamic interplay between different classes of interneurons in influencing local theta power. PMID:26300744

Autapse-Induced Spiral Wave in Network of Neurons under Noise

PubMed Central

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property. PMID:24967577

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.

Autapse-induced spiral wave in network of neurons under noise.

PubMed

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property.

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

Graph Design via Convex Optimization: Online and Distributed Perspectives

NASA Astrophysics Data System (ADS)

Meng, De

Network and graph have long been natural abstraction of relations in a variety of applications, e.g. transportation, power system, social network, communication, electrical circuit, etc. As a large number of computation and optimization problems are naturally defined on graphs, graph structures not only enable important properties of these problems, but also leads to highly efficient distributed and online algorithms. For example, graph separability enables the parallelism for computation and operation as well as limits the size of local problems. More interestingly, graphs can be defined and constructed in order to take best advantage of those problem properties. This dissertation focuses on graph structure and design in newly proposed optimization problems, which establish a bridge between graph properties and optimization problem properties. We first study a new optimization problem called Geodesic Distance Maximization Problem (GDMP). Given a graph with fixed edge weights, finding the shortest path, also known as the geodesic, between two nodes is a well-studied network flow problem. We introduce the Geodesic Distance Maximization Problem (GDMP): the problem of finding the edge weights that maximize the length of the geodesic subject to convex constraints on the weights. We show that GDMP is a convex optimization problem for a wide class of flow costs, and provide a physical interpretation using the dual. We present applications of the GDMP in various fields, including optical lens design, network interdiction, and resource allocation in the control of forest fires. We develop an Alternating Direction Method of Multipliers (ADMM) by exploiting specific problem structures to solve large-scale GDMP, and demonstrate its effectiveness in numerical examples. We then turn our attention to distributed optimization on graph with only local communication. Distributed optimization arises in a variety of applications, e.g. distributed tracking and localization, estimation problems in sensor networks, multi-agent coordination. Distributed optimization aims to optimize a global objective function formed by summation of coupled local functions over a graph via only local communication and computation. We developed a weighted proximal ADMM for distributed optimization using graph structure. This fully distributed, single-loop algorithm allows simultaneous updates and can be viewed as a generalization of existing algorithms. More importantly, we achieve faster convergence by jointly designing graph weights and algorithm parameters. Finally, we propose a new problem on networks called Online Network Formation Problem: starting with a base graph and a set of candidate edges, at each round of the game, player one first chooses a candidate edge and reveals it to player two, then player two decides whether to accept it; player two can only accept limited number of edges and make online decisions with the goal to achieve the best properties of the synthesized network. The network properties considered include the number of spanning trees, algebraic connectivity and total effective resistance. These network formation games arise in a variety of cooperative multiagent systems. We propose a primal-dual algorithm framework for the general online network formation game, and analyze the algorithm performance by the competitive ratio and regret.

Node-node correlations and transport properties in scale-free networks

NASA Astrophysics Data System (ADS)

Obregon, Bibiana; Guzman, Lev

2011-03-01

We study some transport properties of complex networks. We focus our attention on transport properties of scale-free and small-world networks and compare two types of transport: Electric and max-flow cases. In particular, we construct scale-free networks, with a given degree sequence, to estimate the distribution of conductances for different values of assortative/dissortative mixing. For the electric case we find that the distributions of conductances are affect ed by the assortative mixing of the network whereas for the max-flow case, the distributions almost do not show changes when node-node correlations are altered. Finally, we compare local and global transport in terms of the average conductance for the small-world (Watts-Strogatz) model

Random Boolean networks for autoassociative memory: Optimization and sequential learning

NASA Astrophysics Data System (ADS)

Sherrington, D.; Wong, K. Y. M.

Conventional neural networks are based on synaptic storage of information, even when the neural states are discrete and bounded. In general, the set of potential local operations is much greater. Here we discuss some aspects of the properties of networks of binary neurons with more general Boolean functions controlling the local dynamics. Two specific aspects are emphasised; (i) optimization in the presence of noise and (ii) a simple model for short-term memory exhibiting primacy and recency in the recall of sequentially taught patterns.

Structure-function relationship of biological gels revealed by multiple-particle tracking and differential interference contrast microscopy: The case of human lamin networks

NASA Astrophysics Data System (ADS)

Panorchan, Porntula; Wirtz, Denis; Tseng, Yiider

2004-10-01

Lamin B1 filaments organize into a thin dense meshwork underlying the nucleoplasmic side of the nuclear envelope. Recent experiments in vivo suggest that lamin B1 plays a key structural role in the nuclear envelope, but the intrinsic mechanical properties of lamin B1 networks remain unknown. To assess the potential mechanical contribution of lamin B1 in maintaining the integrity and providing structural support to the nucleus, we measured the micromechanical properties and examined the ultrastructural distribution of lamin B1 networks in vitro using particle tracking methods and differential interference contrast (DIC) microscopy. We exploit various surface chemistries of the probe microspheres (carboxylated, polyethylene glycol-coated, and amine-modified) to differentiate lamin-rich from lamin-poor regions and to rigorously extract local viscoelastic moduli from the mean-squared displacements of noninteracting particles. Our results show that human lamin B1 can, even in the absence of auxiliary proteins, form stiff and yet extremely porous networks that are well suited to provide structural strength to the nuclear lamina. Combining DIC microscopy and particle tracking allows us to relate directly the local organization of a material to its local mechanical properties, a general methodology that can be extended to living cells.

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks

PubMed Central

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial. PMID:23563395

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

PubMed

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy

2013-01-01

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

Scaling properties of multitension domain wall networks

NASA Astrophysics Data System (ADS)

Oliveira, M. F.; Martins, C. J. A. P.

2015-02-01

We study the asymptotic scaling properties of domain wall networks with three different tensions in various cosmological epochs. We discuss the conditions under which a scale-invariant evolution of the network (which is well established for simpler walls) still applies and also consider the limiting case where defects are locally planar and the curvature is concentrated in the junctions. We present detailed quantitative predictions for scaling densities in various contexts, which should be testable by means of future high-resolution numerical simulations.

Study on spin filtering and switching action in a double-triangular network chain

NASA Astrophysics Data System (ADS)

Zhang, Yongmei

2018-04-01

Spin transport properties of a double-triangular quantum network with local magnetic moment on backbones and magnetic flux penetrating the network plane are studied. Numerical simulation results show that such a quantum network will be a good candidate for spin filter and spin switch. Local dispersion and density of states are considered in the framework of tight-binding approximation. Transmission coefficients are calculated by the method of transfer matrix. Spin transmission is regulated by substrate magnetic moment and magnetic flux piercing those triangles. Experimental realization of such theoretical research will be conducive to designing of new spintronic devices.

Diversity of key players in the microbial ecosystems of the human body

PubMed Central

Jordán, Ferenc; Lauria, Mario; Scotti, Marco; Nguyen, Thanh-Phuong; Praveen, Paurush; Morine, Melissa; Priami, Corrado

2015-01-01

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different. PMID:26514870

Diversity of key players in the microbial ecosystems of the human body.

PubMed

Jordán, Ferenc; Lauria, Mario; Scotti, Marco; Nguyen, Thanh-Phuong; Praveen, Paurush; Morine, Melissa; Priami, Corrado

2015-10-30

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different.

Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

PubMed

Zhong, Suyu; He, Yong; Gong, Gaolang

2015-05-01

Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties. © 2015 Wiley Periodicals, Inc.

Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

PubMed

Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

2018-03-12

Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround.

PubMed

Boucsein, Clemens; Nawrot, Martin P; Schnepel, Philipp; Aertsen, Ad

2011-01-01

Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing.

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.

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

PubMed

Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

2018-07-01

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p < 0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

Stress Response of Granular Systems

NASA Astrophysics Data System (ADS)

Ramola, Kabir; Chakraborty, Bulbul

2017-10-01

We develop a framework for stress response in two dimensional granular media, with and without friction, that respects vector force balance at the microscopic level. We introduce local gauge degrees of freedom that determine the response of contact forces between constituent grains on a given, disordered, contact network, to external perturbations. By mapping this response to the spectral properties of the graph Laplacian corresponding to the underlying contact network, we show that this naturally leads to spatial localization of forces. We present numerical evidence for localization using exact diagonalization studies of network Laplacians of soft disk packings. Finally, we discuss the role of other constraints, such as torque balance, in determining the stability of a granular packing to external perturbations.

Physical limits to biomechanical sensing in disordered fibre networks

NASA Astrophysics Data System (ADS)

Beroz, Farzan; Jawerth, Louise M.; Münster, Stefan; Weitz, David A.; Broedersz, Chase P.; Wingreen, Ned S.

2017-07-01

Cells actively probe and respond to the stiffness of their surroundings. Since mechanosensory cells in connective tissue are surrounded by a disordered network of biopolymers, their in vivo mechanical environment can be extremely heterogeneous. Here we investigate how this heterogeneity impacts mechanosensing by modelling the cell as an idealized local stiffness sensor inside a disordered fibre network. For all types of networks we study, including experimentally-imaged collagen and fibrin architectures, we find that measurements applied at different points yield a strikingly broad range of local stiffnesses, spanning roughly two decades. We verify via simulations and scaling arguments that this broad range of local stiffnesses is a generic property of disordered fibre networks. Finally, we show that to obtain optimal, reliable estimates of global tissue stiffness, a cell must adjust its size, shape, and position to integrate multiple stiffness measurements over extended regions of space.

Epidemic spreading in metapopulation networks with heterogeneous infection rates

NASA Astrophysics Data System (ADS)

Gong, Yong-Wang; Song, Yu-Rong; Jiang, Guo-Ping

2014-12-01

In this paper, we study epidemic spreading in metapopulation networks wherein each node represents a subpopulation symbolizing a city or an urban area and links connecting nodes correspond to the human traveling routes among cities. Differently from previous studies, we introduce a heterogeneous infection rate to characterize the effect of nodes' local properties, such as population density, individual health habits, and social conditions, on epidemic infectivity. By means of a mean-field approach and Monte Carlo simulations, we explore how the heterogeneity of the infection rate affects the epidemic dynamics, and find that large fluctuations of the infection rate have a profound impact on the epidemic threshold as well as the temporal behavior of the prevalence above the epidemic threshold. This work can refine our understanding of epidemic spreading in metapopulation networks with the effect of nodes' local properties.

The role of syntax in complex networks: Local and global importance of verbs in a syntactic dependency network

NASA Astrophysics Data System (ADS)

Čech, Radek; Mačutek, Ján; Žabokrtský, Zdeněk

2011-10-01

Syntax of natural language has been the focus of linguistics for decades. The complex network theory, being one of new research tools, opens new perspectives on syntax properties of the language. Despite numerous partial achievements, some fundamental problems remain unsolved. Specifically, although statistical properties typical for complex networks can be observed in all syntactic networks, the impact of syntax itself on these properties is still unclear. The aim of the present study is to shed more light on the role of syntax in the syntactic network structure. In particular, we concentrate on the impact of the syntactic function of a verb in the sentence on the complex network structure. Verbs play the decisive role in the sentence structure (“local” importance). From this fact we hypothesize the importance of verbs in the complex network (“global” importance). The importance of verb in the complex network is assessed by the number of links which are directed from the node representing verb to other nodes in the network. Six languages (Catalan, Czech, Dutch, Hungarian, Italian, Portuguese) were used for testing the hypothesis.

The Local Structure of Globalization. The Network Dynamics of Foreign Direct Investments in the International Electricity Industry

NASA Astrophysics Data System (ADS)

Koskinen, Johan; Lomi, Alessandro

2013-05-01

We study the evolution of the network of foreign direct investment (FDI) in the international electricity industry during the period 1994-2003. We assume that the ties in the network of investment relations between countries are created and deleted in continuous time, according to a conditional Gibbs distribution. This assumption allows us to take simultaneously into account the aggregate predictions of the well-established gravity model of international trade as well as local dependencies between network ties connecting the countries in our sample. According to the modified version of the gravity model that we specify, the probability of observing an investment tie between two countries depends on the mass of the economies involved, their physical distance, and the tendency of the network to self-organize into local configurations of network ties. While the limiting distribution of the data generating process is an exponential random graph model, we do not assume the system to be in equilibrium. We find evidence of the effects of the standard gravity model of international trade on evolution of the global FDI network. However, we also provide evidence of significant dyadic and extra-dyadic dependencies between investment ties that are typically ignored in available research. We show that local dependencies between national electricity industries are sufficient for explaining global properties of the network of foreign direct investments. We also show, however, that network dependencies vary significantly over time giving rise to a time-heterogeneous localized process of network evolution.

The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.

PubMed

Weng, Jian; Xie, Ye; Wang, Chunjie; Chen, Feiyan

2017-08-18

Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.

Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

PubMed

Hébert-Dufresne, Laurent; Grochow, Joshua A; Allard, Antoine

2016-08-18

We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

Differential network entropy reveals cancer system hallmarks

PubMed Central

West, James; Bianconi, Ginestra; Severini, Simone; Teschendorff, Andrew E.

2012-01-01

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network we here demonstrate that cancer cells are characterised by an increase in network entropy. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local network entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local correlation patterns. In particular, we find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in network entropy. These findings may have potential implications for identifying novel drug targets. PMID:23150773

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.

Paving the Way Towards Reactive Planar Spanner Construction in Wireless Networks

NASA Astrophysics Data System (ADS)

Frey, Hannes; Rührup, Stefan

A spanner is a subgraph of a given graph that supports the original graph's shortest path lengths up to a constant factor. Planar spanners and their distributed construction are of particular interest for geographic routing, which is an efficient localized routing scheme for wireless ad hoc and sensor networks. Planarity of the network graph is a key criterion for guaranteed delivery, while the spanner property supports efficiency in terms of path length. We consider the problem of reactive local spanner construction, where a node's local topology is determined on demand. Known message-efficient reactive planarization algorithms do not preserve the spanner property, while reactive spanner constructions with a low message overhead have not been described so far. We introduce the concept of direct planarization which may be an enabler of efficient reactive spanner construction. Given an edge, nodes check for all incident intersecting edges a certain geometric criterion and withdraw the edge if this criterion is not satisfied. We use this concept to derive a generic reactive topology control mechanism and consider two geometric criteria. Simulation results show that direct planarization increases the performance of localized geographic routing by providing shorter paths than existing reactive approaches.

Competing dynamic phases of active polymer networks

NASA Astrophysics Data System (ADS)

Freedman, Simon; Banerjee, Shiladitya; Dinner, Aaron R.

Recent experiments on in-vitro reconstituted assemblies of F-actin, myosin-II motors, and cross-linking proteins show that tuning local network properties can changes the fundamental biomechanical behavior of the system. For example, by varying cross-linker density and actin bundle rigidity, one can switch between contractile networks useful for reshaping cells, polarity sorted networks ideal for directed molecular transport, and frustrated networks with robust structural properties. To efficiently investigate the dynamic phases of actomyosin networks, we developed a coarse grained non-equilibrium molecular dynamics simulation of model semiflexible filaments, molecular motors, and cross-linkers with phenomenologically defined interactions. The simulation's accuracy was verified by benchmarking the mechanical properties of its individual components and collective behavior against experimental results at the molecular and network scales. By adjusting the model's parameters, we can reproduce the qualitative phases observed in experiment and predict the protein characteristics where phase crossovers could occur in collective network dynamics. Our model provides a framework for understanding cells' multiple uses of actomyosin networks and their applicability in materials research. Supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.

Development of an Empirical Local Magnitude Formula for Northern Oklahoma

NASA Astrophysics Data System (ADS)

Spriggs, N.; Karimi, S.; Moores, A. O.

2015-12-01

In this paper we focus on determining a local magnitude formula for northern Oklahoma that is unbiased with distance by empirically constraining the attenuation properties within the region of interest based on the amplitude of observed seismograms. For regional networks detecting events over several hundred kilometres, distance correction terms play an important role in determining the magnitude of an event. Standard distance correction terms such as Hutton and Boore (1987) may have a significant bias with distance if applied in a region with different attenuation properties, resulting in an incorrect magnitude. We have presented data from a regional network of broadband seismometers installed in bedrock in northern Oklahoma. The events with magnitude in the range of 2.0 and 4.5, distributed evenly across this network are considered. We find that existing models show a bias with respect to hypocentral distance. Observed amplitude measurements demonstrate that there is a significant Moho bounce effect that mandates the use of a trilinear attenuation model in order to avoid bias in the distance correction terms. We present two different approaches of local magnitude calibration. The first maintains the classic definition of local magnitude as proposed by Richter. The second method calibrates local magnitude so that it agrees with moment magnitude where a regional moment tensor can be computed. To this end, regional moment tensor solutions and moment magnitudes are computed for events with magnitude larger than 3.5 to allow calibration of local magnitude to moment magnitude. For both methods the new formula results in magnitudes systematically lower than previous values computed with Eaton's (1992) model. We compare the resulting magnitudes and discuss the benefits and drawbacks of each method. Our results highlight the importance of correct calibration of the distance correction terms for accurate local magnitude assessment in regional networks.

Increased entropy of signal transduction in the cancer metastasis phenotype.

PubMed

Teschendorff, Andrew E; Severini, Simone

2010-07-30

The statistical study of biological networks has led to important novel biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis and provide examples of de-novo discoveries of gene modules with known roles in apoptosis, immune-mediated tumour suppression, cell-cycle and tumour invasion. Importantly, we also identify a novel gene module within the insulin growth factor signalling pathway, alteration of which may predispose the tumour to metastasize. These results demonstrate that a metastatic cancer phenotype is characterised by an increase in the randomness of the local information flux patterns. Measures of local randomness in integrated protein interaction mRNA expression networks may therefore be useful for identifying genes and signalling pathways disrupted in one phenotype relative to another. Further exploration of the statistical properties of such integrated cancer expression and protein interaction networks will be a fruitful endeavour.

Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks

NASA Astrophysics Data System (ADS)

Yilmaz, Ergin; Baysal, Veli; Ozer, Mahmut; Perc, Matjaž

2016-02-01

We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman-Watts small-world network consisting of stochastic Hodgkin-Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obtained results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin-Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm.

Hierarchical coefficient of a multifractal based network

NASA Astrophysics Data System (ADS)

Moreira, Darlan A.; Lucena, Liacir dos Santos; Corso, Gilberto

2014-02-01

The hierarchical property for a general class of networks stands for a power-law relation between clustering coefficient, CC and connectivity k: CC∝kβ. This relation is empirically verified in several biologic and social networks, as well as in random and deterministic network models, in special for hierarchical networks. In this work we show that the hierarchical property is also present in a Lucena network. To create a Lucena network we use the dual of a multifractal lattice ML, the vertices are the sites of the ML and links are established between neighbouring lattices, therefore this network is space filling and planar. Besides a Lucena network shows a scale-free distribution of connectivity. We deduce a relation for the maximal local clustering coefficient CCimax of a vertex i in a planar graph. This condition expresses that the number of links among neighbour, N△, of a vertex i is equal to its connectivity ki, that means: N△=ki. The Lucena network fulfils the condition N△≃ki independent of ki and the anisotropy of ML. In addition, CCmax implies the threshold β=1 for the hierarchical property for any scale-free planar network.

A Three-Dimensional Computational Model of Collagen Network Mechanics

PubMed Central

Lee, Byoungkoo; Zhou, Xin; Riching, Kristin; Eliceiri, Kevin W.; Keely, Patricia J.; Guelcher, Scott A.; Weaver, Alissa M.; Jiang, Yi

2014-01-01

Extracellular matrix (ECM) strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model) and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned). We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions. PMID:25386649

Reduced hemispheric asymmetry of brain anatomical networks in attention deficit hyperactivity disorder.

PubMed

Li, Dandan; Li, Ting; Niu, Yan; Xiang, Jie; Cao, Rui; Liu, Bo; Zhang, Hui; Wang, Bin

2018-05-11

Despite many studies reporting a variety of alterations in brain networks in patients with attention deficit hyperactivity disorder (ADHD), alterations in hemispheric anatomical networks are still unclear. In this study, we investigated topology alterations in hemispheric white matter in patients with ADHD and the relationship between these alterations and clinical features of the illness. Weighted hemispheric brain anatomical networks were first constructed for each of 40 right-handed patients with ADHD and 53 matched normal controls. Then, graph theoretical approaches were utilized to compute hemispheric topological properties. The small-world property was preserved in the hemispheric network. Furthermore, a significant group-by-hemisphere interaction was revealed in global efficiency, local efficiency and characteristic path length, attributed to the significantly reduced hemispheric asymmetry of global and local integration in patients with ADHD compared with normal controls. Specifically, reduced asymmetric regional efficiency was found in three regions. Finally, we found that the abnormal asymmetry of hemispheric brain anatomical network topology and regional efficiency were both associated with clinical features (the Adult ADHD Self-Report Scale and Wechsler Adult Intelligence Scale) in patients. Our findings provide new insights into the lateralized nature of hemispheric dysconnectivity and highlight the potential for using brain network measures of hemispheric asymmetry as neural biomarkers for ADHD and its clinical features.

A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks.

PubMed

Zhu, Mingqiang; Song, Fei; Xu, Lei; Seo, Jung Taek; You, Ilsun

2017-11-29

As the key element, sensor networks are widely investigated by the Internet of Things (IoT) community. When massive numbers of devices are well connected, malicious attackers may deliberately propagate fake position information to confuse the ordinary users and lower the network survivability in belt-type situation. However, most existing positioning solutions only focus on the algorithm accuracy and do not consider any security aspects. In this paper, we propose a comprehensive scheme for node localization protection, which aims to improve the energy-efficient, reliability and accuracy. To handle the unbalanced resource consumption, a node deployment mechanism is presented to satisfy the energy balancing strategy in resource-constrained scenarios. According to cooperation localization theory and network connection property, the parameter estimation model is established. To achieve reliable estimations and eliminate large errors, an improved localization algorithm is created based on modified average hop distances. In order to further improve the algorithms, the node positioning accuracy is enhanced by using the steepest descent method. The experimental simulations illustrate the performance of new scheme can meet the previous targets. The results also demonstrate that it improves the belt-type sensor networks' survivability, in terms of anti-interference, network energy saving, etc.

Topological properties of complex networks in protein structures

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

2014-03-01

We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

Energy landscapes for a machine learning application to series data

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

Ballard, Andrew J.; Stevenson, Jacob D.; Das, Ritankar

2016-03-28

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in termsmore » of distributions of local minima and their properties.« less

Persistent homology analysis of ion aggregations and hydrogen-bonding networks.

PubMed

Xia, Kelin

2018-05-16

Despite the great advancement of experimental tools and theoretical models, a quantitative characterization of the microscopic structures of ion aggregates and their associated water hydrogen-bonding networks still remains a challenging problem. In this paper, a newly-invented mathematical method called persistent homology is introduced, for the first time, to quantitatively analyze the intrinsic topological properties of ion aggregation systems and hydrogen-bonding networks. The two most distinguishable properties of persistent homology analysis of assembly systems are as follows. First, it does not require a predefined bond length to construct the ion or hydrogen-bonding network. Persistent homology results are determined by the morphological structure of the data only. Second, it can directly measure the size of circles or holes in ion aggregates and hydrogen-bonding networks. To validate our model, we consider two well-studied systems, i.e., NaCl and KSCN solutions, generated from molecular dynamics simulations. They are believed to represent two morphological types of aggregation, i.e., local clusters and extended ion networks. It has been found that the two aggregation types have distinguishable topological features and can be characterized by our topological model very well. Further, we construct two types of networks, i.e., O-networks and H2O-networks, for analyzing the topological properties of hydrogen-bonding networks. It is found that for both models, KSCN systems demonstrate much more dramatic variations in their local circle structures with a concentration increase. A consistent increase of large-sized local circle structures is observed and the sizes of these circles become more and more diverse. In contrast, NaCl systems show no obvious increase of large-sized circles. Instead a consistent decline of the average size of the circle structures is observed and the sizes of these circles become more and more uniform with a concentration increase. As far as we know, these unique intrinsic topological features in ion aggregation systems have never been pointed out before. More importantly, our models can be directly used to quantitatively analyze the intrinsic topological invariants, including circles, loops, holes, and cavities, of any network-like structures, such as nanomaterials, colloidal systems, biomolecular assemblies, among others. These topological invariants cannot be described by traditional graph and network models.

An evidential link prediction method and link predictability based on Shannon entropy

NASA Astrophysics Data System (ADS)

Yin, Likang; Zheng, Haoyang; Bian, Tian; Deng, Yong

2017-09-01

Predicting missing links is of both theoretical value and practical interest in network science. In this paper, we empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks. Most of the previous studies focus on the accuracy, however, it is crucial to consider the link predictability as an initial property of networks itself. Hence, this paper has proposed a new link prediction approach called evidential measure (EM) based on Dempster-Shafer theory. Moreover, this paper proposed a new method to measure link predictability via local information and Shannon entropy.

Investigating Molecular Level Stress-Strain Relationships in Entangled F-Actin Networks by Combined Force-Measuring Optical Tweezers and Fluorescence Microscopy

NASA Astrophysics Data System (ADS)

Lee, Kent; Henze, Dean; Robertson-Anderson, Rae

2013-03-01

Actin is an important cytoskeletal protein involved in cell structure and motility, cancer invasion and metastasis, and muscle contraction. The intricate viscoelastic properties of filamentous actin (F-actin) networks allow for the many dynamic roles of actin, thus warranting investigation. Exploration of this unique stress-strain/strain-rate relationship in complex F-actin networks can also improve biomimetic materials engineering. Here, we use optical tweezers with fluorescence microscopy to study the viscoelastic properties of F-actin networks on the microscopic level. Optically trapped microspheres embedded in various F-actin networks are moved through the network using a nanoprecision piezoelectric stage. The force exerted on the microspheres by the F-actin network and subsequent force relaxation are measured, while a fraction of the filaments in the network are fluorescent-labeled to observe filament deformation in real-time. The dependence of the viscoelastic properties of the network on strain rates and amplitudes as well as F-actin concentration is quantified. This approach provides the much-needed link between induced force and deformation over localized regimes (tens of microns) and down to the single molecule level.

Naming games in two-dimensional and small-world-connected random geometric networks.

PubMed

Lu, Qiming; Korniss, G; Szymanski, B K

2008-01-01

We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game [Baronchelli, J. Stat. Mech.: Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.

Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks.

PubMed

Huang, Dengfeng; Ren, Aifeng; Shang, Jing; Lei, Qiao; Zhang, Yun; Yin, Zhongliang; Li, Jun; von Deneen, Karen M; Huang, Liyu

2016-01-01

The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed. The local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.

Exploring the patterns and evolution of self-organized urban street networks through modeling

NASA Astrophysics Data System (ADS)

Rui, Yikang; Ban, Yifang; Wang, Jiechen; Haas, Jan

2013-03-01

As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.

Dynamics of subway networks based on vehicles operation timetable

NASA Astrophysics Data System (ADS)

Xiao, Xue-mei; Jia, Li-min; Wang, Yan-hui

2017-05-01

In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices' and edges' local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network.

Schizophrenia classification using functional network features

NASA Astrophysics Data System (ADS)

Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

2012-03-01

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Complete stability of delayed recurrent neural networks with Gaussian activation functions.

PubMed

Liu, Peng; Zeng, Zhigang; Wang, Jun

2017-01-01

This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3 k equilibrium points with 0≤k≤n, among which 2 k and 3 k -2 k equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

The geometry of chaotic dynamics — a complex network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

2011-12-01

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

Polymerization speed and diffractive experiments in polymer network LC test cells

NASA Astrophysics Data System (ADS)

Braun, Larissa; Gong, Zhen; Habibpourmoghadam, Atefeh; Schafforz, Samuel L.; Wolfram, Lukas; Lorenz, Alexander

2018-02-01

Polymer-network liquid crystals (LCs), where the response properties of a LC can be enhanced by the presence of a porous polymer network, are investigated. In the reported experiments, liquid crystals were doped with a small amount (< 10%) of photo-curable acrylate monomers. Samples with surface grafted photoinitiators, dissolvable photoinitiators, and samples with both kinds of photoinitiators were prepared. Both conventional (planar electrodes) and diffractive (interdigitated electrodes) test cells were used. These samples were exposed with a UV light source and changes of their capacitance were investigated with an LCR meter during exposure. Due to the presence of the in-situ generated polymer network, the electro-optic response properties of photo cured samples were enhanced. For example, their continuous phase modulation properties led to more localized responses in samples with interdigitated electrodes, which caused suppression of selected diffraction orders in the diffraction patterns recorded in polymer network LC samples. Moreover, capacitance changes were investigated during photopolymerization of a blue phase LC.

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Reconstructing cerebrovascular networks under local physiological constraints by integer programming

DOE PAGES

Rempfler, Markus; Schneider, Matthias; Ielacqua, Giovanna D.; ...

2015-04-23

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to the probabilistic model. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (µCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of ourmore » probabilistic model. As a result, we perform experiments on micro magnetic resonance angiography (µMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.« less

Random walks with long-range steps generated by functions of Laplacian matrices

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Michelitsch, T. M.; Collet, B. A.; Nowakowski, A. F.; Nicolleau, F. C. G. A.

2018-04-01

In this paper, we explore different Markovian random walk strategies on networks with transition probabilities between nodes defined in terms of functions of the Laplacian matrix. We generalize random walk strategies with local information in the Laplacian matrix, that describes the connections of a network, to a dynamic determined by functions of this matrix. The resulting processes are non-local allowing transitions of the random walker from one node to nodes beyond its nearest neighbors. We find that only two types of Laplacian functions are admissible with distinct behaviors for long-range steps in the infinite network limit: type (i) functions generate Brownian motions, type (ii) functions Lévy flights. For this asymptotic long-range step behavior only the lowest non-vanishing order of the Laplacian function is relevant, namely first order for type (i), and fractional order for type (ii) functions. In the first part, we discuss spectral properties of the Laplacian matrix and a series of relations that are maintained by a particular type of functions that allow to define random walks on any type of undirected connected networks. Once described general properties, we explore characteristics of random walk strategies that emerge from particular cases with functions defined in terms of exponentials, logarithms and powers of the Laplacian as well as relations of these dynamics with non-local strategies like Lévy flights and fractional transport. Finally, we analyze the global capacity of these random walk strategies to explore networks like lattices and trees and different types of random and complex networks.

A Small World of Neuronal Synchrony

PubMed Central

Yu, Shan; Huang, Debin; Singer, Wolf

2008-01-01

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding. PMID:18400792

A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks

PubMed Central

Zhu, Mingqiang; Song, Fei; Xu, Lei; Seo, Jung Taek

2017-01-01

As the key element, sensor networks are widely investigated by the Internet of Things (IoT) community. When massive numbers of devices are well connected, malicious attackers may deliberately propagate fake position information to confuse the ordinary users and lower the network survivability in belt-type situation. However, most existing positioning solutions only focus on the algorithm accuracy and do not consider any security aspects. In this paper, we propose a comprehensive scheme for node localization protection, which aims to improve the energy-efficient, reliability and accuracy. To handle the unbalanced resource consumption, a node deployment mechanism is presented to satisfy the energy balancing strategy in resource-constrained scenarios. According to cooperation localization theory and network connection property, the parameter estimation model is established. To achieve reliable estimations and eliminate large errors, an improved localization algorithm is created based on modified average hop distances. In order to further improve the algorithms, the node positioning accuracy is enhanced by using the steepest descent method. The experimental simulations illustrate the performance of new scheme can meet the previous targets. The results also demonstrate that it improves the belt-type sensor networks’ survivability, in terms of anti-interference, network energy saving, etc. PMID:29186072

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.

PubMed

Karuza, Elisabeth A; Thompson-Schill, Sharon L; Bassett, Danielle S

2016-08-01

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Local and global responses in complex gene regulation networks

NASA Astrophysics Data System (ADS)

Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

2009-04-01

An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

Modeling online social signed networks

NASA Astrophysics Data System (ADS)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

PubMed Central

McDonnell, Mark D.; Ward, Lawrence M.

2014-01-01

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity. PMID:24743633

Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

PubMed Central

Sacchet, Matthew D.; Prasad, Gautam; Foland-Ross, Lara C.; Thompson, Paul M.; Gotlib, Ian H.

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities. PMID:25762941

Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

[Aberrant topological properties of whole-brain functional network in chronic right-sided sensorineural hearing loss: a resting-state functional MRI study].

PubMed

Zhang, Lingling; Liu, Bin; Xu, Yangwen; Yang, Ming; Feng, Yuan; Huang, Yaqing; Huan, Zhichun; Hou, Zhaorui

2015-02-03

To investigate the topological properties of the functional brain network in unilateral sensorineural hearing loss patients. In this study, we acquired resting-state BOLD- fMRI data from 19 right-sided SNHL patients and 31 healthy controls with normal hearing and constructed their whole brain functional networks. Two-sample two-tailed t-tests were performed to investigate group differences in topological parameters between the USNHL patients and the controls. Partial correlation analysis was conducted to determine the relationships between the network metrics and USNHL-related variables. Both USNHL patients and controls exhibited small-word architecture in their brain functional networks within the range 0. 1 - 0. 2 of sparsity. Compared to the controls, USNHL patients showed significant increase in characteristic path length and normalized characteristic path length, but significant decrease in global efficiency. Clustering coefficient, local efficiency and normalized clustering coefficient demonstrated no significant difference. Furthermore, USNHL patients exhibited no significant association between the altered network metrics and the duration of USNHL or the severity of hearing loss. Our results indicated the altered topological properties of whole brain functional networks in USNHL patients, which may help us to understand pathophysiologic mechanism of USNHL patients.

A generalized LSTM-like training algorithm for second-order recurrent neural networks

PubMed Central

Monner, Derek; Reggia, James A.

2011-01-01

The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

Nonrigid synthetic aperture radar and optical image coregistration by combining local rigid transformations using a Kohonen network.

PubMed

Salehpour, Mehdi; Behrad, Alireza

2017-10-01

This study proposes a new algorithm for nonrigid coregistration of synthetic aperture radar (SAR) and optical images. The proposed algorithm employs point features extracted by the binary robust invariant scalable keypoints algorithm and a new method called weighted bidirectional matching for initial correspondence. To refine false matches, we assume that the transformation between SAR and optical images is locally rigid. This property is used to refine false matches by assigning scores to matched pairs and clustering local rigid transformations using a two-layer Kohonen network. Finally, the thin plate spline algorithm and mutual information are used for nonrigid coregistration of SAR and optical images.

Neocortical dynamics due to axon propagation delays in cortico-cortical fibers: EEG traveling and standing waves with implications for top-down influences on local networks and white matter disease

PubMed Central

Nunez, Paul L.; Srinivasan, Ramesh

2013-01-01

The brain is treated as a nested hierarchical complex system with substantial interactions across spatial scales. Local networks are pictured as embedded within global fields of synaptic action and action potentials. Global fields may act top-down on multiple networks, acting to bind remote networks. Because of scale-dependent properties, experimental electrophysiology requires both local and global models that match observational scales. Multiple local alpha rhythms are embedded in a global alpha rhythm. Global models are outlined in which cm-scale dynamic behaviors result largely from propagation delays in cortico-cortical axons and cortical background excitation level, controlled by neuromodulators on long time scales. The idealized global models ignore the bottom-up influences of local networks on global fields so as to employ relatively simple mathematics. The resulting models are transparently related to several EEG and steady state visually evoked potentials correlated with cognitive states, including estimates of neocortical coherence structure, traveling waves, and standing waves. The global models suggest that global oscillatory behavior of self-sustained (limit-cycle) modes lower than about 20 Hz may easily occur in neocortical/white matter systems provided: Background cortical excitability is sufficiently high; the strength of long cortico-cortical axon systems is sufficiently high; and the bottom-up influence of local networks on the global dynamic field is sufficiently weak. The global models provide "entry points" to more detailed studies of global top-down influences, including binding of weakly connected networks, modulation of gamma oscillations by theta or alpha rhythms, and the effects of white matter deficits. PMID:24505628

Harnessing Electrostatic Forces to Grow Bio-inspired Hierarchical Vascular Networks

NASA Astrophysics Data System (ADS)

Behler, Kristopher; Melrose, Zachary; Schott, Andrew; Wetzel, Eric

2012-02-01

Vascular networks provide a system for fluid distribution. Artificial vascular materials with enhanced properties are currently being developed that could ultimately be integrated into systems reliant upon fluid transport while retaining their structural properties. An uninterrupted and controllable supply of liquid is optimal for many applications such as continual self-healing materials, in-situ delivery of index matched fluids, thermal management and drug delivery systems could benefit from a bio-inspired vascular approach that combines complex network geometries with minimal processing parameters. Two such approaches to induce vascular networks are electrohydrodynamic viscous fingering (EHVF) and electrical treeing (ET). EHVF is a phenomenon that occurs when a low viscosity liquid is forced through a high viscosity fluid or matrix, resulting in branches due to capillary and viscous forces in the high viscosity material. By applying voltages of 0 -- 60 kV, finger diameter is reduced. ET is the result of partial discharges in a dielectric material. In the vicinity of a small diameter electrode, the local electric field is greater than the global dielectric strength, causing a localized, step-wise, breakdown to occur forming a highly branched interconnected structure. ET is a viable method to produce networks on a smaller, micron, scale than the products of the EHVF method.

Towards a unified description of the hydrogen bond network of liquid water: A dynamics based approach

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

Ozkanlar, Abdullah, E-mail: abdullah.ozkanlar@wsu.edu; Zhou, Tiecheng; Clark, Aurora E., E-mail: auclark@wsu.edu

2014-12-07

The definition of a hydrogen bond (H-bond) is intimately related to the topological and dynamic properties of the hydrogen bond network within liquid water. The development of a universal H-bond definition for water is an active area of research as it would remove many ambiguities in the network properties that derive from the fixed definition employed to assign whether a water dimer is hydrogen bonded. This work investigates the impact that an electronic-structure based definition, an energetic, and a geometric definition of the H-bond has upon both topological and dynamic network behavior of simulated water. In each definition, the usemore » of a cutoff (either geometric or energetic) to assign the presence of a H-bond leads to the formation of transiently bonded or broken dimers, which have been quantified within the simulation data. The relative concentration of transient species, and their duration, results in two of the three definitions sharing similarities in either topological or dynamic features (H-bond distribution, H-bond lifetime, etc.), however no two definitions exhibit similar behavior for both classes of network properties. In fact, two networks with similar local network topology (as indicated by similar average H-bonds) can have dramatically different global network topology (as indicated by the defect state distributions) and altered H-bond lifetimes. A dynamics based correction scheme is then used to remove artificially transient H-bonds and to repair artificially broken bonds within the network such that the corrected network exhibits the same structural and dynamic properties for two H-bond definitions (the properties of the third definition being significantly improved). The algorithm described represents a significant step forward in the development of a unified hydrogen bond network whose properties are independent of the original hydrogen bond definition that is employed.« less

Rapid Neocortical Dynamics: Cellular and Network Mechanisms

PubMed Central

Haider, Bilal; McCormick, David A.

