Network rewiring dynamics with convergence towards a star network.
Whigham, P A; Dick, G; Parry, M
2016-10-01
Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz (Nature393, 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.
Measuring the evolutionary rewiring of biological networks.
Directory of Open Access Journals (Sweden)
Chong Shou
Full Text Available We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.
Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile; Liu, Chen
2013-05-01
The effects of time delay and rewiring probability on stochastic resonance and spatiotemporal order in small-world neuronal networks are studied in this paper. Numerical results show that, irrespective of the pacemaker introduced to one single neuron or all neurons of the network, the phenomenon of stochastic resonance occurs. The time delay in the coupling process can either enhance or destroy stochastic resonance on small-world neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of the pacemaker. More importantly, it is found that the small-world topology can significantly affect the stochastic resonance on excitable neuronal networks. For small time delays, increasing the rewiring probability can largely enhance the efficiency of pacemaker-driven stochastic resonance. We argue that the time delay and the rewiring probability both play a key role in determining the ability of the small-world neuronal network to improve the noise-induced outreach of the localized subthreshold pacemaker.
Impact of constrained rewiring on network structure and node dynamics.
Rattana, P; Berthouze, L; Kiss, I Z
2014-11-01
In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.
Efficient rewirings for enhancing synchronizability of dynamical networks
Rad, Ali Ajdari; Jalili, Mahdi; Hasler, Martin
2008-09-01
In this paper, we present an algorithm for optimizing synchronizability of complex dynamical networks. Starting with an undirected and unweighted network, we end up with an undirected and unweighted network with the same number of nodes and edges having enhanced synchronizability. To this end, based on some network properties, rewirings, i.e., eliminating an edge and creating a new edge elsewhere, are performed iteratively avoiding always self-loops and multiple edges between the same nodes. We show that the method is able to enhance the synchronizability of networks of any size and topological properties in a small number of steps that scales with the network size. For numerical simulations, an optimization algorithm based on simulated annealing is used. Also, the evolution of different topological properties of the network such as distribution of node degree, node and edge betweenness centrality is tracked with the iteration steps. We use networks such as scale-free, Strogatz-Watts and random to start with and we show that regardless of the initial network, the final optimized network becomes homogeneous. In other words, in the network with high synchronizability, parameters, such as, degree, shortest distance, node, and edge betweenness centralities are almost homogeneously distributed. Also, parameters, such as, maximum node and edge betweenness centralities are small for the rewired network. Although we take the eigenratio of the Laplacian as the target function for optimization, we show that it is also possible to choose other appropriate target functions exhibiting almost the same performance. Furthermore, we show that even if the network is optimized taking into account another interpretation of synchronizability, i.e., synchronization cost, the optimal network has the same synchronization properties. Indeed, in networks with optimized synchronizability, different interpretations of synchronizability coincide. The optimized networks are Ramanujan graphs
Rewiring of neuronal networks during synaptic silencing.
Wrosch, Jana Katharina; Einem, Vicky von; Breininger, Katharina; Dahlmanns, Marc; Maier, Andreas; Kornhuber, Johannes; Groemer, Teja Wolfgang
2017-09-15
Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity.
Rewiring Chemical Networks Based on Dynamic Dithioacetal and Disulfide Bonds.
Orrillo, A Gastón; La-Venia, Agustina; Escalante, Andrea M; Furlan, Ricardo L E
2018-01-18
The control of the connectivity between nodes of synthetic networks is still largely unexplored. To address this point we take advantage of a simple dynamic chemical system with two exchange levels that are mutually connected and can be activated simultaneously or sequentially. Dithioacetals and disulfides can be exchanged simultaneously under UV light in the presence of a sensitizer. Crossover reactions between both exchange processes produce a fully connected chemical network. On the other hand, the use of acid, base or UV light connects different nodes allowing network rewiring. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Evolutionary rewiring of bacterial regulatory networks
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Tiffany B. Taylor
2015-07-01
Full Text Available Bacteria have evolved complex regulatory networks that enable integration of multiple intracellular and extracellular signals to coordinate responses to environmental changes. However, our knowledge of how regulatory systems function and evolve is still relatively limited. There is often extensive homology between components of different networks, due to past cycles of gene duplication, divergence, and horizontal gene transfer, raising the possibility of cross-talk or redundancy. Consequently, evolutionary resilience is built into gene networks – homology between regulators can potentially allow rapid rescue of lost regulatory function across distant regions of the genome. In our recent study [Taylor, et al. Science (2015, 347(6225] we find that mutations that facilitate cross-talk between pathways can contribute to gene network evolution, but that such mutations come with severe pleiotropic costs. Arising from this work are a number of questions surrounding how this phenomenon occurs.
Coevolution of Cooperation and Partner Rewiring Range in Spatial Social Networks
Khoo, Tommy; Pauls, Scott
2016-01-01
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 clusterin...
Network evolution: rewiring and signatures of conservation in signaling.
Directory of Open Access Journals (Sweden)
Mark G F Sun
Full Text Available The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3 domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.
Ma, Tianle; Zhang, Aidong
2017-07-15
Reconstructing context-specific transcriptional regulatory network is crucial for deciphering principles of regulatory mechanisms underlying various conditions. Recently studies that reconstructed transcriptional networks have focused on individual organisms or cell types and relied on data repositories of context-free regulatory relationships. Here we present a comprehensive framework to systematically derive putative regulator-target pairs in any given context by integrating context-specific transcriptional profiling and public data repositories of gene regulatory networks. Moreover, our framework can identify core regulatory modules and signature genes underlying global regulatory circuitry, and detect network rewiring and core rewired modules in different contexts by considering gene modules and edge (gene interaction) modules collaboratively. We applied our methods to analyzing Autism RNA-seq experiment data and produced biologically meaningful results. In particular, all 11 hub genes in a predicted rewired autistic regulatory subnetwork have been linked to autism based on literature review. The predicted rewired autistic regulatory network may shed some new insight into disease mechanism. Published by Elsevier Inc.
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
Jarman, Nicholas; Trengove, Chris; Steur, Erik; Tyukin, Ivan; van Leeuwen, Cees
2014-12-01
A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.
2014-01-01
Background Plant secondary metabolites are critical to various biological processes. However, the regulations of these metabolites are complex because of regulatory rewiring or crosstalk. To unveil how regulatory behaviors on secondary metabolism reshape biological processes, we constructed and analyzed a dynamic regulatory network of secondary metabolic pathways in Arabidopsis. Results The dynamic regulatory network was constructed through integrating co-expressed gene pairs and regulatory interactions. Regulatory interactions were either predicted by conserved transcription factor binding sites (TFBSs) or proved by experiments. We found that integrating two data (co-expression and predicted regulatory interactions) enhanced the number of highly confident regulatory interactions by over 10% compared with using single data. The dynamic changes of regulatory network systematically manifested regulatory rewiring to explain the mechanism of regulation, such as in terpenoids metabolism, the regulatory crosstalk of RAV1 (AT1G13260) and ATHB1 (AT3G01470) on HMG1 (hydroxymethylglutaryl-CoA reductase, AT1G76490); and regulation of RAV1 on epoxysqualene biosynthesis and sterol biosynthesis. Besides, we investigated regulatory rewiring with expression, network topology and upstream signaling pathways. Regulatory rewiring was revealed by the variability of genes’ expression: pathway genes and transcription factors (TFs) were significantly differentially expressed under different conditions (such as terpenoids biosynthetic genes in tissue experiments and E2F/DP family members in genotype experiments). Both network topology and signaling pathways supported regulatory rewiring. For example, we discovered correlation among the numbers of pathway genes, TFs and network topology: one-gene pathways (such as δ-carotene biosynthesis) were regulated by a fewer TFs, and were not critical to metabolic network because of their low degrees in topology. Upstream signaling pathways of 50
MacLeod, Molly; Genung, Mark A; Ascher, John S; Winfree, Rachael
2016-11-01
Recent studies of mutualistic networks show that interactions between partners change across years. Both biological mechanisms and chance could drive these patterns, but the relative importance of these factors has not been separated. We established a field experiment consisting of 102 monospecific plots of 17 native plant species, from which we collected 6713 specimens of 52 bee species over four years. We used these data and a null model to determine whether bee species' foraging choices varied more or less over time beyond the variation expected by chance. Thus we provide the first quantitative definition of rewiring and fidelity as these terms are used in the literature on interaction networks. All 52 bee species varied in plant partner choice across years, but for 27 species this variation was indistinguishable from random partner choice. Another 11 species showed rewiring, varying more across years than expected by chance, while 14 species showed fidelity, indicating that they both prefer certain plant species and are consistent in those preferences across years. Our study shows that rewiring and fidelity both exist in mutualist networks, but that once sampling effects have been accounted for, they are less common than has been reported in the ecological literature. © 2016 by the Ecological Society of America.