2011-01-01

The highly interconnected local and large-scale networks of the neocortical sheet rapidly and dynamically modulate their functional connectivity according to behavioral demands. This basic operating principle of the neocortex is mediated by the continuously changing flow of excitatory and inhibitory synaptic barrages that not only control participation of neurons in networks but also define the networks themselves. The rapid control of neuronal responsiveness via synaptic bombardment is a fundamental property of cortical dynamics that may provide the basis of diverse behaviors, including sensory perception, motor integration, working memory, and attention. PMID:19409263

Constructively determining the MBL spectrum using Tensor Networks

NASA Astrophysics Data System (ADS)

Clark, Bryan; Yu, Xiongjie; Pekker, David

All the eigenstates of a many-body localized phase can be compactly represented in the tensor-network language. Current algorithms to find these states often only target single states and/or require difficult optimization to find. In this talk we will show how to generate every eigenstate in the spectrum constructively and discuss its implication for the properties of the MBL phase.

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

PubMed Central

Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-01-01

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. PMID:29267252

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

PubMed

Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-12-21

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

Collective multipartite Einstein-Podolsky-Rosen steering: more secure optical networks.

PubMed

Wang, Meng; Gong, Qihuang; He, Qiongyi

2014-12-01

Collective multipartite Einstein-Podolsky-Rosen (EPR) steering is a type of quantum correlation shared among N parties, where the EPR paradox of one party can only be realized by performing local measurements on all the remaining N-1 parties. We formalize the collective tripartite steering in terms of local hidden state model and give the steering inequalities that act as signatures and suggest how to optimize collective tripartite steering in specific optical schemes. The special entangled states with property of collective multipartite steering may have potential applications in ultra-secure multiuser communication networks where the issue of trust is critical.

Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

PubMed

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.

Topological Organization of Functional Brain Networks in Healthy Children: Differences in Relation to Age, Sex, and Intelligence

PubMed Central

Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C.; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

2013-01-01

Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence. PMID:23390528

Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence.

PubMed

Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

2013-01-01

Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.

A coevolving model based on preferential triadic closure for social media networks

PubMed Central

Li, Menghui; Zou, Hailin; Guan, Shuguang; Gong, Xiaofeng; Li, Kun; Di, Zengru; Lai, Choy-Heng

2013-01-01

The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. PMID:23979061

Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits

PubMed Central

Mai, Naikeng; Zhong, Xiaomei; Chen, Ben; Peng, Qi; Wu, Zhangying; Zhang, Weiru; Ouyang, Cong; Ning, Yuping

2017-01-01

Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients. PMID:28878666

Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits.

PubMed

Mai, Naikeng; Zhong, Xiaomei; Chen, Ben; Peng, Qi; Wu, Zhangying; Zhang, Weiru; Ouyang, Cong; Ning, Yuping

2017-01-01

Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.

Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.

PubMed

Baek, K; Morris, L S; Kundu, P; Voon, V

2017-03-01

The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks

PubMed Central

Gjorgjieva, Julijana; Mease, Rebecca A.; Moody, William J.; Fairhall, Adrienne L.

2014-01-01

Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission. PMID:25474701

Correlation Between Fracture Network Properties and Stress Variability in Geological Media

NASA Astrophysics Data System (ADS)

Lei, Qinghua; Gao, Ke

2018-05-01

We quantitatively investigate the stress variability in fractured geological media under tectonic stresses. The fracture systems studied include synthetic fracture networks following power law length scaling and natural fracture patterns based on outcrop mapping. The stress field is derived from a finite-discrete element model, and its variability is analyzed using a set of mathematical formulations that honor the tensorial nature of stress data. We show that local stress perturbation, quantified by the Euclidean distance of a local stress tensor to the mean stress tensor, has a positive, linear correlation with local fracture intensity, defined as the total fracture length per unit area within a local sampling window. We also evaluate the stress dispersion of the entire stress field using the effective variance, that is, a scalar-valued measure of the overall stress variability. The results show that a well-connected fracture system under a critically stressed state exhibits strong local and global stress variabilities.

Properties of centralized cooperative sensing in cognitive radio networks

NASA Astrophysics Data System (ADS)

Skokowski, Paweł; Malon, Krzysztof; Łopatka, Jerzy

2017-04-01

Spectrum sensing is a functionality that enables network creation in the cognitive radio technology. Spectrum sensing is use for building the situation awareness knowledge for better use of radio resources and to adjust network parameters in case of jamming, interferences from legacy systems, decreasing link quality caused e.g. by nodes positions changes. This paper presents results from performed tests to compare cooperative centralized sensing versus local sensing. All tests were performed in created simulator developed in Matlab/Simulink environment.

From Discrete Breathers to Many Body Localization and Flatbands

NASA Astrophysics Data System (ADS)

Flach, Sergej

Discrete breathers (DB) and intrinsic localized modes (ILM) are synonymic dynamical states on nonlinear lattices - periodic in time and localized in space, and widely observed in many applications. I will discuss the connections between DBs and many-body localization (MBL) and the properties of DBs on flatband networks. A dense quantized gas of strongly excited DBs can lead to a MBL phase in a variety of different lattice models. Its classical counterpart corresponds to a 'nonergodic metal' in the MBL language, or to a nonGibbsean selftrapped state in the language of nonlinear dynamics. Flatband networks are lattices with small amplitude waves exhibiting macroscopic degeneracy in their band structure due to local symmetries, destructive interference, compact localized eigenstates and horizontal flat bands. DBs can preserve the compactness of localization in the presence of nonlinearity with properly tuned internal phase relationships, making them promising tools for control of the phase coherence of waves. Also at New Zealand Institute of Advanced Study, Massey University, Auckland, New Zealand.

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2016-04-01

Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type

NASA Astrophysics Data System (ADS)

Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

2015-03-01

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

Disrupted White Matter Network and Cognitive Decline in Type 2 Diabetes Patients.

PubMed

Zhang, Junying; Liu, Zhen; Li, Zixiao; Wang, Yunxia; Chen, Yaojing; Li, Xin; Chen, Kewei; Shu, Ni; Zhang, Zhanjun

2016-05-06

Type 2 diabetes mellitus is accompanied by cognitive impairment and is associated with an increased risk of dementia. Damage to brain structures such as white matter network disruption may underlie this cognitive disturbance. In the present study, 886 non-diabetic and 163 type 2 diabetic participants completed a battery of neuropsychological tests. Among them, 38 diabetic patients and 34 non-diabetic participants that matched the patients for age/sex/education received a magnetic resonance imaging-based diffusion tensor imaging. Then we calculated the topological properties of the white matter network using a graph theoretical method to investigate network efficiency differences between groups. We found that type 2 diabetic patients had inferior performances compared to the non-diabetic controls, in several cognitive domains involving executive function, spatial processing, memory, and attention. We also found that diabetic patients exhibited a disrupted topological organization of the white matter network (including the global network properties, i.e., network strength, global efficiency, local efficiency and shortest path length, and the nodal efficiency of the right rolandic operculum) in the brain. Moreover, those global network properties and the nodal efficiency of the right rolandic operculum both had positive correlations with executive function in the patient group. The results suggest that type 2 diabetes mellitus leads to an alteration in the topological organization of the cortical white matter network and this alteration may account for the observed cognitive decline.

Regional and transported aerosols during DRAGON-Japan experiment

NASA Astrophysics Data System (ADS)

Sano, I.; Holben, B. N.; Mukai, S.; Nakata, M.; Nakaguchi, Y.; Sugimoto, N.; Hatakeyama, S.; Nishizawa, T.; Takamura, T.; Takemura, T.; Yonemitsu, M.; Fujito, T.; Schafer, J.; Eck, T. F.; Sorokin, M.; Kenny, P.; Goto, M.; Hiraki, T.; Iguchi, N.; Kouzai, K.; KUJI, M.; Muramatsu, K.; Okada, Y.; Sadanaga, Y.; Tohno, S.; Toyazaki, Y.; Yamamoto, K.

2013-12-01

Aerosol properties over Japan have been monitored by AERONET sun / sky photometers since 2000. These measurements provides us with long term information of local aerosols, which are influenced by transported aerosols, such as Asian dusts or anthropogenic pollutants due to rapid increasing of energy consumption in Asian countries. A new aerosol monitoring experiment, Distributed Regional Aerosol Gridded Observation Networks (DRAGON) - Japan is operated in spring of 2012. The main instrument of DRAGON network is AERONET sun/sky radiometers. Some of them are sparsely set along the Japanese coast and some others make a dense network in Osaka, which is the second-largest city in Japan and famous for manufacturing town. Several 2ch NIES-LIDAR systems are also co-located with AERONET instrument to monitor Asian dusts throughout the campaign. The objects of Dragon-Japan are to characterize local aerosols as well as transported ones from the continent of China, and to acquire the detailed aerosol information for validating satellite data with high resolved spatial scale. This work presents the comprehensive results of aerosol properties with respect to regional- and/or transported- scale during DRAGON-Japan experiments.

Epidemic Process over the Commute Network in a Metropolitan Area

PubMed Central

Yashima, Kenta; Sasaki, Akira

2014-01-01

An understanding of epidemiological dynamics is important for prevention and control of epidemic outbreaks. However, previous studies tend to focus only on specific areas, indicating that application to another area or intervention strategy requires a similar time-consuming simulation. Here, we study the epidemic dynamics of the disease-spread over a commute network, using the Tokyo metropolitan area as an example, in an attempt to elucidate the general properties of epidemic spread over a commute network that could be used for a prediction in any metropolitan area. The model is formulated on the basis of a metapopulation network in which local populations are interconnected by actual commuter flows in the Tokyo metropolitan area and the spread of infection is simulated by an individual-based model. We find that the probability of a global epidemic as well as the final epidemic sizes in both global and local populations, the timing of the epidemic peak, and the time at which the epidemic reaches a local population are mainly determined by the joint distribution of the local population sizes connected by the commuter flows, but are insensitive to geographical or topological structure of the network. Moreover, there is a strong relation between the population size and the time that the epidemic reaches this local population and we are able to determine the reason for this relation as well as its dependence on the commute network structure and epidemic parameters. This study shows that the model based on the connection between the population size classes is sufficient to predict both global and local epidemic dynamics in metropolitan area. Moreover, the clear relation of the time taken by the epidemic to reach each local population can be used as a novel measure for intervention; this enables efficient intervention strategies in each local population prior to the actual arrival. PMID:24905831

Odd–even structural sensitivity on dynamics in network-forming ionic liquids

DOE PAGES

Yang, Ke; Cai, Zhikun; Tyagi, Madhusudan; ...

2016-04-13

Understanding structural sensitivity on properties of materials is an important step toward the rational design of materials. As a compelling case of sensitive structure-property relationship, an odd-even effect refers to the alternating trend of physical or chemical properties on odd/even number of repeating structural units. In crystalline or semi-crystalline materials, such odd-even variations of macroscopic properties emerge as manifestations of differences in the periodic packing patterns of molecules. Therefore, due to the lack of long-range order, such odd-even phenomenon is not expected in liquids. Herein, we report the discovery of a remarkable odd-even effect of the dynamical properties in themore » liquid phase, which challenges the traditional periodic packing explanations. In a class of network-forming ionic liquid (NIL), using incoherent quasi-elastic neutron scattering measurements, we measured the dynamical properties including the diffusion coefficient and the rotational relaxation time. These dynamical properties showed pronounced alternating trends with increased number of methylene (–CH 2– ) groups in the backbone. Meanwhile, the structure factor S(Q) showed no long-range periodic packing of molecules, while the pair distribution function g(r) revealed subtle differences in the local molecular morphology. As a result, the observed dynamical odd-even phenomenon in liquids showed that profound dynamical changes originate from subtle local structural differences.« less

Efficient weighting strategy for enhancing synchronizability of complex networks

NASA Astrophysics Data System (ADS)

Wang, Youquan; Yu, Feng; Huang, Shucheng; Tu, Juanjuan; Chen, Yan

2018-04-01

Networks with high propensity to synchronization are desired in many applications ranging from biology to engineering. In general, there are two ways to enhance the synchronizability of a network: link rewiring and/or link weighting. In this paper, we propose a new link weighting strategy based on the concept of the neighborhood subgroup. The neighborhood subgroup of a node i through node j in a network, i.e. Gi→j, means that node u belongs to Gi→j if node u belongs to the first-order neighbors of j (not include i). Our proposed weighting schema used the local and global structural properties of the networks such as the node degree, betweenness centrality and closeness centrality measures. We applied the method on scale-free and Watts-Strogatz networks of different structural properties and show the good performance of the proposed weighting scheme. Furthermore, as model networks cannot capture all essential features of real-world complex networks, we considered a number of undirected and unweighted real-world networks. To the best of our knowledge, the proposed weighting strategy outperformed the previously published weighting methods by enhancing the synchronizability of these real-world networks.

Complex networks with scale-free nature and hierarchical modularity

NASA Astrophysics Data System (ADS)

Shekatkar, Snehal M.; Ambika, G.

2015-09-01

Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

Comparisons of Neuronal and Excitatory Network Properties between the Rat Brainstem Nuclei that Participate in Vertical and Horizontal Gaze Holding

PubMed Central

Sugimura, Taketoshi; Yanagawa, Yuchio

2017-01-01

Gaze holding is primarily controlled by neural structures including the prepositus hypoglossi nucleus (PHN) for horizontal gaze and the interstitial nucleus of Cajal (INC) for vertical and torsional gaze. In contrast to the accumulating findings of the PHN, there is no report regarding the membrane properties of INC neurons or the local networks in the INC. In this study, to verify whether the neural structure of the INC is similar to that of the PHN, we investigated the neuronal and network properties of the INC using whole-cell recordings in rat brainstem slices. Three types of afterhyperpolarization (AHP) profiles and five firing patterns observed in PHN neurons were also observed in INC neurons. However, the overall distributions based on the AHP profile and the firing patterns of INC neurons were different from those of PHN neurons. The application of burst stimulation to a nearby site of a recorded INC neuron induced an increase in the frequency of spontaneous EPSCs. The duration of the increased EPSC frequency of INC neurons was not significantly different from that of PHN neurons. The percent of duration reduction induced by a Ca2+-permeable AMPA (CP-AMPA) receptor antagonist was significantly smaller in the INC than in the PHN. These findings suggest that local excitatory networks that activate sustained EPSC responses also exist in the INC, but their activation mechanisms including the contribution of CP-AMPA receptors differ between the INC and the PHN. PMID:28966973

Exact evaluation of the causal spectrum and localization properties of electronic states on a scale-free network

NASA Astrophysics Data System (ADS)

Xie, Pinchen; Yang, Bingjia; Zhang, Zhongzhi; Andrade, Roberto F. S.

2018-07-01

A deterministic network with tree structure is considered, for which the spectrum of its adjacency matrix can be exactly evaluated by a recursive renormalization approach. It amounts to successively increasing number of contributions at any finite step of construction of the tree, resulting in a causal chain. The resulting eigenvalues can be related the full energy spectrum of a nearest-neighbor tight-binding model defined on this structure. Given this association, it turns out that further properties of the eigenvectors can be evaluated, like the degree of quantum localization of the tight-binding eigenstates, expressed by the inverse participation ratio (IPR). It happens that, for the current model, the IPR's are also suitable to be analytically expressed in terms in corresponding eigenvalue chain. The resulting IPR scaling behavior is expressed by the tails of eigenvalue chains as well.

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

PubMed Central

Hu, Meng; He, Julong; Zhao, Zhisheng; Strobel, Timothy A.; Hu, Wentao; Yu, Dongli; Sun, Hao; Liu, Lingyu; Li, Zihe; Ma, Mengdong; Kono, Yoshio; Shu, Jinfu; Mao, Ho-kwang; Fei, Yingwei; Shen, Guoyin; Wang, Yanbin; Juhl, Stephen J.; Huang, Jian Yu; Liu, Zhongyuan; Xu, Bo; Tian, Yongjun

2017-01-01

Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties. PMID:28630918

IsoWAN: A NASA Science and Engineering Information and Services Framework

NASA Technical Reports Server (NTRS)

Korsmeyer, David J.; Chow, Edward T.; Conroy, Michael P.; Swanson, Keith (Technical Monitor)

2000-01-01

We believe that the next evolutionary step in supporting wide-area application and services delivery to customers is a network framework that provides for collocation of applications and services at distinct sites in the network, an interconnection between these sites that is performance optimized for these applications, and value-added services for applications. We use the term IsoWAN to describe an advanced, isolated network interconnect services framework that will enable applications to be more secure, and able to access and be in use in both local and remote environments. The main functions of an IsoWAN are virtual localization of application services, an application service interface, coordinated delivery of applications and associated data to the customer, and supporting collaborative application development for customers. An initial pilot network between three NASA Centers: Ames Research Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, has been built and its properties will be discussed.

Development of the brain's functional network architecture.

PubMed

Vogel, Alecia C; Power, Jonathan D; Petersen, Steven E; Schlaggar, Bradley L

2010-12-01

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

Development of the Brain's Functional Network Architecture

PubMed Central

Power, Jonathan D.; Petersen, Steven E.; Schlaggar, Bradley L.

2013-01-01

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks. PMID:20976563

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.

Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

PubMed Central

Liu, Yaou; Duan, Yunyun; Li, Kuncheng

2015-01-01

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain. PMID:26539535

Harmony in the small-world

NASA Astrophysics Data System (ADS)

Marchiori, Massimo; Latora, Vito

2000-10-01

The small-world phenomenon, popularly known as six degrees of separation, has been mathematically formalized by Watts and Strogatz in a study of the topological properties of a network. Small-world networks are defined in terms of two quantities: they have a high clustering coefficient C like regular lattices and a short characteristic path length L typical of random networks. Physical distances are of fundamental importance in applications to real cases; nevertheless, this basic ingredient is missing in the original formulation. Here, we introduce a new concept, the connectivity length D, that gives harmony to the whole theory. D can be evaluated on a global and on a local scale and plays in turn the role of L and 1/ C. Moreover, it can be computed for any metrical network and not only for the topological cases. D has a precise meaning in terms of information propagation and describes in a unified way, both the structural and the dynamical aspects of a network: small-worlds are defined by a small global and local D, i.e., by a high efficiency in propagating information both on a local and global scale. The neural system of the nematode C. elegans, the collaboration graph of film actors, and the oldest US subway system, can now be studied also as metrical networks and are shown to be small-worlds.

The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model.

PubMed

Aćimović, Jugoslava; Mäki-Marttunen, Tuomo; Linne, Marja-Leena

2015-01-01

We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes.

Social Networks and Community-Based Natural Resource Management

NASA Astrophysics Data System (ADS)

Lauber, T. Bruce; Decker, Daniel J.; Knuth, Barbara A.

2008-10-01

We conducted case studies of three successful examples of collaborative, community-based natural resource conservation and development. Our purpose was to: (1) identify the functions served by interactions within the social networks of involved stakeholders; (2) describe key structural properties of these social networks; and (3) determine how these structural properties varied when the networks were serving different functions. The case studies relied on semi-structured, in-depth interviews of 8 to 11 key stakeholders at each site who had played a significant role in the collaborative projects. Interview questions focused on the roles played by key stakeholders and the functions of interactions between them. Interactions allowed the exchange of ideas, provided access to funding, and enabled some stakeholders to influence others. The exchange of ideas involved the largest number of stakeholders, the highest percentage of local stakeholders, and the highest density of interactions. Our findings demonstrated the value of tailoring strategies for involving stakeholders to meet different needs during a collaborative, community-based natural resource management project. Widespread involvement of local stakeholders may be most appropriate when ideas for a project are being developed. During efforts to exert influence to secure project approvals or funding, however, involving specific individuals with political connections or influence on possible sources of funds may be critical. Our findings are consistent with past work that has postulated that social networks may require specific characteristics to meet different needs in community-based environmental management.

White-Matter Structural Connectivity Underlying Human Laughter-Related Traits Processing.

PubMed

Wu, Ching-Lin; Zhong, Suyu; Chan, Yu-Chen; Chen, Hsueh-Chih; Gong, Gaolang; He, Yong; Li, Ping

2016-01-01

Most research into the neural mechanisms of humor has not explicitly focused on the association between emotion and humor on the brain white matter networks mediating this connection. However, this connection is especially salient in gelotophobia (the fear of being laughed at), which is regarded as the presentation of humorlessness, and two related traits, gelotophilia (the enjoyment of being laughed at) and katagelasticism (the enjoyment of laughing at others). Here, we explored whether the topological properties of white matter networks can account for the individual differences in the laughter-related traits of 31 healthy adults. We observed a significant negative correlation between gelotophobia scores and the clustering coefficient, local efficiency and global efficiency, but a positive association between gelotophobia scores and path length in the brain's white matter network. Moreover, the current study revealed that with increasing individual fear of being laughed at, the linking efficiencies in superior frontal gyrus, anterior cingulate cortex, parahippocampal gyrus, and middle temporal gyrus decreased. However, there were no significant correlations between either gelotophilia or katagelasticism scores or the topological properties of the brain white matter network. These findings suggest that the fear of being laughed at is directly related to the level of local and global information processing of the brain network, which might provide new insights into the neural mechanisms of the humor information processing.

White-Matter Structural Connectivity Underlying Human Laughter-Related Traits Processing

PubMed Central

Wu, Ching-Lin; Zhong, Suyu; Chan, Yu-Chen; Chen, Hsueh-Chih; Gong, Gaolang; He, Yong; Li, Ping

2016-01-01

Most research into the neural mechanisms of humor has not explicitly focused on the association between emotion and humor on the brain white matter networks mediating this connection. However, this connection is especially salient in gelotophobia (the fear of being laughed at), which is regarded as the presentation of humorlessness, and two related traits, gelotophilia (the enjoyment of being laughed at) and katagelasticism (the enjoyment of laughing at others). Here, we explored whether the topological properties of white matter networks can account for the individual differences in the laughter-related traits of 31 healthy adults. We observed a significant negative correlation between gelotophobia scores and the clustering coefficient, local efficiency and global efficiency, but a positive association between gelotophobia scores and path length in the brain's white matter network. Moreover, the current study revealed that with increasing individual fear of being laughed at, the linking efficiencies in superior frontal gyrus, anterior cingulate cortex, parahippocampal gyrus, and middle temporal gyrus decreased. However, there were no significant correlations between either gelotophilia or katagelasticism scores or the topological properties of the brain white matter network. These findings suggest that the fear of being laughed at is directly related to the level of local and global information processing of the brain network, which might provide new insights into the neural mechanisms of the humor information processing. PMID:27833572

Network community structure and loop coefficient method

NASA Astrophysics Data System (ADS)

Vragović, I.; Louis, E.

2006-07-01

A modular structure, in which groups of tightly connected nodes could be resolved as separate entities, is a property that can be found in many complex networks. In this paper, we propose a algorithm for identifying communities in networks. It is based on a local measure, so-called loop coefficient that is a generalization of the clustering coefficient. Nodes with a large loop coefficient tend to be core inner community nodes, while other vertices are usually peripheral sites at the borders of communities. Our method gives satisfactory results for both artificial and real-world graphs, if they have a relatively pronounced modular structure. This type of algorithm could open a way of interpreting the role of nodes in communities in terms of the local loop coefficient, and could be used as a complement to other methods.

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

PubMed Central

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

2018-01-01

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

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

NASA Astrophysics Data System (ADS)

Xie, Tian; Grossman, Jeffrey C.

2018-04-01

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

Research of Hubs Location Method for Weighted Brain Network Based on NoS-FA.

PubMed

Weng, Zhengkui; Wang, Bin; Xue, Jie; Yang, Baojie; Liu, Hui; Xiong, Xin

2017-01-01

As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies.

Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.

PubMed

Tanaka, Takuma; Aoyagi, Toshio; Kaneko, Takeshi

2012-10-01

We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

Dark Fiber and Distributed Acoustic Sensing: Applications to Monitoring Seismicity and Near-Surface Properties

NASA Astrophysics Data System (ADS)

Ajo Franklin, J. B.; Lindsey, N.; Dou, S.; Freifeld, B. M.; Daley, T. M.; Tracy, C.; Monga, I.

2017-12-01

"Dark Fiber" refers to the large number of fiber-optic lines installed for telecommunication purposes but not currently utilized. With the advent of distributed acoustic sensing (DAS), these unused fibers have the potential to become a seismic sensing network with unparalleled spatial extent and density with applications to monitoring both natural seismicity as well as near-surface soil properties. While the utility of DAS for seismic monitoring has now been conclusively shown on built-for-purpose networks, dark fiber deployments have been challenged by the heterogeneity of fiber installation procedures in telecommunication as well as access limitations. However, the potential of telecom networks to augment existing broadband monitoring stations provides a strong incentive to explore their utilization. We present preliminary results demonstrating the application of DAS to seismic monitoring on a 20 km run of "dark" telecommunications fiber between West Sacramento, CA and Woodland CA, part of the Dark Fiber Testbed maintained by the DOE's ESnet user facility. We show a small catalog of local and regional earthquakes detected by the array and evaluate fiber coupling by using variations in recorded frequency content. Considering the low density of broadband stations across much of the Sacramento Basin, such DAS recordings could provide a crucial data source to constrain small-magnitude local events. We also demonstrate the application of ambient noise interferometry using DAS-recorded waveforms to estimate soil properties under selected sections of the dark fiber transect; the success of this test suggests that the network could be utilized for environmental monitoring at the basin scale. The combination of these two examples demonstrates the exciting potential for combining DAS with ubiquitous dark fiber to greatly extend the reach of existing seismic monitoring networks.

Spatially localized structure-function relations in the elastic properties of sheared articular cartilage

NASA Astrophysics Data System (ADS)

Silverberg, Jesse; Bonassar, Lawrence; Cohen, Itai

2013-03-01

Contemporary developments in therapeutic tissue engineering have been enabled by basic research efforts in the field of biomechanics. Further integration of technology in medicine requires a deeper understanding of the mechanical properties of soft biological materials and the structural origins of their response under extreme stresses and strains. Drawing on the science generated by the ``Extreme Mechanics'' community, we present experimental results on the mechanical properties of articular cartilage, a hierarchically structured soft biomaterial found in the joints of mammalian long bones. Measurements of the spatially localized structure and mechanical properties will be compared with theoretical descriptions based on networks of deformed rods, poro-visco-elasticity, and standard continuum models. Discrepancies between experiment and theory will be highlighted, and suggestions for how models can be improved will be given.

[Cognitive advantages of the third age: a neural network model of brain aging].

PubMed

Karpenko, M P; Kachalova, L M; Budilova, E V; Terekhin, A T

2009-01-01

We consider a neural network model of age-related cognitive changes in aging brain based on Hopfield network with a sigmoid function of neuron activation. Age is included in the activation function as a parameter in the form of exponential rate denominator, which makes it possible to take into account the weakening of interneuronal links really observed in the aging brain. Analysis of properties of the Lyapunov function associated with the network shows that, with increasing parameter of age, its relief becomes smoother and the number of local minima (network attractors) decreases. As a result, the network gets less frequently stuck in the nearest local minima of the Lyapunov function and reaches a global minimum corresponding to the most effective solution of the cognitive task. It is reasonable to assume that similar changes really occur in the aging brain. Phenomenologically, these changes can be manifested as emergence in aged people of a cognitive quality such as wisdom i.e. ability to find optimal decisions in difficult controversial situations, to distract from secondary aspects and to see the problem as a whole.

A novel neural-wavelet approach for process diagnostics and complex system modeling

NASA Astrophysics Data System (ADS)

Gao, Rong

Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation.

NASA Astrophysics Data System (ADS)

Jablonski, Piotr; Poe, Gina; Zochowski, Michal

2007-03-01

The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

Structural network heterogeneities and network dynamics: A possible dynamical mechanism for hippocampal memory reactivation

NASA Astrophysics Data System (ADS)

Jablonski, Piotr; Poe, Gina R.; Zochowski, Michal

2007-01-01

The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

PubMed

Yu, Zhibin; Wang, Yubo; Zheng, Bing; Zheng, Haiyong; Wang, Nan; Gu, Zhaorui

2017-01-01

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

Altered characteristic of brain networks in mild cognitive impairment during a selective attention task: An EEG study.

PubMed

Wei, Ling; Li, Yingjie; Yang, Xiaoli; Xue, Qing; Wang, Yuping

2015-10-01

The present study evaluated the topological properties of whole brain networks using graph theoretical concepts and investigated the time-evolution characteristic of brain network in mild cognitive impairment patients during a selective attention task. Electroencephalography (EEG) activities were recorded in 10 MCI patients and 17 healthy subjects when they performed a color match task. We calculated the phase synchrony index between each possible pairs of EEG channels in alpha and beta frequency bands and analyzed the local interconnectedness, overall connectedness and small-world characteristic of brain network in different degree for two groups. Relative to healthy normal controls, the properties of cortical networks in MCI patients tend to be a shift of randomization. Lower σ of MCI had suggested that patients had a further loss of small-world attribute both during active and resting states. Our results provide evidence for the functional disconnection of brain regions in MCI. Furthermore, we found the properties of cortical networks could reflect the processing of conflict information in the selective attention task. The human brain tends to be a more regular and efficient neural architecture in the late stage of information processing. In addition, the processing of conflict information needs stronger information integration and transfer between cortical areas. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Relating the large-scale structure of time series and visibility networks.

PubMed

Rodríguez, Miguel A

2017-06-01

The structure of time series is usually characterized by means of correlations. A new proposal based on visibility networks has been considered recently. Visibility networks are complex networks mapped from surfaces or time series using visibility properties. The structures of time series and visibility networks are closely related, as shown by means of fractional time series in recent works. In these works, a simple relationship between the Hurst exponent H of fractional time series and the exponent of the distribution of edges γ of the corresponding visibility network, which exhibits a power law, is shown. To check and generalize these results, in this paper we delve into this idea of connected structures by defining both structures more properly. In addition to the exponents used before, H and γ, which take into account local properties, we consider two more exponents that, as we will show, characterize global properties. These are the exponent α for time series, which gives the scaling of the variance with the size as var∼T^{2α}, and the exponent κ of their corresponding network, which gives the scaling of the averaged maximum of the number of edges, 〈k_{M}〉∼N^{κ}. With this representation, a more precise connection between the structures of general time series and their associated visibility network is achieved. Similarities and differences are more clearly established, and new scaling forms of complex networks appear in agreement with their respective classes of time series.

Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

PubMed Central

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies. PMID:22412922

Altered Cerebral Blood Flow Covariance Network in Schizophrenia.

PubMed

Liu, Feng; Zhuo, Chuanjun; Yu, Chunshui

2016-01-01

Many studies have shown abnormal cerebral blood flow (CBF) in schizophrenia; however, it remains unclear how topological properties of CBF network are altered in this disorder. Here, arterial spin labeling (ASL) MRI was employed to measure resting-state CBF in 96 schizophrenia patients and 91 healthy controls. CBF covariance network of each group was constructed by calculating across-subject CBF covariance between 90 brain regions. Graph theory was used to compare intergroup differences in global and nodal topological measures of the network. Both schizophrenia patients and healthy controls had small-world topology in CBF covariance networks, implying an optimal balance between functional segregation and integration. Compared with healthy controls, schizophrenia patients showed reduced small-worldness, normalized clustering coefficient and local efficiency of the network, suggesting a shift toward randomized network topology in schizophrenia. Furthermore, schizophrenia patients exhibited altered nodal centrality in the perceptual-, affective-, language-, and spatial-related regions, indicating functional disturbance of these systems in schizophrenia. This study demonstrated for the first time that schizophrenia patients have disrupted topological properties in CBF covariance network, which provides a new perspective (efficiency of blood flow distribution between brain regions) for understanding neural mechanisms of schizophrenia.

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.

Charge transport network dynamics in molecular aggregates

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

Jackson, Nicholas E.; Chen, Lin X.; Ratner, Mark A.

2016-07-20

Due to the nonperiodic nature of charge transport in disordered systems, generating insight into static charge transport networks, as well as analyzing the network dynamics, can be challenging. Here, we apply time-dependent network analysis to scrutinize the charge transport networks of two representative molecular semiconductors: a rigid n-type molecule, perylenediimide, and a flexible p-type molecule, bBDT(TDPP)2. Simulations reveal the relevant timescale for local transfer integral decorrelation to be ~100 fs, which is shown to be faster than that of a crystalline morphology of the same molecule. Using a simple graph metric, global network changes are observed over timescales competitive withmore » charge carrier lifetimes. These insights demonstrate that static charge transport networks are qualitatively inadequate, whereas average networks often overestimate network connectivity. Finally, a simple methodology for tracking dynamic charge transport properties is proposed.« less

Structural Properties of the Brazilian Air Transportation Network.

PubMed

Couto, Guilherme S; da Silva, Ana Paula Couto; Ruiz, Linnyer B; Benevenuto, Fabrício

2015-09-01

The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City) is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

Establishing the reliability of rhesus macaque social network assessment from video observations

PubMed Central

Feczko, Eric; Mitchell, Thomas A. J.; Walum, Hasse; Brooks, Jenna M.; Heitz, Thomas R.; Young, Larry J.; Parr, Lisa A.

2015-01-01

Understanding the properties of a social environment is important for understanding the dynamics of social relationships. Understanding such dynamics is relevant for multiple fields, ranging from animal behaviour to social and cognitive neuroscience. To quantify social environment properties, recent studies have incorporated social network analysis. Social network analysis quantifies both the global and local properties of a social environment, such as social network efficiency and the roles played by specific individuals, respectively. Despite the plethora of studies incorporating social network analysis, methods to determine the amount of data necessary to derive reliable social networks are still being developed. Determining the amount of data necessary for a reliable network is critical for measuring changes in the social environment, for example following an experimental manipulation, and therefore may be critical for using social network analysis to statistically assess social behaviour. In this paper, we extend methods for measuring error in acquired data and for determining the amount of data necessary to generate reliable social networks. We derived social networks from a group of 10 male rhesus macaques, Macaca mulatta, for three behaviours: spatial proximity, grooming and mounting. Behaviours were coded using a video observation technique, where video cameras recorded the compound where the 10 macaques resided. We collected, coded and used 10 h of video data to construct these networks. Using the methods described here, we found in our data that 1 h of spatial proximity observations produced reliable social networks. However, this may not be true for other studies due to differences in data acquisition. Our results have broad implications for measuring and predicting the amount of error in any social network, regardless of species. PMID:26392632

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.

Changes in functional organization and white matter integrity in the connectome in Parkinson's disease.

PubMed

Tinaz, Sule; Lauro, Peter M; Ghosh, Pritha; Lungu, Codrin; Horovitz, Silvina G

2017-01-01

Parkinson's disease (PD) leads to dysfunction in multiple cortico-striatal circuits. The neurodegeneration has also been associated with impaired white matter integrity. This structural and functional "disconnection" in PD needs further characterization. We investigated the structural and functional organization of the PD whole brain connectome consisting of 200 nodes using diffusion tensor imaging and resting-state functional MRI, respectively. Data from 20 non-demented PD patients on dopaminergic medication and 20 matched controls were analyzed using graph theory-based methods. We focused on node strength, clustering coefficient, and local efficiency as measures of local network properties; and network modularity as a measure of information flow. PD patients showed reduced white matter connectivity in frontoparietal-striatal nodes compared to controls, but no change in modular organization of the white matter tracts. PD group also showed reduction in functional local network metrics in many nodes distributed across the connectome. There was also decreased functional modularity in the core cognitive networks including the default mode and dorsal attention networks, and sensorimotor network, as well as a lack of modular distinction in the orbitofrontal and basal ganglia nodes in the PD group compared to controls. Our results suggest that despite subtle white matter connectivity changes, the overall structural organization of the PD connectome remains robust at relatively early disease stages. However, there is a breakdown in the functional modular organization of the PD connectome.

Changes in functional brain networks following sports-related concussion in adolescents.

PubMed

Virji-Babul, Naznin; Hilderman, Courtney G E; Makan, Nadia; Liu, Aiping; Smith-Forrester, Jenna; Franks, Chris; Wang, Z J

2014-12-01

Sports-related concussion is a major public health issue; however, little is known about the underlying changes in functional brain networks in adolescents following injury. Our aim was to use the tools from graph theory to evaluate the changes in brain network properties following concussion in adolescent athletes. We recorded resting state electroencephalography (EEG) in 33 healthy adolescent athletes and 9 adolescent athletes with a clinical diagnosis of subacute concussion. Graph theory analysis was applied to these data to evaluate changes in brain networks. Global and local metrics of the structural properties of the graph were calculated for each group and correlated with Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) scores. Brain networks of both groups showed small-world topology with no statistically significant differences in the global metrics; however, significant differences were found in the local metrics. Specifically, in the concussed group, we noted: 1) increased values of betweenness and degree in frontal electrode sites corresponding to the (R) dorsolateral prefrontal cortex and the (R) inferior frontal gyrus and 2) decreased values of degree in the region corresponding to the (R) frontopolar prefrontal cortex. In addition, there was significant negative correlation between degree and hub value, with total symptom score at the electrode site corresponding to the (R) prefrontal cortex. This preliminary report in adolescent athletes shows for the first time that resting-state EEG combined with graph theoretical analysis may provide an objective method of evaluating changes in brain networks following concussion. This approach may be useful in identifying individuals at risk for future injury.

Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network.

PubMed

Sciaraffa, Nicolina; Borghini, Gianluca; Aricò, Pietro; Di Flumeri, Gianluca; Colosimo, Alfredo; Bezerianos, Anastasios; Thakor, Nitish V; Babiloni, Fabio

2017-07-21

Subjects' interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects' activities, due to high workload tendencies, were less coordinated.

Dopamine precursor depletion impairs structure and efficiency of resting state brain functional networks.

PubMed

Carbonell, Felix; Nagano-Saito, Atsuko; Leyton, Marco; Cisek, Paul; Benkelfat, Chawki; He, Yong; Dagher, Alain

2014-09-01

Spatial patterns of functional connectivity derived from resting brain activity may be used to elucidate the topological properties of brain networks. Such networks are amenable to study using graph theory, which shows that they possess small world properties and can be used to differentiate healthy subjects and patient populations. Of particular interest is the possibility that some of these differences are related to alterations in the dopamine system. To investigate the role of dopamine in the topological organization of brain networks at rest, we tested the effects of reducing dopamine synthesis in 13 healthy subjects undergoing functional magnetic resonance imaging. All subjects were scanned twice, in a resting state, following ingestion of one of two amino acid drinks in a randomized, double-blind manner. One drink was a nutritionally balanced amino acid mixture, and the other was tyrosine and phenylalanine deficient. Functional connectivity between 90 cortical and subcortical regions was estimated for each individual subject under each dopaminergic condition. The lowered dopamine state caused the following network changes: reduced global and local efficiency of the whole brain network, reduced regional efficiency in limbic areas, reduced modularity of brain networks, and greater connection between the normally anti-correlated task-positive and default-mode networks. We conclude that dopamine plays a role in maintaining the efficient small-world properties and high modularity of functional brain networks, and in segregating the task-positive and default-mode networks. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'. Copyright © 2014 Elsevier Ltd. All rights reserved.

Propagation, cascades, and agreement dynamics in complex communication and social networks

NASA Astrophysics Data System (ADS)

Lu, Qiming

Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

NASA Astrophysics Data System (ADS)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological function.

Machine learning prediction for classification of outcomes in local minimisation

NASA Astrophysics Data System (ADS)

Das, Ritankar; Wales, David J.

2017-01-01

Machine learning schemes are employed to predict which local minimum will result from local energy minimisation of random starting configurations for a triatomic cluster. The input data consists of structural information at one or more of the configurations in optimisation sequences that converge to one of four distinct local minima. The ability to make reliable predictions, in terms of the energy or other properties of interest, could save significant computational resources in sampling procedures that involve systematic geometry optimisation. Results are compared for two energy minimisation schemes, and for neural network and quadratic functions of the inputs.

On the number of different dynamics in Boolean networks with deterministic update schedules.

PubMed

Aracena, J; Demongeot, J; Fanchon, E; Montalva, M

2013-04-01

Deterministic Boolean networks are a type of discrete dynamical systems widely used in the modeling of genetic networks. The dynamics of such systems is characterized by the local activation functions and the update schedule, i.e., the order in which the nodes are updated. In this paper, we address the problem of knowing the different dynamics of a Boolean network when the update schedule is changed. We begin by proving that the problem of the existence of a pair of update schedules with different dynamics is NP-complete. However, we show that certain structural properties of the interaction diagraph are sufficient for guaranteeing distinct dynamics of a network. In [1] the authors define equivalence classes which have the property that all the update schedules of a given class yield the same dynamics. In order to determine the dynamics associated to a network, we develop an algorithm to efficiently enumerate the above equivalence classes by selecting a representative update schedule for each class with a minimum number of blocks. Finally, we run this algorithm on the well known Arabidopsis thaliana network to determine the full spectrum of its different dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

NASA Astrophysics Data System (ADS)

Holben, Brent N.; Kim, Jhoon; Sano, Itaru; Mukai, Sonoyo; Eck, Thomas F.; Giles, David M.; Schafer, Joel S.; Sinyuk, Aliaksandr; Slutsker, Ilya; Smirnov, Alexander; Sorokin, Mikhail; Anderson, Bruce E.; Che, Huizheng; Choi, Myungje; Crawford, James H.; Ferrare, Richard A.; Garay, Michael J.; Jeong, Ukkyo; Kim, Mijin; Kim, Woogyung; Knox, Nichola; Li, Zhengqiang; Lim, Hwee S.; Liu, Yang; Maring, Hal; Nakata, Makiko; Pickering, Kenneth E.; Piketh, Stuart; Redemann, Jens; Reid, Jeffrey S.; Salinas, Santo; Seo, Sora; Tan, Fuyi; Tripathi, Sachchida N.; Toon, Owen B.; Xiao, Qingyang

2018-01-01

Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso- and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

Inferring general relations between network characteristics from specific network ensembles.

PubMed

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

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

Maximal switchability of centralized networks

NASA Astrophysics Data System (ADS)

Vakulenko, Sergei; Morozov, Ivan; Radulescu, Ovidiu

2016-08-01

We consider continuous time Hopfield-like recurrent networks as dynamical models for gene regulation and neural networks. We are interested in networks that contain n high-degree nodes preferably connected to a large number of N s weakly connected satellites, a property that we call n/N s -centrality. If the hub dynamics is slow, we obtain that the large time network dynamics is completely defined by the hub dynamics. Moreover, such networks are maximally flexible and switchable, in the sense that they can switch from a globally attractive rest state to any structurally stable dynamics when the response time of a special controller hub is changed. In particular, we show that a decrease of the controller hub response time can lead to a sharp variation in the network attractor structure: we can obtain a set of new local attractors, whose number can increase exponentially with N, the total number of nodes of the nework. These new attractors can be periodic or even chaotic. We provide an algorithm, which allows us to design networks with the desired switching properties, or to learn them from time series, by adjusting the interactions between hubs and satellites. Such switchable networks could be used as models for context dependent adaptation in functional genetics or as models for cognitive functions in neuroscience.

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

PubMed

Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

2015-05-01

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

Conditional repair by locally switching the thermal healing capability of dynamic covalent polymers with light

PubMed Central

Fuhrmann, Anne; Göstl, Robert; Wendt, Robert; Kötteritzsch, Julia; Hager, Martin D.; Schubert, Ulrich S.; Brademann-Jock, Kerstin; Thünemann, Andreas F.; Nöchel, Ulrich; Behl, Marc; Hecht, Stefan

2016-01-01

Healable materials could play an important role in reducing the environmental footprint of our modern technological society through extending the life cycles of consumer products and constructions. However, as most healing processes are carried out by heat alone, the ability to heal damage generally kills the parent material's thermal and mechanical properties. Here we present a dynamic covalent polymer network whose thermal healing ability can be switched ‘on' and ‘off' on demand by light, thereby providing local control over repair while retaining the advantageous macroscopic properties of static polymer networks. We employ a photoswitchable furan-based crosslinker, which reacts with short and mobile maleimide-substituted poly(lauryl methacrylate) chains forming strong covalent bonds while simultaneously allowing the reversible, spatiotemporally resolved control over thermally induced de- and re-crosslinking. We reason that our system can be adapted to more complex materials and has the potential to impact applications in responsive coatings, photolithography and microfabrication. PMID:27941924

Conditional repair by locally switching the thermal healing capability of dynamic covalent polymers with light

NASA Astrophysics Data System (ADS)

Fuhrmann, Anne; Göstl, Robert; Wendt, Robert; Kötteritzsch, Julia; Hager, Martin D.; Schubert, Ulrich S.; Brademann-Jock, Kerstin; Thünemann, Andreas F.; Nöchel, Ulrich; Behl, Marc; Hecht, Stefan

2016-12-01

Healable materials could play an important role in reducing the environmental footprint of our modern technological society through extending the life cycles of consumer products and constructions. However, as most healing processes are carried out by heat alone, the ability to heal damage generally kills the parent material's thermal and mechanical properties. Here we present a dynamic covalent polymer network whose thermal healing ability can be switched `on' and `off' on demand by light, thereby providing local control over repair while retaining the advantageous macroscopic properties of static polymer networks. We employ a photoswitchable furan-based crosslinker, which reacts with short and mobile maleimide-substituted poly(lauryl methacrylate) chains forming strong covalent bonds while simultaneously allowing the reversible, spatiotemporally resolved control over thermally induced de- and re-crosslinking. We reason that our system can be adapted to more complex materials and has the potential to impact applications in responsive coatings, photolithography and microfabrication.

Synchronization of three electrochemical oscillators: From local to global coupling

NASA Astrophysics Data System (ADS)

Liu, Yifan; Sebek, Michael; Mori, Fumito; Kiss, István Z.

2018-04-01

We investigate the formation of synchronization patterns in an oscillatory nickel electrodissolution system in a network obtained by superimposing local and global coupling with three electrodes. We explored the behavior through numerical simulations using kinetic ordinary differential equations, Kuramoto type phase models, and experiments, in which the local to global coupling could be tuned by cross resistances between the three nickel wires. At intermediate coupling strength with predominant global coupling, two of the three oscillators, whose natural frequencies are closer, can synchronize. By adding even a relatively small amount of local coupling (about 9%-25%), a spatially organized partially synchronized state can occur where one of the two synchronized elements is in the center. A formula was derived for predicting the critical coupling strength at which full synchronization will occur independent of the permutation of the natural frequencies of the oscillators over the network. The formula correctly predicts the variation of the critical coupling strength as a function of the global coupling fraction, e.g., with local coupling the critical coupling strength is about twice than that required with global coupling. The results show the importance of the topology of the network on the synchronization properties in a simple three-oscillator setup and could provide guidelines for decrypting coupling topology from identification of synchronization patterns.

Emergence of communities and diversity in social networks

PubMed Central

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross

2017-01-01

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics. PMID:28235785

Emergence of communities and diversity in social networks.

PubMed

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

2017-03-14

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

Aberrant topological organization of the functional brain network associated with prior overt hepatic encephalopathy in cirrhotic patients.

PubMed

Chen, Hua-Jun; Chen, Qiu-Feng; Yang, Zhe-Ting; Shi, Hai-Bin

2018-05-30

A higher risk of cognitive impairments has been found after an overt hepatic encephalopathy (OHE) episode in cirrhotic patients. We investigated the effect of prior OHE episodes on the topological organization of the functional brain network and its association with the relevant cognitive impairments. Resting-state functional MRI data were acquired from 41 cirrhotic patients (19 with prior OHE (Prior-OHE) and 22 without (Non-Prior-OHE)) and 21 healthy controls (HC). A Psychometric Hepatic Encephalopathy Score (PHES) assessed cognition. The whole-brain functional network was constructed by thresholding functional correlation matrices of 90 brain regions (derived from the Automated Anatomic Labeling atlas). The topological properties of the brain network, including small-worldness, network efficiency, and nodal efficiency, were examined using graph theory-based analysis. Globally, the Prior-OHE group had a significantly decreased clustering coefficient and local efficiency, compared with the controls. Locally, the nodal efficiency in the bilateral medial superior frontal gyrus and the right postcentral gyrus decreased in the Prior-OHE group, while the nodal efficiency in the bilateral anterior cingulate/paracingulate gyri and right superior parietal gyrus increased in the Prior-OHE group. The alterations of global and regional network parameters progressed from Non-Prior-OHE to Prior-OHE and the clustering coefficient and local efficiency values were significantly correlated with PHES results. In conclusion, cirrhosis leads to the reduction of brain functional network efficiency, which could be aggravated by a prior OHE episode. Aberrant topological organization of the functional brain network may contribute to a higher risk of cognitive impairments in Prior-OHE patients.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons.

PubMed

Yger, Pierre; El Boustani, Sami; Destexhe, Alain; Frégnac, Yves

2011-10-01

The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order "mean-field" statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.

Modeling the average shortest-path length in growth of word-adjacency networks

NASA Astrophysics Data System (ADS)

Kulig, Andrzej; DroŻdŻ, Stanisław; Kwapień, Jarosław; OświÈ©cimka, Paweł

2015-03-01

We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest-path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest-path length. Then, we identify the local chainlike linear growth induced by grammar and style as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.

Quantum entanglement percolation

NASA Astrophysics Data System (ADS)

Siomau, Michael

2016-09-01

Quantum communication demands efficient distribution of quantum entanglement across a network of connected partners. The search for efficient strategies for the entanglement distribution may be based on percolation theory, which describes evolution of network connectivity with respect to some network parameters. In this framework, the probability to establish perfect entanglement between two remote partners decays exponentially with the distance between them before the percolation transition point, which unambiguously defines percolation properties of any classical network or lattice. Here we introduce quantum networks created with local operations and classical communication, which exhibit non-classical percolation transition points leading to striking communication advantages over those offered by the corresponding classical networks. We show, in particular, how to establish perfect entanglement between any two nodes in the simplest possible network—the 1D chain—using imperfectly entangled pairs of qubits.

Molecular clock on a neutral network.

PubMed

Raval, Alpan

2007-09-28

The number of fixed mutations accumulated in an evolving population often displays a variance that is significantly larger than the mean (the overdispersed molecular clock). By examining a generic evolutionary process on a neutral network of high-fitness genotypes, we establish a formalism for computing all cumulants of the full probability distribution of accumulated mutations in terms of graph properties of the neutral network, and use the formalism to prove overdispersion of the molecular clock. We further show that significant overdispersion arises naturally in evolution when the neutral network is highly sparse, exhibits large global fluctuations in neutrality, and small local fluctuations in neutrality. The results are also relevant for elucidating aspects of neutral network topology from empirical measurements of the substitution process.

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Molecular Clock on a Neutral Network

NASA Astrophysics Data System (ADS)

Raval, Alpan

2007-09-01

The number of fixed mutations accumulated in an evolving population often displays a variance that is significantly larger than the mean (the overdispersed molecular clock). By examining a generic evolutionary process on a neutral network of high-fitness genotypes, we establish a formalism for computing all cumulants of the full probability distribution of accumulated mutations in terms of graph properties of the neutral network, and use the formalism to prove overdispersion of the molecular clock. We further show that significant overdispersion arises naturally in evolution when the neutral network is highly sparse, exhibits large global fluctuations in neutrality, and small local fluctuations in neutrality. The results are also relevant for elucidating aspects of neutral network topology from empirical measurements of the substitution process.

Non-Hermitian localization in biological networks.

PubMed

Amir, Ariel; Hatano, Naomichi; Nelson, David R

2016-04-01

We explore the spectra and localization properties of the N-site banded one-dimensional non-Hermitian random matrices that arise naturally in sparse neural networks. Approximately equal numbers of random excitatory and inhibitory connections lead to spatially localized eigenfunctions and an intricate eigenvalue spectrum in the complex plane that controls the spontaneous activity and induced response. A finite fraction of the eigenvalues condense onto the real or imaginary axes. For large N, the spectrum has remarkable symmetries not only with respect to reflections across the real and imaginary axes but also with respect to 90^{∘} rotations, with an unusual anisotropic divergence in the localization length near the origin. When chains with periodic boundary conditions become directed, with a systematic directional bias superimposed on the randomness, a hole centered on the origin opens up in the density-of-states in the complex plane. All states are extended on the rim of this hole, while the localized eigenvalues outside the hole are unchanged. The bias-dependent shape of this hole tracks the bias-independent contours of constant localization length. We treat the large-N limit by a combination of direct numerical diagonalization and using transfer matrices, an approach that allows us to exploit an electrostatic analogy connecting the "charges" embodied in the eigenvalue distribution with the contours of constant localization length. We show that similar results are obtained for more realistic neural networks that obey "Dale's law" (each site is purely excitatory or inhibitory) and conclude with perturbation theory results that describe the limit of large directional bias, when all states are extended. Related problems arise in random ecological networks and in chains of artificial cells with randomly coupled gene expression patterns.

Non-Hermitian localization in biological networks

NASA Astrophysics Data System (ADS)

Amir, Ariel; Hatano, Naomichi; Nelson, David R.

2016-04-01

We explore the spectra and localization properties of the N -site banded one-dimensional non-Hermitian random matrices that arise naturally in sparse neural networks. Approximately equal numbers of random excitatory and inhibitory connections lead to spatially localized eigenfunctions and an intricate eigenvalue spectrum in the complex plane that controls the spontaneous activity and induced response. A finite fraction of the eigenvalues condense onto the real or imaginary axes. For large N , the spectrum has remarkable symmetries not only with respect to reflections across the real and imaginary axes but also with respect to 90∘ rotations, with an unusual anisotropic divergence in the localization length near the origin. When chains with periodic boundary conditions become directed, with a systematic directional bias superimposed on the randomness, a hole centered on the origin opens up in the density-of-states in the complex plane. All states are extended on the rim of this hole, while the localized eigenvalues outside the hole are unchanged. The bias-dependent shape of this hole tracks the bias-independent contours of constant localization length. We treat the large-N limit by a combination of direct numerical diagonalization and using transfer matrices, an approach that allows us to exploit an electrostatic analogy connecting the "charges" embodied in the eigenvalue distribution with the contours of constant localization length. We show that similar results are obtained for more realistic neural networks that obey "Dale's law" (each site is purely excitatory or inhibitory) and conclude with perturbation theory results that describe the limit of large directional bias, when all states are extended. Related problems arise in random ecological networks and in chains of artificial cells with randomly coupled gene expression patterns.

Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients

PubMed Central

Chang, Won Hyuk; Kim, Yun-Hee; Lee, Seong-Whan; Kwon, Gyu Hyun

2015-01-01

Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal functional characteristics on brain network for a stroke. PMID:26656269

Thermostability of In Vitro Evolved Bacillus subtilis Lipase A: A Network and Dynamics Perspective

PubMed Central

Srivastava, Ashutosh; Sinha, Somdatta

2014-01-01

Proteins in thermophilic organisms remain stable and function optimally at high temperatures. Owing to their important applicability in many industrial processes, such thermostable proteins have been studied extensively, and several structural factors attributed to their enhanced stability. How these factors render the emergent property of thermostability to proteins, even in situations where no significant changes occur in their three-dimensional structures in comparison to their mesophilic counter-parts, has remained an intriguing question. In this study we treat Lipase A from Bacillus subtilis and its six thermostable mutants in a unified manner and address the problem with a combined complex network-based analysis and molecular dynamic studies to find commonality in their properties. The Protein Contact Networks (PCN) of the wild-type and six mutant Lipase A structures developed at a mesoscopic scale were analyzed at global network and local node (residue) level using network parameters and community structure analysis. The comparative PCN analysis of all proteins pointed towards important role of specific residues in the enhanced thermostability. Network analysis results were corroborated with finer-scale molecular dynamics simulations at both room and high temperatures. Our results show that this combined approach at two scales can uncover small but important changes in the local conformations that add up to stabilize the protein structure in thermostable mutants, even when overall conformation differences among them are negligible. Our analysis not only supports the experimentally determined stabilizing factors, but also unveils the important role of contacts, distributed throughout the protein, that lead to thermostability. We propose that this combined mesoscopic-network and fine-grained molecular dynamics approach is a convenient and useful scheme not only to study allosteric changes leading to protein stability in the face of negligible over-all conformational changes due to mutations, but also in other molecular networks where change in function does not accompany significant change in the network structure. PMID:25122499

World Input-Output Network

PubMed Central

Cerina, Federica; Zhu, Zhen; Chessa, Alessandro; Riccaboni, Massimo

2015-01-01

Production systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the global multi-regional input-output (GMRIO) tables. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we analyze respectively the global, regional, and local network properties of the so-called world input-output network (WION) and document its evolution over time. At global level, we find that the industries are highly but asymmetrically connected, which implies that micro shocks can lead to macro fluctuations. At regional level, we find that the world production is still operated nationally or at most regionally as the communities detected are either individual economies or geographically well defined regions. Finally, at local level, for each industry we compare the network-based measures with the traditional methods of backward linkages. We find that the network-based measures such as PageRank centrality and community coreness measure can give valuable insights into identifying the key industries. PMID:26222389

Emergent Low-Symmetry Phases and Large Property Enhancements in Ferroelectric KNbO 3 Bulk Crystals [Emergent Low-Symmetry Phases with Large Property Enhancement in Ferroelectric KNbO 3 Bulk Crystals

DOE PAGES

Lummen, Tom T. A.; Leung, J.; Kumar, Amit; ...

2017-06-19

The design of new or enhanced functionality in materials is traditionally viewed as requiring the discovery of new chemical compositions through synthesis. Large property enhancements may however also be hidden within already well-known materials, when their structural symmetry is deviated from equilibrium through a small local strain or field. Here, the discovery of enhanced material properties associated with a new metastable phase of monoclinic symmetry within bulk KNbO 3 is reported. This phase is found to coexist with the nominal orthorhombic phase at room temperature, and is both induced by and stabilized with local strains generated by a network ofmore » ferroelectric domain walls. While the local microstructural shear strain involved is only ≈0.017%, the concurrent symmetry reduction results in an optical second harmonic generation response that is over 550% higher at room temperature. Moreover, the meandering walls of the low-symmetry domains also exhibit enhanced electrical conductivity on the order of 1 S m -1. In conclusion, this discovery reveals a potential new route to local engineering of significant property enhancements and conductivity through symmetry lowering in ferroelectric crystals.« less

Emergence of localized patterns in globally coupled networks of relaxation oscillators with heterogeneous connectivity

NASA Astrophysics Data System (ADS)

Leiser, Randolph J.; Rotstein, Horacio G.

2017-08-01

Oscillations in far-from-equilibrium systems (e.g., chemical, biochemical, biological) are generated by the nonlinear interplay of positive and negative feedback effects operating at different time scales. Relaxation oscillations emerge when the time scales between the activators and the inhibitors are well separated. In addition to the large-amplitude oscillations (LAOs) or relaxation type, these systems exhibit small-amplitude oscillations (SAOs) as well as abrupt transitions between them (canard phenomenon). Localized cluster patterns in networks of relaxation oscillators consist of one cluster oscillating in the LAO regime or exhibiting mixed-mode oscillations (LAOs interspersed with SAOs), while the other oscillates in the SAO regime. Because the individual oscillators are monostable, localized patterns are a network phenomenon that involves the interplay of the connectivity and the intrinsic dynamic properties of the individual nodes. Motivated by experimental and theoretical results on the Belousov-Zhabotinsky reaction, we investigate the mechanisms underlying the generation of localized patterns in globally coupled networks of piecewise-linear relaxation oscillators where the global feedback term affects the rate of change of the activator (fast variable) and depends on the weighted sum of the inhibitor (slow variable) at any given time. We also investigate whether these patterns are affected by the presence of a diffusive type of coupling whose synchronizing effects compete with the symmetry-breaking global feedback effects.

Interplay between population firing stability and single neuron dynamics in hippocampal networks

PubMed Central

Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna

2015-01-01

Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699

Folding energy landscape and network dynamics of small globular proteins

PubMed Central

Hori, Naoto; Chikenji, George; Berry, R. Stephen; Takada, Shoji

2009-01-01

The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties. PMID:19114654

Folding energy landscape and network dynamics of small globular proteins.

PubMed

Hori, Naoto; Chikenji, George; Berry, R Stephen; Takada, Shoji

2009-01-06

The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties.

Toughening mystery of natural rubber deciphered by double network incorporating hierarchical structures

PubMed Central

Zhou, Weiming; Li, Xiangyang; Lu, Jie; Huang, Ningdong; Chen, Liang; Qi, Zeming; Li, Liangbin; Liang, Haiyi

2014-01-01

As an indispensible material for modern society, natural rubber possesses peerless mechanical properties such as strength and toughness over its artificial analogues, which remains a mystery. Intensive experimental and theoretical investigations have revealed the self-enhancement of natural rubber due to strain-induced crystallization. However a rigorous model on the self-enhancement, elucidating natural rubber's extraordinary mechanical properties, is obscured by deficient understanding of the local hierarchical structure under strain. With spatially resolved synchrotron radiation micro-beam scanning X-ray diffraction we discover weak oscillation in distributions of strain-induced crystallinity around crack tip for stretched natural rubber film, demonstrating a soft-hard double network structure. The fracture energy enhancement factor obtained by utilizing the double network model indicates an enhancement of toughness by 3 orders. It's proposed that upon stretching spontaneously developed double network structures integrating hierarchy at multi length-scale in natural rubber play an essential role in its remarkable mechanical performance. PMID:25511479

Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability

NASA Astrophysics Data System (ADS)

Kar, Soummya; Moura, José M. F.

2011-04-01

The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.

Inference of financial networks using the normalised mutual information rate.

PubMed

Goh, Yong Kheng; Hasim, Haslifah M; Antonopoulos, Chris G

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.

Inference of financial networks using the normalised mutual information rate

PubMed Central

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics. PMID:29420644

Hydrophobic-domain-dependent protein-protein interactions mediate the localization of GPAT enzymes to ER subdomains

USDA-ARS?s Scientific Manuscript database

The endoplasmic reticulum (ER) is a dynamic network that consists of numerous regions or subdomains with discrete morphological features and functional properties, including those involved in protein and oil-body formation, anterograde transport of secretory proteins, the exchange of macromolecules ...

Auxetic metamaterials from disordered networks

NASA Astrophysics Data System (ADS)

Reid, Daniel R.; Pashine, Nidhi; Wozniak, Justin M.; Jaeger, Heinrich M.; Liu, Andrea J.; Nagel, Sidney R.; de Pablo, Juan J.

2018-02-01

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as Poisson’s ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruning-based theoretical ideas have not been successful. Here we introduce a more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and in the nonlinear regimes. In particular, it is shown that Poisson’s ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting 2D sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks’ susceptibility to tuning to design networks that possess a distribution of stiffer and more compliant bonds and whose auxetic behavior is even greater than that of homogeneous networks. Taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of unique mechanical metamaterials.

Scale-Free Distribution of Avian Influenza Outbreaks

NASA Astrophysics Data System (ADS)

Small, Michael; Walker, David M.; Tse, Chi Kong

2007-11-01

Using global case data for the period from 25 November 2003 to 10 March 2007, we construct a network of plausible transmission pathways for the spread of avian influenza among domestic and wild birds. The network structure we obtain is complex and exhibits scale-free (although not necessarily small-world) properties. Communities within this network are connected with a distribution of links with infinite variance. Hence, the disease transmission model does not exhibit a threshold and so the infection will continue to propagate even with very low transmissibility. Consequentially, eradication with methods applicable to locally homogeneous populations is not possible. Any control measure needs to focus explicitly on the hubs within this network structure.

Multistability and instability analysis of recurrent neural networks with time-varying delays.

PubMed

Zhang, Fanghai; Zeng, Zhigang

2018-01-01

This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k 0 is a nonnegative integer such that k 0 ≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties. This method improves and extends the existing results in the literature. Finally, one numerical example is provided to show the superiority and effectiveness of the presented results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Clique-Based Neural Associative Memories with Local Coding and Precoding.

PubMed

Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

2016-08-01

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.

PubMed

Jovanović, Stojan; Rotter, Stefan

2016-06-01

The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

Developmental time windows for axon growth influence neuronal network topology.

PubMed

Lim, Sol; Kaiser, Marcus

2015-04-01

Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

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.

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

Lummen, Tom T. A.; Leung, J.; Kumar, Amit

The design of new or enhanced functionality in materials is traditionally viewed as requiring the discovery of new chemical compositions through synthesis. Large property enhancements may however also be hidden within already well-known materials, when their structural symmetry is deviated from equilibrium through a small local strain or field. Here, the discovery of enhanced material properties associated with a new metastable phase of monoclinic symmetry within bulk KNbO3 is reported. This phase is found to coexist with the nominal orthorhombic phase at room temperature, and is both induced by and stabilized with local strains generated by a network of ferroelectricmore » domain walls. While the local microstructural shear strain involved is only approximate to 0.017%, the concurrent symmetry reduction results in an optical second harmonic generation response that is over 550% higher at room temperature. Moreover, the meandering walls of the low-symmetry domains also exhibit enhanced electrical conductivity on the order of 1 S m(-1). This discovery reveals a potential new route to local engineering of significant property enhancements and conductivity through symmetry lowering in ferroelectric crystals.« less

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

Lummen, Tom T. A.; Leung, J.; Kumar, Amit

The design of new or enhanced functionality in materials is traditionally viewed as requiring the discovery of new chemical compositions through synthesis. Large property enhancements may however also be hidden within already well-known materials, when their structural symmetry is deviated from equilibrium through a small local strain or field. Here, the discovery of enhanced material properties associated with a new metastable phase of monoclinic symmetry within bulk KNbO 3 is reported. This phase is found to coexist with the nominal orthorhombic phase at room temperature, and is both induced by and stabilized with local strains generated by a network ofmore » ferroelectric domain walls. While the local microstructural shear strain involved is only ≈0.017%, the concurrent symmetry reduction results in an optical second harmonic generation response that is over 550% higher at room temperature. Moreover, the meandering walls of the low-symmetry domains also exhibit enhanced electrical conductivity on the order of 1 S m -1. In conclusion, this discovery reveals a potential new route to local engineering of significant property enhancements and conductivity through symmetry lowering in ferroelectric crystals.« less

From Sociocultural Disintegration to Community Connectedness Dimensions of Local Community Concepts and Their Effects on Psychological Health of Its Residents

PubMed Central

Sørensen, Tom; Kleiner, Robert; Ngo, Paul; Sørensen, Andreas; Bøe, Nils

2013-01-01

In a series of community mental health promotion studies in Lofoten, Norway, the concept of sociocultural integration is used to describe properties of a local community that are related to people's psychological health. Starting with Durkheim's description of a cohesive society, we compare different concepts that are related to sociocultural integration, for example, sense of community, social capital, and social cohesion. We then examine the relationship of various individual oriented social psychological concepts to sociocultural integration. These concepts often share theoretical and operational definitions. The concept of sociocultural integration in the Lofoten studies was proved to be very valuable in understanding how the properties of a community can affect people's mental health and their social psychological properties. It has also shown its value in the planning of mental health services and demonstrating its success in concrete community-based mental health promotion projects. Thus they could make important contributions to further studies and actions in local communities where the intersection between the individual, their social network, and their local community occurs. PMID:24236288

PhysDoc: A Distributed Network of Physics Institutions: Collecting, Indexing, and Searching High Quality Documents by Using Harvest; The Dublin Core Metadata Initiative: Mission, Current Activities, and Future Directions; Information Services for Higher Education: A New Competitive Space; Intellectual Property Conservancies.

ERIC Educational Resources Information Center

Severiens, Thomas; Hohlfeld, Michael; Zimmermann, Kerstin; Hilf, Eberhard R.; von Ossietzky, Carl; Weibel, Stuart L.; Koch, Traugott; Hughes, Carol Ann; Bearman, David

2000-01-01

Includes four articles that discuss a variety to topics, including a distributed network of physics institutions documents called PhysDocs which harvests information from the local Web-servers of professional physics institutions; the Dublin Core metadata initiative; information services for higher education in a competitive environment; and…

Route Prediction on Tracking Data to Location-Based Services

NASA Astrophysics Data System (ADS)

Petróczi, Attila István; Gáspár-Papanek, Csaba

Wireless networks have become so widespread, it is beneficial to determine the ability of cellular networks for localization. This property enables the development of location-based services, providing useful information. These services can be improved by route prediction under the condition of using simple algorithms, because of the limited capabilities of mobile stations. This study gives alternative solutions for this problem of route prediction based on a specific graph model. Our models provide the opportunity to reach our destinations with less effort.

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.

Dynamic aftereffects in supplementary motor network following inhibitory transcranial magnetic stimulation protocols.

PubMed

Ji, Gong-Jun; Yu, Fengqiong; Liao, Wei; Wang, Kai

2017-04-01

The supplementary motor area (SMA) is a key node of the motor network. Inhibitory repetitive transcranial magnetic stimulation (rTMS) of the SMA can potentially improve movement disorders. However, the aftereffects of inhibitory rTMS on brain function remain largely unknown. Using a single-blind, crossover within-subject design, we investigated the role of aftereffects with two inhibitory rTMS protocols [1800 pulses of either 1-Hz repetitive stimulation or continuous theta burst stimulation (cTBS)] on the left SMA. A total of 19 healthy volunteers participated in the rTMS sessions on 2 separate days. Firstly, short-term aftereffects were estimated at three levels (functional connectivity, local activity, and network properties) by comparing the resting-state functional magnetic resonance imaging datasets (9min) acquired before and after each rTMS session. Local activity and network properties were not significantly altered by either protocol. Functional connectivity within the SMA network was increased (in the left paracentral gyrus) by 1-Hz stimulation and decreased (in the left inferior frontal gyrus and SMA/middle cingulate cortex) by cTBS. The subsequent three-way analysis of variance (site×time×protocol) did not show a significant interaction effect or "protocol" main effect, suggesting that the two protocols share an underlying mechanism. Secondly, sliding-window analysis was used to evaluate the dynamic features of aftereffects in the ~29min after the end of stimulation. Aftereffects were maintained for a maximum of 9.8 and 6.6min after the 1-Hz and cTBS protocols, respectively. In summary, this study revealed topographical and temporal aftereffects in the SMA network following inhibitory rTMS protocols, providing valuable information for their application in future neuroscience and clinical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses.

PubMed

Xu, Man; Tan, Xiangliang; Zhang, Xinyuan; Guo, Yihao; Mei, Yingjie; Feng, Qianjin; Xu, Yikai; Feng, Yanqiu

2017-01-01

Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

Positive and negative affective processing exhibit dissociable functional hubs during the viewing of affective pictures.

PubMed

Zhang, Wenhai; Li, Hong; Pan, Xiaohong

2015-02-01

Recent resting-state functional magnetic resonance imaging (fMRI) studies using graph theory metrics have revealed that the functional network of the human brain possesses small-world characteristics and comprises several functional hub regions. However, it is unclear how the affective functional network is organized in the brain during the processing of affective information. In this study, the fMRI data were collected from 25 healthy college students as they viewed a total of 81 positive, neutral, and negative pictures. The results indicated that affective functional networks exhibit weaker small-worldness properties with higher local efficiency, implying that local connections increase during viewing affective pictures. Moreover, positive and negative emotional processing exhibit dissociable functional hubs, emerging mainly in task-positive regions. These functional hubs, which are the centers of information processing, have nodal betweenness centrality values that are at least 1.5 times larger than the average betweenness centrality of the network. Positive affect scores correlated with the betweenness values of the right orbital frontal cortex (OFC) and the right putamen in the positive emotional network; negative affect scores correlated with the betweenness values of the left OFC and the left amygdala in the negative emotional network. The local efficiencies in the left superior and inferior parietal lobe correlated with subsequent arousal ratings of positive and negative pictures, respectively. These observations provide important evidence for the organizational principles of the human brain functional connectome during the processing of affective information. © 2014 Wiley Periodicals, Inc.

Community Detection in Complex Networks via Clique Conductance.

PubMed

Lu, Zhenqi; Wahlström, Johan; Nehorai, Arye

2018-04-13

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

Supply network science: Emergence of a new perspective on a classical field

NASA Astrophysics Data System (ADS)

Brintrup, Alexandra; Ledwoch, Anna

2018-03-01

Supply networks emerge as companies procure goods from one another to produce their own products. Due to a chronic lack of data, studies on these emergent structures have long focussed on local neighbourhoods, assuming simple, chain-like structures. However, studies conducted since 2001 have shown that supply chains are indeed complex networks that exhibit similar organisational patterns to other network types. In this paper, we present a critical review of theoretical and model based studies which conceptualise supply chains from a network science perspective, showing that empirical data do not always support theoretical models that were developed, and argue that different industrial settings may present different characteristics. Consequently, a need that arises is the development and reconciliation of interpretation across different supply network layers such as contractual relations, material flow, financial links, and co-patenting, as these different projections tend to remain in disciplinary siloes. Other gaps include a lack of null models that show whether the observed properties are meaningful, a lack of dynamical models that can inform how layers evolve and adopt to changes, and a lack of studies that investigate how local decisions enable emergent outcomes. We conclude by asking the network science community to help bridge these gaps by engaging with this important area of research.

Supply network science: Emergence of a new perspective on a classical field.

PubMed

Brintrup, Alexandra; Ledwoch, Anna

2018-03-01

Supply networks emerge as companies procure goods from one another to produce their own products. Due to a chronic lack of data, studies on these emergent structures have long focussed on local neighbourhoods, assuming simple, chain-like structures. However, studies conducted since 2001 have shown that supply chains are indeed complex networks that exhibit similar organisational patterns to other network types. In this paper, we present a critical review of theoretical and model based studies which conceptualise supply chains from a network science perspective, showing that empirical data do not always support theoretical models that were developed, and argue that different industrial settings may present different characteristics. Consequently, a need that arises is the development and reconciliation of interpretation across different supply network layers such as contractual relations, material flow, financial links, and co-patenting, as these different projections tend to remain in disciplinary siloes. Other gaps include a lack of null models that show whether the observed properties are meaningful, a lack of dynamical models that can inform how layers evolve and adopt to changes, and a lack of studies that investigate how local decisions enable emergent outcomes. We conclude by asking the network science community to help bridge these gaps by engaging with this important area of research.

Assessing cortical synchronization during transcranial direct current stimulation: A graph-theoretical analysis.

PubMed

Mancini, Matteo; Brignani, Debora; Conforto, Silvia; Mauri, Piercarlo; Miniussi, Carlo; Pellicciari, Maria Concetta

2016-10-15

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that can alter cortical excitability and modulate behaviour in a polarity-dependent way. Despite the widespread use of this method in the neuroscience field, its effects on ongoing local or global (network level) neuronal activity are still not foreseeable. A way to shed light on the neuronal mechanisms underlying the cortical connectivity changes induced by tDCS is provided by the combination of tDCS with electroencephalography (EEG). In this study, twelve healthy subjects underwent online tDCS-EEG recording (i.e., simultaneous), during resting-state, using 19 EEG channels. The protocol involved anodal, cathodal and sham stimulation conditions, with the active and the reference electrodes in the left frontocentral area (FC3) and on the forehead over the right eyebrow, respectively. The data were processed using a network model, based on graph theory and the synchronization likelihood. The resulting graphs were analysed for four frequency bands (theta, alpha, beta and gamma) to evaluate the presence of tDCS-induced differences in synchronization patterns and graph theory measures. The resting state network connectivity resulted altered during tDCS, in a polarity-specific manner for theta and alpha bands. Anodal tDCS weakened synchronization with respect to the baseline over the fronto-central areas in the left hemisphere, for theta band (p<0.05). In contrast, during cathodal tDCS a significant increase in inter-hemispheric synchronization connectivity was observed over the centro-parietal, centro-occipital and parieto-occipital areas for the alpha band (p<0.05). Local graph measures showed a tDCS-induced polarity-specific differences that regarded modifications of network activities rather than specific region properties. Our results show that applying tDCS during the resting state modulates local synchronization as well as network properties in slow frequency bands, in a polarity-specific manner. Copyright © 2016 Elsevier Inc. All rights reserved.

Linking microscopic and macroscopic response in disordered solids

NASA Astrophysics Data System (ADS)

Hexner, Daniel; Liu, Andrea J.; Nagel, Sidney R.

2018-06-01

The modulus of a rigid network of harmonic springs depends on the sum of the energies in each of the bonds due to an applied distortion such as compression in the case of the bulk modulus or shear in the case of the shear modulus. However, the distortion need not be global. Here we introduce a local modulus, Li, associated with changing the equilibrium length of a single bond, i , in the network. We show that Li is useful for understanding many aspects of the mechanical response of the entire system. It allows an efficient computation of how the removal of any bond changes the global properties such as the bulk and shear moduli. Furthermore, it allows a prediction of the distribution of these changes and clarifies why the changes of these two moduli due to removal of a bond are uncorrelated; these are the essential ingredients necessary for the efficient manipulation of network properties by bond removal.

Locality preserving non-negative basis learning with graph embedding.

PubMed

Ghanbari, Yasser; Herrington, John; Gur, Ruben C; Schultz, Robert T; Verma, Ragini

2013-01-01

The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graphtheoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.

Transmembrane domain-dependent protein-protein interactions participate in the localization of GPAT enzymes to ER subdomains

USDA-ARS?s Scientific Manuscript database

The endoplasmic reticulum (ER) is a dynamic network that consists of numerous regions or subdomains with discrete morphological features and functional properties, including those involved in protein and oil-body formation, anterograde transport of secretory proteins, the exchange of macromolecules ...

Magnetic domain wall gratings for magnetization reversal tuning and confined dynamic mode localization.

PubMed

Trützschler, Julia; Sentosun, Kadir; Mozooni, Babak; Mattheis, Roland; McCord, Jeffrey

2016-08-04

High density magnetic domain wall gratings are imprinted in ferromagnetic-antiferromagnetic thin films by local ion irradiation by which alternating head-to-tail-to-head-to-tail and head-to-head-to-tail-to-tail spatially overlapping domain wall networks are formed. Unique magnetic domain processes result from the interaction of anchored domain walls. Non-linear magnetization response is introduced by the laterally distributed magnetic anisotropy phases. The locally varying magnetic charge distribution gives rise to localized and guided magnetization spin-wave modes directly constrained by the narrow domain wall cores. The exchange coupled multiphase material structure leads to unprecedented static and locally modified dynamic magnetic material properties.