Dynamics of the cell-cycle network under genome-rewiring perturbations.
Katzir, Yair; Elhanati, Yuval; Averbukh, Inna; Braun, Erez
2013-12-01
The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycle's promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein-DNA and protein-protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited 'resources' and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes, without a
Rewiring the network. What helps an innovation to diffuse?
Sznajd-Weron, Katarzyna; Szwabiński, Janusz; Weron, Rafał; Weron, Tomasz
2014-03-01
A fundamental question related to innovation diffusion is how the structure of the social network influences the process. Empirical evidence regarding real-world networks of influence is very limited. On the other hand, agent-based modeling literature reports different, and at times seemingly contradictory, results. In this paper we study innovation diffusion processes for a range of Watts-Strogatz networks in an attempt to shed more light on this problem. Using the so-called Sznajd model as the backbone of opinion dynamics, we find that the published results are in fact consistent and allow us to predict the role of network topology in various situations. In particular, the diffusion of innovation is easier on more regular graphs, i.e. with a higher clustering coefficient. Moreover, in the case of uncertainty—which is particularly high for innovations connected to public health programs or ecological campaigns—a more clustered network will help the diffusion. On the other hand, when social influence is less important (i.e. in the case of perfect information), a shorter path will help the innovation to spread in the society and—as a result—the diffusion will be easiest on a random graph.
Energy Technology Data Exchange (ETDEWEB)
Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)
2015-03-01
Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.
Nonlinear preferential rewiring in fixed-size networks as a diffusion process.
Johnson, Samuel; Torres, Joaquín J; Marro, Joaquín
2009-05-01
We present an evolving network model in which the total numbers of nodes and edges are conserved, but in which edges are continuously rewired according to nonlinear preferential detachment and reattachment. Assuming power-law kernels with exponents alpha and beta , the stationary states which the degree distributions evolve toward exhibit a second-order phase transition-from relatively homogeneous to highly heterogeneous (with the emergence of starlike structures) at alpha=beta . Temporal evolution of the distribution in this critical regime is shown to follow a nonlinear diffusion equation, arriving at either pure or mixed power laws of exponents -alpha and 1-alpha .
Depolarizing γ-aminobutyric acid contributes to glutamatergic network rewiring in epilepsy.
Kourdougli, Nazim; Pellegrino, Christophe; Renko, Juho-Matti; Khirug, Stanislav; Chazal, Geneviève; Kukko-Lukjanov, Tiina-Kaisa; Lauri, Sari E; Gaiarsa, Jean-Luc; Zhou, Liang; Peret, Angélique; Castrén, Eero; Tuominen, Raimo K; Crépel, Valérie; Rivera, Claudio
2017-02-01
Rewiring of excitatory glutamatergic neuronal circuits is a major abnormality in epilepsy. Besides the rewiring of excitatory circuits, an abnormal depolarizing γ-aminobutyric acidergic (GABAergic) drive has been hypothesized to participate in the epileptogenic processes. However, a remaining clinically relevant question is whether early post-status epilepticus (SE) evoked chloride dysregulation is important for the remodeling of aberrant glutamatergic neuronal circuits. Osmotic minipumps were used to infuse intracerebrally a specific inhibitor of depolarizing GABAergic transmission as well as a functionally blocking antibody toward the pan-neurotrophin receptor p75 (p75NTR ). The compounds were infused between 2 and 5 days after pilocarpine-induced SE. Immunohistochemistry for NKCC1, KCC2, and ectopic recurrent mossy fiber (rMF) sprouting as well as telemetric electroencephalographic and electrophysiological recordings were performed at day 5 and 2 months post-SE. Blockade of NKCC1 after SE with the specific inhibitor bumetanide restored NKCC1 and KCC2 expression, normalized chloride homeostasis, and significantly reduced the glutamatergic rMF sprouting within the dentate gyrus. This mechanism partially involves p75NTR signaling, as bumetanide application reduced SE-induced p75NTR expression and functional blockade of p75NTR decreased rMF sprouting. The early transient (3 days) post-SE infusion of bumetanide reduced rMF sprouting and recurrent seizures in the chronic epileptic phase. Our findings show that early post-SE abnormal depolarizing GABA and p75NTR signaling fosters a long-lasting rearrangement of glutamatergic network that contributes to the epileptogenic process. This finding defines promising and novel targets to constrain reactive glutamatergic network rewiring in adult epilepsy. Ann Neurol 2017;81:251-265. © 2017 American Neurological Association.
Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling
DEFF Research Database (Denmark)
Creixell, Pau; Schoof, Erwin M; Simpson, Craig D.
2015-01-01
Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network...... rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified......-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks....
Iorio, Francesco; Bernardo-Faura, Marti; Gobbi, Andrea; Cokelaer, Thomas; Jurman, Giuseppe; Saez-Rodriguez, Julio
2016-12-20
Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment. Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. BiRewire3 it is freely available at https://www.bioconductor.org/packages/BiRewire/ , and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.
Ablation as targeted perturbation to rewire communication network of persistent atrial fibrillation.
Tao, Susumu; Way, Samuel F; Garland, Joshua; Chrispin, Jonathan; Ciuffo, Luisa A; Balouch, Muhammad A; Nazarian, Saman; Spragg, David D; Marine, Joseph E; Berger, Ronald D; Calkins, Hugh; Ashikaga, Hiroshi
2017-01-01
Persistent atrial fibrillation (AF) can be viewed as disintegrated patterns of information transmission by action potential across the communication network consisting of nodes linked by functional connectivity. To test the hypothesis that ablation of persistent AF is associated with improvement in both local and global connectivity within the communication networks, we analyzed multi-electrode basket catheter electrograms of 22 consecutive patients (63.5 ± 9.7 years, 78% male) during persistent AF before and after the focal impulse and rotor modulation-guided ablation. Eight patients (36%) developed recurrence within 6 months after ablation. We defined communication networks of AF by nodes (cardiac tissue adjacent to each electrode) and edges (mutual information between pairs of nodes). To evaluate patient-specific parameters of communication, thresholds of mutual information were applied to preserve 10% to 30% of the strongest edges. There was no significant difference in network parameters between both atria at baseline. Ablation effectively rewired the communication network of persistent AF to improve the overall connectivity. In addition, successful ablation improved local connectivity by increasing the average clustering coefficient, and also improved global connectivity by decreasing the characteristic path length. As a result, successful ablation improved the efficiency and robustness of the communication network by increasing the small-world index. These changes were not observed in patients with AF recurrence. Furthermore, a significant increase in the small-world index after ablation was associated with synchronization of the rhythm by acute AF termination. In conclusion, successful ablation rewires communication networks during persistent AF, making it more robust, efficient, and easier to synchronize. Quantitative analysis of communication networks provides not only a mechanistic insight that AF may be sustained by spatially localized sources and
Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans
Watson, Emma; Olin-Sandoval, Viridiana; Hoy, Michael J; Li, Chi-Hua; Louisse, Timo; Yao, Victoria; Mori, Akihiro; Holdorf, Amy D; Troyanskaya, Olga G; Ralser, Markus; Walhout, Albertha JM
2016-01-01
Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild. DOI: http://dx.doi.org/10.7554/eLife.17670.001 PMID:27383050
Interaction rewiring and the rapid turnover of plant-pollinator networks.
CaraDonna, Paul J; Petry, William K; Brennan, Ross M; Cunningham, James L; Bronstein, Judith L; Waser, Nickolas M; Sanders, Nathan J
2017-03-01
Whether species interactions are static or change over time has wide-reaching ecological and evolutionary consequences. However, species interaction networks are typically constructed from temporally aggregated interaction data, thereby implicitly assuming that interactions are fixed. This approach has advanced our understanding of communities, but it obscures the timescale at which interactions form (or dissolve) and the drivers and consequences of such dynamics. We address this knowledge gap by quantifying the within-season turnover of plant-pollinator interactions from weekly censuses across 3 years in a subalpine ecosystem. Week-to-week turnover of interactions (1) was high, (2) followed a consistent seasonal progression in all years of study and (3) was dominated by interaction rewiring (the reassembly of interactions among species). Simulation models revealed that species' phenologies and relative abundances constrained both total interaction turnover and rewiring. Our findings reveal the diversity of species interactions that may be missed when the temporal dynamics of networks are ignored. © 2017 John Wiley & Sons Ltd/CNRS.