Magnetic domain wall gratings for magnetization reversal tuning and confined dynamic mode localization

NASA Astrophysics Data System (ADS)

Trützschler, Julia; Sentosun, Kadir; Mozooni, Babak; Mattheis, Roland; McCord, Jeffrey

2016-08-01

High density magnetic domain wall gratings are imprinted in ferromagnetic-antiferromagnetic thin films by local ion irradiation by which alternating head-to-tail-to-head-to-tail and head-to-head-to-tail-to-tail spatially overlapping domain wall networks are formed. Unique magnetic domain processes result from the interaction of anchored domain walls. Non-linear magnetization response is introduced by the laterally distributed magnetic anisotropy phases. The locally varying magnetic charge distribution gives rise to localized and guided magnetization spin-wave modes directly constrained by the narrow domain wall cores. The exchange coupled multiphase material structure leads to unprecedented static and locally modified dynamic magnetic material properties.

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes.

PubMed

Vegué, Marina; Perin, Rodrigo; Roxin, Alex

2017-08-30

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. Copyright © 2017 the authors 0270-6474/17/378498-13$15.00/0.

Graph theoretical analysis of complex networks in the brain

PubMed Central

Stam, Cornelis J; Reijneveld, Jaap C

2007-01-01

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern. PMID:17908336

International migration network: Topology and modeling

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

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

International migration network: topology and modeling.

PubMed

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

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

Dynamic information routing in complex networks

PubMed Central

Kirst, Christoph; Timme, Marc; Battaglia, Demian

2016-01-01

Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

Prestimulus Network Integration of Auditory Cortex Predisposes Near-Threshold Perception Independently of Local Excitability

PubMed Central

Leske, Sabine; Ruhnau, Philipp; Frey, Julia; Lithari, Chrysa; Müller, Nadia; Hartmann, Thomas; Weisz, Nathan

2015-01-01

An ever-increasing number of studies are pointing to the importance of network properties of the brain for understanding behavior such as conscious perception. However, with regards to the influence of prestimulus brain states on perception, this network perspective has rarely been taken. Our recent framework predicts that brain regions crucial for a conscious percept are coupled prior to stimulus arrival, forming pre-established pathways of information flow and influencing perceptual awareness. Using magnetoencephalography (MEG) and graph theoretical measures, we investigated auditory conscious perception in a near-threshold (NT) task and found strong support for this framework. Relevant auditory regions showed an increased prestimulus interhemispheric connectivity. The left auditory cortex was characterized by a hub-like behavior and an enhanced integration into the brain functional network prior to perceptual awareness. Right auditory regions were decoupled from non-auditory regions, presumably forming an integrated information processing unit with the left auditory cortex. In addition, we show for the first time for the auditory modality that local excitability, measured by decreased alpha power in the auditory cortex, increases prior to conscious percepts. Importantly, we were able to show that connectivity states seem to be largely independent from local excitability states in the context of a NT paradigm. PMID:26408799

Variations on a theme - the evolution of hydrocarbon solids. I. Compositional and spectral modelling - the eRCN and DG models

NASA Astrophysics Data System (ADS)

Jones, A. P.

2012-04-01

Context. The compositional properties of hydrogenated amorphous carbons are known to evolve in response to the local conditions. Aims: We present a model for low-temperature, amorphous hydrocarbon solids, based on the microphysical properties of random and defected networks of carbon and hydrogen atoms, that can be used to study and predict the evolution of their properties in the interstellar medium. Methods: We adopt an adaptable and prescriptive approach to model these materials, which is based on a random covalent network (RCN) model, extended here to a full compositional derivation (the eRCN model), and a defective graphite (DG) model for the hydrogen poorer materials where the eRCN model is no longer valid. Results: We provide simple expressions that enable the determination of the structural, infrared and spectral properties of amorphous hydrocarbon grains as a function of the hydrogen atomic fraction, XH. Structural annealing, resulting from hydrogen atom loss, results in a transition from H-rich, aliphatic-rich to H-poor, aromatic-rich materials. Conclusions: The model predicts changes in the optical properties of hydrogenated amorphous carbon dust in response to the likely UV photon-driven and/or thermal annealing processes resulting, principally, from the radiation field in the environment. We show how this dust component will evolve, compositionally and structurally in the interstellar medium in response to the local conditions. Appendices A and B are available in electronic form at http://www.aanda.org

Continuous-time quantum walks on multilayer dendrimer networks

NASA Astrophysics Data System (ADS)

Galiceanu, Mircea; Strunz, Walter T.

2016-08-01

We consider continuous-time quantum walks (CTQWs) on multilayer dendrimer networks (MDs) and their application to quantum transport. A detailed study of properties of CTQWs is presented and transport efficiency is determined in terms of the exact and average return probabilities. The latter depends only on the eigenvalues of the connectivity matrix, which even for very large structures allows a complete analytical solution for this particular choice of network. In the case of MDs we observe an interplay between strong localization effects, due to the dendrimer topology, and good efficiency from the linear segments. We show that quantum transport is enhanced by interconnecting more layers of dendrimers.

Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

NASA Astrophysics Data System (ADS)

Papadopoulos, Lia; Kim, Jason Z.; Kurths, Jürgen; Bassett, Danielle S.

2017-07-01

Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule—which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators—can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by considering a time-dependent interplay between structure and dynamics, this work offers a mechanism through which emergent phenomena and organization can arise in complex systems utilizing local rules.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

The Mathematics of Dispatchability Revisited

NASA Technical Reports Server (NTRS)

Morris, Paul

2016-01-01

Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatchable STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, the dispatchability property has both theoretical and practical interest. One thing that hampers further work in this area is the underdeveloped theory. The existing definitions are expressed in terms of algorithms, and are less suitable for mathematical proofs. In this paper, we develop a new formal theory of dispatchability in terms of execution sequences. We exploit this to prove a characterization of dispatchability involving the structural properties of the STN graph. This facilitates the potential application of the theory to uncertainty reasoning.

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

USGS Publications Warehouse

Song, W.-Z.; Huang, R.; Shirazi, B.; Husent, R.L.

2009-01-01

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and frame into slots. Parent determines children's frame assigmnent based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. Firstly, given any node, at any time slot, there is at most one active sender in its neighborhood (includ ing itself). Secondly, the packet scheduling with TreelMAC is bufferless, which therefore minimizes the probability of network congestion. Thirdly, the data throughput to gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24 node test bed demonstrate that TreeMAC protocol significantly improves network throughput and energy efficiency, by comparing to the TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC[8]. ?? 2009 IEEE.

Evolution and selection of river networks: Statics, dynamics, and complexity

PubMed Central

Rinaldo, Andrea; Rigon, Riccardo; Banavar, Jayanth R.; Maritan, Amos; Rodriguez-Iturbe, Ignacio

2014-01-01

Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal channel networks (OCNs) are feasible optimal configurations of a spanning network mimicking landscape evolution and network selection through imperfect searches for dynamically accessible states. OCNs are spanning loopless configurations, however, only under precise physical requirements that arise under the constraints imposed by river dynamics—every spanning tree is exactly a local minimum of total energy dissipation. It is remarkable that dynamically accessible configurations, the local optima, stabilize into diverse metastable forms that are nevertheless characterized by universal statistical features. Such universal features explain very well the statistics of, and the linkages among, the scaling features measured for fluvial landforms across a broad range of scales regardless of geology, exposed lithology, vegetation, or climate, and differ significantly from those of the ground state, known exactly. Results are provided on the emergence of criticality through adaptative evolution and on the yet-unexplored range of applications of the OCN concept. PMID:24550264

Coevolution of Cooperation and Partner Rewiring Range in Spatial Social Networks

NASA Astrophysics Data System (ADS)

Khoo, Tommy; Fu, Feng; Pauls, Scott

2016-11-01

In recent years, there has been growing interest in the study of coevolutionary games on networks. Despite much progress, little attention has been paid to spatially embedded networks, where the underlying geographic distance, rather than the graph distance, is an important and relevant aspect of the partner rewiring process. It thus remains largely unclear how individual partner rewiring range preference, local vs. global, emerges and affects cooperation. Here we explicitly address this issue using a coevolutionary model of cooperation and partner rewiring range preference in spatially embedded social networks. In contrast to local rewiring, global rewiring has no distance restriction but incurs a one-time cost upon establishing any long range link. We find that under a wide range of model parameters, global partner switching preference can coevolve with cooperation. Moreover, the resulting partner network is highly degree-heterogeneous with small average shortest path length while maintaining high clustering, thereby possessing small-world properties. We also discover an optimum availability of reputation information for the emergence of global cooperators, who form distant partnerships at a cost to themselves. From the coevolutionary perspective, our work may help explain the ubiquity of small-world topologies arising alongside cooperation in the real world.

Multiparameter Estimation in Networked Quantum Sensors

NASA Astrophysics Data System (ADS)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-01

We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.

An agronomic field-scale sensor network for monitoring soil water and temperature variation

NASA Astrophysics Data System (ADS)

Brown, D. J.; Gasch, C.; Brooks, E. S.; Huggins, D. R.; Campbell, C. S.; Cobos, D. R.

2014-12-01

Environmental sensor networks have been deployed in a variety of contexts to monitor plant, air, water and soil properties. To date, there have been relatively few such networks deployed to monitor dynamic soil properties in cropped fields. Here we report on experience with a distributed soil sensor network that has been deployed for seven years in a research farm with ongoing agronomic field operations. The Washington State University R. J. Cook Agronomy Farm (CAF), Pullman, WA, USA has recently been designated a United States Department of Agriculture (USDA) Long-Term Agro-Ecosystem Research (LTAR) site. In 2007, 12 geo-referenced locations at CAF were instrumented, then in 2009 this network was expended to 42 locations distributed across the 37-ha farm. At each of this locations, Decagon 5TE probes (Decagon Devices Inc., Pullman, WA, USA) were installed at five depths (30, 60, 90, 120, and 150 cm), with temperature and volumetric soil moisture content recorded hourly. Initially, data loggers were wirelessly connected to a data station that could be accessed through a cell connection, but due to the logistics of agronomic field operations, we later buried the dataloggers at each site and now periodically download data via local radio transmission. In this presentation, we share our experience with the installation, maintenance, calibration and data processing associated with an agronomic soil monitoring network. We also present highlights of data derived from this network, including seasonal fluctuations of soil temperature and volumetric water content at each depth, and how these measurements are influenced by crop type, soil properties, landscape position, and precipitation events.

Probing the water distribution in porous model sands with two immiscible fluids: A nuclear magnetic resonance micro-imaging study

NASA Astrophysics Data System (ADS)

Lee, Bum Han; Lee, Sung Keun

2017-10-01

The effect of the structural heterogeneity of porous networks on the water distribution in porous media, initially saturated with immiscible fluid followed by increasing durations of water injection, remains one of the important problems in hydrology. The relationship among convergence rates (i.e., the rate of fluid saturation with varying injection time) and the macroscopic properties and structural parameters of porous media have been anticipated. Here, we used nuclear magnetic resonance (NMR) micro-imaging to obtain images (down to ∼50 μm resolution) of the distribution of water injected for varying durations into porous networks that were initially saturated with silicone oil. We then established the relationships among the convergence rates, structural parameters, and transport properties of porous networks. The volume fraction of the water phase increases as the water injection duration increases. The 3D images of the water distributions for silica gel samples are similar to those of the glass bead samples. The changes in water saturation (and the accompanying removal of silicone oil) and the variations in the volume fraction, specific surface area, and cube-counting fractal dimension of the water phase fit well with the single-exponential recovery function { f (t) = a [ 1 -exp (- λt) ] } . The asymptotic values (a, i.e., saturated value) of the properties of the volume fraction, specific surface area, and cube-counting fractal dimension of the glass bead samples were greater than those for the silica gel samples primarily because of the intrinsic differences in the porous networks and local distribution of the pore size and connectivity. The convergence rates of all of the properties are inversely proportional to the entropy length and permeability. Despite limitations of the current study, such as insufficient resolution and uncertainty for the estimated parameters due to sparsely selected short injection times, the observed trends highlight the first analyses of the cube-counting fractal dimension (and other structural properties) and convergence rates in porous networks consisting of two fluid components. These results indicate that the convergence rates correlate with the geometric factor that characterizes the porous networks and transport property of the porous networks.

Topology of molecular interaction networks.

PubMed

Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

2013-09-16

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Topology of molecular interaction networks

PubMed Central

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

Spatial Variability of AERONET Aerosol Optical Properties and Satellite Data in South Korea during NASA DRAGON-Asia Campaign.

PubMed

Lee, Hyung Joo; Son, Youn-Suk

2016-04-05

We investigated spatial variability in aerosol optical properties, including aerosol optical depth (AOD), fine-mode fraction (FMF), and single scattering albedo (SSA), observed at 21 Aerosol Robotic Network (AERONET) sites and satellite remote sensing data in South Korea during the spring of 2012. These dense AERONET networks established in a National Aeronautics and Space Administration (NASA) field campaign enabled us to examine the spatially detailed aerosol size distribution and composition as well as aerosol levels. The springtime particle air quality was characterized by high background aerosol levels and high contributions of coarse-mode aerosols to total aerosols. We found that between-site correlations and coefficient of divergence for AOD and FMF strongly relied on the distance between sites, particularly in the south-north direction. Higher AOD was related to higher population density and lower distance from highways, and the aerosol size distribution and composition reflected source-specific characteristics. The ratios of satellite NO2 to AOD, which indicate the relative contributions of local combustion sources to aerosol levels, represented higher local contributions in metropolitan Seoul and Pusan. Our study demonstrates that the aerosol levels were determined by both local and regional pollution and that the relative contributions of these pollutions to aerosols generated spatial heterogeneity in the particle air quality.

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Phenomenological network models: Lessons for epilepsy surgery.

PubMed

Hebbink, Jurgen; Meijer, Hil; Huiskamp, Geertjan; van Gils, Stephan; Leijten, Frans

2017-10-01

The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Shear Stress Induced Reorganization of the Keratin Intermediate Filament Network Requires Phosphorylation by Protein Kinase C ζ

PubMed Central

Sivaramakrishnan, Sivaraj; Schneider, Jaime L.; Sitikov, Albert; Goldman, Robert D.

2009-01-01

Keratin intermediate filaments (KIFs) form a fibrous polymer network that helps epithelial cells withstand external mechanical forces. Recently, we established a correlation between the structure of the KIF network and its local mechanical properties in alveolar epithelial cells. Shear stress applied across the cell surface resulted in the structural remodeling of KIF and a substantial increase in the elastic modulus of the network. This study examines the mechanosignaling that regulates the structural remodeling of the KIF network. We report that the shear stress–mediated remodeling of the KIF network is facilitated by a twofold increase in the dynamic exchange rate of KIF subunits, which is regulated in a PKC ζ and 14-3-3–dependent manner. PKC ζ phosphorylates K18pSer33, and this is required for the structural reorganization because the KIF network in A549 cells transfected with a dominant negative PKC ζ, or expressing the K18Ser33Ala mutation, is unchanged. Blocking the shear stress–mediated reorganization results in reduced cellular viability and increased apoptotic levels. These data suggest that shear stress mediates the phosphorylation of K18pSer33, which is required for the reorganization of the KIF network, resulting in changes in mechanical properties of the cell that help maintain the integrity of alveolar epithelial cells. PMID:19357195

The Anatomical Distance of Functional Connections Predicts Brain Network Topology in Health and Schizophrenia

PubMed Central

Vértes, Petra E.; Stidd, Reva; Lalonde, François; Clasen, Liv; Rapoport, Judith; Giedd, Jay; Bullmore, Edward T.; Gogtay, Nitin

2013-01-01

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive “pruning” of short-distance functional connections in schizophrenia. PMID:22275481

Cortical connectivity in fronto-temporal focal epilepsy from EEG analysis: A study via graph theory.

PubMed

Vecchio, Fabrizio; Miraglia, Francesca; Curcio, Giuseppe; Della Marca, Giacomo; Vollono, Catello; Mazzucchi, Edoardo; Bramanti, Placido; Rossini, Paolo Maria

2015-06-01

It is believed that effective connectivity and optimal network structure are essential for proper information processing in the brain. Indeed, functional abnormalities of the brain are found to be associated with pathological changes in connectivity and network structures. The aim of the present study was to explore the interictal network properties of EEG signals from temporal lobe structures in the context of fronto-temporal lobe epilepsy. To complete this aim, the graph characteristics of the EEG data of 17 patients suffering from focal epilepsy of the fronto-temporal type, recorded during interictal periods, were examined and compared in terms of the affected versus the unaffected hemispheres. EEG connectivity analysis was performed using eLORETA software in 15 fronto-temporal regions (Brodmann Areas BAs 8, 9, 10, 11, 20, 21, 22, 37, 38, 41, 42, 44, 45, 46, 47) on both affected and unaffected hemispheres. The evaluation of the graph analysis parameters, such as 'global' (characteristic path length) and 'local' connectivity (clustering coefficient) showed a statistically significant interaction among side (affected and unaffected hemisphere) and Band (delta, theta, alpha, beta, gamma). Duncan post hoc testing showed an increase of the path length in the alpha band in the affected hemisphere with respect to the unaffected one, as evaluated by an inter-hemispheric marker. The affected hemisphere also showed higher values of local connectivity in the alpha band. In general, an increase of local and global graph theory parameters in the alpha band was found in the affected hemisphere. It was also demonstrated that these effects were more evident in drug-free patients than in those undergoing pharmacological therapy. The increased measures in the affected hemisphere of both functional local segregation and global integration could result from the combination of overlapping mechanisms, including reactive neuroplastic changes seeking to maintain constant integration and segregation properties. This reactive neuroplastic mechanism seeking to maintain constant integration and segregation properties seems to be more evident in the absence of antiepileptic treatment. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures.

PubMed

Erdeniz, Burak; Serin, Emin; İbadi, Yelda; Taş, Cumhur

2017-12-30

Schizophrenia is a complex disorder in which abnormalities in brain connectivity and social functioning play a central role. The aim of this study is to explore small-world network properties, and understand their relationship with social functioning and social cognition in the context of schizophrenia, by testing functional connectivity differences in network properties and its relation to clinical behavioral measures. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia and 23 healthy volunteers. The results revealed that patients with schizophrenia show significantly decreased connectivity between a range of brain regions, particularly involving connections among the right orbitofrontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia were characterized by reduced path length compared to healthy controls; however, no significant difference was found for clustering coefficient, local efficiency or global efficiency. Additionally, we found that nodal efficiency of the amygdala and the putamen were significantly correlated with the independence-performance subscale of social functioning scale (SFC), and Reading the Mind in the Eyes test; however, the correlations do not survive correction for multiple comparison. The current results help to clarify the relationship between social functioning deficits and topological brain measures in schizophrenia. Copyright © 2017 Elsevier B.V. All rights reserved.

Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

PubMed

Jeub, Lucas G S; Balachandran, Prakash; Porter, Mason A; Mucha, Peter J; Mahoney, Michael W

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

PubMed Central

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2016-01-01

It is common in the study of networks to investigate intermediate-sized (or “meso-scale”) features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify “communities,” which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that “communities” are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for “size-resolved community structure” that can arise in real (and realistic) networks: (i) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify “good” communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally-biased methods that focus on communities that are centered around a given seed node might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate subtler structural properties that are important to consider in the development of better benchmark networks to test methods for community detection. PMID:25679670

Think locally, act locally: Detection of small, medium-sized, and large communities in large networks

NASA Astrophysics Data System (ADS)

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

The Role of Network Architecture in Collagen Mechanics.

PubMed

Jansen, Karin A; Licup, Albert J; Sharma, Abhinav; Rens, Robbie; MacKintosh, Fred C; Koenderink, Gijsje H

2018-06-05

Collagen forms fibrous networks that reinforce tissues and provide an extracellular matrix for cells. These networks exhibit remarkable strain-stiffening properties that tailor the mechanical functions of tissues and regulate cell behavior. Recent models explain this nonlinear behavior as an intrinsic feature of disordered networks of stiff fibers. Here, we experimentally validate this theoretical framework by measuring the elastic properties of collagen networks over a wide range of self-assembly conditions. We show that the model allows us to quantitatively relate both the linear and nonlinear elastic behavior of collagen networks to their underlying architecture. Specifically, we identify the local coordination number (or connectivity) 〈z〉 as a key architectural parameter that governs the elastic response of collagen. The network elastic response reveals that 〈z〉 decreases from 3.5 to 3 as the polymerization temperature is raised from 26 to 37°C while being weakly dependent on concentration. We furthermore infer a Young's modulus of 1.1 MPa for the collagen fibrils from the linear modulus. Scanning electron microscopy confirms that 〈z〉 is between three and four but is unable to detect the subtle changes in 〈z〉 with polymerization conditions that rheology is sensitive to. Finally, we show that, consistent with the model, the initial stress-stiffening response of collagen networks is controlled by the negative normal stress that builds up under shear. Our work provides a predictive framework to facilitate future studies of the regulatory effect of extracellular matrix molecules on collagen mechanics. Moreover, our findings can aid mechanobiological studies of wound healing, fibrosis, and cancer metastasis, which require collagen matrices with tunable mechanical properties. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Embedding dynamical networks into distributed models

NASA Astrophysics Data System (ADS)

Innocenti, Giacomo; Paoletti, Paolo

2015-07-01

Large networks of interacting dynamical systems are well-known for the complex behaviours they are able to display, even when each node features a quite simple dynamics. Despite examples of such networks being widespread both in nature and in technological applications, the interplay between the local and the macroscopic behaviour, through the interconnection topology, is still not completely understood. Moreover, traditional analytical methods for dynamical response analysis fail because of the intrinsically large dimension of the phase space of the network which makes the general problem intractable. Therefore, in this paper we develop an approach aiming to condense all the information in a compact description based on partial differential equations. By focusing on propagative phenomena, rigorous conditions under which the original network dynamical properties can be successfully analysed within the proposed framework are derived as well. A network of Fitzhugh-Nagumo systems is finally used to illustrate the effectiveness of the proposed method.

Shock waves on complex networks

NASA Astrophysics Data System (ADS)

Mones, Enys; Araújo, Nuno A. M.; Vicsek, Tamás; Herrmann, Hans J.

2014-05-01

Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.

Using complex networks for text classification: Discriminating informative and imaginative documents

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-01-01

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Functional Brain Networks Develop from a “Local to Distributed” Organization

PubMed Central

Power, Jonathan D.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Schlaggar, Bradley L.; Petersen, Steven E.

2009-01-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways. PMID:19412534

White matter structural network abnormalities underlie executive dysfunction in amyotrophic lateral sclerosis.

PubMed

Dimond, Dennis; Ishaque, Abdullah; Chenji, Sneha; Mah, Dennell; Chen, Zhang; Seres, Peter; Beaulieu, Christian; Kalra, Sanjay

2017-03-01

Research in amyotrophic lateral sclerosis (ALS) suggests that executive dysfunction, a prevalent cognitive feature of the disease, is associated with abnormal structural connectivity and white matter integrity. In this exploratory study, we investigated the white matter constructs of executive dysfunction, and attempted to detect structural abnormalities specific to cognitively impaired ALS patients. Eighteen ALS patients and 22 age and education matched healthy controls underwent magnetic resonance imaging on a 4.7 Tesla scanner and completed neuropsychometric testing. ALS patients were categorized into ALS cognitively impaired (ALSci, n = 9) and ALS cognitively competent (ALScc, n = 5) groups. Tract-based spatial statistics and connectomics were used to compare white matter integrity and structural connectivity of ALSci and ALScc patients. Executive function performance was correlated with white matter FA and network metrics within the ALS group. Executive function performance in the ALS group correlated with global and local network properties, as well as FA, in regions throughout the brain, with a high predilection for the frontal lobe. ALSci patients displayed altered local connectivity and structural integrity in these same frontal regions that correlated with executive dysfunction. Our results suggest that executive dysfunction in ALS is related to frontal network disconnectivity, which potentially mediates domain-specific, or generalized cognitive impairment, depending on the degree of global network disruption. Furthermore, reported co-localization of decreased network connectivity and diminished white matter integrity suggests white matter pathology underlies this topological disruption. We conclude that executive dysfunction in ALSci is associated with frontal and global network disconnectivity, underlined by diminished white matter integrity. Hum Brain Mapp 38:1249-1268, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Functional brain networks develop from a "local to distributed" organization.

PubMed

Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

2009-05-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Structural and Luminescent property of Holmium doped Borate Glasses

NASA Astrophysics Data System (ADS)

Usharani, V. L.; Eraiah, B.

2018-02-01

Holmium doped Lithium Lead Borate glasses of different compositions were prepared by melt quenching technique. Fourier transform infrared investigations on lithium lead borate glasses have been made to study the local order and vibrations of atoms in the glass network and it contains mainly BO3 and BO4 structural units. Photoluminescence techniques were employed to investigate the luminescent property of these glasses excited at 451nm. Blue emission have been observed from the transition 495 (5F3 → 5I8).

An analysis of current source density profiles activated by local stimulation in the mouse auditory cortex in vitro.

PubMed

Yamamura, Daiki; Sano, Ayaka; Tateno, Takashi

2017-03-15

To examine local network properties of the mouse auditory cortex in vitro, we recorded extracellular spatiotemporal laminar profiles driven by short electric local stimulation on a planar multielectrode array substrate. The recorded local field potentials were subsequently evaluated using current source density (CSD) analysis to identify sources and sinks. Current sinks are thought to be an indicator of net synaptic current in the small volume of cortex surrounding the recording site. Thus, CSD analysis combined with multielectrode arrays enabled us to compare mean synaptic activity in response to small current stimuli on a layer-by-layer basis. We also used senescence-accelerated mice (SAM), some strains of which show earlier onset of age-related hearing loss, to examine the characteristic spatiotemporal CSD profiles stimulated by electrodes in specific cortical layers. Thus, the CSD patterns were classified into several clusters based on stimulation sites in the cortical layers. We also found some differences in CSD patterns between the two SAM strains in terms of aging according to principle component analysis with dimension reduction. For simultaneous two-site stimulation, we modeled the obtained CSD profiles as a linear superposition of the CSD profiles to individual single-site stimulation. The model analysis indicated the nonlinearity of spatiotemporal integration over stimulus-driven activity in a layer-specific manner. Finally, on the basis of these results, we discuss the auditory cortex local network properties and the effects of aging on these mouse strains. Copyright © 2017 Elsevier B.V. All rights reserved.

Bridging, brokerage and betweenness

PubMed Central

Everett, Martin G.; Valente, Thomas W.

2017-01-01

Valente and Fujimoto (2010) proposed a measure of brokerage in networks based on Granovetter’s classic work on the strength of weak ties. Their paper identified the need for finding node-based measures of brokerage that consider the entire network structure, not just a node’s local environment. The measures they propose, aggregating the average change in cohesion for a node’s links, has several limitations. In this paper we review their method and show how the idea can be modified by using betweenness centrality as an underpinning concept. We explore the properties of the new method and provide point, normalized, and network level variations. This new approach has two advantages, first it provides a more robust means to normalize the measure to control for network size, and second, the modified measure is computationally less demanding making it applicable to larger networks. PMID:28239229

Mean-field approach to evolving spatial networks, with an application to osteocyte network formation

NASA Astrophysics Data System (ADS)

Taylor-King, Jake P.; Basanta, David; Chapman, S. Jonathan; Porter, Mason A.

2017-07-01

We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can have a spatial location and a velocity, or it can have some more abstract internal property that describes something like a social trait. Edges between nodes are created and destroyed, and new nodes enter the system. We introduce a "local state degree distribution" (LSDD) as the degree distribution at a particular point in state space. We then make a mean-field assumption and thereby derive an integro-partial differential equation that is satisfied by the LSDD. We perform numerical experiments and find good agreement between solutions of the integro-differential equation and the LSDD from stochastic simulations of the full model. To illustrate our theory, we apply it to a simple model for osteocyte network formation within bones, with a view to understanding changes that may take place during cancer. Our results suggest that increased rates of differentiation lead to higher densities of osteocytes, but with a smaller number of dendrites. To help provide biological context, we also include an introduction to osteocytes, the formation of osteocyte networks, and the role of osteocytes in bone metastasis.

Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease.

PubMed

Xu, Jinping; Zhang, Jiuquan; Zhang, Jinlei; Wang, Yue; Zhang, Yanling; Wang, Jian; Li, Guanglin; Hu, Qingmao; Zhang, Yuanchao

2017-01-01

Although abnormal cortical morphology and connectivity between brain regions (structural covariance) have been reported in Parkinson's disease (PD), the topological organizations of large-scale structural brain networks are still poorly understood. In this study, we investigated large-scale structural brain networks in a sample of 37 PD patients and 34 healthy controls (HC) by assessing the structural covariance of cortical gyrification with local gyrification index (lGI). We demonstrated prominent small-world properties of the structural brain networks for both groups. Compared with the HC group, PD patients showed significantly increased integrated characteristic path length and integrated clustering coefficient, as well as decreased integrated global efficiency in structural brain networks. Distinct distributions of hub regions were identified between the two groups, showing more hub regions in the frontal cortex in PD patients. Moreover, the modular analyses revealed significantly decreased integrated regional efficiency in lateral Fronto-Insula-Temporal module, and increased integrated regional efficiency in Parieto-Temporal module in the PD group as compared to the HC group. In summary, our study demonstrated altered topological properties of structural networks at a global, regional and modular level in PD patients. These findings suggests that the structural networks of PD patients have a suboptimal topological organization, resulting in less effective integration of information between brain regions.

Grey matter networks in people at increased familial risk for schizophrenia.

PubMed

Tijms, Betty M; Sprooten, Emma; Job, Dominic; Johnstone, Eve C; Owens, David G C; Willshaw, David; Seriès, Peggy; Lawrie, Stephen M

2015-10-01

Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls. The following network properties were calculated: size, connectivity density, degree, path length, clustering coefficient, betweenness centrality and small world properties. People at risk of schizophrenia showed decreased path length and clustering in mostly prefrontal and temporal areas. Within the high risk sample, the path length of the posterior cingulate cortex and the betweenness centrality of the left inferior frontal operculum explained 81% of the variance in schizotypal cognitions, which was previously shown to be the strongest behavioural predictor of schizophrenia in the study. In contrast, local grey matter volume measurements explained 48% of variance in schizotypy. The present results suggest that single-subject grey matter networks can quantify behaviourally relevant biological alterations in people at increased risk for schizophrenia before disease onset. Copyright © 2015 Elsevier B.V. All rights reserved.

Exploring Maps with Greedy Navigators

NASA Astrophysics Data System (ADS)

Lee, Sang Hoon; Holme, Petter

2012-03-01

During the last decade of network research focusing on structural and dynamical properties of networks, the role of network users has been more or less underestimated from the bird’s-eye view of global perspective. In this era of global positioning system equipped smartphones, however, a user’s ability to access local geometric information and find efficient pathways on networks plays a crucial role, rather than the globally optimal pathways. We present a simple greedy spatial navigation strategy as a probe to explore spatial networks. These greedy navigators use directional information in every move they take, without being trapped in a dead end based on their memory about previous routes. We suggest that the centralities measures have to be modified to incorporate the navigators’ behavior, and present the intriguing effect of navigators’ greediness where removing some edges may actually enhance the routing efficiency, which is reminiscent of Braess’s paradox. In addition, using samples of road structures in large cities around the world, it is shown that the navigability measure we define reflects unique structural properties, which are not easy to predict from other topological characteristics. In this respect, we believe that our routing scheme significantly moves the routing problem on networks one step closer to reality, incorporating the inevitable incompleteness of navigators’ information.

Structural properties of hydration shell around various conformations of simple polypeptides.

PubMed

Czapiewski, Dariusz; Zielkiewicz, Jan

2010-04-08

In this paper we investigate structural properties of water within the solvation shell around the peptide core created by a well-defined conformation of polypeptide chain. The following secondary structures are investigated: linear (straight chain), and three helices PII (polyproline-like), 3(10), and alpha. We propose using the two-particle contribution to entropy as a rational measure of the water structural ordering within the solvation layer. This contribution divides into two terms, depending on the peptide-water and water-water interactions, respectively, and in this paper both terms are investigated. The structure of "solvation" water is described by the second term, and therefore it mainly attracts our attention. Determination of this term, however, is not an easy task, requiring some controversial approximations. Therefore, we have transformed this term to the form of some rational parameter which measures the local structural ordering of water within the solvation shell. Moreover, the results of several independent investigations are reported: we adopt the harmonic approximation for an independent estimation of the water entropy within the solvation shell, and we also study structure of the water-water hydrogen bond network, mean geometry of a single hydrogen bond, the self-diffusion coefficients (both translational and rotational) of water, and the mean lifetimes of water-water and water-peptide hydrogen bonds. All the obtained results lead to the conclusion that the local structure of water within the solvation shell changes only slightly in comparison to the bulk one. If so, the measure of local water ordering proposed by us is exploited with the aim to gain the deeper insight on the structural properties of "solvation" water. It has been shown that this parameter can be factored into three terms, which measure translational, configurational, and orientational ordering, respectively. Using this factoring, the ordering map for a precise description of the water local ordering has been built. An interesting correlation is observed: the points on this map lie approximately on the straight line, while the linear conformations clearly deviate from the general tendency. Further analysis of the obtained results allows us to express the supposition that an increasing local ordering of water around given secondary structure corresponds to an increasing relative stability of this structure in aqueous solution. Analyzing the geometry of the water-water hydrogen bond network within the solvation layer, we find some systematic deviations of this geometry from the bulk water properties. We also observe that the alanine peptides (excluding the linear form) disturb the hydrogen bond network in the less range, and in another way than the various conformations of polyglycine, while the linear form of polyalanine behaves very similarly to the glycine ones. Next, investigating the dynamic properties, we also conclude that water near the peptide surface creates a pseudorigid structure, a "halo" around the peptide core. This "halo" is stabilized by slightly higher energy of the hydrogen bonds network: we have found that within this region the hydrogen bonds network is slightly less distorted, the water-water hydrogen bonds are a little more stable and their mean lifetime is clearly longer that that of bulk water. Significant differences between the alanine- and glycine-based polypeptides are also visible. It has also been found that this solvation layer interacts with the polyalanine in another way than with polyglycine. Although in the case of the glycine-based polypeptide this layer slides relatively freely over the peptide surface, for the alanine-based polypeptide this sliding is strongly hindered by the presence of the methyl groups, and this effect is additionally enhanced by a rise in the solvation layer rigidity. Thus, the survey of various dynamic properties allows us to perceive and to explain distinct differences in behavior of water within the solvation shell around both glycine and alanine peptides.

Beyond topology: coevolution of structure and flux in metabolic networks.

PubMed

Morrison, E S; Badyaev, A V

2017-10-01

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals' metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage-bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among-individual variation in flux occurred in networks with the strongest among-compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Topological Mechanics of Origami and Kirigami

NASA Astrophysics Data System (ADS)

Chen, Bryan Gin-ge; Liu, Bin; Evans, Arthur A.; Paulose, Jayson; Cohen, Itai; Vitelli, Vincenzo; Santangelo, C. D.

2016-04-01

Origami and kirigami have emerged as potential tools for the design of mechanical metamaterials whose properties such as curvature, Poisson ratio, and existence of metastable states can be tuned using purely geometric criteria. A major obstacle to exploiting this property is the scarcity of tools to identify and program the flexibility of fold patterns. We exploit a recent connection between spring networks and quantum topological states to design origami with localized folding motions at boundaries and study them both experimentally and theoretically. These folding motions exist due to an underlying topological invariant rather than a local imbalance between constraints and degrees of freedom. We give a simple example of a quasi-1D folding pattern that realizes such topological states. We also demonstrate how to generalize these topological design principles to two dimensions. A striking consequence is that a domain wall between two topologically distinct, mechanically rigid structures is deformable even when constraints locally match the degrees of freedom.

Network properties of interstitial cells of Cajal affect intestinal pacemaker activity and motor patterns, according to a mathematical model of weakly coupled oscillators.

PubMed

Wei, Ruihan; Parsons, Sean P; Huizinga, Jan D

2017-03-01

What is the central question of this study? What are the effects of interstitial cells of Cajal (ICC) network perturbations on intestinal pacemaker activity and motor patterns? What is the main finding and its importance? Two-dimensional modelling of the ICC pacemaker activity according to a phase model of weakly coupled oscillators showed that network properties (coupling strength between oscillators, frequency gradient and frequency noise) strongly influence pacemaker network activity and subsequent motor patterns. The model explains motor patterns observed in physiological conditions and provides predictions and testable hypotheses for effects of ICC loss and frequency modulation on the motor patterns. Interstitial cells of Cajal (ICC) are the pacemaker cells of gut motility and are associated with motility disorders. Interstitial cells of Cajal form a network, but the contributions of its network properties to gut physiology and dysfunction are poorly understood. We modelled an ICC network as a two-dimensional network of weakly coupled oscillators with a frequency gradient and showed changes over time in video and graphical formats. Model parameters were obtained from slow-wave-driven contraction patterns in the mouse intestine and pacemaker slow-wave activities from the cat intestine. Marked changes in propagating oscillation patterns (including changes from propagation to non-propagating) were observed by changing network parameters (coupling strength between oscillators, the frequency gradient and frequency noise), which affected synchronization, propagation velocity and occurrence of dislocations (termination of an oscillation). Complete uncoupling of a circumferential ring of oscillators caused the proximal and distal section to desynchronize, but complete synchronization was maintained with only a single oscillator connecting the sections with high enough coupling. The network of oscillators could withstand loss; even with 40% of oscillators lost randomly within the network, significant synchronization and anterograde propagation remained. A local increase in pacemaker frequency diminished anterograde propagation; the effects were strongly dependent on location, frequency gradient and coupling strength. In summary, the model puts forth the hypothesis that fundamental changes in oscillation patterns (ICC slow-wave activity or circular muscle contractions) can occur through physiological modulation of network properties. Strong evidence is provided to accept the ICC network as a system of coupled oscillators. © 2016 The Authors. Experimental Physiology © 2016 The Physiological Society.

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.

Localization of binary neutron star mergers with second and third generation gravitational-wave detectors

NASA Astrophysics Data System (ADS)

Mills, Cameron; Tiwari, Vaibhav; Fairhurst, Stephen

2018-05-01

The observation of gravitational wave signals from binary black hole and binary neutron star mergers has established the field of gravitational wave astronomy. It is expected that future networks of gravitational wave detectors will possess great potential in probing various aspects of astronomy. An important consideration for successive improvement of current detectors or establishment on new sites is knowledge of the minimum number of detectors required to perform precision astronomy. We attempt to answer this question by assessing the ability of future detector networks to detect and localize binary neutron stars mergers on the sky. Good localization ability is crucial for many of the scientific goals of gravitational wave astronomy, such as electromagnetic follow-up, measuring the properties of compact binaries throughout cosmic history, and cosmology. We find that although two detectors at improved sensitivity are sufficient to get a substantial increase in the number of observed signals, at least three detectors of comparable sensitivity are required to localize majority of the signals, typically to within around 10 deg2 —adequate for follow-up with most wide field of view optical telescopes.

Overlapping communities from dense disjoint and high total degree clusters

NASA Astrophysics Data System (ADS)

Zhang, Hongli; Gao, Yang; Zhang, Yue

2018-04-01

Community plays an important role in the field of sociology, biology and especially in domains of computer science, where systems are often represented as networks. And community detection is of great importance in the domains. A community is a dense subgraph of the whole graph with more links between its members than between its members to the outside nodes, and nodes in the same community probably share common properties or play similar roles in the graph. Communities overlap when nodes in a graph belong to multiple communities. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is one of the most successful techniques dealing with large networks. The paper presents a density-based seeding method, in which dense disjoint local clusters are searched and selected as seeds. The proposed method selects a seed by the total degree and density of local clusters utilizing merely local structures of the network. Furthermore, this paper proposes a novel community refining phase via minimizing the conductance of each community, through which the quality of identified communities is largely improved in linear time. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art and the proposed refining method largely enhances the quality of the identified communities. Experimental results in real graphs with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with higher quality, and it obtains much more accurate community structure than the current local algorithms without any priori information.

Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

PubMed Central

Sun, Yu; Li, Junhua; Suckling, John; Feng, Lei

2017-01-01

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging. PMID:29209197

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network1[OPEN

PubMed Central

Kim, Taehyong; Dreher, Kate; Nilo-Poyanco, Ricardo; Lee, Insuk; Fiehn, Oliver; Lange, Bernd Markus; Nikolau, Basil J.; Sumner, Lloyd; Welti, Ruth; Wurtele, Eve S.; Rhee, Seung Y.

2015-01-01

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes. PMID:25670818

Probabilistic diffusion tractography reveals improvement of structural network in musicians.

PubMed

Li, Jianfu; Luo, Cheng; Peng, Yueheng; Xie, Qiankun; Gong, Jinnan; Dong, Li; Lai, Yongxiu; Li, Hong; Yao, Dezhong

2014-01-01

Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

Micro-Macro Analysis of Complex Networks

PubMed Central

Marchiori, Massimo; Possamai, Lino

2015-01-01

Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a “classic” approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (“micro”) to a different scale level (“macro”), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability. PMID:25635812

Programmable disorder in random DNA tilings

NASA Astrophysics Data System (ADS)

Tikhomirov, Grigory; Petersen, Philip; Qian, Lulu

2017-03-01

Scaling up the complexity and diversity of synthetic molecular structures will require strategies that exploit the inherent stochasticity of molecular systems in a controlled fashion. Here we demonstrate a framework for programming random DNA tilings and show how to control the properties of global patterns through simple, local rules. We constructed three general forms of planar network—random loops, mazes and trees—on the surface of self-assembled DNA origami arrays on the micrometre scale with nanometre resolution. Using simple molecular building blocks and robust experimental conditions, we demonstrate control of a wide range of properties of the random networks, including the branching rules, the growth directions, the proximity between adjacent networks and the size distribution. Much as combinatorial approaches for generating random one-dimensional chains of polymers have been used to revolutionize chemical synthesis and the selection of functional nucleic acids, our strategy extends these principles to random two-dimensional networks of molecules and creates new opportunities for fabricating more complex molecular devices that are organized by DNA nanostructures.

Convergence and rate analysis of neural networks for sparse approximation.

PubMed

Balavoine, Aurèle; Romberg, Justin; Rozell, Christopher J

2012-09-01

We present an analysis of the Locally Competitive Algorithm (LCA), which is a Hopfield-style neural network that efficiently solves sparse approximation problems (e.g., approximating a vector from a dictionary using just a few nonzero coefficients). This class of problems plays a significant role in both theories of neural coding and applications in signal processing. However, the LCA lacks analysis of its convergence properties, and previous results on neural networks for nonsmooth optimization do not apply to the specifics of the LCA architecture. We show that the LCA has desirable convergence properties, such as stability and global convergence to the optimum of the objective function when it is unique. Under some mild conditions, the support of the solution is also proven to be reached in finite time. Furthermore, some restrictions on the problem specifics allow us to characterize the convergence rate of the system by showing that the LCA converges exponentially fast with an analytically bounded convergence rate. We support our analysis with several illustrative simulations.

Lightweight filter architecture for energy efficient mobile vehicle localization based on a distributed acoustic sensor network.

PubMed

Kim, Keonwook

2013-08-23

The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably.

Design of robust flow processing networks with time-programmed responses

NASA Astrophysics Data System (ADS)

Kaluza, P.; Mikhailov, A. S.

2012-04-01

Can artificially designed networks reach the levels of robustness against local damage which are comparable with those of the biochemical networks of a living cell? We consider a simple model where the flow applied to an input node propagates through the network and arrives at different times to the output nodes, thus generating a pattern of coordinated responses. By using evolutionary optimization algorithms, functional networks - with required time-programmed responses - were constructed. Then, continuing the evolution, such networks were additionally optimized for robustness against deletion of individual nodes or links. In this manner, large ensembles of functional networks with different kinds of robustness were obtained, making statistical investigations and comparison of their structural properties possible. We have found that, generally, different architectures are needed for various kinds of robustness. The differences are statistically revealed, for example, in the Laplacian spectra of the respective graphs. On the other hand, motif distributions of robust networks do not differ from those of the merely functional networks; they are found to belong to the first Alon superfamily, the same as that of the gene transcription networks of single-cell organisms.

Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

PubMed

Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B

2012-01-01

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling complexity in engineered infrastructure system: Water distribution network as an example

NASA Astrophysics Data System (ADS)

Zeng, Fang; Li, Xiang; Li, Ke

2017-02-01

The complex topology and adaptive behavior of infrastructure systems are driven by both self-organization of the demand and rigid engineering solutions. Therefore, engineering complex systems requires a method balancing holism and reductionism. To model the growth of water distribution networks, a complex network model was developed following the combination of local optimization rules and engineering considerations. The demand node generation is dynamic and follows the scaling law of urban growth. The proposed model can generate a water distribution network (WDN) similar to reported real-world WDNs on some structural properties. Comparison with different modeling approaches indicates that a realistic demand node distribution and co-evolvement of demand node and network are important for the simulation of real complex networks. The simulation results indicate that the efficiency of water distribution networks is exponentially affected by the urban growth pattern. On the contrary, the improvement of efficiency by engineering optimization is limited and relatively insignificant. The redundancy and robustness, on another aspect, can be significantly improved through engineering methods.

Benford’s Distribution in Complex Networks

PubMed Central

Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

2016-01-01

Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters. PMID:27748398

3D reconstruction of the porous microstructure of Al2O3-coatings based on sequentially revealed surface data

NASA Astrophysics Data System (ADS)

Loftfield, Nina; Kästner, Markus; Reithmeier, Eduard

2018-06-01

Local and global liquid transport properties correlate strongly with the morphology of porous materials. Therefore, by characterizing the porous network information is indirectly gained on the materials properties. Properties like the open-porosity are easily accessible with techniques like mercury porosimetry. However, the 3D image reconstruction, destructive or non-destructive, holds advantages like an accurate spatially resolved representation of the investigated material. Common 3D data acquisition is done by x-ray microtomography or a combination of focused ion beam based milling and scanning electron microscopy. In this work a reconstruction approach similar to the latter one is implemented. The porous network is reconstructed based on an alternating process of milling the surface by fly cutting and measuring the surface data with a confocal laser scanning microscope. This has the benefit of reconstructing the pore network on the basis of surface height data, measuring the structure boundaries directly. The stack of milled surface height data needs to be registered and the pore structure to be segmented. The segmented pore structure is connected throughout each height layer and afterwards meshed. The investigated materials are porous surface coatings of aluminum oxide for the usage in tribological pairings.

Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis.

PubMed

Caeyenberghs, K; Powell, H W R; Thomas, R H; Brindley, L; Church, C; Evans, J; Muthukumaraswamy, S D; Jones, D K; Hamandi, K

2015-01-01

Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.

Hyperconnectivity in juvenile myoclonic epilepsy: A network analysis

PubMed Central

Caeyenberghs, K.; Powell, H.W.R.; Thomas, R.H.; Brindley, L.; Church, C.; Evans, J.; Muthukumaraswamy, S.D.; Jones, D.K.; Hamandi, K.

2014-01-01

Objective Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Methods Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Results Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Conclusions Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients. PMID:25610771

Analysis of a large-scale weighted network of one-to-one human communication

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Saramäki, Jari; Hyvönen, Jörkki; Szabó, Gábor; Argollo de Menezes, M.; Kaski, Kimmo; Barabási, Albert-László; Kertész, János

2007-06-01

We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities. We give an account of motif intensity and coherence distributions and compare them to a randomized reference system. We also use the concept of link overlap to measure the number of common neighbours any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing strong quantitative evidence for the weak ties hypothesis, a central concept in social network analysis. The percolation properties of the network are found to depend on the type and order of removed links, and they can help understand how the local structure of the network manifests itself at the global level. We hope that our results will contribute to modelling weighted large-scale social networks, and believe that the systematic approach followed here can be adopted to study other weighted networks.

Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

PubMed

Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

2017-07-04

This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

Emergence and robustness of target waves in a neuronal network

NASA Astrophysics Data System (ADS)

Xu, Ying; Jin, Wuyin; Ma, Jun

2015-08-01

Target waves in excitable media such as neuronal network can regulate the spatial distribution and orderliness as a continuous pacemaker. Three different schemes are used to develop stable target wave in the network, and the potential mechanism for emergence of target waves in the excitable media is investigated. For example, a local pacing driven by external periodical forcing can generate stable target wave in the excitable media, furthermore, heterogeneity and local feedback under self-feedback coupling are also effective to generate continuous target wave as well. To discern the difference of these target waves, a statistical synchronization factor is defined by using mean field theory and artificial defects are introduced into the network to block the target wave, thus the robustness of these target waves could be detected. However, these target waves developed from the above mentioned schemes show different robustness to the blocking from artificial defects. A regular network of Hindmarsh-Rose neurons is designed in a two-dimensional square array, target waves are induced by using three different ways, and then some artificial defects, which are associated with anatomical defects, are set in the network to detect the effect of defects blocking on the travelling waves. It confirms that the robustness of target waves to defects blocking depends on the intrinsic properties (ways to generate target wave) of target waves.

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

USGS Publications Warehouse

Song, W.-Z.; Huang, R.; Shirazi, B.; LaHusen, R.

2009-01-01

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and each frame into slots. A parent node determines the children's frame assignment based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. First, given any node, at any time slot, there is at most one active sender in its neighborhood (including itself). Second, the packet scheduling with TreeMAC is bufferless, which therefore minimizes the probability of network congestion. Third, the data throughput to the gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24-node testbed show that TreeMAC protocol significantly improves network throughput, fairness, and energy efficiency compared to TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC. Partial results of this paper were published in Song, Huang, Shirazi and Lahusen [W.-Z. Song, R. Huang, B. Shirazi, and R. Lahusen, TreeMAC: Localized TDMA MAC protocol for high-throughput and fairness in sensor networks, in: The 7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom, March 2009]. Our new contributions include analyses of the performance of TreeMAC from various aspects. We also present more implementation detail and evaluate TreeMAC from other aspects. ?? 2009 Elsevier B.V.

How children explore the phonological network in child-directed speech: A survival analysis of children’s first word productions

PubMed Central

Carlson, Matthew T.; Sonderegger, Morgan; Bane, Max

2014-01-01

We explored how phonological network structure influences the age of words’ first appearance in children’s (14–50 months) speech, using a large, longitudinal corpus of spontaneous child-caregiver interactions. We represent the caregiver lexicon as a network in which each word is connected to all of its phonological neighbors, and consider both words’ local neighborhood density (degree), and also their embeddedness among interconnected neighborhoods (clustering coefficient and coreness). The larger-scale structure reflected in the latter two measures is implicated in current theories of lexical development and processing, but its role in lexical development has not yet been explored. Multilevel discrete-time survival analysis revealed that children are more likely to produce new words whose network properties support lexical access for production: high degree, but low clustering coefficient and coreness. These effects appear to be strongest at earlier ages and largely absent from 30 months on. These results suggest that both a word’s local connectivity in the lexicon and its position in the lexicon as a whole influences when it is learned, and they underscore how general lexical processing mechanisms contribute to productive vocabulary development. PMID:25089073

Going Deeper With Contextual CNN for Hyperspectral Image Classification.

PubMed

Lee, Hyungtae; Kwon, Heesung

2017-10-01

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.

Multiparameter Estimation in Networked Quantum Sensors

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

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Multiparameter Estimation in Networked Quantum Sensors

DOE PAGES

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-21

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Temporal efficiency evaluation and small-worldness characterization in temporal networks

PubMed Central

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

Temporal efficiency evaluation and small-worldness characterization in temporal networks

NASA Astrophysics Data System (ADS)

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-09-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

Evolution of a designless nanoparticle network into reconfigurable Boolean logic

NASA Astrophysics Data System (ADS)

Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.

2015-12-01

Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

Membrane Properties and the Balance between Excitation and Inhibition Control Gamma-Frequency Oscillations Arising from Feedback Inhibition

PubMed Central

Economo, Michael N.; White, John A.

2012-01-01

Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30–80 Hz gamma rhythm in which network oscillations arise through ‘stochastic synchrony’ capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs. PMID:22275859

Autoassociative memory retrieval and spontaneous activity bumps in small-world networks of integrate-and-fire neurons.

PubMed

Anishchenko, Anastasia; Treves, Alessandro

2006-10-01

The metric structure of synaptic connections is obviously an important factor in shaping the properties of neural networks, in particular the capacity to retrieve memories, with which are endowed autoassociative nets operating via attractor dynamics. Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise description useful for a better understanding of how the type of connectivity affects memory retrieval? We have simulated an autoassociative memory network of integrate-and-fire units, positioned on a ring, with the network connectivity varied parametrically between ordered and random. We find that the network retrieves previously stored memory patterns when the connectivity is close to random, and displays the characteristic behavior of ordered nets (localized 'bumps' of activity) when the connectivity is close to ordered. Recent analytical work shows that these two behaviors can coexist in a network of simple threshold-linear units, leading to localized retrieval states. We find that they tend to be mutually exclusive behaviors, however, with our integrate-and-fire units. Moreover, the transition between the two occurs for values of the connectivity parameter which are not simply related to the notion of small worlds.

Local structure and structural signature underlying properties in metallic glasses and supercooled liquids

NASA Astrophysics Data System (ADS)

Ding, Jun

Metallic glasses (MGs), discovered five decades ago as a newcomer in the family of glasses, are of current interest because of their unique structures and properties. There are also many fundamental materials science issues that remain unresolved for metallic glasses, as well as their predecessor above glass transition temperature, the supercooled liquids. In particular, it is a major challenge to characterize the local structure and unveil the structure-property relationship for these amorphous materials. This thesis presents a systematic study of the local structure of metallic glasses as well as supercooled liquids via classical and ab initio molecular dynamics simulations. Three typical MG models are chosen as representative candidate, Cu64 Zr36, Pd82Si18 and Mg65Cu 25Y10 systems, while the former is dominant with full icosahedra short-range order and the prism-type short-range order dominate for latter two. Furthermore, we move to unravel the underlying structural signature among several properties in metallic glasses. Firstly, the temperature dependence of specific heat and liquid fragility between Cu-Zr and Mg-Cu-Y (also Pd-Si) in supercooled liquids are quite distinct: gradual versus fast evolution of specific heat and viscosity/relaxation time with undercooling. Their local structural ordering are found to relate with the temperature dependence of specific heat and relaxation time. Then elastic heterogeneity has been studied to correlate with local structure in Cu-Zr MGs. Specifically, this part covers how the degree of elastic deformation correlates with the internal structure at the atomic level, how to quantitatively evaluate the local solidity/liquidity in MGs and how the network of interpenetrating connection of icosahedra determine the corresponding shear modulus. Finally, we have illustrated the structure signature of quasi-localized low-frequency vibrational normal modes, which resides the intriguing vibrational properties in MGs. Specifically, the local atomic packing structure in a model MG strongly correlate with the corresponding participation fraction in quasi-localized soft modes, while the highest and lowest participation correspond to geometrically unfavored motifs and ISRO respectively. In addition, we clearly demonstrate that quasi-localized low-frequency vibrational modes correlate strongly with fertile sites for shear transformations in a MG.

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender

PubMed Central

Pezoulas, Vasileios C.; Zervakis, Michalis; Michelogiannis, Sifis; Klados, Manousos A.

2017-01-01

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks. PMID:28491028

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender.

PubMed

Pezoulas, Vasileios C; Zervakis, Michalis; Michelogiannis, Sifis; Klados, Manousos A

2017-01-01

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.

PubMed

Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong

2018-01-01

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.

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

PubMed

Qin, Chao; Sun, Yongqi; Dong, Yadong

2017-01-01

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

On the use of SPM to probe the interplay between polymer surface chemistry and polymer surface mechanics

NASA Astrophysics Data System (ADS)

Brogly, Maurice; Noel, Olivier; Awada, Houssein; Castelein, Gilles

2007-03-01

Adhesive properties of a polymer surface results from the complex contribution of surface chemistry and activation of sliding and dissipating mechanisms within the polymer surface layer. The purpose of this study is to dissociate the different contributions (chemical and mechanical) included in an AFM force-distance curve in order to establish relationships between the surface viscoelastic properties of the polymer, the surface chemistry of functionalized polymer surfaces and the adhesive forces, as determined by C-AFM experiments. Indeed we are interested in the measurements of local attractive or adhesive forces in AFM contact mode, of controlled chemical and mechanical model substrates. In order to investigate the interplay between mechanical or viscoelastic mechanisms and surface chemistry during the tip - polymer contact, we achieved force measurements on model PDMS polymer networks, whose surfaces are chemically controlled with the same functional groups as before (silicon substrates). On the basis of AFM nano-indentation experiments, surface Young moduli have been determined. The results show that the viscoelastic contribution is dominating in the adhesion force measurement. We propose an original model, which express the local adhesion force to the energy dissipated within the contact and the surface properties of the material (thermodynamic work of adhesion). Moreover we show that the dissipation function is related to Mc, the mass between crosslinks of the network.

A Networked Sensor System for the Analysis of Plot-Scale Hydrology.

PubMed

Villalba, German; Plaza, Fernando; Zhong, Xiaoyang; Davis, Tyler W; Navarro, Miguel; Li, Yimei; Slater, Thomas A; Liang, Yao; Liang, Xu

2017-03-20

This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments.

A Networked Sensor System for the Analysis of Plot-Scale Hydrology

PubMed Central

Villalba, German; Plaza, Fernando; Zhong, Xiaoyang; Davis, Tyler W.; Navarro, Miguel; Li, Yimei; Slater, Thomas A.; Liang, Yao; Liang, Xu

2017-01-01

This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments. PMID:28335534

Local or distributed activation? The view from biology

NASA Astrophysics Data System (ADS)

Reimers, Mark

2011-06-01

There is considerable disagreement among connectionist modellers over whether to represent distinct properties by distinct nodes of a network or whether properties should be represented by patterns of activity across all nodes. This paper draws on the literature of neuroscience to say that a more subtle way of describing how different brain regions contribute to a behaviour, in terms of individual learning and in terms of degrees of importance, may render the current debate moot: both sides of the 'localist' versus 'distributed' debate emphasise different aspects of biology.

Percolation and Reinforcement on Complex Networks

NASA Astrophysics Data System (ADS)

Yuan, Xin

Complex networks appear in almost every aspect of our daily life and are widely studied in the fields of physics, mathematics, finance, biology and computer science. This work utilizes percolation theory in statistical physics to explore the percolation properties of complex networks and develops a reinforcement scheme on improving network resilience. This dissertation covers two major parts of my Ph.D. research on complex networks: i) probe--in the context of both traditional percolation and k-core percolation--the resilience of complex networks with tunable degree distributions or directed dependency links under random, localized or targeted attacks; ii) develop and propose a reinforcement scheme to eradicate catastrophic collapses that occur very often in interdependent networks. We first use generating function and probabilistic methods to obtain analytical solutions to percolation properties of interest, such as the giant component size and the critical occupation probability. We study uncorrelated random networks with Poisson, bi-Poisson, power-law, and Kronecker-delta degree distributions and construct those networks which are based on the configuration model. The computer simulation results show remarkable agreement with theoretical predictions. We discover an increase of network robustness as the degree distribution broadens and a decrease of network robustness as directed dependency links come into play under random attacks. We also find that targeted attacks exert the biggest damage to the structure of both single and interdependent networks in k-core percolation. To strengthen the resilience of interdependent networks, we develop and propose a reinforcement strategy and obtain the critical amount of reinforced nodes analytically for interdependent Erdḧs-Renyi networks and numerically for scale-free and for random regular networks. Our mechanism leads to improvement of network stability of the West U.S. power grid. This dissertation provides us with a deeper understanding of the effects of structural features on network stability and fresher insights into designing resilient interdependent infrastructure networks.

Self-organized magnetic particles to tune the mechanical behavior of a granular system

NASA Astrophysics Data System (ADS)

Cox, Meredith; Wang, Dong; Barés, Jonathan; Behringer, Robert P.

2016-09-01

Above a certain density a granular material jams. This property can be controlled by either tuning a global property, such as the packing fraction or by applying shear strain, or at the micro-scale by tuning grain shape, inter-particle friction or externally controlled organization. Here, we introduce a novel way to change a local granular property by adding a weak anisotropic magnetic interaction between particles. We measure the evolution of the pressure, P, and coordination number, Z, for a packing of 2D photo-elastic disks, subject to uniaxial compression. A fraction R m of the particles have embedded cuboidal magnets. The strength of the magnetic interactions between particles is too weak to have a strong direct effect on P or Z when the system is jammed. However, the magnetic interactions play an important role in the evolution of latent force networks when systems containing a large enough fraction of the particles with magnets are driven through unjammed to jammed states. In this case, a statistically stable network of magnetic chains self-organizes before jamming and overlaps with force chains once jamming occurs, strengthening the granular medium. This property opens a novel way to control mechanical properties of granular materials.

[Brain plastic alterations in subjects with chronic right-sided sensorineural hearing loss: a resting-state MRI study].

PubMed

Zhang, L L; Gong, J P; Xu, Y W; Liu, B

2016-06-21

To investigate the nodal properties and reorganization of whole-brain functional network in subjects with severe right-sided SNHL. From June 2012 to June 2013, a total of 19 patients with severe right-sided SNHL were collected from Zhongda Hospital or the recruitment advertising along with 31 healthy controls.Based on the graph-theoretical analysis, the whole-brain functional networks were constructed using the BOLD-fMRI data of all subjects.Two sample two-tailed t-tests were used to investigate the differences between two groups in nodal metrics, such as node degree, node betweenness, node global efficiency and node local efficiency.All metrics were corrected by multiple comparisons.Partial correlation analysis was used to estimate the relationship between the significant metrics and the duration or severity of hearing loss. The right-sided SNHL showed significantly increased betweenness centrality in left supramarginal gyrus and right fusiform.However, other nodal parameters showed no statistical difference.Besides, patients exhibited no significant association between the altered metrics and clinical variables. Alterations of local topological properties may underlie cerebral cross-modal plastic reorganization in visual or speech-related regions in severe right-sided SNHL patients.

Power-law weighted networks from local attachments

NASA Astrophysics Data System (ADS)

Moriano, P.; Finke, J.

2012-07-01

This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble steady state properties of the empirical citation distributions of i) publications indexed by the Institute for Scientific Information from 1981 to 1997; ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.

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

PubMed

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

2017-08-01

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

Evolutionary transitions in controls reconcile adaptation with continuity of evolution.

PubMed

Badyaev, Alexander V

2018-05-19

Evolution proceeds by accumulating functional solutions, necessarily forming an uninterrupted lineage from past solutions of ancestors to the current design of extant forms. At the population level, this process requires an organismal architecture in which the maintenance of local adaptation does not preclude the ability to innovate in the same traits and their continuous evolution. Representing complex traits as networks enables us to visualize a fundamental principle that resolves tension between adaptation and continuous evolution: phenotypic states encompassing adaptations traverse the continuous multi-layered landscape of past physical, developmental and functional associations among traits. The key concept that captures such traversing is network controllability - the ability to move a network from one state into another while maintaining its functionality (reflecting evolvability) and to efficiently propagate information or products through the network within a phenotypic state (maintaining its robustness). Here I suggest that transitions in network controllability - specifically in the topology of controls - help to explain how robustness and evolvability are balanced during evolution. I will focus on evolutionary transitions in degeneracy of metabolic networks - a ubiquitous property of phenotypic robustness where distinct pathways achieve the same end product - to suggest that associated changes in network controls is a common rule underlying phenomena as distinct as phenotypic plasticity, organismal accommodation of novelties, genetic assimilation, and macroevolutionary diversification. Capitalizing on well understood principles by which network structure translates into function of control nodes, I show that accumulating redundancy in one type of network controls inevitably leads to the emergence of another type of controls, forming evolutionary cycles of network controllability that, ultimately, reconcile local adaptation with continuity of evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

PubMed Central

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

2015-01-01

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic. PMID:26694379

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

PubMed

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S; Theis, Fabian J

2015-12-18

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

Divisibility patterns of natural numbers on a complex network.

PubMed

Shekatkar, Snehal M; Bhagwat, Chandrasheel; Ambika, G

2015-09-16

Investigation of divisibility properties of natural numbers is one of the most important themes in the theory of numbers. Various tools have been developed over the centuries to discover and study the various patterns in the sequence of natural numbers in the context of divisibility. In the present paper, we study the divisibility of natural numbers using the framework of a growing complex network. In particular, using tools from the field of statistical inference, we show that the network is scale-free but has a non-stationary degree distribution. Along with this, we report a new kind of similarity pattern for the local clustering, which we call "stretching similarity", in this network. We also show that the various characteristics like average degree, global clustering coefficient and assortativity coefficient of the network vary smoothly with the size of the network. Using analytical arguments we estimate the asymptotic behavior of global clustering and average degree which is validated using numerical analysis.

Detection and localization of change points in temporal networks with the aid of stochastic block models

NASA Astrophysics Data System (ADS)

De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

2016-11-01

A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

Complex networks untangle competitive advantage in Australian football

NASA Astrophysics Data System (ADS)

Braham, Calum; Small, Michael

2018-05-01

We construct player-based complex network models of Australian football teams for the 2014 Australian Football League season; modelling the passes between players as weighted, directed edges. We show that analysis of these measures can give an insight into the underlying structure and strategy of Australian football teams, quantitatively distinguishing different playing styles. The relationships observed between network properties and match outcomes suggest that successful teams exhibit well-connected passing networks with the passes distributed between all 22 players as evenly as possible. Linear regression models of team scores and match margins show significant improvements in R2 and Bayesian information criterion when network measures are added to models that use conventional measures, demonstrating that network analysis measures contain useful, extra information. Several measures, particularly the mean betweenness centrality, are shown to be useful in predicting the outcomes of future matches, suggesting they measure some aspect of the intrinsic strength of teams. In addition, several local centrality measures are shown to be useful in analysing individual players' differing contributions to the team's structure.

Complex networks untangle competitive advantage in Australian football.

PubMed

Braham, Calum; Small, Michael

2018-05-01

We construct player-based complex network models of Australian football teams for the 2014 Australian Football League season; modelling the passes between players as weighted, directed edges. We show that analysis of these measures can give an insight into the underlying structure and strategy of Australian football teams, quantitatively distinguishing different playing styles. The relationships observed between network properties and match outcomes suggest that successful teams exhibit well-connected passing networks with the passes distributed between all 22 players as evenly as possible. Linear regression models of team scores and match margins show significant improvements in R 2 and Bayesian information criterion when network measures are added to models that use conventional measures, demonstrating that network analysis measures contain useful, extra information. Several measures, particularly the mean betweenness centrality, are shown to be useful in predicting the outcomes of future matches, suggesting they measure some aspect of the intrinsic strength of teams. In addition, several local centrality measures are shown to be useful in analysing individual players' differing contributions to the team's structure.

A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

PubMed Central

Sweeney, Yann; Hellgren Kotaleski, Jeanette; Hennig, Matthias H.

2015-01-01

Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes. PMID:26158556

Resource redistribution in polydomous ant nest networks: local or global?

PubMed Central

Franks, Daniel W.; Robinson, Elva J.H.

2014-01-01

An important problem facing organisms in a heterogeneous environment is how to redistribute resources to where they are required. This is particularly complex in social insect societies as resources have to be moved both from the environment into the nest and between individuals within the nest. Polydomous ant colonies are split between multiple spatially separated, but socially connected, nests. Whether, and how, resources are redistributed between nests in polydomous colonies is unknown. We analyzed the nest networks of the facultatively polydomous wood ant Formica lugubris. Our results indicate that resource redistribution in polydomous F. lugubris colonies is organized at the local level between neighboring nests and not at the colony level. We found that internest trails connecting nests that differed more in their amount of foraging were stronger than trails between nests with more equal foraging activity. This indicates that resources are being exchanged directly from nests with a foraging excess to nests that require resources. In contrast, we found no significant relationships between nest properties, such as size and amount of foraging, and network measures such as centrality and connectedness. This indicates an absence of a colony-level resource exchange. This is a clear example of a complex behavior emerging as a result of local interactions between parts of a system. PMID:25214755

Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

PubMed Central

Timme, Nicholas; Ito, Shinya; Myroshnychenko, Maxym; Yeh, Fang-Chin; Hiolski, Emma; Hottowy, Pawel; Beggs, John M.

2014-01-01

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons. PMID:25536059

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

NASA Astrophysics Data System (ADS)

Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

2016-04-01

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

Collective relaxation dynamics of small-world networks

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N , average degree k , and topological randomness q . We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q , including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Collective relaxation dynamics of small-world networks.

PubMed

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

PubMed Central

Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

2016-01-01

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. PMID:27080396

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

PubMed Central

Kraft, Reuben H.; Mckee, Phillip Justin; Dagro, Amy M.; Grafton, Scott T.

2012-01-01

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the “damaged” network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times () network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight. PMID:22915997

Error and attack tolerance of complex networks

NASA Astrophysics Data System (ADS)

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

2000-07-01

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

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

PubMed Central

Stetz, Gabrielle; Verkhivker, Gennady M.

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms. PMID:28095400

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study.

PubMed

Cai, Lin; Dong, Qi; Niu, Haijing

2018-04-01

Early childhood (7-8 years old) and early adolescence (11-12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A FDG-PET Study of Metabolic Networks in Apolipoprotein E ε4 Allele Carriers

PubMed Central

Yao, Zhijun; Hu, Bin; Zheng, Jiaxiang; Zheng, Weihao; Chen, Xuejiao; Gao, Xiang; Xie, Yuanwei; Fang, Lei

2015-01-01

Recently, some studies have applied the graph theory in brain network analysis in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). However, relatively little research has specifically explored the properties of the metabolic network in apolipoprotein E (APOE) ε4 allele carriers. In our study, all the subjects, including ADs, MCIs and NCs (normal controls) were divided into 165 APOE ε4 carriers and 165 APOE ε4 noncarriers. To establish the metabolic network for all brain regions except the cerebellum, cerebral glucose metabolism data obtained from FDG-PET (18F-fluorodeoxyglu-cose positron emission tomography) were segmented into 90 areas with automated anatomical labeling (AAL) template. Then, the properties of the networks were computed to explore the between-group differences. Our results suggested that both APOE ε4 carriers and noncarriers showed the small-world properties. Besides, compared with APOE ε4 noncarriers, the carriers showed a lower clustering coefficient. In addition, significant changes in 6 hub brain regions were found in between-group nodal centrality. Namely, compared with APOE ε4 noncarriers, significant decreases of the nodal centrality were found in left insula, right insula, right anterior cingulate, right paracingulate gyri, left cuneus, as well as significant increases in left paracentral lobule and left heschl gyrus in APOE ε4 carriers. Increased local short distance interregional correlations and disrupted long distance interregional correlations were found, which may support the point that the APOE ε4 carriers were more similar with AD or MCI in FDG uptake. In summary, the organization of metabolic network in APOE ε4 carriers indicated a less optimal pattern and APOE ε4 might be a risk factor for AD. PMID:26161964

Universities scale like cities.

PubMed

van Raan, Anthony F J

2013-01-01

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the 'gross university income' in terms of total number of citations over 'size' in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. We find that deviations from the expected behavior can indeed be explained by specific local properties of universities, particularly the field-specific composition of a university, and its quality in terms of field-normalized citation impact. By studying both the set of the 500 largest universities worldwide and a specific subset of these 500 universities--the top-100 European universities--we are also able to distinguish between properties of universities with as well as without selection of one specific local property, the quality of a university in terms of its average field-normalized citation impact. It also reveals an interesting observation concerning the working of a crucial property in networked systems, preferential attachment.

Chimera States in Continuous Media: Existence and Distinctness

NASA Astrophysics Data System (ADS)

Nicolaou, Zachary G.; Riecke, Hermann; Motter, Adilson E.

2017-12-01

The defining property of chimera states is the coexistence of coherent and incoherent domains in systems that are structurally and spatially homogeneous. The recent realization that such states might be common in oscillator networks raises the question of whether an analogous phenomenon can occur in continuous media. Here, we show that chimera states can exist in continuous systems even when the coupling is strictly local, as in many fluid and pattern forming media. Using the complex Ginzburg-Landau equation as a model system, we characterize chimera states consisting of a coherent domain of a frozen spiral structure and an incoherent domain of amplitude turbulence. We show that in this case, in contrast with discrete network systems, fluctuations in the local coupling field play a crucial role in limiting the coherent regions. We suggest these findings shed light on new possible forms of coexisting order and disorder in fluid systems.

Local network assessment

NASA Astrophysics Data System (ADS)

Glen, D. V.

1985-04-01

Local networks, related standards activities of the Institute of Electrical and Electronics Engineers the American National Standards Institute and other elements are presented. These elements include: (1) technology choices such as topology, transmission media, and access protocols; (2) descriptions of standards for the 802 local area networks (LAN's); high speed local networks (HSLN's) and military specification local networks; and (3) intra- and internetworking using bridges and gateways with protocols Interconnection (OSI) reference model. The convergence of LAN/PBX technology is also described.

Dynamic Reorganization of Functional Connectivity Reveals Abnormal Temporal Efficiency in Schizophrenia.

PubMed

Sun, Yu; Collinson, Simon L; Suckling, John; Sim, Kang

2018-06-07

Emerging evidence suggests that schizophrenia is associated with brain dysconnectivity. Nonetheless, the implicit assumption of stationary functional connectivity (FC) adopted in most previous resting-state functional magnetic resonance imaging (fMRI) studies raises an open question of schizophrenia-related aberrations in dynamic properties of resting-state FC. This study introduces an empirical method to examine the dynamic functional dysconnectivity in patients with schizophrenia. Temporal brain networks were estimated from resting-state fMRI of 2 independent datasets (patients/controls = 18/19 and 53/57 for self-recorded dataset and a publicly available replication dataset, respectively) by the correlation of sliding time-windowed time courses among regions of a predefined atlas. Through the newly introduced temporal efficiency approach and temporal random network models, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found that although prominent temporal small-world properties were revealed in both groups, temporal brain networks of patients with schizophrenia in both datasets showed a significantly higher temporal global efficiency, which cannot be simply attributable to head motion and sampling error. Specifically, we found localized changes of temporal nodal properties in the left frontal, right medial parietal, and subcortical areas that were associated with clinical features of schizophrenia. Our findings demonstrate that altered dynamic FC may underlie abnormal brain function and clinical symptoms observed in schizophrenia. Moreover, we provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network and highlight the potential of aberrant brain dynamic FC in unraveling the pathophysiologic mechanisms of the disease.

Onset age of L2 acquisition influences language network in early and late Cantonese-Mandarin bilinguals.

PubMed

Liu, Xiaojin; Tu, Liu; Wang, Junjing; Jiang, Bo; Gao, Wei; Pan, Ximin; Li, Meng; Zhong, Miao; Zhu, Zhenzhen; Niu, Meiqi; Li, Yanyan; Zhao, Ling; Chen, Xiaoxi; Liu, Chang; Lu, Zhi; Huang, Ruiwang

2017-11-01

Early second language (L2) experience influences the neural organization of L2 in neuro-plastic terms. Previous studies tried to reveal these plastic effects of age of second language acquisition (AoA-L2) and proficiency-level in L2 (PL-L2) on the neural basis of language processing in bilinguals. Although different activation patterns have been observed during language processing in early and late bilinguals by task-fMRI, few studies reported the effect of AoA-L2 and high PL-L2 on language network at resting state. In this study, we acquired resting-state fMRI (R-fMRI) data from 10 Cantonese (L1)-Mandarin (L2) early bilinguals (acquired L2: 3years old) and 11 late bilinguals (acquired L2: 6years old), and analyzed their topological properties of language networks after controlling the language daily exposure and usage as well as PL in L1 and L2. We found that early bilinguals had significantly a higher clustering coefficient, global and local efficiency, but significantly lower characteristic path length compared to late bilinguals. Modular analysis indicated that compared to late bilinguals, early bilinguals showed significantly stronger intra-modular functional connectivity in the semantic and phonetic modules, stronger inter-modular functional connectivity between the semantic and phonetic modules as well as between the phonetic and syntactic modules. Differences in global and local parameters may reflect different patterns of neuro-plasticity respectively for early and late bilinguals. These results suggested that different L2 experience influences topological properties of language network, even if late bilinguals achieve high PL-L2. Our findings may provide a new perspective of neural mechanisms related to early and late bilinguals. Copyright © 2017 Elsevier Inc. All rights reserved.

Cascading Failures and Recovery in Networks of Networks

NASA Astrophysics Data System (ADS)

Havlin, Shlomo

Network science have been focused on the properties of a single isolated network that does not interact or depends on other networks. In reality, many real-networks, such as power grids, transportation and communication infrastructures interact and depend on other networks. I will present a framework for studying the vulnerability and the recovery of networks of interdependent networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes like certain locations play a role in two networks -multiplex. This may happen recursively and can lead to a cascade of failures and to a sudden fragmentation of the system. I will present analytical solutions for the critical threshold and the giant component of a network of n interdependent networks. I will show, that the general theory has many novel features that are not present in the classical network theory. When recovery of components is possible global spontaneous recovery of the networks and hysteresis phenomena occur and the theory suggests an optimal repairing strategy of system of systems. I will also show that interdependent networks embedded in space are significantly more vulnerable compared to non embedded networks. In particular, small localized attacks may lead to cascading failures and catastrophic consequences.Thus, analyzing data of real network of networks is highly required to understand the system vulnerability. DTRA, ONR, Israel Science Foundation.