Directory of Open Access Journals (Sweden)
Mardelle Atkins
2013-08-01
Full Text Available Organ development is directed by selector gene networks. Eye development in the fruit fly Drosophila melanogaster is driven by the highly conserved selector gene network referred to as the "retinal determination gene network," composed of approximately 20 factors, whose core comprises twin of eyeless (toy, eyeless (ey, sine oculis (so, dachshund (dac, and eyes absent (eya. These genes encode transcriptional regulators that are each necessary for normal eye development, and sufficient to direct ectopic eye development when misexpressed. While it is well documented that the downstream genes so, eya, and dac are necessary not only during early growth and determination stages but also during the differentiation phase of retinal development, it remains unknown how the retinal determination gene network terminates its functions in determination and begins to promote differentiation. Here, we identify a switch in the regulation of ey by the downstream retinal determination genes, which is essential for the transition from determination to differentiation. We found that central to the transition is a switch from positive regulation of ey transcription to negative regulation and that both types of regulation require so. Our results suggest a model in which the retinal determination gene network is rewired to end the growth and determination stage of eye development and trigger terminal differentiation. We conclude that changes in the regulatory relationships among members of the retinal determination gene network are a driving force for key transitions in retinal development.
DEFF Research Database (Denmark)
Thomsen, Bodil Marie Stavning; Luksch, Manu
2016-01-01
Bidraget kommenterer filmen Dreams Rewired og skaber refleksive spor i det vidt forgrenede film-arkiviske materiale om mere end et århundredes tele- og real-time kommunikation, hvor kropslig bevægelse bl.a gøres til data.......Bidraget kommenterer filmen Dreams Rewired og skaber refleksive spor i det vidt forgrenede film-arkiviske materiale om mere end et århundredes tele- og real-time kommunikation, hvor kropslig bevægelse bl.a gøres til data....
Directory of Open Access Journals (Sweden)
Rohith Srivas
2013-12-01
Full Text Available Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions among 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks.
Yao, Yuangen; Deng, Haiyou; Ma, Chengzhang; Yi, Ming; Ma, Jun
2017-01-01
Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed.
Yang, Mingfeng; Li, Xuefeng; Bu, Chunya; Wang, Hui; Shi, Guanglu; Yang, Xiushan; Hu, Yong; Wang, Xiaoqin
2014-11-01
Pyruvate decarboxylase and alcohol dehydrogenase are efficient enzymes for ethanol production in Zymomonas mobilis. These two enzymes were over-expressed in Escherichia coli, a promising candidate for industrial ethanol production, resulting in high ethanol production in the engineered E. coli. To investigate the intracellular changes to the enzyme overexpression for homoethanol production, 2-DE and LC-MS/MS were performed. More than 1,000 protein spots were reproducibly detected in the gel by image analysis. Compared to the wild-type, 99 protein spots showed significant changes in abundance in the recombinant E. coli, in which 46 were down-regulated and 53 were up-regulated. Most proteins related to tricarboxylic acid cycle, glycerol metabolism and other energy metabolism were up-regulated, whereas proteins involved in glycolysis and glyoxylate pathway were down-regulated, indicating the rewired metabolism in the engineered E. coli. As glycolysis is the main pathway for ethanol production, and it was inhibited significantly in engineered E. coli, further efforts should be directed at minimizing the repression of glycolysis to optimize metabolism network for higher yields of ethanol production.
Xu, Xiaohua; Warrington, Arthur E; Bieber, Allan J; Rodriguez, Moses
2011-07-01
Repair of the central nervous system (CNS) constitutes an integral part of treating neurological disease and plays a crucial role in restoring CNS architecture and function. Distinct strategies have been developed to reconstruct the damaged neural tissue, with many tested preclinically in animal models. We review cell replacement-based repair strategies. By taking spinal cord injury, cerebral ischaemia and degenerative CNS disorders as examples for CNS repair, we discuss progress and potential problems in utilizing embryonic stem cells and adult neural/non-neural stem cells to repair cell loss in the CNS. Nevertheless, CNS repair is not simply a matter of cell transplantation. The major challenge is to induce regenerating neural cells to integrate into the neural network and compensate for damaged neural function. The neural cells confront an environment very different from that of the developmental stage in which these cells differentiate to form interwoven networks. During the repair process, one of the challenges is neurodegeneration, which can develop from interrupted innervations to/from the targets, chronic inflammation, ischaemia, aging or idiopathic neural toxicity. Neurodegeneration, which occurs on the basis of a characteristic vascular and neural web, usually presents as a chronically progressive process with unknown aetiology. Currently, there is no effective treatment to stop or slow down neurodegeneration. Pathological changes from patients with Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis indicate a broken homeostasis in the CNS. We discuss how the blood-brain barrier and neural networks are formed to maintain CNS homeostasis and their contribution to neurodegeneration in diseased conditions. Another challenge is that some inhibitors produced by CNS injury do not facilitate the regenerating neural cells to incorporate into a pre-existing network. We review glial responses to CNS injury. Of note, the reactive astrocytes
Yang, Dong-Hoon; Jung, Kwang-Woo; Bang, Soohyun; Lee, Jang-Won; Song, Min-Hee; Floyd-Averette, Anna; Festa, Richard A; Ianiri, Giuseppe; Idnurm, Alexander; Thiele, Dennis J; Heitman, Joseph; Bahn, Yong-Sun
2017-01-01
Thermotolerance is a crucial virulence attribute for human pathogens, including the fungus Cryptococcus neoformans that causes fatal meningitis in humans. Loss of the protein kinase Sch9 increases C. neoformans thermotolerance, but its regulatory mechanism has remained unknown. Here, we studied the Sch9-dependent and Sch9-independent signaling networks modulating C. neoformans thermotolerance by using genome-wide transcriptome analysis and reverse genetic approaches. During temperature upshift, genes encoding for molecular chaperones and heat shock proteins were upregulated, whereas those for translation, transcription, and sterol biosynthesis were highly suppressed. In this process, Sch9 regulated basal expression levels or induced/repressed expression levels of some temperature-responsive genes, including heat shock transcription factor (HSF1) and heat shock proteins (HSP104 and SSA1). Notably, we found that the HSF1 transcript abundance decreased but the Hsf1 protein became transiently phosphorylated during temperature upshift. Nevertheless, Hsf1 is essential for growth and its overexpression promoted C. neoformans thermotolerance. Transcriptome analysis using an HSF1 overexpressing strain revealed a dual role of Hsf1 in the oxidative stress response and thermotolerance. Chromatin immunoprecipitation demonstrated that Hsf1 binds to the step-type like heat shock element (HSE) of its target genes more efficiently than to the perfect- or gap-type HSE. This study provides insight into the thermotolerance of C. neoformans by elucidating the regulatory mechanisms of Sch9 and Hsf1 through the genome-scale identification of temperature-dependent genes. Copyright © 2017 by the Genetics Society of America.
Malarz, K.; Szvetelszky, Z.; Szekf, B.; Kulakowski, K.
2006-11-01
We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erd{õ}s and Rényi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value pc, for which half of agents are informed, scales with the system size as N-gamma with gamma approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.
Quantifying randomness in real networks
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.
Coevolution of quantum and classical strategies on evolving random networks.
Directory of Open Access Journals (Sweden)
Qiang Li
Full Text Available We study the coevolution of quantum and classical strategies on weighted and directed random networks in the realm of the prisoner's dilemma game. During the evolution, agents can break and rewire their links with the aim of maximizing payoffs, and they can also adjust the weights to indicate preferences, either positive or negative, towards their neighbors. The network structure itself is thus also subject to evolution. Importantly, the directionality of links does not affect the accumulation of payoffs nor the strategy transfers, but serves only to designate the owner of each particular link and with it the right to adjust the link as needed. We show that quantum strategies outperform classical strategies, and that the critical temptation to defect at which cooperative behavior can be maintained rises, if the network structure is updated frequently. Punishing neighbors by reducing the weights of their links also plays an important role in maintaining cooperation under adverse conditions. We find that the self-organization of the initially random network structure, driven by the evolutionary competition between quantum and classical strategies, leads to the spontaneous emergence of small average path length and a large clustering coefficient.