Global Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks

DTIC Science & Technology

2014-03-31

Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks M.M. Asadi H. Mahboubi A...2014 Global Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks Contract Report # AMBUSH.1.1 Contract...pi j /= 0. The sensor network considered in this work is composed of underwater sensors , which use acoustic waves for

Estimating local scaling properties for the classification of interstitial lung disease patterns

NASA Astrophysics Data System (ADS)

Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel

2011-03-01

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Intelligent routing protocol for ad hoc wireless network

NASA Astrophysics Data System (ADS)

Peng, Chaorong; Chen, Chang Wen

2006-05-01

A novel routing scheme for mobile ad hoc networks (MANETs), which combines hybrid and multi-inter-routing path properties with a distributed topology discovery route mechanism using control agents is proposed in this paper. In recent years, a variety of hybrid routing protocols for Mobile Ad hoc wireless networks (MANETs) have been developed. Which is proactively maintains routing information for a local neighborhood, while reactively acquiring routes to destinations beyond the global. The hybrid protocol reduces routing discovery latency and the end-to-end delay by providing high connectivity without requiring much of the scarce network capacity. On the other side the hybrid routing protocols in MANETs likes Zone Routing Protocol still need route "re-discover" time when a route between zones link break. Sine the topology update information needs to be broadcast routing request on local zone. Due to this delay, the routing protocol may not be applicable for real-time data and multimedia communication. We utilize the advantages of a clustering organization and multi-routing path in routing protocol to achieve several goals at the same time. Firstly, IRP efficiently saves network bandwidth and reduces route reconstruction time when a routing path fails. The IRP protocol does not require global periodic routing advertisements, local control agents will automatically monitor and repair broke links. Secondly, it efficiently reduces congestion and traffic "bottlenecks" for ClusterHeads in clustering network. Thirdly, it reduces significant overheads associated with maintaining clusters. Fourthly, it improves clusters stability due to dynamic topology changing frequently. In this paper, we present the Intelligent Routing Protocol. First, we discuss the problem of routing in ad hoc networks and the motivation of IRP. We describe the hierarchical architecture of IRP. We describe the routing process and illustrate it with an example. Further, we describe the control manage mechanisms, which are used to control active route and reduce the traffic amount in the route discovery procedure. Finial, the numerical experiments are given to show the effectiveness of IRP routing protocol.

Optical network unit placement in Fiber-Wireless (FiWi) access network by Moth-Flame optimization algorithm

NASA Astrophysics Data System (ADS)

Singh, Puja; Prakash, Shashi

2017-07-01

Hybrid wireless-optical broadband access network (WOBAN) or Fiber-Wireless (FiWi) is the integration of wireless access network and optical network. This hybrid multi-domain network adopts the advantages of wireless and optical domains and serves the demand of technology savvy users. FiWi exhibits the properties of cost effectiveness, robustness, flexibility, high capacity, reliability and is self organized. Optical Network Unit (ONU) placement problem in FiWi contributes in simplifying the network design and enhances the performance in terms of cost efficiency and increased throughput. Several individual-based algorithms, such as Simulated Annealing (SA), Tabu Search, etc. have been suggested for ONU placement, but these algorithms suffer from premature convergence (trapping in a local optima). The present research work undertakes the deployment of FiWi and proposes a novel nature-inspired heuristic paradigm called Moth-Flame optimization (MFO) algorithm for multiple optical network units' placement. MFO is a population based algorithm. Population-based algorithms are better in handling local optima avoidance. The simulation results are compared with the existing Greedy and Simulated Annealing algorithms to optimize the position of ONUs. To the best of our knowledge, MFO algorithm has been used for the first time in this domain, moreover it has been able to provide very promising and competitive results. The performance of MFO algorithm has been analyzed by varying the 'b' parameter. MFO algorithm results in faster convergence than the existing strategies of Greedy and SA and returns a lower value of overall cost function. The results exhibit the dependence of the objective function on the distribution of wireless users also.

Tribological Properties of AlSi12-Al₂O₃ Interpenetrating Composite Layers in Comparison with Unreinforced Matrix Alloy.

PubMed

Dolata, Anna Janina

2017-09-06

Alumina-Aluminum composites with interpenetrating network structures are a new class of advanced materials with potentially better properties than composites reinforced by particles or fibers. Local casting reinforcement was proposed to take into account problems with the machinability of this type of materials and the shaping of the finished products. The centrifugal infiltration process fabricated composite castings in the form of locally reinforced shafts. The main objective of the research presented in this work was to compare the tribological properties (friction coefficient, wear resistance) of AlSi12/Al₂O₃ interpenetrating composite layers with unreinforced AlSi12 matrix areas. Profilometric tests enabled both quantitative and qualitative analyses of the wear trace that formed on investigated surfaces. It has been shown that interpenetrating composite layers are characterized by lower and more stable coefficients of friction (μ), as well as higher wear resistance than unreinforced matrix areas. At the present stage, the study confirmed that the tribological properties of the composite layers depend on the spatial structure of the ceramic reinforcement, and primarily the volume and size of alumina foam cells.

Photon Localization and Dicke Superradiance in Atomic Gases

NASA Astrophysics Data System (ADS)

Akkermans, E.; Gero, A.; Kaiser, R.

2008-09-01

Photon propagation in a gas of N atoms is studied using an effective Hamiltonian describing photon-mediated atomic dipolar interactions. The density P(Γ) of photon escape rates is determined from the spectrum of the N×N random matrix Γij=sin⁡(xij)/xij, where xij is the dimensionless random distance between any two atoms. Varying disorder and system size, a scaling behavior is observed for the escape rates. It is explained using microscopic calculations and a stochastic model which emphasizes the role of cooperative effects in photon localization and provides an interesting relation with statistical properties of “small world networks.”

Energy-aware scheduling of surveillance in wireless multimedia sensor networks.

PubMed

Wang, Xue; Wang, Sheng; Ma, Junjie; Sun, Xinyao

2010-01-01

Wireless sensor networks involve a large number of sensor nodes with limited energy supply, which impacts the behavior of their application. In wireless multimedia sensor networks, sensor nodes are equipped with audio and visual information collection modules. Multimedia contents are ubiquitously retrieved in surveillance applications. To solve the energy problems during target surveillance with wireless multimedia sensor networks, an energy-aware sensor scheduling method is proposed in this paper. Sensor nodes which acquire acoustic signals are deployed randomly in the sensing fields. Target localization is based on the signal energy feature provided by multiple sensor nodes, employing particle swarm optimization (PSO). During the target surveillance procedure, sensor nodes are adaptively grouped in a totally distributed manner. Specially, the target motion information is extracted by a forecasting algorithm, which is based on the hidden Markov model (HMM). The forecasting results are utilized to awaken sensor node in the vicinity of future target position. According to the two properties, signal energy feature and residual energy, the sensor nodes decide whether to participate in target detection separately with a fuzzy control approach. Meanwhile, the local routing scheme of data transmission towards the observer is discussed. Experimental results demonstrate the efficiency of energy-aware scheduling of surveillance in wireless multimedia sensor network, where significant energy saving is achieved by the sensor awakening approach and data transmission paths are calculated with low computational complexity.

Extending the Stabilized Supralinear Network model for binocular image processing.

PubMed

Selby, Ben; Tripp, Bryan

2017-06-01

The visual cortex is both extensive and intricate. Computational models are needed to clarify the relationships between its local mechanisms and high-level functions. The Stabilized Supralinear Network (SSN) model was recently shown to account for many receptive field phenomena in V1, and also to predict subtle receptive field properties that were subsequently confirmed in vivo. In this study, we performed a preliminary exploration of whether the SSN is suitable for incorporation into large, functional models of the visual cortex, considering both its extensibility and computational tractability. First, whereas the SSN receives abstract orientation signals as input, we extended it to receive images (through a linear-nonlinear stage), and found that the extended version behaved similarly. Secondly, whereas the SSN had previously been studied in a monocular context, we found that it could also reproduce data on interocular transfer of surround suppression. Finally, we reformulated the SSN as a convolutional neural network, and found that it scaled well on parallel hardware. These results provide additional support for the plausibility of the SSN as a model of lateral interactions in V1, and suggest that the SSN is well suited as a component of complex vision models. Future work will use the SSN to explore relationships between local network interactions and sophisticated vision processes in large networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dendritic Connectivity, Heterogeneity, and Scaling in Urban Stormwater Networks: Implications for Socio-Hydrology

NASA Astrophysics Data System (ADS)

Mejia, A.; Jovanovic, T.; Hale, R. L.; Gironas, J. A.

2017-12-01

Urban stormwater networks (USNs) are unique dendritic (tree-like) structures that combine both artificial (e.g., swales and pipes) and natural (e.g., streams and wetlands) components. They are central to stream ecosystem structure and function in urban watersheds. The emphasis of conventional stormwater management, however, has been on localized, temporal impacts (e.g., changes to hydrographs at discrete locations), and the performance of individual stormwater control measures. This is the case even though control measures are implemented to prevent impacts on the USN. We develop a modeling approach to retrospectively study hydrological fluxes and states in USNs and apply the model to an urban watershed in Scottsdale, Arizona, USA. Using outputs from the model, we analyze over space and time the network properties of dendritic connectivity, heterogeneity, and scaling. Results show that as the network growth over time, due to increasing urbanization, it tends to become more homogenous in terms of topological features but increasingly heterogeneous in terms of dynamic features. We further use the modeling results to address socio-hydrological implications for USNs. We find that the adoption over time of evolving management strategies (e.g., widespread implementation of vegetated swales and retention ponds versus pipes) may be locally beneficial to the USN but benefits may not propagate systematically through the network. The latter can be reinforced by sudden, perhaps unintended, changes to the overall dendritic connectivity.

Reason for high strength and good ductility in dual phase steels composed of soft ferrite and hard martensite

NASA Astrophysics Data System (ADS)

Terada, Daisuke; Ikeda, Gosuke; Park, Myeong-heom; Shibata, Akinobu; Tsuji, Nobuhiro

2017-07-01

Dual phase (DP) steels in which the microstructures are composed of a soft ferrite phase and a hard martensite phase are known to show good strain-hardening, high strength and large elongation, but reasons for their superior mechanical properties are still unclear. In the present study, two types of DP structures, having either networked martensite or isolated martensite were fabricated in a low-carbon steel by different heat treatment routes, and their tensile deformation behavior was analyzed using the digital image correlation (DIC) technique. It was revealed that the DP specimens having networked martensite microstructures showed a better strength-ductility balance than the DP specimens with isolated martensite structures. The microscopic DIC analysis of identical areas showed that the strain distribution within the DP microstructures was not uniform and the plastic strain was localized in soft ferrite grains. The strain localized regions tended to detour around hard martensite but eventually propagated across the martensite. It was found also from the DIC analysis that the degree of strain partitioning between ferrite and martensite in the networked DP structure was lower than that in the isolated DP structure. The deformation became more homogeneous when the hard phase (martensite) was connected to form a network structure, which could be one of the reasons for the better strength-ductility balance in the networked DP structure compared to that in the isolated DP structure.

Our Selections and Decisions: Inherent Features of the Nervous System?

NASA Astrophysics Data System (ADS)

Rösler, Frank

The chapter summarizes findings on the neuronal bases of decisionmaking. Taking the phenomenon of selection it will be explained that systems built only from excitatory and inhibitory neuron (populations) have the emergent property of selecting between different alternatives. These considerations suggest that there exists a hierarchical architecture with central selection switches. However, in such a system, functions of selection and decision-making are not localized, but rather emerge from an interaction of several participating networks. These are, on the one hand, networks that process specific input and output representations and, on the other hand, networks that regulate the relative activation/inhibition of the specific input and output networks. These ideas are supported by recent empirical evidence. Moreover, other studies show that rather complex psychological variables, like subjective probability estimates, expected gains and losses, prediction errors, etc., do have biological correlates, i.e., they can be localized in time and space as activation states of neural networks and single cells. These findings suggest that selections and decisions are consequences of an architecture which, seen from a biological perspective, is fully deterministic. However, a transposition of such nomothetic functional principles into the idiographic domain, i.e., using them as elements for comprehensive 'mechanistic' explanations of individual decisions, seems not to be possible because of principle limitations. Therefore, individual decisions will remain predictable by means of probabilistic models alone.

A characterization of the coupled evolution of grain fabric and pore space using complex networks: Pore connectivity and optimized flows in the presence of shear bands

NASA Astrophysics Data System (ADS)

Russell, Scott; Walker, David M.; Tordesillas, Antoinette

2016-03-01

A framework for the multiscale characterization of the coupled evolution of the solid grain fabric and its associated pore space in dense granular media is developed. In this framework, a pseudo-dual graph transformation of the grain contact network produces a graph of pores which can be readily interpreted as a pore space network. Survivability, a new metric succinctly summarizing the connectivity of the solid grain and pore space networks, measures material robustness. The size distribution and the connectivity of pores can be characterized quantitatively through various network properties. Assortativity characterizes the pore space with respect to the parity of the number of particles enclosing the pore. Multiscale clusters of odd parity versus even parity contact cycles alternate spatially along the shear band: these represent, respectively, local jamming and unjamming regions that continually switch positions in time throughout the failure regime. Optimal paths, established using network shortest paths in favor of large pores, provide clues on preferential paths for interstitial matter transport. In systems with higher rolling resistance at contacts, less tortuous shortest paths thread through larger pores in shear bands. Notably the structural patterns uncovered in the pore space suggest that more robust models of interstitial pore flow through deforming granular systems require a proper consideration of the evolution of in situ shear band and fracture patterns - not just globally, but also inside these localized failure zones.

Cell-ECM Interactions During Cancer Invasion

NASA Astrophysics Data System (ADS)

Jiang, Yi

The extracellular matrix (ECM), a fibrous material that forms a network in a tissue, significantly affects many aspects of cellular behavior, including cell movement and proliferation. Transgenic mouse tumor studies indicate that excess collagen, a major component of ECM, enhances tumor formation and invasiveness. Clinically, tumor associated collagen signatures are strong markers for breast cancer survival. However, the underlying mechanisms are unclear since the properties of ECM are complex, with diverse structural and mechanical properties depending on various biophysical parameters. We have developed a three-dimensional elastic fiber network model, and parameterized it with in vitro collagen mechanics. Using this model, we study ECM remodeling as a result of local deformation and cell migration through the ECM as a network percolation problem. We have also developed a three-dimensional, multiscale model of cell migration and interaction with ECM. Our model reproduces quantitative single cell migration experiments. This model is a first step toward a fully biomechanical cell-matrix interaction model and may shed light on tumor associated collagen signatures in breast cancer. This work was partially supported by NIH-U01CA143069.

Structural and configurational properties of nanoconfined monolayer ice from first principles

PubMed Central

Corsetti, Fabiano; Matthews, Paul; Artacho, Emilio

2016-01-01

Understanding the structural tendencies of nanoconfined water is of great interest for nanoscience and biology, where nano/micro-sized objects may be separated by very few layers of water. Here we investigate the properties of ice confined to a quasi-2D monolayer by a featureless, chemically neutral potential, in order to characterize its intrinsic behaviour. We use density-functional theory simulations with a non-local van der Waals density functional. An ab initio random structure search reveals all the energetically competitive monolayer configurations to belong to only two of the previously-identified families, characterized by a square or honeycomb hydrogen-bonding network, respectively. We discuss the modified ice rules needed for each network, and propose a simple point dipole 2D lattice model that successfully explains the energetics of the square configurations. All identified stable phases for both networks are found to be non-polar (but with a topologically non-trivial texture for the square) and, hence, non-ferroelectric, in contrast to previous predictions from a five-site empirical force-field model. Our results are in good agreement with very recently reported experimental observations. PMID:26728125

Structural and configurational properties of nanoconfined monolayer ice from first principles

NASA Astrophysics Data System (ADS)

Corsetti, Fabiano; Matthews, Paul; Artacho, Emilio

2016-01-01

Understanding the structural tendencies of nanoconfined water is of great interest for nanoscience and biology, where nano/micro-sized objects may be separated by very few layers of water. Here we investigate the properties of ice confined to a quasi-2D monolayer by a featureless, chemically neutral potential, in order to characterize its intrinsic behaviour. We use density-functional theory simulations with a non-local van der Waals density functional. An ab initio random structure search reveals all the energetically competitive monolayer configurations to belong to only two of the previously-identified families, characterized by a square or honeycomb hydrogen-bonding network, respectively. We discuss the modified ice rules needed for each network, and propose a simple point dipole 2D lattice model that successfully explains the energetics of the square configurations. All identified stable phases for both networks are found to be non-polar (but with a topologically non-trivial texture for the square) and, hence, non-ferroelectric, in contrast to previous predictions from a five-site empirical force-field model. Our results are in good agreement with very recently reported experimental observations.

Quaternion-valued echo state networks.

PubMed

Xia, Yili; Jahanchahi, Cyrus; Mandic, Danilo P

2015-04-01

Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction of QESNs is made possible by the recent emergence of quaternion nonlinear activation functions with local analytic properties, required by nonlinear gradient descent training algorithms. To make QENSs second-order optimal for the generality of quaternion signals (both circular and noncircular), we employ augmented quaternion statistics to introduce widely linear QESNs. To that end, the standard widely linear model is modified so as to suit the properties of dynamical reservoir, typically realized by recurrent neural networks. This allows for a full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances, and a rigorous account of second-order noncircularity (improperness), and the corresponding power mismatch and coupling between the data components. Simulations in the prediction setting on both benchmark circular and noncircular signals and on noncircular real-world 3-D body motion data support the analysis.

Graph partitions and cluster synchronization in networks of oscillators

PubMed Central

Schaub, Michael T.; O’Clery, Neave; Billeh, Yazan N.; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio

2017-01-01

Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators. PMID:27781454

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling.

PubMed

Suarez-Diez, Maria; Adam, Jonathan; Adamski, Jerzy; Chasapi, Styliani A; Luchinat, Claudio; Peters, Annette; Prehn, Cornelia; Santucci, Claudio; Spyridonidis, Alexandros; Spyroulias, Georgios A; Tenori, Leonardo; Wang-Sattler, Rui; Saccenti, Edoardo

2017-07-07

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA-MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

PubMed Central

2017-01-01

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids. PMID:28517934

Lightweight Filter Architecture for Energy Efficient Mobile Vehicle Localization Based on a Distributed Acoustic Sensor Network

PubMed Central

Kim, Keonwook

2013-01-01

The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably. PMID:23979482

The structural and electronic properties of amorphous HgCdTe from first-principles calculations

NASA Astrophysics Data System (ADS)

Zhao, Huxian; Chen, Xiaoshuang; Lu, Jianping; Shu, Haibo; Lu, Wei

2014-01-01

Amorphous mercury cadmium telluride (a-MCT) model structures, with x being 0.125 and 0.25, are obtained from first-principles calculations. We generate initial structures by computation alchemy method. It is found that most atoms in the network of amorphous structures tend to be fourfold and form tetrahedral structures, implying that the chemical ordered continuous random network with some coordination defects is the ideal structure for a-MCT. The electronic structure is also concerned. The gap is found to be 0.30 and 0.26 eV for a-Hg0.875Cd0.125Te and a-Hg0.75Cd0.25Te model structures, independent of the composition. By comparing with the properties of crystalline MCT with the same composition, we observe a blue-shift of energy band gap. The localization of tail states and its atomic origin are also discussed.

The Mathematics of Dispatchability, Revisited

NASA Technical Reports Server (NTRS)

Morris, Paul

2016-01-01

Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatchable STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently, it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, dispatchability has considerable theoretical as well as practical significance. One thing that hampers further work in this area is the underdeveloped theory. Moreover, the existing foundation is inadequate in certain respects. In this paper, we develop a new mathematical theory of dispatchability and its relationship to execution. We also provide several characterizations of dispatchability, including characterizations in terms of the structural properties of the STN graph. This facilitates the potential application of the theory to other areas.

Fracture network created by 3D printer and its validation using CT images

NASA Astrophysics Data System (ADS)

Suzuki, A.; Watanabe, N.; Li, K.; Horne, R. N.

2017-12-01

Understanding flow mechanisms in fractured media is essential for geoscientific research and geological development industries. This study used 3D printed fracture networks in order to control the properties of fracture distributions inside the sample. The accuracy and appropriateness of creating samples by the 3D printer was investigated by using a X-ray CT scanner. The CT scan images suggest that the 3D printer is able to reproduce complex three-dimensional spatial distributions of fracture networks. Use of hexane after printing was found to be an effective way to remove wax for the post-treatment. Local permeability was obtained by the cubic law and used to calculate the global mean. The experimental value of the permeability was between the arithmetic and geometric means of the numerical results, which is consistent with conventional studies. This methodology based on 3D printed fracture networks can help validate existing flow modeling and numerical methods.

Georgia's Surface-Water Resources and Streamflow Monitoring Network, 2006

USGS Publications Warehouse

Nobles, Patricia L.; ,

2006-01-01

The U.S. Geological Survey (USGS) network of 223 real-time monitoring stations, the 'Georgia HydroWatch,' provides real-time water-stage data, with streamflow computed at 198 locations, and rainfall recorded at 187 stations. These sites continuously record data on 15-minute intervals and transmit the data via satellite to be incorporated into the USGS National Water Information System database. These data are automatically posted to the USGS Web site for public dissemination (http://waterdata.usgs.gov/ga/nwis/nwis). The real-time capability of this network provides information to help emergency-management officials protect human life and property during floods, and mitigate the effects of prolonged drought. The map at right shows the USGS streamflow monitoring network for Georgia and major watersheds. Streamflow is monitored at 198 sites statewide, more than 80 percent of which include precipitation gages. Various Federal, State, and local agencies fund these streamflow monitoring stations.

The global transmission network of HIV-1.

PubMed

Wertheim, Joel O; Leigh Brown, Andrew J; Hepler, N Lance; Mehta, Sanjay R; Richman, Douglas D; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-01-15

Human immunodeficiency virus type 1 (HIV-1) is pandemic, but its contemporary global transmission network has not been characterized. A better understanding of the properties and dynamics of this network is essential for surveillance, prevention, and eventual eradication of HIV. Here, we apply a simple and computationally efficient network-based approach to all publicly available HIV polymerase sequences in the global database, revealing a contemporary picture of the spread of HIV-1 within and between countries. This approach automatically recovered well-characterized transmission clusters and extended other clusters thought to be contained within a single country across international borders. In addition, previously undescribed transmission clusters were discovered. Together, these clusters represent all known modes of HIV transmission. The extent of international linkage revealed by our comprehensive approach demonstrates the need to consider the global diversity of HIV, even when describing local epidemics. Finally, the speed of this method allows for near-real-time surveillance of the pandemic's progression.

Organization and hierarchy of the human functional brain network lead to a chain-like core.

PubMed

Mastrandrea, Rossana; Gabrielli, Andrea; Piras, Fabrizio; Spalletta, Gianfranco; Caldarelli, Guido; Gili, Tommaso

2017-07-07

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming a highly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.

Modelling students' knowledge organisation: Genealogical conceptual networks

NASA Astrophysics Data System (ADS)

Koponen, Ismo T.; Nousiainen, Maija

2018-04-01

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

Counting motifs in dynamic networks.

PubMed

Mukherjee, Kingshuk; Hasan, Md Mahmudul; Boucher, Christina; Kahveci, Tamer

2018-04-11

A network motif is a sub-network that occurs frequently in a given network. Detection of such motifs is important since they uncover functions and local properties of the given biological network. Finding motifs is however a computationally challenging task as it requires solving the costly subgraph isomorphism problem. Moreover, the topology of biological networks change over time. These changing networks are called dynamic biological networks. As the network evolves, frequency of each motif in the network also changes. Computing the frequency of a given motif from scratch in a dynamic network as the network topology evolves is infeasible, particularly for large and fast evolving networks. In this article, we design and develop a scalable method for counting the number of motifs in a dynamic biological network. Our method incrementally updates the frequency of each motif as the underlying network's topology evolves. Our experiments demonstrate that our method can update the frequency of each motif in orders of magnitude faster than counting the motif embeddings every time the network changes. If the network evolves more frequently, the margin with which our method outperforms the existing static methods, increases. We evaluated our method extensively using synthetic and real datasets, and show that our method is highly accurate(≥ 96%) and that it can be scaled to large dense networks. The results on real data demonstrate the utility of our method in revealing interesting insights on the evolution of biological processes.

Towards an Earthquake and Tsunami Early Warning in the Caribbean

NASA Astrophysics Data System (ADS)

Huerfano Moreno, V. A.; Vanacore, E. A.

2017-12-01

The Caribbean region (CR) has a documented history of large damaging earthquakes and tsunamis that have affected coastal areas, including the events of Jamaica in 1692, Virgin Islands in 1867, Puerto Rico in 1918, the Dominican Republic in 1946 and Haiti in 2010. There is clear evidence that tsunamis have been triggered by large earthquakes that deformed the ocean floor around the Caribbean Plate boundary. The CR is monitored jointly by national/regional/local seismic, geodetic and sea level networks. All monitoring institutions are participating in the UNESCO ICG/Caribe EWS, the purpose of this initiative is to minimize loss of life and destruction of property, and to mitigate against catastrophic economic impacts via promoting local research, real time (RT) earthquake, geodetic and sea level data sharing and improving warning capabilities and enhancing education and outreach strategies. Currently more than, 100 broad-band seismic, 65 sea levels and 50 GPS high rate stations are available in real or near real-time. These real-time streams are used by Local/Regional or Worldwide detection and warning institutions to provide earthquake source parameters in a timely manner. Currently, any Caribbean event detected to have a magnitude greater than 4.5 is evaluated, and sea level is measured, by the TWC for tsumanigenic potential. The regional cooperation is motivated both by research interests as well as geodetic, seismic and tsunami hazard monitoring and warning. It will allow the imaging of the tectonic structure of the Caribbean region to a high resolution which will consequently permit further understanding of the seismic source properties for moderate and large events and the application of this knowledge to procedures of civil protection. To reach its goals, the virtual network has been designed following the highest technical standards: BB sensors, 24 bits A/D converters with 140 dB dynamic range, real-time telemetry. Here we will discuss the state of the PR component of this virtual network as well as current advances in the imaging of the PR tectonic structure. The goal of this presentation is to describe the Puerto Rico Seismic Network (PRSN) system, including the real time earthquake and tsunami monitoring as well as the specific protocols used to broadcast earthquake/tsunami messages locally.

Stimulated Emission Depletion Live-Cell Super-Resolution Imaging Shows Proliferative Remodeling of T-Tubule Membrane Structures After Myocardial Infarction

PubMed Central

Wagner, Eva; Lauterbach, Marcel A.; Kohl, Tobias; Westphal, Volker; Williams, George S.B.; Steinbrecher, Julia H.; Streich, Jan-Hendrik; Korff, Brigitte; Tuan, Hoang-Trong M.; Hagen, Brian; Luther, Stefan; Hasenfuss, Gerd; Parlitz, Ulrich; Jafri, M. Saleet; Hell, Stefan W.; Lederer, W. Jonathan; Lehnart, Stephan E.

2014-01-01

Rationale Transverse tubules (TTs) couple electric surface signals to remote intracellular Ca2+ release units (CRUs). Diffraction-limited imaging studies have proposed loss of TT components as disease mechanism in heart failure (HF). Objectives Objectives were to develop quantitative super-resolution strategies for live-cell imaging of TT membranes in intact cardiomyocytes and to show that TT structures are progressively remodeled during HF development, causing early CRU dysfunction. Methods and Results Using stimulated emission depletion (STED) microscopy, we characterized individual TTs with nanometric resolution as direct readout of local membrane morphology 4 and 8 weeks after myocardial infarction (4pMI and 8pMI). Both individual and network TT properties were investigated by quantitative image analysis. The mean area of TT cross sections increased progressively from 4pMI to 8pMI. Unexpectedly, intact TT networks showed differential changes. Longitudinal and oblique TTs were significantly increased at 4pMI, whereas transversal components appeared decreased. Expression of TT-associated proteins junctophilin-2 and caveolin-3 was significantly changed, correlating with network component remodeling. Computational modeling of spatial changes in HF through heterogeneous TT reorganization and RyR2 orphaning (5000 of 20 000 CRUs) uncovered a local mechanism of delayed subcellular Ca2+ release and action potential prolongation. Conclusions This study introduces STED nanoscopy for live mapping of TT membrane structures. During early HF development, the local TT morphology and associated proteins were significantly altered, leading to differential network remodeling and Ca2+ release dyssynchrony. Our data suggest that TT remodeling during HF development involves proliferative membrane changes, early excitation-contraction uncoupling, and network fracturing. PMID:22723297

Relations between macropore network characteristics and the degree of preferential solute transport

NASA Astrophysics Data System (ADS)

Larsbo, M.; Koestel, J.; Jarvis, N.

2014-12-01

The characteristics of the soil macropore network determine the potential for fast transport of agrochemicals and contaminants through the soil. The objective of this study was to examine the relationships between macropore network characteristics, hydraulic properties and state variables and measures of preferential transport. Experiments were carried out under near-saturated conditions on undisturbed columns sampled from four agricultural topsoils of contrasting texture and structure. Macropore network characteristics were computed from 3-D X-ray tomography images of the soil pore system. Non-reactive solute transport experiments were carried out at five steady-state water flow rates from 2 to 12 mm h-1. The degree of preferential transport was evaluated by the normalised 5% solute arrival time and the apparent dispersivity calculated from the resulting breakthrough curves. Near-saturated hydraulic conductivities were measured on the same samples using a tension disc infiltrometer placed on top of the columns. Results showed that many of the macropore network characteristics were inter-correlated. For example, large macroporosities were associated with larger specific macropore surface areas and better local connectivity of the macropore network. Generally, an increased flow rate resulted in earlier solute breakthrough and a shifting of the arrival of peak concentration towards smaller drained volumes. Columns with smaller macroporosities, poorer local connectivity of the macropore network and smaller near-saturated hydraulic conductivities exhibited a greater degree of preferential transport. This can be explained by the fact that, with only two exceptions, global (i.e. sample scale) continuity of the macropore network was still preserved at low macroporosities. Thus, for any given flow rate, pores of larger diameter were actively conducting solute in soils of smaller near-saturated hydraulic conductivity. This was associated with larger local transport velocities and, hence, less time for equilibration between the macropores and the surrounding matrix which made the transport more preferential. Conversely, the large specific macropore surface area and well-connected macropore networks associated with columns with large macroporosities limit the degree of preferential transport because they increase the diffusive flux between macropores and the soil matrix and they increase the near-saturated hydraulic conductivity. The normalised 5% arrival times were most strongly correlated with the estimated hydraulic state variables (e.g. with the degree of saturation in the macropores R2 = 0.589), since these combine into one measure the effects of irrigation rate and the near-saturated hydraulic conductivity function, which in turn implicitly depends on the volume, size distribution, global continuity, local connectivity and tortuosity of the macropore network.

Phase synchronization of bursting neurons in clustered small-world networks

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Lameu, E. L.; Batista, A. M.; Lopes, S. R.; Pereira, T.; Zamora-López, G.; Kurths, J.; Viana, R. L.

2012-07-01

We investigate the collective dynamics of bursting neurons on clustered networks. The clustered network model is composed of subnetworks, each of them presenting the so-called small-world property. This model can also be regarded as a network of networks. In each subnetwork a neuron is connected to other ones with regular as well as random connections, the latter with a given intracluster probability. Moreover, in a given subnetwork each neuron has an intercluster probability to be connected to the other subnetworks. The local neuron dynamics has two time scales (fast and slow) and is modeled by a two-dimensional map. In such small-world network the neuron parameters are chosen to be slightly different such that, if the coupling strength is large enough, there may be synchronization of the bursting (slow) activity. We give bounds for the critical coupling strength to obtain global burst synchronization in terms of the network structure, that is, the probabilities of intracluster and intercluster connections. We find that, as the heterogeneity in the network is reduced, the network global synchronizability is improved. We show that the transitions to global synchrony may be abrupt or smooth depending on the intercluster probability.

Networks in plant epidemiology: from genes to landscapes, countries, and continents.

PubMed

Moslonka-Lefebvre, Mathieu; Finley, Ann; Dorigatti, Ilaria; Dehnen-Schmutz, Katharina; Harwood, Tom; Jeger, Michael J; Xu, Xiangming; Holdenrieder, Ottmar; Pautasso, Marco

2011-04-01

There is increasing use of networks in ecology and epidemiology, but still relatively little application in phytopathology. Networks are sets of elements (nodes) connected in various ways by links (edges). Network analysis aims to understand system dynamics and outcomes in relation to network characteristics. Many existing natural, social, and technological networks have been shown to have small-world (local connectivity with short-cuts) and scale-free (presence of super-connected nodes) properties. In this review, we discuss how network concepts can be applied in plant pathology from the molecular to the landscape and global level. Wherever disease spread occurs not just because of passive/natural dispersion but also due to artificial movements, it makes sense to superimpose realistic models of the trade in plants on spatially explicit models of epidemic development. We provide an example of an emerging pathosystem (Phytophthora ramorum) where a theoretical network approach has proven particularly fruitful in analyzing the spread of disease in the UK plant trade. These studies can help in assessing the future threat posed by similar emerging pathogens. Networks have much potential in plant epidemiology and should become part of the standard curriculum.

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

PubMed Central

Hass, Joachim; Hertäg, Loreen; Durstewitz, Daniel

2016-01-01

The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition. PMID:27203563

Probing the Topological Properties of Complex Networks Modeling Short Written Texts

PubMed Central

Amancio, Diego R.

2015-01-01

In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well—many informative discoveries have been made this way—but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyses performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks. PMID:25719799

Competition between global and local online social networks

NASA Astrophysics Data System (ADS)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-01

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

Competition between global and local online social networks.

PubMed

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-27

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

Modeling microcirculatory blood flow: current state and future perspectives.

PubMed

Gompper, Gerhard; Fedosov, Dmitry A

2016-01-01

Microvascular blood flow determines a number of important physiological processes of an organism in health and disease. Therefore, a detailed understanding of microvascular blood flow would significantly advance biophysical and biomedical research and its applications. Current developments in modeling of microcirculatory blood flow already allow to go beyond available experimental measurements and have a large potential to elucidate blood flow behavior in normal and diseased microvascular networks. There exist detailed models of blood flow on a single cell level as well as simplified models of the flow through microcirculatory networks, which are reviewed and discussed here. The combination of these models provides promising prospects for better understanding of blood flow behavior and transport properties locally as well as globally within large microvascular networks. © 2015 Wiley Periodicals, Inc.

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

PubMed

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

2012-02-21

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

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

PubMed Central

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

2012-01-01

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

Multiple μ-stability of neural networks with unbounded time-varying delays.

PubMed

Wang, Lili; Chen, Tianping

2014-05-01

In this paper, we are concerned with a class of recurrent neural networks with unbounded time-varying delays. Based on the geometrical configuration of activation functions, the phase space R(n) can be divided into several Φη-type subsets. Accordingly, a new set of regions Ωη are proposed, and rigorous mathematical analysis is provided to derive the existence of equilibrium point and its local μ-stability in each Ωη. It concludes that the n-dimensional neural networks can exhibit at least 3(n) equilibrium points and 2(n) of them are μ-stable. Furthermore, due to the compatible property, a set of new conditions are presented to address the dynamics in the remaining 3(n)-2(n) subset regions. As direct applications of these results, we can get some criteria on the multiple exponential stability, multiple power stability, multiple log-stability, multiple log-log-stability and so on. In addition, the approach and results can also be extended to the neural networks with K-level nonlinear activation functions and unbounded time-varying delays, in which there can store (2K+1)(n) equilibrium points, (K+1)(n) of them are locally μ-stable. Numerical examples are given to illustrate the effectiveness of our results. Copyright © 2014 Elsevier Ltd. All rights reserved.

Network dynamics: The World Wide Web

NASA Astrophysics Data System (ADS)

Adamic, Lada Ariana

Despite its rapidly growing and dynamic nature, the Web displays a number of strong regularities which can be understood by drawing on methods of statistical physics. This thesis finds power-law distributions in website sizes, traffic, and links, and more importantly, develops a stochastic theory which explains them. Power-law link distributions are shown to lead to network characteristics which are especially suitable for scalable localized search. It is also demonstrated that the Web is a "small world": to reach one site from any other takes an average of only 4 hops, while most related sites cluster together. Additional dynamical properties of the Web graph are extracted from diffusion processes.

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states.

PubMed

Kohn, Lucas; Tschirsich, Ferdinand; Keck, Maximilian; Plenio, Martin B; Tamascelli, Dario; Montangero, Simone

2018-01-01

We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond or local dimension of up to 24 times in quasi-two-dimensional cylindrical systems.

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states

NASA Astrophysics Data System (ADS)

Kohn, Lucas; Tschirsich, Ferdinand; Keck, Maximilian; Plenio, Martin B.; Tamascelli, Dario; Montangero, Simone

2018-01-01

We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond or local dimension of up to 24 times in quasi-two-dimensional cylindrical systems.

43 CFR 2806.43 - How does BLM calculate rent for passive reflectors and local exchange networks?

Code of Federal Regulations, 2011 CFR

2011-10-01

... reflectors and local exchange networks? 2806.43 Section 2806.43 Public Lands: Interior Regulations Relating...-Of-Way § 2806.43 How does BLM calculate rent for passive reflectors and local exchange networks? (a) BLM calculates rent for passive reflectors and local exchange networks by using the same rent...

Introduction to Local Area Networks for Microcomputers — Characteristics, Costs, Implementation Considerations

PubMed Central

Haney, James P.

1984-01-01

The essence of a local area network (LAN) is that the whole is greater than the sum of its parts. A local area network can save in hardware costs when expensive peripherals are shared; it can save time when large blocks of data are rapidly exchanged among users. The need for more cost-effective and capable communications has inspired the emergence of rapidly developing markets and technologies for local area networks. The purpose of this paper is to provide an understanding of the characteristics, components, costs, and implementation considerations of local area networks. The paper does not compare or define specific vendor offerings; however, recent IBM announcements regarding local area networks are summarized in the last section of the paper.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

PubMed

Coman, Alin; Momennejad, Ida; Drach, Rae D; Geana, Andra

2016-07-19

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

Multisector Health Policy Networks in 15 Large US Cities.

PubMed

Harris, Jenine K; Leider, J P; Carothers, Bobbi J; Castrucci, Brian C; Hearne, Shelley

2016-01-01

Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas core local funding networks had structures consistent with lower performing networks.

Multisector Health Policy Networks in 15 Large US Cities

PubMed Central

Leider, J. P.; Carothers, Bobbi J.; Castrucci, Brian C.; Hearne, Shelley

2016-01-01

Context: Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. Design: We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. Setting/Participants: We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. Outcome Measures: Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. Results: Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. Conclusion: Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas core local funding networks had structures consistent with lower performing networks. PMID:26910868

Emergence of Adaptive Computation by Single Neurons in the Developing Cortex

PubMed Central

Famulare, Michael; Gjorgjieva, Julijana; Moody, William J.

2013-01-01

Adaptation is a fundamental computational motif in neural processing. To maintain stable perception in the face of rapidly shifting input, neural systems must extract relevant information from background fluctuations under many different contexts. Many neural systems are able to adjust their input–output properties such that an input's ability to trigger a response depends on the size of that input relative to its local statistical context. This “gain-scaling” strategy has been shown to be an efficient coding strategy. We report here that this property emerges during early development as an intrinsic property of single neurons in mouse sensorimotor cortex, coinciding with the disappearance of spontaneous waves of network activity, and can be modulated by changing the balance of spike-generating currents. Simultaneously, developing neurons move toward a common intrinsic operating point and a stable ratio of spike-generating currents. This developmental trajectory occurs in the absence of sensory input or spontaneous network activity. Through a combination of electrophysiology and modeling, we demonstrate that developing cortical neurons develop the ability to perform nearly perfect gain scaling by virtue of the maturing spike-generating currents alone. We use reduced single neuron models to identify the conditions for this property to hold. PMID:23884925

Synthesis, characterization and in vivo evaluation of biocompatible ferrogels

NASA Astrophysics Data System (ADS)

Lopez-Lopez, M. T.; Rodriguez, I. A.; Rodriguez-Arco, L.; Carriel, V.; Bonhome-Espinosa, A. B.; Campos, F.; Zubarev, A.; Duran, J. D. G.

2017-06-01

A hydrogel is a 3-D network of polymer chains in which water is the dispersion medium. Hydrogels have found extensive applications in the biomedical field due to their resemblance to living tissues. Furthermore, hydrogels can be endowed with exceptional properties by addition of synthetic materials. For example, magnetic field-sensitive gels, called ferrogels, are obtained by embedding magnetic particles in the polymer network. Novel living tissues with unique magnetic field-sensitive properties were recently prepared by 3-D cell culture in biocompatible ferrogels. This paper critically reviews the most recent progress and perspectives in their synthesis, characterization and biocompatibility evaluation. Optimization of ferrogels for this novel application requires low-density, strongly magnetic, multi-domain particles. Interestingly, the rheological properties of the resulting ferrogels in the absence of field were largely enhanced with respect to nonmagnetic hydrogels, which can only be explained by the additional cross-linking imparted by the embedded magnetic particles. Remarkably, rheological measurements under an applied magnetic field demonstrated that ferrogels presented reversibly tunable mechanical properties, which constitutes a unique advantage with respect to nonmagnetic hydrogels. In vivo evaluation of ferrogels showed good biocompatibility, with only some local inflammatory response, and no particle migration or damage to distant organs.

Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury.

PubMed

Yan, Yan; Song, Jian; Xu, Guozheng; Yao, Shun; Cao, Chenglong; Li, Chang; Peng, Guibao; Du, Hao

2017-10-01

This study investigated the characteristics of the small-world brain network architecture of patients with mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7days after injury respectively. After preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 templates. The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these parameters between the MTBI and control group. Significantly different parameters were investigated for correlations with SAC scores; those that correlated were chosen for further curve fitting. The clustering coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local networks, and strength of mean connectivity. These features may be associated with brain function compensation. The network parameters significantly correlated with SAC total and attention scores. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Computational Framework for 3D Mechanical Modeling of Plant Morphogenesis with Cellular Resolution

PubMed Central

Gilles, Benjamin; Hamant, Olivier; Boudaoud, Arezki; Traas, Jan; Godin, Christophe

2015-01-01

The link between genetic regulation and the definition of form and size during morphogenesis remains largely an open question in both plant and animal biology. This is partially due to the complexity of the process, involving extensive molecular networks, multiple feedbacks between different scales of organization and physical forces operating at multiple levels. Here we present a conceptual and modeling framework aimed at generating an integrated understanding of morphogenesis in plants. This framework is based on the biophysical properties of plant cells, which are under high internal turgor pressure, and are prevented from bursting because of the presence of a rigid cell wall. To control cell growth, the underlying molecular networks must interfere locally with the elastic and/or plastic extensibility of this cell wall. We present a model in the form of a three dimensional (3D) virtual tissue, where growth depends on the local modulation of wall mechanical properties and turgor pressure. The model shows how forces generated by turgor-pressure can act both cell autonomously and non-cell autonomously to drive growth in different directions. We use simulations to explore lateral organ formation at the shoot apical meristem. Although different scenarios lead to similar shape changes, they are not equivalent and lead to different, testable predictions regarding the mechanical and geometrical properties of the growing lateral organs. Using flower development as an example, we further show how a limited number of gene activities can explain the complex shape changes that accompany organ outgrowth. PMID:25569615

Local-Area-Network Simulator

NASA Technical Reports Server (NTRS)

Gibson, Jim; Jordan, Joe; Grant, Terry

1990-01-01

Local Area Network Extensible Simulator (LANES) computer program provides method for simulating performance of high-speed local-area-network (LAN) technology. Developed as design and analysis software tool for networking computers on board proposed Space Station. Load, network, link, and physical layers of layered network architecture all modeled. Mathematically models according to different lower-layer protocols: Fiber Distributed Data Interface (FDDI) and Star*Bus. Written in FORTRAN 77.

Spatial spreading of infectious disease via local and national mobility networks in South Korea

NASA Astrophysics Data System (ADS)

Kwon, Okyu; Son, Woo-Sik

2017-12-01

We study the spread of infectious disease based on local- and national-scale mobility networks. We construct a local mobility network using data on urban bus services to estimate local-scale movement of people. We also construct a national mobility network from orientation-destination data of vehicular traffic between highway tollgates to evaluate national-scale movement of people. A metapopulation model is used to simulate the spread of epidemics. Thus, the number of infected people is simulated using a susceptible-infectious-recovered (SIR) model within the administrative division, and inter-division spread of infected people is determined through local and national mobility networks. In this paper, we consider two scenarios for epidemic spread. In the first, the infectious disease only spreads through local-scale movement of people, that is, the local mobility network. In the second, it spreads via both local and national mobility networks. For the former, the simulation results show infected people sequentially spread to neighboring divisions. Yet for the latter, we observe a faster spreading pattern to distant divisions. Thus, we confirm the national mobility network enhances synchronization among the incidence profiles of all administrative divisions.

Disrupted Nodal and Hub Organization Account for Brain Network Abnormalities in Parkinson’s Disease

PubMed Central

Koshimori, Yuko; Cho, Sang-Soo; Criaud, Marion; Christopher, Leigh; Jacobs, Mark; Ghadery, Christine; Coakeley, Sarah; Harris, Madeleine; Mizrahi, Romina; Hamani, Clement; Lang, Anthony E.; Houle, Sylvain; Strafella, Antonio P.

2016-01-01

The recent application of graph theory to brain networks promises to shed light on complex diseases such as Parkinson’s disease (PD). This study aimed to investigate functional changes in sensorimotor and cognitive networks in Parkinsonian patients, with a focus on inter- and intra-connectivity organization in the disease-associated nodal and hub regions using the graph theoretical analyses. Resting-state functional MRI data of a total of 65 participants, including 23 healthy controls (HCs) and 42 patients, were investigated in 120 nodes for local efficiency, betweenness centrality, and degree. Hub regions were identified in the HC and patient groups. We found nodal and hub changes in patients compared with HCs, including the right pre-supplementary motor area (SMA), left anterior insula, bilateral mid-insula, bilateral dorsolateral prefrontal cortex (DLPFC), and right caudate nucleus. In general, nodal regions within the sensorimotor network (i.e., right pre-SMA and right mid-insula) displayed weakened connectivity, with the former node associated with more severe bradykinesia, and impaired integration with default mode network regions. The left mid-insula also lost its hub properties in patients. Within the executive networks, the left anterior insular cortex lost its hub properties in patients, while a new hub region was identified in the right caudate nucleus, paralleled by an increased level of inter- and intra-connectivity in the bilateral DLPFC possibly representing compensatory mechanisms. These findings highlight the diffuse changes in nodal organization and regional hub disruption accounting for the distributed abnormalities across brain networks and the clinical manifestations of PD. PMID:27891090

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

PubMed

Li, Huaizhou; Zhou, Haiyan; Yang, Yang; Wang, Haiyuan; Zhong, Ning

2017-10-01

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Disrupted Nodal and Hub Organization Account for Brain Network Abnormalities in Parkinson's Disease.

PubMed

Koshimori, Yuko; Cho, Sang-Soo; Criaud, Marion; Christopher, Leigh; Jacobs, Mark; Ghadery, Christine; Coakeley, Sarah; Harris, Madeleine; Mizrahi, Romina; Hamani, Clement; Lang, Anthony E; Houle, Sylvain; Strafella, Antonio P

2016-01-01

The recent application of graph theory to brain networks promises to shed light on complex diseases such as Parkinson's disease (PD). This study aimed to investigate functional changes in sensorimotor and cognitive networks in Parkinsonian patients, with a focus on inter- and intra-connectivity organization in the disease-associated nodal and hub regions using the graph theoretical analyses. Resting-state functional MRI data of a total of 65 participants, including 23 healthy controls (HCs) and 42 patients, were investigated in 120 nodes for local efficiency, betweenness centrality, and degree. Hub regions were identified in the HC and patient groups. We found nodal and hub changes in patients compared with HCs, including the right pre-supplementary motor area (SMA), left anterior insula, bilateral mid-insula, bilateral dorsolateral prefrontal cortex (DLPFC), and right caudate nucleus. In general, nodal regions within the sensorimotor network (i.e., right pre-SMA and right mid-insula) displayed weakened connectivity, with the former node associated with more severe bradykinesia, and impaired integration with default mode network regions. The left mid-insula also lost its hub properties in patients. Within the executive networks, the left anterior insular cortex lost its hub properties in patients, while a new hub region was identified in the right caudate nucleus, paralleled by an increased level of inter- and intra-connectivity in the bilateral DLPFC possibly representing compensatory mechanisms. These findings highlight the diffuse changes in nodal organization and regional hub disruption accounting for the distributed abnormalities across brain networks and the clinical manifestations of PD.

Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks

PubMed Central

Newberry, Mitchell G; Ennis, Daniel B; Savage, Van M

2015-01-01

Scientists have long sought to understand how vascular networks supply blood and oxygen to cells throughout the body. Recent work focuses on principles that constrain how vessel size changes through branching generations from the aorta to capillaries and uses scaling exponents to quantify these changes. Prominent scaling theories predict that combinations of these exponents explain how metabolic, growth, and other biological rates vary with body size. Nevertheless, direct measurements of individual vessel segments have been limited because existing techniques for measuring vasculature are invasive, time consuming, and technically difficult. We developed software that extracts the length, radius, and connectivity of in vivo vessels from contrast-enhanced 3D Magnetic Resonance Angiography. Using data from 20 human subjects, we calculated scaling exponents by four methods—two derived from local properties of branching junctions and two from whole-network properties. Although these methods are often used interchangeably in the literature, we do not find general agreement between these methods, particularly for vessel lengths. Measurements for length of vessels also diverge from theoretical values, but those for radius show stronger agreement. Our results demonstrate that vascular network models cannot ignore certain complexities of real vascular systems and indicate the need to discover new principles regarding vessel lengths. PMID:26317654

Effect of mixed Ge/Si cross-linking on the physical properties of amorphous Ge-Si-Te networks

NASA Astrophysics Data System (ADS)

Gunasekera, K.; Boolchand, P.; Micoulaut, M.

2014-04-01

Amorphous GexSixTe1-2x glasses are studied as a function of composition by a combination of experimental and theoretical methods, allowing for a full description of the network structure in relationship with physico-chemical properties. Calorimetric and thermal measurements reveal that such glasses display an anomalous behavior across a range of compositions xc1=7.5% and

Syntactic Computations in the Language Network: Characterizing Dynamic Network Properties Using Representational Similarity Analysis

PubMed Central

Tyler, Lorraine K.; Cheung, Teresa P. L.; Devereux, Barry J.; Clarke, Alex

2013-01-01

The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG) and posterior middle temporal gyrus (LMTG) and the anatomical connections between them. Here we use magnetoencephalography (MEG) to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g., “… landing planes …”), at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA) to characterize syntactic information represented in the LIFG and left posterior middle temporal gyrus (LpMTG) over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity. PMID:23730293

Multifunctional graded dielectrics fabricated using dry powder printing

NASA Astrophysics Data System (ADS)

Good, Austin J.; Roper, David; Good, Brandon; Yarlagadda, Shridhar; Mirotznik, Mark S.

2017-09-01

The ability to fabricate multifunctional devices that combine good structural properties with embedded electromagnetic functionality has many practical applications, including antireflective surfaces for structural radomes, load bearing conformal antennas, integrated RF transmission lines and passive beam forming networks. We describe here a custom made 3D printer that can print high dielectric constant ceramic powders within a low-loss structural composite substrate to produce mechanically robust parts with integrated graded dielectric properties. We fabricated a number of these parts and evaluated their anisotropic dielectric properties by determining the complete permittivity tensor of the printed samples as a function of local powder weight. This data was then experimentally validated using two practical examples: a Chebyshev antireflective stack and a 2D passive beamsteering network. The results of both electromagnetic systems displayed acceptable agreement between the simulated and measured results. This agreement shows that powder printing is a potential approach for fabricating spatially graded dielectric electromagnetic systems. This paper was submitted for review on 15 February 2017. The project is funded by the Office of Naval Research, Code 331.

Fuels planning: science synthesis and integration; social issues fact sheet 05: The importance of working locally

Treesearch

Rocky Mountain Research Station USDA Forest Service

2004-01-01

People who evaluate their actions in terms of what others think are often said to be guided by community norms. With respect to fuels management, this means that when you are "selling" a property owner on taking steps to reduce fuels, you are not just "selling" to one person, but to a network of people. This fact sheet discusses three tools to help...

A biophysical signature of network affiliation and sensory processing in mitral cells

PubMed Central

Angelo, Kamilla; Rancz, Ede A.; Pimentel, Diogo; Hundahl, Christian; Hannibal, Jens; Fleischmann, Alexander; Pichler, Bruno; Margrie, Troy W.

2012-01-01

One defining characteristic of the mammalian brain is its neuronal diversity1. For a given region, substructure or layer and even cell type2, variability in neuronal morphology and connectivity2-5 persists. While it is well established that such cellular properties vary considerably according to neuronal type, the significant biophysical diversity of neurons of the same morphological class is typically averaged out and ignored. Here we show that the amplitude of hyperpolarization-evoked membrane potential sag recorded in olfactory bulb mitral cells is an emergent, homotypic property of local networks and sensory information processing. Simultaneous whole-cell recordings from pairs of cells reveal that the amount of hyperpolarization-evoked sag potential and current6 is stereotypic for mitral cells belonging to the same glomerular circuit. This is corroborated by a mosaic, glomerulus-based pattern of expression of the HCN2 subunit of the hyperpolarization-activated current (Ih) channel. Furthermore, inter-glomerular differences in both membrane potential sag and HCN2 protein are diminished when sensory input to glomeruli is genetically and globally altered so only one type of odorant receptor is universally expressed7. We therefore suggest that population diversity in the intrinsic profile of mitral cells reflect functional adaptations of distinct local circuits dedicated to processing subtly different odor-related information. PMID:22820253

Complex network view of evolving manifolds

NASA Astrophysics Data System (ADS)

da Silva, Diamantino C.; Bianconi, Ginestra; da Costa, Rui A.; Dorogovtsev, Sergey N.; Mendes, José F. F.

2018-03-01

We study complex networks formed by triangulations and higher-dimensional simplicial complexes representing closed evolving manifolds. In particular, for triangulations, the set of possible transformations of these networks is restricted by the condition that at each step, all the faces must be triangles. Stochastic application of these operations leads to random networks with different architectures. We perform extensive numerical simulations and explore the geometries of growing and equilibrium complex networks generated by these transformations and their local structural properties. This characterization includes the Hausdorff and spectral dimensions of the resulting networks, their degree distributions, and various structural correlations. Our results reveal a rich zoo of architectures and geometries of these networks, some of which appear to be small worlds while others are finite dimensional with Hausdorff dimension equal or higher than the original dimensionality of their simplices. The range of spectral dimensions of the evolving triangulations turns out to be from about 1.4 to infinity. Our models include simplicial complexes representing manifolds with evolving topologies, for example, an h -holed torus with a progressively growing number of holes. This evolving graph demonstrates features of a small-world network and has a particularly heavy-tailed degree distribution.

Glucose Metabolism during Resting State Reveals Abnormal Brain Networks Organization in the Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Martínez-Montes, Eduardo

2013-01-01

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network’s attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI. PMID:23894356

Universities Scale Like Cities

PubMed Central

van Raan, Anthony F. J.

2013-01-01

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the ‘gross university income’ in terms of total number of citations over ‘size’ in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. We find that deviations from the expected behavior can indeed be explained by specific local properties of universities, particularly the field-specific composition of a university, and its quality in terms of field-normalized citation impact. By studying both the set of the 500 largest universities worldwide and a specific subset of these 500 universities -the top-100 European universities- we are also able to distinguish between properties of universities with as well as without selection of one specific local property, the quality of a university in terms of its average field-normalized citation impact. It also reveals an interesting observation concerning the working of a crucial property in networked systems, preferential attachment. PMID:23544062

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation

PubMed Central

Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

2015-01-01

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence. PMID:26089794

Complex Networks in Different Languages: A Study of an Emergent Multilingual Encyclopedia

NASA Astrophysics Data System (ADS)

Pembe, F. Canan; Bingol, Haluk

There is an increasing interest to the study of complex networks in an interdisciplinary way. Language, as a complex network, has been a part of this study due to its importance in human life. Moreover, the Internet has also been at the center of this study by making access to large amounts of information possible. With these ideas in mind, this work aims to evaluate conceptual networks in different languages with the data from a large and open source of information in the Internet, namely Wikipedia. As an evolving multilingual encyclopedia that can be edited by any Internet user, Wikipedia is a good example of an emergent complex system. In this paper, different from previous work on conceptual networks which usually concentrated on single languages, we concentrate on possible ways to compare the usages of different languages and possibly the underlying cultures. This also involves the analysis of local network properties around certain coneepts in different languages. For an initial evaluation, the concept "family" is used to compare the English and German Wikipedias. Although, the work is currently at the beginning, the results are promising.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

PubMed

Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

PubMed Central

Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. PMID:29387141

LOCAL AIR: Local Aerosol monitoring combining in-situ and Remote Sensing observations

NASA Astrophysics Data System (ADS)

Mona, Lucia; Caggiano, Rosa; Donvito, Angelo; Giannini, Vincenzo; Papagiannopoulos, Nikolaos; Sarli, Valentina; Trippetta, Serena

2015-04-01

The atmospheric aerosols have effects on climate, environment and health. Although the importance of the study of aerosols is well recognized, the current knowledge of the characteristics and their distribution is still insufficient, and there are large uncertainties in the current understanding of the role of aerosols on climate and the environment, both on a regional and local level. Overcoming these uncertainties requires a search strategy that integrates data from multiple platforms (eg, terrestrial, satellite, ships and planes) and the different acquisition techniques (for example, in situ measurements, remote sensing, modeling numerical and data assimilation) (Yu et al., 2006). To this end, in recent years, there have been many efforts such as the creation of networks dedicated to systematic observation of aerosols (eg, European Monitoring and Evaluation Programme-EMEP, European Aerosol Research Lidar NETwork-EARLINET, MicroPulse Lidar Network- MPLNET, and Aerosol Robotic NETwork-AERONET), the development and implementation of new satellite sensors and improvement of numerical models. The recent availability of numerous data to the ground, columnar and profiles of aerosols allows to investigate these aspects. An integrated approach between these different techniques could be able to provide additional information, providing greater insight into the properties of aerosols and their distribution and overcoming the limits of each single technique. In fact, the ground measurements allow direct determination of the physico-chemical properties of aerosols, but cannot be considered representative for large spatial and temporal scales and do not provide any information about the vertical profile of aerosols. On the other hand, the remote sensing techniques from the ground and satellite provide information on the vertical distribution of atmospheric aerosols both in the Planetary Boundary Layer (PBL), mainly characterized by the presence of aerosols originating from local sources, which in the troposphere, where there are aerosols transported over long distances by the phenomena of atmospheric circulation. The purpose of the LOCAL AIR project is the development of a methodology for using synergistic data at different resolutions (ground measurements, remote sensing from ground and satellite) as an effective tool for the characterization of tropospheric aerosols on a local scale. The backbone of the project is the long-term ground-based measurements collected at CIAO (CNR-IMAA Atmospheric Observatory) plus the CALIPSO observations.. The location of the plethora of instruments and measurements of atmospheric interest available at CNR-IMAA makes it a sample site not only for the realization of the methodology, but also allows a feasibility study of this method in the absence of some by analysis of the measures considered in the scaling down of the algorithm developed. It will be evaluated the applicability and reliability of the algorithm implemented for the characterization of the aerosol content to the ground in other places of special interest. Acknowledgments: LOCAL AIR is supported by PO FSE Basilicata 2007-2013 Azione n. 45/AP/05/2013/REG - CUP: G53G13000300009.

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

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza

2017-05-01

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

Coastal storm monitoring in Virginia

USGS Publications Warehouse

Wicklein, Shaun M.; Bennett, Mark

2014-01-01

Coastal communities in Virginia are prone to flooding, particularly during hurricanes, nor’easters, and other coastal low-pressure systems. These weather systems affect public safety, personal and public property, and valuable infrastructure, such as transportation, water and sewer, and electric-supply networks. Local emergency managers, utility operators, and the public are tasked with making difficult decisions regarding evacuations, road closures, and post-storm recovery efforts as a result of coastal flooding. In coastal Virginia these decisions often are made on the basis of anecdotal knowledge from past events or predictions based on data from monitoring sites located far away from the affected area that may not reflect local conditions. Preventing flood hazards, such as hurricane-induced storm surge, from becoming human disasters requires an understanding of the relative risks that flooding poses to specific communities. The risk to life and property can be very high if decisions about evacuations and road closures are made too late or not at all.

Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study.

PubMed

Koenis, Marinka M G; Brouwer, Rachel M; van den Heuvel, Martijn P; Mandl, René C W; van Soelen, Inge L C; Kahn, René S; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

2015-12-01

The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence, is unresolved in adolescence. In addition, the influence of genetic factors in the developing network is not known. Therefore, in a longitudinal study of 162 healthy adolescent twins and their siblings (mean age at baseline 9.9 [range 9.0-15.0] years), we mapped local and global structural network efficiency of cerebral fiber pathways (weighted with mean FA and streamline count) and assessed intelligence over a three-year interval. We find that the efficiency of the brain's structural network is highly heritable (locally up to 74%). FA-based local and global efficiency increases during early adolescence. Streamline count based local efficiency both increases and decreases, and global efficiency reorganizes to a net decrease. Local FA-based efficiency was correlated to IQ. Moreover, increases in FA-based network efficiency (global and local) and decreases in streamline count based local efficiency are related to increases in intellectual functioning. Individual changes in intelligence and local FA-based efficiency appear to go hand in hand in frontal and temporal areas. More widespread local decreases in streamline count based efficiency (frontal cingulate and occipital) are correlated with increases in intelligence. We conclude that the teenage brain is a network in progress in which individual differences in maturation relate to level of intellectual functioning. © 2015 Wiley Periodicals, Inc.

Identifying important nodes by adaptive LeaderRank

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei

2017-03-01

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

PREFACE: Complex Networks: from Biology to Information Technology

NASA Astrophysics Data System (ADS)

Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

2008-06-01

The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm for counting large directed loops. This work proposes a belief-propagation algorithm for counting long loops in directed networks, which is then applied to networks of different sizes and loop structure. In The anatomy of a large query graph, Baeza-Yates and Tiberi show that scale invariance is present also in the structure of a graph derived from query logs. This graph is determined not only by the queries but also by the subsequent actions of the users. The graph analysed in this study is generated by more than twenty million queries and is less sparse than suggested by previous studies. A different class of networks is considered by Travençolo and da F Costa in Hierarchical spatial organisation of geographical networks. This work proposes a hierarchical extension of the polygonality index as a means to characterise geographical planar networks and, in particular, to obtain more complete information about the spatial order of the network at progressive spatial scales. The paper Border trees of complex networks by Villas Boas et al focuses instead on the statistical properties of the boundary of graphs, constituted by the vertices of degree one (the leaves of border trees). The authors study the local properties, the depth, and the number of leaves of these border trees, finding that in some real networks more than half of the nodes belong to the border trees. The last contribution to the first section is The generation of random directed networks with prescribed 1-node and 2-node degree correlations by Zamora-López et al. This study deals with the generation of random directed networks and shows that often a large number of links cannot be 'randomised' without altering the degree correlations. This permits fast generation of ensembles of maximally random networks. In the section Methods: The Dynamics, significant attention is given to the study of synchronisation processes on networks: Díaz-Guilera's contribution Dynamics towards synchronisation in hierarchical networks consists of an overview of recent studies on hierarchical networks of phase oscillators. By analysing the evolution of the synchronous dynamics, one can infer details about the underlying network topology. Thus a connection between the dynamical and topological properties of the system is established. The paper Network synchronisation: optimal and pessimal scale-free topologies by Donetti et al explores an optimisation algorithm to study the properties of optimally synchronisable unweighted networks with scale-free degree distribution. It is shown that optimisation leads to a tendency towards disassortativity while networks that are optimally 'un-synchronisable' have a highly assortative string-like structure. The paper Critical line in undirected Kauffman Boolean networks—the role of percolation by Fronczak and Fronczak demonstrates that the percolation underlying the process of damage spreading impacts the position of the critical line in random boolean networks. The critical line results from the fact that the ordered behaviour of small clusters shields the chaotic behaviour of the giant component. In Impact of the updating scheme on stationary states of networks, Radicchi et al explore an interpolation between synchronous and asynchronous updating in a one-dimensional chain of Ising spins to locate a phase transition between phases with an absorbing and a fluctuating stationary state. The properties of attractors in the yeast cell-cycle network are also shown to depend sensitively on the updating mode. As this last contribution shows, a large part of the theoretical activity in the field can be applied to the study of biological systems. The section Biological Applications brings together the following contributions: In Applying weighted network measures to microarray distance matrices, Ahnert et al present a new approach to the analysis of weighted networks, which provides a generalisation to any network measure defined on unweighted networks. The clustering coefficient constructed using this approach is used to identify a number of biologically significant genes in data sets from microarray experiments. The paper Quantifying the taxonomic diversity in real species communities by Caretta Cartozo et al reports on universal statistical properties in taxonomic trees. The results, which are obtained by sampling a large pool of species from all over the world, suggest that it is possible to quantitatively distinguish real species assemblage from random collections. In the contribution Insights into biological information processing: structural and dynamical analysis of a human protein signalling network, de la Fuente et al investigate the dynamical properties of a human protein signalling network while accounting for edge directionality and topological properties both at the local and global scale. The relationship between the node degrees and the distribution of signals through the network is characterised using degree correlation profiles. A study of a brain network is presented by de Vico Fallani et al in Persistent patterns of interconnection in time-varying cortical networks estimated from high-resolution EEG recordings in humans during a simple motor act. The authors introduce an approach based on the estimate of time-varying graph indexes that allows the capture of schemes of communication within the network. The method is applied to a set of high resolution EEG data recorded from a group of subjects performing a simple foot movement. The last section, devoted to Social and Technological Applications, includes nine contributions in the broad area of infrastructure, economic, and social systems: The paper Uncovering individual and collective human dynamics from mobile phone records by Cándia et al explores extensive phone records resolved in both time and space to study collective behaviour and the occurrence of anomalous events. At the individual level, it is shown that the distribution of time intervals between consecutive calls is heavy tailed, which agrees with results previously reported on other human activities. In Mining the inner structure of the Web graph, Donato et al present a series of measurements of the Web, which offer a better understanding of the individual components of its bow-tie structure. The scale-free properties permeate all bow-tie components although they do not exhibit self-similarity and their inner structure is quite distinct. Effects of network topology on wealth distributions, by Garlaschelli and Loffredo, shows that a networked economic system self-organises towards a stationary state whose associated wealth distribution depends crucially on the underlying interaction network. In particular, this study implies that first-order topological properties alone (such as the scale-free property) are not enough to explain the emergence of the empirically observed mixed form of the wealth distribution. In the paper Resource allocation pattern in infrastructure networks, Kim and Motter show that real communication and transportation networks tend to exhibit larger load-to-capacity ratio in nodes and links with larger capacities. This surprising pattern, which is a consequence of decentralised evolution and network traffic fluctuations, suggests that infrastructure networks have evolved to prevent local failures but not necessarily large-scale failures that can be caused by cascading processes. The paper Consensus formation on coevolving networks: groups' formation and structure by Kozma and Barrat addresses the effect of adaptivity on a social model of opinion dynamics and consensus formation. The authors find that on adaptive networks the rewiring process fosters group formation by enhancing communication between agents of similar opinion, though it also makes possible the division of clusters. This result is significantly different from the percolation phenomena observed to govern the process in static networks. Capocci and Caldarelli, in the paper Folksonomies and clustering in the collaborative system CiteULike, analyse an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tripartite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied in order to uncover hidden relationships between tags. The authors find that the clustering coefficient reflects the semantical patterns among tags. Lambiotte's contribution, Majority rule on heterogeneous networks, focuses on the majority rule model for opinion formation when the agents interact through a complex network. It is shown that on networks with modular structures the system may exhibit an asymmetric regime, where nodes in different communities reach opposite average opinions. In addition, the node degree heterogeneity is shown to play an important role in the emergence of collective behaviour. In Structural analysis of behavioural networks from the Internet, Meiss et al analyse the structure of the Internet. The authors present a characterisation of the properties of the behavioural networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. The final contribution, A social network's changing statistical properties and the quality of human innovation by Uzzi, is an analysis of the collaboration network of artists that made Broadway musicals in the post World War II period. It is shown that when the clustering coefficient in this network is low or high, the financial and artistic success of the industry is low while an intermediate level of clustering is associated with successful shows. We hope that this special issue will serve as a reference of the state of the knowledge in this exciting area of interdisciplinary research and that it will appeal to both experts and newcomers to the field. Finally, we would like to thank all participants of the workshop for their very significant contributions and the IOP Publishing team, particularly Rebecca Gillan, for the careful production of this special issue.

A Structural Finite Element Model for Lamellar Unit of Aortic Media Indicates Heterogeneous Stress Field After Collagen Recruitment

PubMed Central

Thunes, James R.; Pal, Siladitya; Fortunato, Ronald N.; Phillippi, Julie A.; Gleason, Thomas G.; Vorp, David A.; Maiti, Spandan

2016-01-01

Incorporation of collagen structural information into the study of biomechanical behavior of ascending thoracic aortic (ATA) wall tissue should provide better insight into the pathophysiology of ATA. Structurally motivated constitutive models that include fiber dispersion and recruitment can successfully capture overall mechanical response of the arterial wall tissue. However, these models cannot examine local microarchitectural features of the collagen network, such as the effect of fiber disruptions and interaction between fibrous and non-fibrous components, which may influence emergent biomechanical properties of the tissue. Motivated by this need, we developed a finite element based three-dimensional structural model of the lamellar units of the ATA media that directly incorporates the collagen fiber microarchitecture. The fiber architecture was computer generated utilizing network features, namely fiber orientation distribution, intersection density and areal concentration, obtained from image analysis of multiphoton microscopy images taken from human aneurysmal ascending thoracic aortic media specimens with bicuspid aortic valve (BAV) phenotype. Our model reproduces the typical J-shaped constitutive response of the aortic wall tissue. We found that the stress state in the non-fibrous matrix was homogeneous until the collagen fibers were recruited, but became highly heterogeneous after that event. The degree of heterogeneity was dependent upon local network architecture with high stresses observed near disrupted fibers. The magnitude of non-fibrous matrix stress at higher stretch levels was negatively correlated with local fiber density. The localized stress concentrations, elucidated by this model, may be a factor in the degenerative changes in aneurysmal ATA tissue. PMID:27113538

Deep graphs—A general framework to represent and analyze heterogeneous complex systems across scales

NASA Astrophysics Data System (ADS)

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Deep graphs-A general framework to represent and analyze heterogeneous complex systems across scales.

PubMed

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

NASA Astrophysics Data System (ADS)

Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

2004-12-01

Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various large heterogeneous spatial-temporal datasets provide evidence that the benefits of the proposed methodology for efficient and accurate learning exist beyond the area of retrieval of geophysical parameters.

Evolution of network architecture in a granular material under compression

NASA Astrophysics Data System (ADS)

Papadopoulos, Lia; Puckett, James G.; Daniels, Karen E.; Bassett, Danielle S.

2016-09-01

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup.

Is the Internet gaming-addicted brain close to be in a pathological state?

PubMed

Park, Chang-Hyun; Chun, Ji-Won; Cho, Huyn; Jung, Young-Chul; Choi, Jihye; Kim, Dai Jin

2017-01-01

Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks. © 2015 Society for the Study of Addiction.

Detection of core-periphery structure in networks based on 3-tuple motifs

NASA Astrophysics Data System (ADS)

Ma, Chuang; Xiang, Bing-Bing; Chen, Han-Shuang; Small, Michael; Zhang, Hai-Feng

2018-05-01

Detecting mesoscale structure, such as community structure, is of vital importance for analyzing complex networks. Recently, a new mesoscale structure, core-periphery (CP) structure, has been identified in many real-world systems. In this paper, we propose an effective algorithm for detecting CP structure based on a 3-tuple motif. In this algorithm, we first define a 3-tuple motif in terms of the patterns of edges as well as the property of nodes, and then a motif adjacency matrix is constructed based on the 3-tuple motif. Finally, the problem is converted to find a cluster that minimizes the smallest motif conductance. Our algorithm works well in different CP structures: including single or multiple CP structure, and local or global CP structures. Results on the synthetic and the empirical networks validate the high performance of our method.

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

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

Stochastic win-stay-lose-shift strategy with dynamic aspirations in evolutionary social dilemmas

NASA Astrophysics Data System (ADS)

Amaral, Marco A.; Wardil, Lucas; Perc, Matjaž; da Silva, Jafferson K. L.

2016-09-01

In times of plenty expectations rise, just as in times of crisis they fall. This can be mathematically described as a win-stay-lose-shift strategy with dynamic aspiration levels, where individuals aspire to be as wealthy as their average neighbor. Here we investigate this model in the realm of evolutionary social dilemmas on the square lattice and scale-free networks. By using the master equation and Monte Carlo simulations, we find that cooperators coexist with defectors in the whole phase diagram, even at high temptations to defect. We study the microscopic mechanism that is responsible for the striking persistence of cooperative behavior and find that cooperation spreads through second-order neighbors, rather than by means of network reciprocity that dominates in imitation-based models. For the square lattice the master equation can be solved analytically in the large temperature limit of the Fermi function, while for other cases the resulting differential equations must be solved numerically. Either way, we find good qualitative agreement with the Monte Carlo simulation results. Our analysis also reveals that the evolutionary outcomes are to a large degree independent of the network topology, including the number of neighbors that are considered for payoff determination on lattices, which further corroborates the local character of the microscopic dynamics. Unlike large-scale spatial patterns that typically emerge due to network reciprocity, here local checkerboard-like patterns remain virtually unaffected by differences in the macroscopic properties of the interaction network.

A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

Rearrangement moves on rooted phylogenetic networks

PubMed Central

Gambette, Philippe; van Iersel, Leo; Jones, Mark; Scornavacca, Celine

2017-01-01

Phylogenetic tree reconstruction is usually done by local search heuristics that explore the space of the possible tree topologies via simple rearrangements of their structure. Tree rearrangement heuristics have been used in combination with practically all optimization criteria in use, from maximum likelihood and parsimony to distance-based principles, and in a Bayesian context. Their basic components are rearrangement moves that specify all possible ways of generating alternative phylogenies from a given one, and whose fundamental property is to be able to transform, by repeated application, any phylogeny into any other phylogeny. Despite their long tradition in tree-based phylogenetics, very little research has gone into studying similar rearrangement operations for phylogenetic network—that is, phylogenies explicitly representing scenarios that include reticulate events such as hybridization, horizontal gene transfer, population admixture, and recombination. To fill this gap, we propose “horizontal” moves that ensure that every network of a certain complexity can be reached from any other network of the same complexity, and “vertical” moves that ensure reachability between networks of different complexities. When applied to phylogenetic trees, our horizontal moves—named rNNI and rSPR—reduce to the best-known moves on rooted phylogenetic trees, nearest-neighbor interchange and rooted subtree pruning and regrafting. Besides a number of reachability results—separating the contributions of horizontal and vertical moves—we prove that rNNI moves are local versions of rSPR moves, and provide bounds on the sizes of the rNNI neighborhoods. The paper focuses on the most biologically meaningful versions of phylogenetic networks, where edges are oriented and reticulation events clearly identified. Moreover, our rearrangement moves are robust to the fact that networks with higher complexity usually allow a better fit with the data. Our goal is to provide a solid basis for practical phylogenetic network reconstruction. PMID:28763439

Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

PubMed

Grossi, Giuliano

2009-08-01

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph theory.

Tribological Properties of AlSi12-Al2O3 Interpenetrating Composite Layers in Comparison with Unreinforced Matrix Alloy

PubMed Central

Dolata, Anna Janina

2017-01-01

Alumina–Aluminum composites with interpenetrating network structures are a new class of advanced materials with potentially better properties than composites reinforced by particles or fibers. Local casting reinforcement was proposed to take into account problems with the machinability of this type of materials and the shaping of the finished products. The centrifugal infiltration process fabricated composite castings in the form of locally reinforced shafts. The main objective of the research presented in this work was to compare the tribological properties (friction coefficient, wear resistance) of AlSi12/Al2O3 interpenetrating composite layers with unreinforced AlSi12 matrix areas. Profilometric tests enabled both quantitative and qualitative analyses of the wear trace that formed on investigated surfaces. It has been shown that interpenetrating composite layers are characterized by lower and more stable coefficients of friction (μ), as well as higher wear resistance than unreinforced matrix areas. At the present stage, the study confirmed that the tribological properties of the composite layers depend on the spatial structure of the ceramic reinforcement, and primarily the volume and size of alumina foam cells. PMID:28878162

Evaluating the hydraulic and transport properties of peat soil using pore network modeling and X-ray micro computed tomography

NASA Astrophysics Data System (ADS)

Gharedaghloo, Behrad; Price, Jonathan S.; Rezanezhad, Fereidoun; Quinton, William L.

2018-06-01

Micro-scale properties of peat pore space and their influence on hydraulic and transport properties of peat soils have been given little attention so far. Characterizing the variation of these properties in a peat profile can increase our knowledge on the processes controlling contaminant transport through peatlands. As opposed to the common macro-scale (or bulk) representation of groundwater flow and transport processes, a pore network model (PNM) simulates flow and transport processes within individual pores. Here, a pore network modeling code capable of simulating advective and diffusive transport processes through a 3D unstructured pore network was developed; its predictive performance was evaluated by comparing its results to empirical values and to the results of computational fluid dynamics (CFD) simulations. This is the first time that peat pore networks have been extracted from X-ray micro-computed tomography (μCT) images of peat deposits and peat pore characteristics evaluated in a 3D approach. Water flow and solute transport were modeled in the unstructured pore networks mapped directly from μCT images. The modeling results were processed to determine the bulk properties of peat deposits. Results portray the commonly observed decrease in hydraulic conductivity with depth, which was attributed to the reduction of pore radius and increase in pore tortuosity. The increase in pore tortuosity with depth was associated with more decomposed peat soil and decreasing pore coordination number with depth, which extended the flow path of fluid particles. Results also revealed that hydraulic conductivity is isotropic locally, but becomes anisotropic after upscaling to core-scale; this suggests the anisotropy of peat hydraulic conductivity observed in core-scale and field-scale is due to the strong heterogeneity in the vertical dimension that is imposed by the layered structure of peat soils. Transport simulations revealed that for a given solute, the effective diffusion coefficient decreases with depth due to the corresponding increase of diffusional tortuosity. Longitudinal dispersivity of peat also was computed by analyzing advective-dominant transport simulations that showed peat dispersivity is similar to the empirical values reported in the same peat soil; it is not sensitive to soil depth and does not vary much along the soil profile.

SIR model on a dynamical network and the endemic state of an infectious disease

NASA Astrophysics Data System (ADS)

Dottori, M.; Fabricius, G.

2015-09-01

In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.