Growing random networks with fitness
Ergun, G.; Rodgers, GJ
2001-01-01
Three models of growing random networks with fitness dependent growth rates are analysed using the rate equations for the distribution of their connectivities. In the first model (A), a network is built by connecting incoming nodes to nodes of connectivity $k$ and random additive fitness $\\eta$, with rate $(k-1)+ \\eta $. For $\\eta >0$ we find the connectivity distribution is power law with exponent $\\gamma=+2$. In the second model (B), the network is built by connecting nodes to nodes of conn...
Asymmetric evolving random networks
Coulomb, S.; Bauer, M.
2003-10-01
We generalize the Poissonian evolving random graph model of M. Bauer and D. Bernard (2003), to deal with arbitrary degree distributions. The motivation comes from biological networks, which are well-known to exhibit non Poissonian degree distributions. A node is added at each time step and is connected to the rest of the graph by oriented edges emerging from older nodes. This leads to a statistical asymmetry between incoming and outgoing edges. The law for the number of new edges at each time step is fixed but arbitrary. Thermodynamical behavior is expected when this law has a large time limit. Although (by construction) the incoming degree distributions depend on this law, this is not the case for most qualitative features concerning the size distribution of connected components, as long as the law has a finite variance. As the variance grows above 1/4, the average being < 1/2, a giant component emerges, which connects a finite fraction of the vertices. Below this threshold, the distribution of component sizes decreases algebraically with a continuously varying exponent. The transition is of infinite order, in sharp contrast with the case of static graphs. The local-in-time profiles for the components of finite size allow to give a refined description of the system.
Sacco, Francesca; Silvestri, Alessandra; Posca, Daniela; Pirrò, Stefano; Gherardini, Pier Federico; Castagnoli, Luisa; Mann, Matthias; Cesareni, Gianni
2016-03-23
Metformin is the most frequently prescribed drug for type 2 diabetes. In addition to its hypoglycemic effects, metformin also lowers cancer incidence. This anti-cancer activity is incompletely understood. Here, we profiled the metformin-dependent changes in the proteome and phosphoproteome of breast cancer cells using high-resolution mass spectrometry. In total, we quantified changes of 7,875 proteins and 15,813 phosphosites after metformin changes. To interpret these datasets, we developed a generally applicable strategy that overlays metformin-dependent changes in the proteome and phosphoproteome onto a literature-derived network. This approach suggested that metformin treatment makes cancer cells more sensitive to apoptotic stimuli and less sensitive to pro-growth stimuli. These hypotheses were tested in vivo; as a proof-of-principle, we demonstrated that metformin inhibits the p70S6K-rpS6 axis in a PP2A-phosphatase dependent manner. In conclusion, analysis of deep proteomics reveals both detailed and global mechanisms that contribute to the anti-cancer activity of metformin. Copyright © 2016 Elsevier Inc. All rights reserved.
Generating random networks and graphs
Coolen, Ton; Roberts, Ekaterina
2017-01-01
This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of rand...
Evolution of random catalytic networks
Energy Technology Data Exchange (ETDEWEB)
Fraser, S.M. [Santa Fe Inst., NM (United States); Reidys, C.M. [Santa Fe Inst., NM (United States)]|[Los Alamos National Lab., NM (United States)
1997-06-01
In this paper the authors investigate the evolution of populations of sequences on a random catalytic network. Sequences are mapped into structures, between which are catalytic interactions that determine their instantaneous fitness. The catalytic network is constructed as a random directed graph. They prove that at certain parameter values, the probability of some relevant subgraphs of this graph, for example cycles without outgoing edges, is maximized. Populations evolving under point mutations realize a comparatively small induced subgraph of the complete catalytic network. They present results which show that populations reliably discover and persist on directed cycles in the catalytic graph, though these may be lost because of stochastic effects, and study the effect of population size on this behavior.
Rescue of endemic states in interconnected networks with adaptive coupling
Vazquez, F; Miguel, M San
2015-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads only if the two layers are interconnected, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network, the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that finite-size effects are amplified by the rewiring, as the...
Organization of growing random networks
Energy Technology Data Exchange (ETDEWEB)
Krapivsky, P. L.; Redner, S.
2001-06-01
The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.
Random Network Coding over Composite Fields
DEFF Research Database (Denmark)
Geil, Olav; Lucani Rötter, Daniel Enrique
2017-01-01
Random network coding is a method that achieves multicast capacity asymptotically for general networks [1, 7]. In this approach, vertices in the network randomly and linearly combine incoming information in a distributed manner before forwarding it through their outgoing edges. To ensure success...
Handbook of Large-Scale Random Networks
Bollobas, Bela; Miklos, Dezso
2008-01-01
Covers various aspects of large-scale networks, including mathematical foundations and rigorous results of random graph theory, modeling and computational aspects of large-scale networks, as well as areas in physics, biology, neuroscience, sociology and technical areas
Importance of randomness in biological networks: A random matrix ...
Indian Academy of Sciences (India)
2015-01-29
Jan 29, 2015 ... We show that in spite of huge differences these interaction networks, representing real-world systems, posses from random matrix models, the spectral properties of the underlying matrices of these networks follow random matrix theory bringing them into the same universality class. We further demonstrate ...
Entropy Characterization of Random Network Models
Directory of Open Access Journals (Sweden)
Pedro J. Zufiria
2017-06-01
Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.
Statistical properties of random clique networks
Ding, Yi-Min; Meng, Jun; Fan, Jing-Fang; Ye, Fang-Fu; Chen, Xiao-Song
2017-10-01
In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erdös and Rényi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.
Firing Correlation in Spiking Neurons with Watts-Strogatz Rewiring
Yamanishi, Teruya; Nishimura, Haruhiko
The brain is organized as neuron assemblies with hierarchies of complex network connectivity. In 1998, Watts and Strogatz conjectured that the structures of most complex networks in the real world have the so-called small-world properties of a small mean path between nodes and a high cluster value, regardless of whether they are artificial networks, such as the Internet, or natural networks, such as the brain. Here we explore the nature of a small-world network of neuron assemblies by simulating the network structural dependence of Izhikevich's spiking neuron model. The synchronized rhythmical firing is estimated in terms of rewiring probabilities, and the structural dependence of the firing correlation coefficient is discussed.
Topological properties of random wireless networks
Indian Academy of Sciences (India)
Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). In addition to their extensive application in the modelling of wireless networks, RGGs ﬁnd many new applications and are being studied in their own right. In this paper we ﬁrst provide a brief introduction to the ...
RMBNToolbox: random models for biochemical networks
Directory of Open Access Journals (Sweden)
Niemi Jari
2007-05-01
Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.
Thermodynamics of random reaction networks.
Directory of Open Access Journals (Sweden)
Jakob Fischer
Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.
Thermodynamics of Random Reaction Networks
Fischer, Jakob; Kleidon, Axel; Dittrich, Peter
2015-01-01
Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa −1.5 for linear and −1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks. PMID:25723751
Random walk centrality for temporal networks
Rocha, Luis E. C.; Masuda, Naoki
2014-06-01
Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.
Cross over of recurrence networks to random graphs and random ...
Indian Academy of Sciences (India)
2017-01-27
Jan 27, 2017 ... analysis based on net theoretic measures has developed into a major field, .... with the value of the scaling index γ falling between 2 and 3. To compute the network measures, we first construct an ensemble of synthetic networks, both random and ... higher k values are present to maintain the same γ. In both.
Modelling complex networks by random hierarchical graphs
Directory of Open Access Journals (Sweden)
M.Wróbel
2008-06-01
Full Text Available Numerous complex networks contain special patterns, called network motifs. These are specific subgraphs, which occur oftener than in randomized networks of Erdős-Rényi type. We choose one of them, the triangle, and build a family of random hierarchical graphs, being Sierpiński gasket-based graphs with random "decorations". We calculate the important characteristics of these graphs - average degree, average shortest path length, small-world graph family characteristics. They depend on probability of decorations. We analyze the Ising model on our graphs and describe its critical properties using a renormalization-group technique.
Exploring complex networks through random walks.