Synthesis of Road Networks by Data Conflation

DTIC Science & Technology

2014-04-01

Transform requires basic trigonometric properties. Suppose we have a line oriented as shown in Figure 9 then by defining the parameters, ρ, and θ we...location. Rather than searching for the remaining three parameters, the major and minor axes and the orientation angle, the axes ratio is utilized to...axes ratio and orientation angle are searched on a coarse quantization level and then the local maxima are obtained and a finer resolution area is

Network rigidity and properties of SiO2 and GeO2 glasses under pressure.

PubMed

Trachenko, Kostya; Dove, Martin T; Brazhkin, Vadim; El'kin, F S

2004-09-24

We report in situ studies of SiO2 glass under pressure and find that temperature-induced densification takes place in a pressure window. To explain this effect, we study how rigidity of glasses changes under pressure, with rigidity percolation affecting the dynamics of local relaxation events. We link rigidity percolation in glasses to other effects, including a large increase of crystallization temperature and logarithmic relaxation under pressure.

Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics

PubMed Central

Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay

2015-01-01

Intracellular transport is essential for maintaining proper cellular function in most eukaryotic cells, with perturbations in active transport resulting in several types of disease. Efficient delivery of critical cargos to specific locations is accomplished through a combination of passive diffusion and active transport by molecular motors that ballistically move along a network of cytoskeletal filaments. Although motor-based transport is known to be necessary to overcome cytoplasmic crowding and the limited range of diffusion within reasonable timescales, the topological features of the cytoskeletal network that regulate transport efficiency and robustness have not been established. Using a continuum diffusion model, we observed that the time required for cellular transport was minimized when the network was localized near the nucleus. In simulations that explicitly incorporated network spatial architectures, total filament mass was the primary driver of network transit times. However, filament traps that redirect cargo back to the nucleus caused large variations in network transport. Filament polarity was more important than filament orientation in reducing average transit times, and transport properties were optimized in networks with intermediate motor on and off rates. Our results provide important insights into the functional constraints on intracellular transport under which cells have evolved cytoskeletal structures, and have potential applications for enhancing reactions in biomimetic systems through rational transport network design. PMID:26488648

Neural network approach for characterizing structural transformations by X-ray absorption fine structure

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

Timoshenko, Janis; Frenkel, Anatoly I.; Cintins, Arturs

The knowledge of coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use artificial neural network approach to extract the information on the local structure and its in-situ changes directly from the X-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic andmore » austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from body-centered to face-centered cubic arrangement of iron atoms. Furthermore, this method is attractive for a broad range of materials and experimental conditions« less

Elastic properties of compressed emulsions

NASA Astrophysics Data System (ADS)

Jorjadze, Ivane; Brujic, Jasna

2012-02-01

Visualizing the packing of a dense emulsion in 3D as a function of the external pressure allows us to characterize the geometry and the local stress distribution inside this jammed system. We first test the scaling laws of the pressure and average coordination number over two orders of magnitude in density. We find deviations from theoretical exponents due to the non-affine motion of the particles. Second, we observe that the distribution of forces changes from a broad exponential at the jamming point to a narrower Gaussian-like distribution under high compression. Finally, we calculate the density of states from the measured force network in the approximation of a harmonic potential. Close to jamming, the number of low frequency modes is high, while the application of pressure shifts the distribution to higher frequencies, indicative of a rigid network. The confocal images reveal the structural features associated with the low frequency modes, as well as their localization within the packing. These data are then compared with published results from numerical simulations.

Neural network approach for characterizing structural transformations by X-ray absorption fine structure

DOE PAGES

Timoshenko, Janis; Frenkel, Anatoly I.; Cintins, Arturs; ...

2018-05-25

The knowledge of coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use artificial neural network approach to extract the information on the local structure and its in-situ changes directly from the X-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic andmore » austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from body-centered to face-centered cubic arrangement of iron atoms. Furthermore, this method is attractive for a broad range of materials and experimental conditions« less

Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy

NASA Astrophysics Data System (ADS)

Timoshenko, Janis; Anspoks, Andris; Cintins, Arturs; Kuzmin, Alexei; Purans, Juris; Frenkel, Anatoly I.

2018-06-01

The knowledge of the coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use an artificial neural network approach to extract the information on the local structure and its in situ changes directly from the x-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic and austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from a body-centered to a face-centered cubic arrangement of iron atoms. This method is attractive for a broad range of materials and experimental conditions.

Uncertainty Quantification of Hypothesis Testing for the Integrated Knowledge Engine

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

Cuellar, Leticia

2012-05-31

The Integrated Knowledge Engine (IKE) is a tool of Bayesian analysis, based on Bayesian Belief Networks or Bayesian networks for short. A Bayesian network is a graphical model (directed acyclic graph) that allows representing the probabilistic structure of many variables assuming a localized type of dependency called the Markov property. The Markov property in this instance makes any node or random variable to be independent of any non-descendant node given information about its parent. A direct consequence of this property is that it is relatively easy to incorporate new evidence and derive the appropriate consequences, which in general is notmore » an easy or feasible task. Typically we use Bayesian networks as predictive models for a small subset of the variables, either the leave nodes or the root nodes. In IKE, since most applications deal with diagnostics, we are interested in predicting the likelihood of the root nodes given new observations on any of the children nodes. The root nodes represent the various possible outcomes of the analysis, and an important problem is to determine when we have gathered enough evidence to lean toward one of these particular outcomes. This document presents criteria to decide when the evidence gathered is sufficient to draw a particular conclusion or decide in favor of a particular outcome by quantifying the uncertainty in the conclusions that are drawn from the data. The material in this document is organized as follows: Section 2 presents briefly a forensics Bayesian network, and we explore evaluating the information provided by new evidence by looking first at the posterior distribution of the nodes of interest, and then at the corresponding posterior odds ratios. Section 3 presents a third alternative: Bayes Factors. In section 4 we finalize by showing the relation between the posterior odds ratios and Bayes factors and showing examples these cases, and in section 5 we conclude by providing clear guidelines of how to use these for the type of Bayesian networks used in IKE.« less

Brain Network Analysis from High-Resolution EEG Signals

NASA Astrophysics Data System (ADS)

de Vico Fallani, Fabrizio; Babiloni, Fabio

Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an effective methodology improving the comprehension of the complex interactions in the brain.

Hydrogel Actuation by Electric Field Driven Effects

NASA Astrophysics Data System (ADS)

Morales, Daniel Humphrey

Hydrogels are networks of crosslinked, hydrophilic polymers capable of absorbing and releasing large amounts of water while maintaining their structural integrity. Polyelectrolyte hydrogels are a subset of hydrogels that contain ionizable moieties, which render the network sensitive to the pH and the ionic strength of the media and provide mobile counterions, which impart conductivity. These networks are part of a class of "smart" material systems that can sense and adjust their shape in response to the external environment. Hence, the ability to program and modulate hydrogel shape change has great potential for novel biomaterial and soft robotics applications. We utilized electric field driven effects to manipulate the interaction of ions within polyelectrolyte hydrogels in order to induce controlled deformation and patterning. Additionally, electric fields can be used to promote the interactions of separate gel networks, as modular components, and particle assemblies within gel networks to develop new types of soft composite systems. First, we present and analyze a walking gel actuator comprised of cationic and anionic gel legs attached by electric field-promoted polyion complexation. We characterize the electro-osmotic response of the hydrogels as a function of charge density and external salt concentration. The gel walkers achieve unidirectional motion on flat elastomer substrates and exemplify a simple way to move and manipulate soft matter devices in aqueous solutions. An 'ionoprinting' technique is presented with the capability to topographically structure and actuate hydrated gels in two and three dimensions by locally patterning ions induced by electric fields. The bound charges change the local mechanical properties of the gel to induce relief patterns and evoke localized stress, causing rapid folding in air. The ionically patterned hydrogels exhibit programmable temporal and spatial shape transitions which can be tuned by the duration and/or strength of the applied electric field. We extend the use of ionoprinting to develop multi-responsive bilayer gel systems capable of more complex shape transformation. The localized crosslinked regions determine the bending axis as the gel responds to the external environment. The bending can be tuned to reverse direction isothermally by changing the solvent quality or by changing the temperature at a fixed concentration. The multi-responsive behavior is caused by the volume transitions of a non-ionic, thermos-sensitive hydrogel coupled with a superabsorbent ionic hydrogel. Lastly, electric field driven microparticle assembly, using dielectrophoretic (DEP) forces, organized colloidal microparticles within a hydrogel matrix. The use of DEP forces enables rapid, efficient and precise control over the colloidal distribution. The resulting supracolloidal endoskeleton structures impart directional bending as the hydrogel shrinks. We compare the ordered particles structures to random particle distributions in affecting the hydrogel sheet bending response. This study demonstrates a universal technique for imparting directional properties in hydrogels towards new generations of hybrid soft materials.

On Connection Between Topology and Memory Loss in Sheared Granular Materials

NASA Astrophysics Data System (ADS)

Kovalcinova, Lenka; Kramar, Miro; Mischaikow, Konstantin; Kondic, Lou

We present combined results of discrete element simulations and topological data analysis that allows us to characterize the geometrical properties of force networks. Our numerical setup consists of the system of cylindrical particles placed inside rectangular box with periodic boundary conditions along the horizontal direction. System dynamics is driven by constant shearing speed of the top and bottom walls (in the opposite directions) and pressure applied on the top wall in a dense flow regime. Our study reveals the origin of memory loss in granular systems through local rapid changes in force networks. To understand these rapid events we analyze the evolution of the largest Lyapunov exponent in a simpler case of granular system without inter-particle friction and explore a correlation with topological measures. Surprisingly, our results suggest that the memory loss is driven mainly by pressure even in the case of fixed inertial number. We conclude that the interplay between physical properties of the granular system and force network geometry is a key to understand the dynamics of the sheared systems. This research was supported by NSF Grant No. DMS-1521717 and DARPA No. HR0011-16-2-0033.

Transmission of FDDI signals over low-frequency media

NASA Astrophysics Data System (ADS)

Ater, Dan

1992-03-01

The `FDDI' designation is gaining an increasing place in the local area network (LAN) jargon along with the `Ethernet' and `Token Ring.' The four letters stand for fiber-optic distributed data interface, but as in the case of other LANs the meaning of the name looses its importance as the properties of the network become familiar to the community of the users, implementors, and designers. Further, more new properties are added to the original set, sometimes beyond the boundaries hinted by the name. This paper presents the actual stage of an attempt to change the `F' (fiber-optic) to `M' (metal) specifically to unshielded twisted pair (UTP). The sense in doing so is the fact that a huge installed basis of metallic wires for telephone and data transmission already exists, and reaches an immense number of desktops. This paper describes: (1) An hierarchical network architecture emphasizing the segment to be implemented over UTP. (2) A systemic approach to the definition of the parameters for the physical medium dependent (PMD) module that should interface the MDDI (FDDI over metallic media) to the UTP cable plant. (3) Measurement results available at the time of the presentation.

Network analysis of the COSMOS galaxy field

NASA Astrophysics Data System (ADS)

de Regt, R.; Apunevych, S.; von Ferber, C.; Holovatch, Yu; Novosyadlyj, B.

2018-07-01

The galaxy data provided by COSMOS survey for 1°×1° field of sky are analysed by methods of complex networks. Three galaxy samples (slices) with redshifts ranging within intervals 0.88÷0.91, 0.91÷0.94, and 0.94÷0.97 are studied as two-dimensional projections for the spatial distributions of galaxies. We construct networks and calculate network measures for each sample, in order to analyse the network similarity of different samples, distinguish various topological environments, and find associations between galaxy properties (colour index and stellar mass) and their topological environments. Results indicate a high level of similarity between geometry and topology for different galaxy samples and no clear evidence of evolutionary trends in network measures. The distribution of local clustering coefficient C manifests three modes which allow for discrimination between stand-alone singlets and dumbbells (0 ≤ C ≤ 0.1), intermediately packed (0.1 < C < 0.9) and clique (0.9 ≤ C ≤ 1) like galaxies. Analysing astrophysical properties of galaxies (colour index and stellar masses), we show that distributions are similar in all slices, however weak evolutionary trends can also be seen across redshift slices. To specify different topological environments, we have extracted selections of galaxies from each sample according to different modes of C distribution. We have found statistically significant associations between evolutionary parameters of galaxies and selections of C: the distribution of stellar mass for galaxies with interim C differs from the corresponding distributions for stand-alone and clique galaxies, and this difference holds for all redshift slices. The colour index realizes somewhat different behaviour.

Network analysis of the COSMOS galaxy field

NASA Astrophysics Data System (ADS)

de Regt, R.; Apunevych, S.; Ferber, C. von; Holovatch, Yu; Novosyadlyj, B.

2018-03-01

The galaxy data provided by COSMOS survey for 1° × 1° field of sky are analysed by methods of complex networks. Three galaxy samples (slices) with redshifts ranging within intervals 0.88÷0.91, 0.91÷0.94 and 0.94÷0.97 are studied as two-dimensional projections for the spatial distributions of galaxies. We construct networks and calculate network measures for each sample, in order to analyse the network similarity of different samples, distinguish various topological environments, and find associations between galaxy properties (colour index and stellar mass) and their topological environments. Results indicate a high level of similarity between geometry and topology for different galaxy samples and no clear evidence of evolutionary trends in network measures. The distribution of local clustering coefficient C manifests three modes which allow for discrimination between stand-alone singlets and dumbbells (0 ≤ C ≤ 0.1), intermediately packed (0.1 < C < 0.9) and clique (0.9 ≤ C ≤ 1) like galaxies. Analysing astrophysical properties of galaxies (colour index and stellar masses), we show that distributions are similar in all slices, however weak evolutionary trends can also be seen across redshift slices. To specify different topological environments we have extracted selections of galaxies from each sample according to different modes of C distribution. We have found statistically significant associations between evolutionary parameters of galaxies and selections of C: the distribution of stellar mass for galaxies with interim C differ from the corresponding distributions for stand-alone and clique galaxies, and this difference holds for all redshift slices. The colour index realises somewhat different behaviour.

Atypical language laterality is associated with large-scale disruption of network integration in children with intractable focal epilepsy.

PubMed

Ibrahim, George M; Morgan, Benjamin R; Doesburg, Sam M; Taylor, Margot J; Pang, Elizabeth W; Donner, Elizabeth; Go, Cristina Y; Rutka, James T; Snead, O Carter

2015-04-01

Epilepsy is associated with disruption of integration in distributed networks, together with altered localization for functions such as expressive language. The relation between atypical network connectivity and altered localization is unknown. In the current study we tested whether atypical expressive language laterality was associated with the alteration of large-scale network integration in children with medically-intractable localization-related epilepsy (LRE). Twenty-three right-handed children (age range 8-17) with medically-intractable LRE performed a verb generation task in fMRI. Language network activation was identified and the Laterality index (LI) was calculated within the pars triangularis and pars opercularis. Resting-state data from the same cohort were subjected to independent component analysis. Dual regression was used to identify associations between resting-state integration and LI values. Higher positive values of the LI, indicating typical language localization were associated with stronger functional integration of various networks including the default mode network (DMN). The normally symmetric resting-state networks showed a pattern of lateralized connectivity mirroring that of language function. The association between atypical language localization and network integration implies a widespread disruption of neural network development. These findings may inform the interpretation of localization studies by providing novel insights into reorganization of neural networks in epilepsy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Investigation of primordial black hole bursts using interplanetary network gamma-ray bursts

DOE PAGES

Ukwatta, Tilan Niranjan; Hurley, Kevin; MacGibbon, Jane H.; ...

2016-07-25

The detection of a gamma-ray burst (GRB) in the solar neighborhood would have very important implications for GRB phenomenology. The leading theories for cosmological GRBs would not be able to explain such events. The final bursts of evaporating primordial black holes (PBHs), however, would be a natural explanation for local GRBs. We present a novel technique that can constrain the distance to GRBs using detections from widely separated, non-imaging spacecraft. This method can determine the actual distance to the burst if it is local. We applied this method to constrain distances to a sample of 36 short-duration GRBs detected bymore » the Interplanetary Network (IPN) that show observational properties that are expected from PBH evaporations. These bursts have minimum possible distances in the 10 13–10 18 cm (7–10 5 au) range, which are consistent with the expected PBH energetics and with a possible origin in the solar neighborhood, although none of the bursts can be unambiguously demonstrated to be local. Furthermore, assuming that these bursts are real PBH events, we estimate lower limits on the PBH burst evaporation rate in the solar neighborhood.« less

The eGo grid model: An open-source and open-data based synthetic medium-voltage grid model for distribution power supply systems

NASA Astrophysics Data System (ADS)

Amme, J.; Pleßmann, G.; Bühler, J.; Hülk, L.; Kötter, E.; Schwaegerl, P.

2018-02-01

The increasing integration of renewable energy into the electricity supply system creates new challenges for distribution grids. The planning and operation of distribution systems requires appropriate grid models that consider the heterogeneity of existing grids. In this paper, we describe a novel method to generate synthetic medium-voltage (MV) grids, which we applied in our DIstribution Network GeneratOr (DINGO). DINGO is open-source software and uses freely available data. Medium-voltage grid topologies are synthesized based on location and electricity demand in defined demand areas. For this purpose, we use GIS data containing demand areas with high-resolution spatial data on physical properties, land use, energy, and demography. The grid topology is treated as a capacitated vehicle routing problem (CVRP) combined with a local search metaheuristics. We also consider the current planning principles for MV distribution networks, paying special attention to line congestion and voltage limit violations. In the modelling process, we included power flow calculations for validation. The resulting grid model datasets contain 3608 synthetic MV grids in high resolution, covering all of Germany and taking local characteristics into account. We compared the modelled networks with real network data. In terms of number of transformers and total cable length, we conclude that the method presented in this paper generates realistic grids that could be used to implement a cost-optimised electrical energy system.

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

PubMed

Rangan, Aaditya V; Cai, David

2007-02-01

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in [Formula: see text] operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used.

Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model

PubMed Central

Farinella, Matteo; Ruedt, Daniel T.; Gleeson, Padraig; Lanore, Frederic; Silver, R. Angus

2014-01-01

In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such ‘background’ synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5) pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a ‘balanced’ background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales. PMID:24763087

Topological Alterations of the Intrinsic Brain Network in Patients with Functional Dyspepsia.

PubMed

Nan, Jiaofen; Zhang, Li; Zhu, Fubao; Tian, Xiaorui; Zheng, Qian; Deneen, Karen M von; Liu, Jixin; Zhang, Ming

2016-01-31

Previous studies reported that integrated information in the brain ultimately determines the subjective experience of patients with chronic pain, but how the information is integrated in the brain connectome of functional dyspepsia (FD) patients remains largely unclear. The study aimed to quantify the topological changes of the brain network in FD patients. Small-world properties, network efficiency and nodal centrality were utilized to measure the changes in topological architecture in 25 FD patients and 25 healthy controls based on functional magnetic resonance imaging. Pearson's correlation assessed the relationship of each topological property with clinical symptoms. FD patients showed an increase of clustering coefficients and local efficiency relative to controls from the perspective of a whole network as well as elevated nodal centrality in the right orbital part of the inferior frontal gyrus, left anterior cingulate gyrus and left hippocampus, and decreased nodal centrality in the right posterior cingulate gyrus, left cuneus, right putamen, left middle occipital gyrus and right inferior occipital gyrus. Moreover, the centrality in the anterior cingulate gyrus was significantly associated with symptom severity and duration in FD patients. Nevertheless, the inclusion of anxiety and depression scores as covariates erased the group differences in nodal centralities in the orbital part of the inferior frontal gyrus and hippocampus. The results suggest topological disruption of the functional brain networks in FD patients, presumably in response to disturbances of sensory information integrated with emotion, memory, pain modulation, and selective attention in patients.

EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands

PubMed Central

Dai, Zhongxiang; de Souza, Joshua; Lim, Julian; Ho, Paul M.; Chen, Yu; Li, Junhua; Thakor, Nitish; Bezerianos, Anastasios; Sun, Yu

2017-01-01

Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks. PMID:28553215

EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands.

PubMed

Dai, Zhongxiang; de Souza, Joshua; Lim, Julian; Ho, Paul M; Chen, Yu; Li, Junhua; Thakor, Nitish; Bezerianos, Anastasios; Sun, Yu

2017-01-01

Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n -back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.

DTI-based connectome analysis of adolescents with major depressive disorder reveals hypoconnectivity of the right caudate.

PubMed

Tymofiyeva, Olga; Connolly, Colm G; Ho, Tiffany C; Sacchet, Matthew D; Henje Blom, Eva; LeWinn, Kaja Z; Xu, Duan; Yang, Tony T

2017-01-01

Adolescence is a vulnerable period for the onset of major depressive disorder (MDD). While some studies have shown white matter alterations in adolescent MDD, there is still a gap in understanding how the brain is affected at a network level. We compared diffusion tensor imaging (DTI)-based brain networks in a cohort of 57 adolescents with MDD and 41 well-matched healthy controls who completed self-reports of depression symptoms and stressful life events. Using atlas-based brain regions as network nodes and tractography streamline count or mean fractional anisotropy (FA) as edge weights, we examined weighted local and global network properties and performed Network-Based Statistic (NBS) analysis. While there were no significant group differences in the global network properties, the FA-weighted node strength of the right caudate was significantly lower in depressed adolescents and correlated positively with age across both groups. The NBS analysis revealed a cluster of lower FA-based connectivity in depressed subjects centered on the right caudate, including connections to frontal gyri, insula, and anterior cingulate. Within this cluster, the most robust difference between groups was the connection between the right caudate and middle frontal gyrus. This connection showed a significant diagnosis by stress interaction and a negative correlation with total stress in depressed adolescents. Use of DTI-based tractography, one atlas-based parcellation, and FA values to characterize brain networks represent this study's limitations. Our results allowed us to suggest caudate-centric models of dysfunctional processes underlying adolescent depression, which might guide future studies and help better understand and treat this disorder. Copyright © 2016 Elsevier B.V. All rights reserved.

The topology and dynamics of complex networks

NASA Astrophysics Data System (ADS)

Dezso, Zoltan

We start with a brief introduction about the topological properties of real networks. Most real networks are scale-free, being characterized by a power-law degree distribution. The scale-free nature of real networks leads to unexpected properties such as the vanishing epidemic threshold. Traditional methods aiming to reduce the spreading rate of viruses cannot succeed on eradicating the epidemic on a scale-free network. We demonstrate that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate the virus. We find that the more biased a policy is towards the hubs, the more chance it has to bring the epidemic threshold above the virus' spreading rate. We continue by studying a large Web portal as a model system for a rapidly evolving network. We find that the visitation pattern of a news document decays as a power law, in contrast with the exponential prediction provided by simple models of site visitation. This is rooted in the inhomogeneous nature of the browsing pattern characterizing individual users: the time interval between consecutive visits by the same user to the site follows a power law distribution, in contrast with the exponential expected for Poisson processes. We show that the exponent characterizing the individual user's browsing patterns determines the power-law decay in a document's visitation. Finally, we turn our attention to biological networks and demonstrate quantitatively that protein complexes in the yeast, Saccharomyces cerevisiae, are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or non-essential) and share identical functional classification and cellular localization. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.

Signatures of Solvation Thermodynamics in Spectra of Intermolecular Vibrations

PubMed Central

2017-01-01

This study explores the thermodynamic and vibrational properties of water in the three-dimensional environment of solvated ions and small molecules using molecular simulations. The spectrum of intermolecular vibrations in liquid solvents provides detailed information on the shape of the local potential energy surface, which in turn determines local thermodynamic properties such as the entropy. Here, we extract this information using a spatially resolved extension of the two-phase thermodynamics method to estimate hydration water entropies based on the local vibrational density of states (3D-2PT). Combined with an analysis of solute–water and water–water interaction energies, this allows us to resolve local contributions to the solvation enthalpy, entropy, and free energy. We use this approach to study effects of ions on their surrounding water hydrogen bond network, its spectrum of intermolecular vibrations, and resulting thermodynamic properties. In the three-dimensional environment of polar and nonpolar functional groups of molecular solutes, we identify distinct hydration water species and classify them by their characteristic vibrational density of states and molecular entropies. In each case, we are able to assign variations in local hydration water entropies to specific changes in the spectrum of intermolecular vibrations. This provides an important link for the thermodynamic interpretation of vibrational spectra that are accessible to far-infrared absorption and Raman spectroscopy experiments. Our analysis provides unique microscopic details regarding the hydration of hydrophobic and hydrophilic functional groups, which enable us to identify interactions and molecular degrees of freedom that determine relevant contributions to the solvation entropy and consequently the free energy. PMID:28783431

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Increasing social capital via local networks: analysis in the context of a surgical practice.

PubMed

Thakur, Anjani; Yang, Isaac; Lee, Michael Y; Goel, Arpan; Ashok, Ashwin; Fonkalsrud, Eric W

2002-09-01

The relationship between social capital (support, trust, patient awareness, and increased practice revenue) and local networks (university hospital) in communities has received little attention. The development of computer-based communication networks (social networks) has added a new dimension to the argument, posing the question of whether local networks can (re-)create social capital in local communities. This relationship is examined through a review of the literature on local networks and social capital and a surgeon's practice management from 1990 to 2001 with respect to repair of pectus chest deformities. With respect to pectus repair there was a consistent but small number of new referrals (15-20 new patients/year), lack of patient awareness (eight to 12 self-referred patients/year), and modest practice revenue. Since the inception of an Internet website (social network) dedicated to pectus repair in 1996 there has been increased social participation (n = 630 hits/year to the website); facilitation of spread of information through E-mail messages (n = 430 messages/year); and a greater participation of groups such as women, minorities, adults, and those with disability (n = 120 patients/year). The dissemination of information via the local network has also allowed an "outward movement" with increased participation by interconnecting communities (n = 698,300 global Internet participants based on statistical ratios). We conclude that local networks have enhanced social networks providing new grounds for the development of relationships based on choice and shared interest.

Temporal Interactions between Cortical Rhythms

PubMed Central

Roopun, Anita K.; Kramer, Mark A.; Carracedo, Lucy M.; Kaiser, Marcus; Davies, Ceri H.; Traub, Roger D.; Kopell, Nancy J.; Whittington, Miles A.

2008-01-01

Multiple local neuronal circuits support different, discrete frequencies of network rhythm in neocortex. Relationships between different frequencies correspond to mechanisms designed to minimise interference, couple activity via stable phase interactions, and control the amplitude of one frequency relative to the phase of another. These mechanisms are proposed to form a framework for spectral information processing. Individual local circuits can also transform their frequency through changes in intrinsic neuronal properties and interactions with other oscillating microcircuits. Here we discuss a frequency transformation in which activity in two co-active local circuits may combine sequentially to generate a third frequency whose period is the concatenation sum of the original two. With such an interaction, the intrinsic periodicity in each component local circuit is preserved – alternate, single periods of each original rhythm form one period of a new frequency – suggesting a robust mechanism for combining information processed on multiple concurrent spatiotemporal scales. PMID:19225587

Parameterization of Keeling's network generation algorithm.

PubMed

Badham, Jennifer; Abbass, Hussein; Stocker, Rob

2008-09-01

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

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

PubMed

Egorov, Alexei V; Draguhn, Andreas

2013-01-01

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

Clustering Coefficients for Correlation Networks.

PubMed

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

Clustering Coefficients for Correlation Networks

PubMed Central

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

Topology for Dominance for Network of Multi-Agent System

NASA Astrophysics Data System (ADS)

Szeto, K. Y.

2007-05-01

The resource allocation problem in evolving two-dimensional point patterns is investigated for the existence of good strategies for the construction of initial configuration that leads to fast dominance of the pattern by one single species, which can be interpreted as market dominance by a company in the context of multi-agent systems in econophysics. For hexagonal lattice, certain special topological arrangements of the resource in two-dimensions, such as rings, lines and clusters have higher probability of dominance, compared to random pattern. For more complex networks, a systematic way to search for a stable and dominant strategy of resource allocation in the changing environment is found by means of genetic algorithm. Five typical features can be summarized by means of the distribution function for the local neighborhood of friends and enemies as well as the local clustering coefficients: (1) The winner has more triangles than the loser has. (2) The winner likes to form clusters as the winner tends to connect with other winner rather than with losers; while the loser tends to connect with winners rather than losers. (3) The distribution function of friends as well as enemies for the winner is broader than the corresponding distribution function for the loser. (4) The connectivity at which the peak of the distribution of friends for the winner occurs is larger than that of the loser; while the peak values for friends for winners is lower. (5) The connectivity at which the peak of the distribution of enemies for the winner occurs is smaller than that of the loser; while the peak values for enemies for winners is lower. These five features appear to be general, at least in the context of two-dimensional hexagonal lattices of various sizes, hierarchical lattice, Voronoi diagrams, as well as high-dimensional random networks. These general local topological properties of networks are relevant to strategists aiming at dominance in evolving patterns when the interaction between the agents is local.

Abnormally Malicious Autonomous Systems and their Internet Connectivity

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

Shue, Craig A; Kalafut, Prof. Andrew; Gupta, Prof. Minaxi

While many attacks are distributed across botnets, investigators and network operators have recently targeted malicious networks through high profile autonomous system (AS) de-peerings and network shut-downs. In this paper, we explore whether some ASes indeed are safe havens for malicious activity. We look for ISPs and ASes that exhibit disproportionately high malicious behavior using ten popular blacklists, plus local spam data, and extensive DNS resolutions based on the contents of the blacklists. We find that some ASes have over 80% of their routable IP address space blacklisted. Yet others account for large fractions of blacklisted IP addresses. Several ASes regularlymore » peer with ASes associated with significant malicious activity. We also find that malicious ASes as a whole differ from benign ones in other properties not obviously related to their malicious activities, such as more frequent connectivity changes with their BGP peers. Overall, we conclude that examining malicious activity at AS granularity can unearth networks with lax security or those that harbor cybercrime.« less

Visibility graphlet approach to chaotic time series

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

Mutua, Stephen; Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega; Gu, Changgui, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn

Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems.more » Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.« less

Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

PubMed Central

Hurtado, Rafael G.; Floría, Luis Mario

2016-01-01

We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized. PMID:27853531

Macroscopic and microscopic spectral properties of brain networks during local and global synchronization

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir A.; Lüttjohann, Annika; Makarov, Vladimir V.; Goremyko, Mikhail V.; Koronovskii, Alexey A.; Nedaivozov, Vladimir; Runnova, Anastasia E.; van Luijtelaar, Gilles; Hramov, Alexander E.; Boccaletti, Stefano

2017-07-01

We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators, which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal. Our methodology is then applied to getting a glance into the microscopic interactions occurring in a neurophysiological system, namely, in the thalamocortical neural network of an epileptic brain of a rat, where the group electrical activity is registered by means of multichannel EEG. We demonstrate that it is possible to infer the degree of interaction between the interconnected regions of the brain during different types of brain activities and to estimate the regions' participation in the generation of the different levels of consciousness.

Object class segmentation of RGB-D video using recurrent convolutional neural networks.

PubMed

Pavel, Mircea Serban; Schulz, Hannes; Behnke, Sven

2017-04-01

Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Conserved Circular Network of Coregulated Lipids Modulates Innate Immune Responses

PubMed Central

Köberlin, Marielle S.; Snijder, Berend; Heinz, Leonhard X.; Baumann, Christoph L.; Fauster, Astrid; Vladimer, Gregory I.; Gavin, Anne-Claude; Superti-Furga, Giulio

2015-01-01

Summary Lipid composition affects the biophysical properties of membranes that provide a platform for receptor-mediated cellular signaling. To study the regulatory role of membrane lipid composition, we combined genetic perturbations of sphingolipid metabolism with the quantification of diverse steps in Toll-like receptor (TLR) signaling and mass spectrometry-based lipidomics. Membrane lipid composition was broadly affected by these perturbations, revealing a circular network of coregulated sphingolipids and glycerophospholipids. This evolutionarily conserved network architecture simultaneously reflected membrane lipid metabolism, subcellular localization, and adaptation mechanisms. Integration of the diverse TLR-induced inflammatory phenotypes with changes in lipid abundance assigned distinct functional roles to individual lipid species organized across the network. This functional annotation accurately predicted the inflammatory response of cells derived from patients suffering from lipid storage disorders, based solely on their altered membrane lipid composition. The analytical strategy described here empowers the understanding of higher-level organization of membrane lipid function in diverse biological systems. PMID:26095250

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

a Weighted Local-World Evolving Network Model Based on the Edge Weights Preferential Selection

NASA Astrophysics Data System (ADS)

Li, Ping; Zhao, Qingzhen; Wang, Haitang

2013-05-01

In this paper, we use the edge weights preferential attachment mechanism to build a new local-world evolutionary model for weighted networks. It is different from previous papers that the local-world of our model consists of edges instead of nodes. Each time step, we connect a new node to two existing nodes in the local-world through the edge weights preferential selection. Theoretical analysis and numerical simulations show that the scale of the local-world affect on the weight distribution, the strength distribution and the degree distribution. We give the simulations about the clustering coefficient and the dynamics of infectious diseases spreading. The weight dynamics of our network model can portray the structure of realistic networks such as neural network of the nematode C. elegans and Online Social Network.

Single Sublattice Endotaxial Phase Separation Driven by Charge Frustration in a Complex Oxide

PubMed Central

2013-01-01

Complex transition-metal oxides are important functional materials in areas such as energy and information storage. The cubic ABO3 perovskite is an archetypal example of this class, formed by the occupation of small octahedral B-sites within an AO3 network defined by larger A cations. We show that introduction of chemically mismatched octahedral cations into a cubic perovskite oxide parent phase modifies structure and composition beyond the unit cell length scale on the B sublattice alone. This affords an endotaxial nanocomposite of two cubic perovskite phases with distinct properties. These locally B-site cation-ordered and -disordered phases share a single AO3 network and have enhanced stability against the formation of a competing hexagonal structure over the single-phase parent. Synergic integration of the distinct properties of these phases by the coherent interfaces of the composite produces solid oxide fuel cell cathode performance superior to that expected from the component phases in isolation. PMID:23750709

Networking CD-ROMs: The Decision Maker's Guide to Local Area Network Solutions.

ERIC Educational Resources Information Center

Elshami, Ahmed M.

In an era when patrons want access to CD-ROM resources but few libraries can afford to buy multiple copies, CD-ROM local area networks (LANs) are emerging as a cost-effective way to provide shared access. To help librarians make informed decisions, this manual offers information on: (1) the basics of LANs, a "local area network primer";…

Support for School-to-School Networks: How Networking Teachers Perceive Support Activities of a Local Coordinating Agency

ERIC Educational Resources Information Center

Sartory, Katharina; Jungermann, Anja-Kristin; Järvinen, Hanna

2017-01-01

External support by a local coordinating agency facilitates the work of school-to-school networks. This study provides an innovative theoretical framework to analyse how support provided by local education offices for school-to-school networks is perceived by the participating teachers. Based on a quantitative survey and qualitative interview data…

Localization and Spreading of Diseases in Complex Networks

NASA Astrophysics Data System (ADS)

Goltsev, A. V.; Dorogovtsev, S. N.; Oliveira, J. G.; Mendes, J. F. F.

2012-09-01

Using the susceptible-infected-susceptible model on unweighted and weighted networks, we consider the disease localization phenomenon. In contrast to the well-recognized point of view that diseases infect a finite fraction of vertices right above the epidemic threshold, we show that diseases can be localized on a finite number of vertices, where hubs and edges with large weights are centers of localization. Our results follow from the analysis of standard models of networks and empirical data for real-world networks.

Mesonet Programs - Needs and Best Practices

NASA Astrophysics Data System (ADS)

Usher, J.; Doherty, J.

2010-09-01

Authors: Jeremy Usher Managing Director, Europe WeatherBug® Professional John Doherty Senior Vice President Sales & Marketing WeatherBug® Professional There are many well documented and compelling needs for significant improvements in mesoscale meteorological observations throughout many parts of the world. This is evidenced by the fact that the vast majority of severe weather impacts and related life, property and economic losses are associated with mesoscale events such as tornados, thunderstorms, fronts, squall lines, etc. Additionally, the looming impacts of climate change are likely to vary substantially on a regional basis requiring more detailed information on a finer scale. Hence, development of comprehensive densely spaced observing systems can establish the critical information repositories needed to improve: short- and medium-term weather and wind forecasting down to local scales, climate monitoring on a regional basis, as well as decision support capabilities including plume dispersion modeling and air quality forecasting, to name a few. It is imperative that governmental/public/private/academic partnerships are formed to leverage the collective expertise, assets and technological know-how of each sector. Collaboration of this type is particularly germane given that many existing mesonets (weather networks) have been deployed by local organizations with local considerations in mind. These stakeholders maintain the capacity to react quickly and efficiently and are best positioned to recommend future network evolution within their domains. Additionally, coordination will go a long way toward avoiding duplication of effort and promote both a robust private sector and wise expenditure of public funds. This presentation will outline the major building blocks of a mesonet program and discuss best practices for a multi-tiered, multi-faceted "network of networks" approach that maximizes the value derived from leveraging existing assets and serves multiple needs. On-going activities within the U.S. National Mesonet Program will be highlighted.

Brain network reorganization differs in response to stress in rats genetically predisposed to depression and stress-resilient rats.

PubMed

Gass, N; Becker, R; Schwarz, A J; Weber-Fahr, W; Clemm von Hohenberg, C; Vollmayr, B; Sartorius, A

2016-12-06

Treatment-resistant depression (TRD) remains a pressing clinical problem. Optimizing treatment requires better definition of the specificity of the involved brain circuits. The rat strain bred for negative cognitive state (NC) represents a genetic animal model of TRD with high face, construct and predictive validity. Vice versa, the positive cognitive state (PC) strain represents a stress-resilient phenotype. Although NC rats show depressive-like behavior, some symptoms such as anhedonia require an external trigger, i.e. a stressful event, which is similar to humans when stressful event induces a depressive episode in genetically predisposed individuals (gene-environment interaction). We aimed to distinguish neurobiological predisposition from the depressogenic pathology at the level of brain-network reorganization. For this purpose, resting-state functional magnetic resonance imaging time series were acquired at 9.4 Tesla scanner in NC (N=11) and PC (N=7) rats before and after stressful event. We used a graph theory analytical approach to calculate the brain-network global and local properties. There was no difference in the global characteristics between the strains. At the local level, the response in the risk strain was characterized with an increased internodal role and reduced local clustering and efficiency of the anterior cingulate cortex (ACC) and prelimbic cortex compared to the stress-resilient strain. We suggest that the increased internodal role of these prefrontal regions could be due to the enhancement of some of their long-range connections, given their connectivity with the amygdala and other default-mode-like network hubs, which could create a bias to attend to negative information characteristic for depression.

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

PubMed Central

Coman, Alin; Momennejad, Ida; Drach, Rae D.; Geana, Andra

2016-01-01

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals. PMID:27357678

Advanced Algorithms for Local Routing Strategy on Complex Networks

PubMed Central

Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K.; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

2016-01-01

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. PMID:27434502

Advanced Algorithms for Local Routing Strategy on Complex Networks.

PubMed

Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

2016-01-01

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70-90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex

PubMed Central

Procyk, Emmanuel; Dominey, Peter Ford

2016-01-01

Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs, random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function. PMID:27286251