Costa, Luciano da Fontoura; Travieso, Gonzalo
2007-01-01
Most real complex networks--such as protein interactions, social contacts, and the Internet--are only partially known and available to us. While the process of exploring such networks in many cases resembles a random walk, it becomes a key issue to investigate and characterize how effectively the nodes and edges of such networks can be covered by different strategies. At the same time, it is critically important to infer how well can topological measurements such as the average node degree and average clustering coefficient be estimated during such network explorations. The present article addresses these problems by considering random, Barabási-Albert (BA), and geographical network models with varying connectivity explored by three types of random walks: traditional, preferential to untracked edges, and preferential to unvisited nodes. A series of relevant results are obtained, including the fact that networks of the three studied models with the same size and average node degree allow similar node and edge coverage efficiency, the identification of linear scaling with the size of the network of the random walk step at which a given percentage of the nodes/edges is covered, and the critical result that the estimation of the averaged node degree and clustering coefficient by random walks on BA networks often leads to heavily biased results. Many are the theoretical and practical implications of such results.
Exploring biological network structure with clustered random networks
Directory of Open Access Journals (Sweden)
Bansal Shweta
2009-12-01
Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in
Exploring biological network structure with clustered random networks.
Bansal, Shweta; Khandelwal, Shashank; Meyers, Lauren Ancel
2009-12-09
Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural
On the dynamics of random neuronal networks
Robert, Philippe; Touboul, Jonathan D.
2014-01-01
We study the mean-field limit and stationary distributions of a pulse-coupled network modeling the dynamics of a large neuronal assemblies. Our model takes into account explicitly the intrinsic randomness of firing times, contrasting with the classical integrate-and-fire model. The ergodicity properties of the Markov process associated to finite networks are investigated. We derive the limit in distribution of the sample path of the state of a neuron of the network when its size gets large. T...
Directory of Open Access Journals (Sweden)
Aitor de Las Heras
Full Text Available Prokaryotic transcription factors (TFs that bind small xenobiotic molecules (e.g., TFs that drive genes that respond to environmental pollutants often display a promiscuous effector profile for analogs of the bona fide chemical signals. XylR, the master TF for expression of the m-xylene biodegradation operons encoded in the TOL plasmid pWW0 of Pseudomonas putida, responds not only to the aromatic compound but also, albeit to a lesser extent, to many other aromatic compounds, such as 3-methylbenzylalcohol (3MBA. We have examined whether such a relaxed regulatory scenario can be reshaped into a high-capacity/high-specificity regime by changing the connectivity of this effector-sensing TF within the rest of the circuit rather than modifying XylR structure itself. To this end, the natural negative feedback loop that operates on xylR transcription was modified with a translational attenuator that brings down the response to 3MBA while maintaining the transcriptional output induced by m-xylene (as measured with a luxCDABE reporter system. XylR expression was then subject to a positive feedback loop in which the TF was transcribed from its own target promoters, each known to hold different input/output transfer functions. In the first case (xylR under the strong promoter of the upper TOL operon, Pu, the reporter system displayed an increased transcriptional capacity in the resulting network for both the optimal and the suboptimal XylR effectors. In contrast, when xylR was expressed under the weaker Ps promoter, the resulting circuit unmistakably discriminated m-xylene from 3MBA. The non-natural connectivity engineered in the network resulted both in a higher promoter activity and also in a much-increased signal-to-background ratio. These results indicate that the working regimes of given genetic circuits can be dramatically altered through simple changes in the way upstream transcription factors are self-regulated by positive or negative feedback loops.
Directory of Open Access Journals (Sweden)
Jennifer W Israel
2016-03-01
Full Text Available The ecologically significant shift in developmental strategy from planktotrophic (feeding to lecithotrophic (nonfeeding development in the sea urchin genus Heliocidaris is one of the most comprehensively studied life history transitions in any animal. Although the evolution of lecithotrophy involved substantial changes to larval development and morphology, it is not known to what extent changes in gene expression underlie the developmental differences between species, nor do we understand how these changes evolved within the context of the well-defined gene regulatory network (GRN underlying sea urchin development. To address these questions, we used RNA-seq to measure expression dynamics across development in three species: the lecithotroph Heliocidaris erythrogramma, the closely related planktotroph H. tuberculata, and an outgroup planktotroph Lytechinus variegatus. Using well-established statistical methods, we developed a novel framework for identifying, quantifying, and polarizing evolutionary changes in gene expression profiles across the transcriptome and within the GRN. We found that major changes in gene expression profiles were more numerous during the evolution of lecithotrophy than during the persistence of planktotrophy, and that genes with derived expression profiles in the lecithotroph displayed specific characteristics as a group that are consistent with the dramatically altered developmental program in this species. Compared to the transcriptome, changes in gene expression profiles within the GRN were even more pronounced in the lecithotroph. We found evidence for conservation and likely divergence of particular GRN regulatory interactions in the lecithotroph, as well as significant changes in the expression of genes with known roles in larval skeletogenesis. We further use coexpression analysis to identify genes of unknown function that may contribute to both conserved and derived developmental traits between species
Israel, Jennifer W; Martik, Megan L; Byrne, Maria; Raff, Elizabeth C; Raff, Rudolf A; McClay, David R; Wray, Gregory A
2016-03-01
The ecologically significant shift in developmental strategy from planktotrophic (feeding) to lecithotrophic (nonfeeding) development in the sea urchin genus Heliocidaris is one of the most comprehensively studied life history transitions in any animal. Although the evolution of lecithotrophy involved substantial changes to larval development and morphology, it is not known to what extent changes in gene expression underlie the developmental differences between species, nor do we understand how these changes evolved within the context of the well-defined gene regulatory network (GRN) underlying sea urchin development. To address these questions, we used RNA-seq to measure expression dynamics across development in three species: the lecithotroph Heliocidaris erythrogramma, the closely related planktotroph H. tuberculata, and an outgroup planktotroph Lytechinus variegatus. Using well-established statistical methods, we developed a novel framework for identifying, quantifying, and polarizing evolutionary changes in gene expression profiles across the transcriptome and within the GRN. We found that major changes in gene expression profiles were more numerous during the evolution of lecithotrophy than during the persistence of planktotrophy, and that genes with derived expression profiles in the lecithotroph displayed specific characteristics as a group that are consistent with the dramatically altered developmental program in this species. Compared to the transcriptome, changes in gene expression profiles within the GRN were even more pronounced in the lecithotroph. We found evidence for conservation and likely divergence of particular GRN regulatory interactions in the lecithotroph, as well as significant changes in the expression of genes with known roles in larval skeletogenesis. We further use coexpression analysis to identify genes of unknown function that may contribute to both conserved and derived developmental traits between species. Collectively, our results
Bipartite quantum states and random complex networks
Garnerone, Silvano; Giorda, Paolo; Zanardi, Paolo
2012-01-01
We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of random graphs known as complex networks. In the case of classical random graphs, we derive an analytic expression for the averaged entanglement entropy \\bar S while for general complex networks we rely on numerics. For a large number of nodes n we find a scaling \\bar {S} \\sim c log n +g_{ {e}} where both the prefactor c and the sub-leading O(1) term ge are characteristic of the different classes of complex networks. In particular, ge encodes topological features of the graphs and is named network topological entropy. Our results suggest that quantum entanglement may provide a powerful tool for the analysis of large complex networks with non-trivial topological properties.
Dynamic regimes of random fuzzy logic networks
Energy Technology Data Exchange (ETDEWEB)
Wittmann, Dominik M; Theis, Fabian J, E-mail: dominik.wittmann@helmholtz-muenchen.de [Computational Modeling in Biology, Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764 Munich-Neuherberg (Germany); Centre for Mathematical Sciences, Technische Universitaet Muenchen, Boltzmannstrasse 3, 85748 Garching (Germany)
2011-01-15
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Goedel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Goedel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Random walk centrality in interconnected multilayer networks
Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex
2015-01-01
Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influential nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.
Infection dynamics on spatial small-world network models
Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario
2017-11-01
The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
Random graph models for dynamic networks
Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.
2017-10-01
Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.
Rewiring cell signalling through chimaeric regulatory protein engineering.
Wang, Baojun; Barahona, Mauricio; Buck, Martin; Schumacher, Jörg
2013-10-01
Bacterial cells continuously sense and respond to their environment using their inherent signalling and gene regulatory networks. Cells are equipped with parallel signalling pathways, which can specifically cope with individual input signals, while interconnectivities between pathways lead to an enhanced complexity of regulatory responses that enable sophisticated adaptation. In principle, any cell signalling pathway may be rewired to respond to non-cognate signals by exchanging and recombining their underlying cognate signalling components. In the present article, we review the engineering strategies and use of chimaeric regulatory proteins in cell signalling pathways, especially the TCS (two-component signalling) system in bacteria, to achieve novel customized signalling or regulatory functions. We envisage that engineered chimaeric regulatory proteins can play an important role to aid both forward and reverse engineering of biological systems for many desired applications.
Siri, Benoît; Quoy, Mathias; Delord, Bruno; Cessac, Bruno; Berry, Hugues
2007-01-01
The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the "edge of chaos" where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.
Accessibility and delay in random temporal networks
Tajbakhsh, Shahriar Etemadi; Coon, Justin P.; Simmons, David E.
2017-09-01
In a wide range of complex networks, the links between the nodes are temporal and may sporadically appear and disappear. This temporality is fundamental to analyzing the formation of paths within such networks. Moreover, the presence of the links between the nodes is a random process induced by nature in many real-world networks. In this paper, we study random temporal networks at a microscopic level and formulate the probability of accessibility from a node i to a node j after a certain number of discrete time units T . While solving the original problem is computationally intractable, we provide an upper and two lower bounds on this probability for a very general case with arbitrary time-varying probabilities of the links' existence. Moreover, for a special case where the links have identical probabilities across the network at each time slot, we obtain the exact probability of accessibility between any two nodes. Finally, we discuss scenarios where the information regarding the presence and absence of links is initially available in the form of time duration (of presence or absence intervals) continuous probability distributions rather than discrete probabilities over time slots. We provide a method for transforming such distributions to discrete probabilities, which enables us to apply the given bounds in this paper to a broader range of problem settings.
Statistical Inference for Detecting Structures and Anomalies in Networks
2015-08-27
5 DISTRIBUTION A: Distribution approved for public release asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm...from a null model where the network is rewired randomly. However, maximizing the modularity can lead to many competing partitions , which have almost the...same modularity but which have little in common with each other; it can also overfit, producing illusory “communities” in random graphs where none
Features of Random Metal Nanowire Networks with
Maloth, Thirupathi
2017-05-01
Among the alternatives to conventional Indium Tin Oxide (ITO) used in making transparent conducting electrodes, the random metal nanowire (NW) networks are considered to be superior offering performance at par with ITO. The performance is measured in terms of sheet resistance and optical transmittance. However, as the electrical properties of such random networks are achieved thanks to a percolation network, a minimum size of the electrodes is needed so it actually exceeds the representative volume element (RVE) of the material and the macroscopic electrical properties are achieved. There is not much information about the compatibility of this minimum RVE size with the resolution actually needed in electronic devices. Furthermore, the efficiency of NWs in terms of electrical conduction is overlooked. In this work, we address the above industrially relevant questions - 1) The minimum size of electrodes that can be made based on the dimensions of NWs and the material coverage. For this, we propose a morphology based classification in defining the RVE size and we also compare the same with that is based on macroscopic electrical properties stabilization. 2) The amount of NWs that do not participate in electrical conduction, hence of no practical use. The results presented in this thesis are a design guide to experimentalists to design transparent electrodes with more optimal usage of the material.
Epidemic Spreading in Random Rectangular Networks
Estrada, Ernesto; Moreno, Yamir
2015-01-01
Recently, Estrada and Sheerin (Phys. Rev. E 91, 042805 (2015)) developed the random rectangular graph (RRG) model to account for the spatial distribution of nodes in a network allowing the variation of the shape of the unit square commonly used in random geometric graphs (RGGs). Here, we consider an epidemics dynamics taking place on the nodes and edges of an RRG and we derive analytically a lower bound for the epidemic threshold for a Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Recovered (SIR) model on these networks. Using extensive numerical simulations of the SIS dynamics we show that the lower bound found is very tight. We conclude that the elongation of the area in which the nodes are distributed makes the network more resilient to the propagation of an epidemics due to the fact that the epidemic threshold increases with the elongation of the rectangle. On the other hand, using the "classical" RGG for modeling epidemics on non-squared cities generates a larger error due to the effects...
Holographic coherent states from random tensor networks
Qi, Xiao-Liang; Yang, Zhao; You, Yi-Zhuang
2017-08-01
Random tensor networks provide useful models that incorporate various important features of holographic duality. A tensor network is usually defined for a fixed graph geometry specified by the connection of tensors. In this paper, we generalize the random tensor network approach to allow quantum superposition of different spatial geometries. We setup a framework in which all possible bulk spatial geometries, characterized by weighted adjacient matrices of all possible graphs, are mapped to the boundary Hilbert space and form an overcomplete basis of the boundary. We name such an overcomplete basis as holographic coherent states. A generic boundary state can be expanded in this basis, which describes the state as a superposition of different spatial geometries in the bulk. We discuss how to define distinct classical geometries and small fluctuations around them. We show that small fluctuations around classical geometries define "code subspaces" which are mapped to the boundary Hilbert space isometrically with quantum error correction properties. In addition, we also show that the overlap between different geometries is suppressed exponentially as a function of the geometrical difference between the two geometries. The geometrical difference is measured in an area law fashion, which is a manifestation of the holographic nature of the states considered.
Kaiser-Bunbury, Christopher N; Muff, Stefanie; Memmott, Jane; Müller, Christine B; Caflisch, Amedeo
2010-04-01
Species extinctions pose serious threats to the functioning of ecological communities worldwide. We used two qualitative and quantitative pollination networks to simulate extinction patterns following three removal scenarios: random removal and systematic removal of the strongest and weakest interactors. We accounted for pollinator behaviour by including potential links into temporal snapshots (12 consecutive 2-week networks) to reflect mutualists' ability to 'switch' interaction partners (re-wiring). Qualitative data suggested a linear or slower than linear secondary extinction while quantitative data showed sigmoidal decline of plant interaction strength upon removal of the strongest interactor. Temporal snapshots indicated greater stability of re-wired networks over static systems. Tolerance of generalized networks to species extinctions was high in the random removal scenario, with an increase in network stability if species formed new interactions. Anthropogenic disturbance, however, that promote the extinction of the strongest interactors might induce a sudden collapse of pollination networks.
Cellular plasticity enables adaptation to unforeseen cell-cycle rewiring challenges.
Directory of Open Access Journals (Sweden)
Yair Katzir
Full Text Available The fundamental dynamics of the cell cycle, underlying cell growth and reproduction, were previously found to be robust under a wide range of environmental and internal perturbations. This property was commonly attributed to its network structure, which enables the coordinated interactions among hundreds of proteins. Despite significant advances in deciphering the components and autonomous interactions of this network, understanding the interfaces of the cell cycle with other major cellular processes is still lacking. To gain insight into these interfaces, we used the process of genome-rewiring in yeast by placing an essential metabolic gene HIS3 from the histidine biosynthesis pathway, under the exclusive regulation of different cell-cycle promoters. In a medium lacking histidine and under partial inhibition of the HIS3p, the rewired cells encountered an unforeseen multitasking challenge; the cell-cycle regulatory genes were required to regulate the essential histidine-pathway gene in concert with the other metabolic demands, while simultaneously driving the cell cycle through its proper temporal phases. We show here that chemostat cell populations with rewired cell-cycle promoters adapted within a short time to accommodate the inhibition of HIS3p and stabilized a new phenotypic state. Furthermore, a significant fraction of the population was able to adapt and grow into mature colonies on plates under such inhibiting conditions. The adapted state was shown to be stably inherited across generations. These adaptation dynamics were accompanied by a non-specific and irreproducible genome-wide transcriptional response. Adaptation of the cell-cycle attests to its multitasking capabilities and flexible interface with cellular metabolic processes and requirements. Similar adaptation features were found in our previous work when rewiring HIS3 to the GAL system and switching cells from galactose to glucose. Thus, at the basis of cellular plasticity is
Marginalization in Random Nonlinear Neural Networks
Vasudeva Raju, Rajkumar; Pitkow, Xaq
2015-03-01
Computations involved in tasks like causal reasoning in the brain require a type of probabilistic inference known as marginalization. Marginalization corresponds to averaging over irrelevant variables to obtain the probability of the variables of interest. This is a fundamental operation that arises whenever input stimuli depend on several variables, but only some are task-relevant. Animals often exhibit behavior consistent with marginalizing over some variables, but the neural substrate of this computation is unknown. It has been previously shown (Beck et al. 2011) that marginalization can be performed optimally by a deterministic nonlinear network that implements a quadratic interaction of neural activity with divisive normalization. We show that a simpler network can perform essentially the same computation. These Random Nonlinear Networks (RNN) are feedforward networks with one hidden layer, sigmoidal activation functions, and normally-distributed weights connecting the input and hidden layers. We train the output weights connecting the hidden units to an output population, such that the output model accurately represents a desired marginal probability distribution without significant information loss compared to optimal marginalization. Simulations for the case of linear coordinate transformations show that the RNN model has good marginalization performance, except for highly uncertain inputs that have low amplitude population responses. Behavioral experiments, based on these results, could then be used to identify if this model does indeed explain how the brain performs marginalization.
Small Worlds in the Tree Topologies of Wireless Sensor Networks
DEFF Research Database (Denmark)
Qiao, Li; Lingguo, Cui; Baihai, Zhang
2010-01-01
In this study, the characteristics of small worlds are investigated in the context of the tree topologies of wireless sensor networks. Tree topologies, which construct spatial graphs with larger characteristic path lengths than random graphs and small clustering coefficients, are ubiquitous...... in wireless sensor networks. Suffering from the link rewiring or the link addition, the characteristic path length of the tree topology reduces rapidly and the clustering coefficient increases greatly. The variety of characteristic path length influences the time synchronization characteristics of wireless...... sensor networks greatly. With the increase of the link rewiring or the link addition probability, the time synchronization error decreases drastically. Two novel protocols named LEACH-SW and TREEPSI-SW are proposed to improve the performances of the sensor networks, in which the small world...
Application of Random Matrix Theory to Complex Networks
Rai, Aparna; Jalan, Sarika
The present article provides an overview of recent developments in spectral analysis of complex networks under random matrix theory framework. Adjacency matrix of unweighted networks, reviewed here, differ drastically from a random matrix, as former have only binary entries. Remarkably, short range correlations in corresponding eigenvalues of such matrices exhibit Gaussian orthogonal statistics of RMT and thus bring them into the universality class. Spectral rigidity of spectra provides measure of randomness in underlying networks. We will consider several examples of model networks vastly studied in last two decades. To the end we would provide potential of RMT framework and obtained results to understand and predict behavior of complex systems with underlying network structure.
Cross over of recurrence networks to random graphs and random ...
Indian Academy of Sciences (India)
Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability ...
Holographic duality from random tensor networks
Energy Technology Data Exchange (ETDEWEB)
Hayden, Patrick; Nezami, Sepehr; Qi, Xiao-Liang; Thomas, Nathaniel; Walter, Michael; Yang, Zhao [Stanford Institute for Theoretical Physics, Department of Physics, Stanford University,382 Via Pueblo, Stanford, CA 94305 (United States)
2016-11-02
Tensor networks provide a natural framework for exploring holographic duality because they obey entanglement area laws. They have been used to construct explicit toy models realizing many of the interesting structural features of the AdS/CFT correspondence, including the non-uniqueness of bulk operator reconstruction in the boundary theory. In this article, we explore the holographic properties of networks of random tensors. We find that our models naturally incorporate many features that are analogous to those of the AdS/CFT correspondence. When the bond dimension of the tensors is large, we show that the entanglement entropy of all boundary regions, whether connected or not, obey the Ryu-Takayanagi entropy formula, a fact closely related to known properties of the multipartite entanglement of assistance. We also discuss the behavior of Rényi entropies in our models and contrast it with AdS/CFT. Moreover, we find that each boundary region faithfully encodes the physics of the entire bulk entanglement wedge, i.e., the bulk region enclosed by the boundary region and the minimal surface. Our method is to interpret the average over random tensors as the partition function of a classical ferromagnetic Ising model, so that the minimal surfaces of Ryu-Takayanagi appear as domain walls. Upon including the analog of a bulk field, we find that our model reproduces the expected corrections to the Ryu-Takayanagi formula: the bulk minimal surface is displaced and the entropy is augmented by the entanglement of the bulk field. Increasing the entanglement of the bulk field ultimately changes the minimal surface behavior topologically, in a way similar to the effect of creating a black hole. Extrapolating bulk correlation functions to the boundary permits the calculation of the scaling dimensions of boundary operators, which exhibit a large gap between a small number of low-dimension operators and the rest. While we are primarily motivated by the AdS/CFT duality, the main
Scaling solutions for connectivity and conductivity of continuous random networks.
Galindo-Torres, S A; Molebatsi, T; Kong, X-Z; Scheuermann, A; Bringemeier, D; Li, L
2015-10-01
Connectivity and conductivity of two-dimensional fracture networks (FNs), as an important type of continuous random networks, are examined systematically through Monte Carlo simulations under a variety of conditions, including different power law distributions of the fracture lengths and domain sizes. The simulation results are analyzed using analogies of the percolation theory for discrete random networks. With a characteristic length scale and conductivity scale introduced, we show that the connectivity and conductivity of FNs can be well described by universal scaling solutions. These solutions shed light on previous observations of scale-dependent FN behavior and provide a powerful method for quantifying effective bulk properties of continuous random networks.
Randomizing bipartite networks: the case of the World Trade Web
Saracco, Fabio; Gabrielli, Andrea; Squartini, Tiziano
2015-01-01
Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the W...
Phase transitions for information diffusion in random clustered networks
Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin
2016-09-01
We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.
Application of random matrix theory to biological networks
Energy Technology Data Exchange (ETDEWEB)
Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov
2006-09-25
We show that spectral fluctuation of interaction matrices of a yeast protein-protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.
Random matrix analysis for gene interaction networks in cancer cells
Kikkawa, Ayumi
2016-01-01
Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...
Performance of wireless sensor networks under random node failures
Energy Technology Data Exchange (ETDEWEB)
Bradonjic, Milan [Los Alamos National Laboratory; Hagberg, Aric [Los Alamos National Laboratory; Feng, Pan [Los Alamos National Laboratory
2011-01-28
Networks are essential to the function of a modern society and the consequence of damages to a network can be large. Assessing network performance of a damaged network is an important step in network recovery and network design. Connectivity, distance between nodes, and alternative routes are some of the key indicators to network performance. In this paper, random geometric graph (RGG) is used with two types of node failure, uniform failure and localized failure. Since the network performance are multi-facet and assessment can be time constrained, we introduce four measures, which can be computed in polynomial time, to estimate performance of damaged RGG. Simulation experiments are conducted to investigate the deterioration of networks through a period of time. With the empirical results, the performance measures are analyzed and compared to provide understanding of different failure scenarios in a RGG.
Softening in random networks of non-identical beams
Ban, Ehsan; Barocas, Victor H.; Shephard, Mark S.; Picu, R. Catalin
2016-02-01
Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.
Softening in Random Networks of Non-Identical Beams.
Ban, Ehsan; Barocas, Victor H; Shephard, Mark S; Picu, Catalin R
2016-02-01
Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.
Localization transition of biased random walks on random networks.
Sood, Vishal; Grassberger, Peter
2007-08-31
We study random walks on large random graphs that are biased towards a randomly chosen but fixed target node. We show that a critical bias strength bc exists such that most walks find the target within a finite time when b > bc. For b infinity before hitting the target. The phase transition at b=bc is a critical point in the sense that quantities such as the return probability P(t) show power laws, but finite-size behavior is complex and does not obey the usual finite-size scaling ansatz. By extending rigorous results for biased walks on Galton-Watson trees, we give the exact analytical value for bc and verify it by large scale simulations.
Emergence of Scaling in Random Networks
Barabási, Albert-László; Albert, Réka
1999-10-01
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Sequential defense against random and intentional attacks in complex networks
Chen, Pin-Yu; Cheng, Shin-Ming
2015-02-01
Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.
Cascading failures in spatially-embedded random networks.
Asztalos, Andrea; Sreenivasan, Sameet; Szymanski, Boleslaw K; Korniss, Gyorgy
2014-01-01
Cascading failures constitute an important vulnerability of interconnected systems. Here we focus on the study of such failures on networks in which the connectivity of nodes is constrained by geographical distance. Specifically, we use random geometric graphs as representative examples of such spatial networks, and study the properties of cascading failures on them in the presence of distributed flow. The key finding of this study is that the process of cascading failures is non-self-averaging on spatial networks, and thus, aggregate inferences made from analyzing an ensemble of such networks lead to incorrect conclusions when applied to a single network, no matter how large the network is. We demonstrate that this lack of self-averaging disappears with the introduction of a small fraction of long-range links into the network. We simulate the well studied preemptive node removal strategy for cascade mitigation and show that it is largely ineffective in the case of spatial networks. We introduce an altruistic strategy designed to limit the loss of network nodes in the event of a cascade triggering failure and show that it performs better than the preemptive strategy. Finally, we consider a real-world spatial network viz. a European power transmission network and validate that our findings from the study of random geometric graphs are also borne out by simulations of cascading failures on the empirical network.
Decoding Algorithms for Random Linear Network Codes
DEFF Research Database (Denmark)
Heide, Janus; Pedersen, Morten Videbæk; Fitzek, Frank
2011-01-01
We consider the problem of efficient decoding of a random linear code over a finite field. In particular we are interested in the case where the code is random, relatively sparse, and use the binary finite field as an example. The goal is to decode the data using fewer operations to potentially a...
Robustness of Dengue Complex Network under Targeted versus Random Attack
Directory of Open Access Journals (Sweden)
Hafiz Abid Mahmood Malik
2017-01-01
Full Text Available Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO, 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia.
Selectivity and sparseness in randomly connected balanced networks.
Directory of Open Access Journals (Sweden)
Cengiz Pehlevan
Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.
Maximal information transfer and behavior diversity in Random Threshold Networks.
Andrecut, M; Foster, D; Carteret, H; Kauffman, S A
2009-07-01
Random Threshold Networks (RTNs) are an idealized model of diluted, non-symmetric spin glasses, neural networks or gene regulatory networks. RTNs also serve as an interesting general example of any coordinated causal system. Here we study the conditions for maximal information transfer and behavior diversity in RTNs. These conditions are likely to play a major role in physical and biological systems, perhaps serving as important selective traits in biological systems. We show that the pairwise mutual information is maximized in dynamically critical networks. Also, we show that the correlated behavior diversity is maximized for slightly chaotic networks, close to the critical region. Importantly, critical networks maximize coordinated, diverse dynamical behavior across the network and across time: the information transmission between source and receiver nodes and the diversity of dynamical behaviors, when measured with a time delay between the source and receiver, are maximized for critical networks.
Transition to Chaos in Random Neuronal Networks
National Research Council Canada - National Science Library
Jonathan Kadmon; Haim Sompolinsky
2015-01-01
.... Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e...
Sensitivity and network topology in chemical reaction systems
Okada, Takashi; Mochizuki, Atsushi
2017-08-01
In living cells, biochemical reactions are catalyzed by specific enzymes and connect to one another by sharing substrates and products, forming complex networks. In our previous studies, we established a framework determining the responses to enzyme perturbations only from network topology, and then proved a theorem, called the law of localization, explaining response patterns in terms of network topology. In this paper, we generalize these results to reaction networks with conserved concentrations, which allows us to study any reaction system. We also propose network characteristics quantifying robustness. We compare E. coli metabolic network with randomly rewired networks, and find that the robustness of the E. coli network is significantly higher than that of the random networks.
Different pathogenic stimuli induce specific metabolic rewiring in human monocytes
Lachmandas, Ekta; Boutens, Lily; Ratter, Jacqueline; Hijmans, Anneke; Hooiveld, Guido; Crevel, van Reinout; Netea, Mihai; Stienstra, Rinke
2016-01-01
Recent studies have demonstrated that upon encountering a pathogenic stimulus, robust metabolic rewiring of immune cells occurs. A switch away from oxidative phosphorylation to glycolysis, even in the presence of sufficient amounts of oxygen (akin the Warburg effect), is typically observed in
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
DEFF Research Database (Denmark)
Heide, Janus; Zhang, Qi; Fitzek, Frank
2013-01-01
This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...
Efficient sampling of complex network with modified random walk strategies
Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei
2018-02-01
We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.
Random fracture networks: percolation, geometry and flow
Adler, P. M.; Thovert, J. F.; Mourzenko, V. V.
2015-12-01
This paper reviews some of the basic properties of fracture networks. Most of the data can only be derived numerically, and to be useful they need to be rationalized, i.e., a large set of numbers should be replaced by a simple formula which is easy to apply for estimating orders of magnitude. Three major tools are found useful in this rationalization effort. First, analytical results can usually be derived for infinite fractures, a limit which corresponds to large densities. Second, the excluded volume and the dimensionless density prove crucial to gather data obtained at intermediate densities. Finally, shape factors can be used to further reduce the influence of fracture shapes. Percolation of fracture networks is of primary importance since this characteristic controls transport properties such as permeability. Recent numerical studies for various types of fracture networks (isotropic, anisotropic, heterogeneous in space, polydisperse, mixture of shapes) are summarized; the percolation threshold rho is made dimensionless by means of the excluded volume. A general correlation for rho is proposed as a function of the gyration radius. The statistical characteristics of the blocks which are cut in the solid matrix by the network are presented, since they control transfers between the porous matrix and the fractures. Results on quantities such as the volume, surface and number of faces are given and semi empirical relations are proposed. The possible intersection of a percolating network and of a cubic cavity is also summarized. This might be of importance for the underground storage of wastes. An approximate reasoning based on the excluded volume of the percolating cluster and of the cubic cavity is proposed. Finally, consequences on the permeability of fracture networks are briefly addressed. An empirical formula which verifies some theoretical properties is proposed.
Random walks on activity-driven networks with attractiveness
Alessandretti, Laura; Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola
2017-05-01
Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network.
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
Topological properties of random wireless networks
Indian Academy of Sciences (India)
indicating that a physical infrastructure needs to be put in place before nodes can communicate. Ad hoc and sensor ... edges, the communication paths of the wireless network can be represented by a graph. The representation of the ..... Pr (Gn ∈ P) → 1. Another definition of a threshold is from Friedgut & Kalal (1996). For a.
Directory of Open Access Journals (Sweden)
Shuiqing Yu
2013-01-01
Full Text Available This paper investigates the dynamic output feedback control for nonlinear networked control systems with both random packet dropout and random delay. Random packet dropout and random delay are modeled as two independent random variables. An observer-based dynamic output feedback controller is designed based upon the Lyapunov theory. The quantitative relationship of the dropout rate, transition probability matrix, and nonlinear level is derived by solving a set of linear matrix inequalities. Finally, an example is presented to illustrate the effectiveness of the proposed method.
Reconstruction of a random phase dynamics network from observations
Pikovsky, A.
2018-01-01
We consider networks of coupled phase oscillators of different complexity: Kuramoto-Daido-type networks, generalized Winfree networks, and hypernetworks with triple interactions. For these setups an inverse problem of reconstruction of the network connections and of the coupling function from the observations of the phase dynamics is addressed. We show how a reconstruction based on the minimization of the squared error can be implemented in all these cases. Examples include random networks with full disorder both in the connections and in the coupling functions, as well as networks where the coupling functions are taken from experimental data of electrochemical oscillators. The method can be directly applied to asynchronous dynamics of units, while in the case of synchrony, additional phase resettings are necessary for reconstruction.
Coevolutionary network approach to cultural dynamics controlled by intolerance
Gracia-Lázaro, Carlos; Quijandría, Fernando; Hernández, Laura; Floría, Luis Mario; Moreno, Yamir
2011-12-01
Starting from Axelrod's model of cultural dissemination, we introduce a rewiring probability, enabling agents to cut the links with their unfriendly neighbors if their cultural similarity is below a tolerance parameter. For low values of tolerance, rewiring promotes the convergence to a frozen monocultural state. However, intermediate tolerance values prevent rewiring once the network is fragmented, resulting in a multicultural society even for values of initial cultural diversity in which the original Axelrod model reaches globalization.
Exploring community structure in biological networks with random graphs.
Sah, Pratha; Singh, Lisa O; Clauset, Aaron; Bansal, Shweta
2014-06-25
Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.
Listening to the noise: random fluctuations reveal gene network parameters
Energy Technology Data Exchange (ETDEWEB)
Munsky, Brian [Los Alamos National Laboratory; Khammash, Mustafa [UCSB
2009-01-01
The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.
Transcriptional rewiring of the sex determining dmrt1 gene duplicate by transposable elements.
Directory of Open Access Journals (Sweden)
Amaury Herpin
2010-02-01
Full Text Available Control and coordination of eukaryotic gene expression rely on transcriptional and posttranscriptional regulatory networks. Evolutionary innovations and adaptations often require rapid changes of such networks. It has long been hypothesized that transposable elements (TE might contribute to the rewiring of regulatory interactions. More recently it emerged that TEs might bring in ready-to-use transcription factor binding sites to create alterations to the promoters by which they were captured. A process where the gene regulatory architecture is of remarkable plasticity is sex determination. While the more downstream components of the sex determination cascades are evolutionary conserved, the master regulators can switch between groups of organisms even on the interspecies level or between populations. In the medaka fish (Oryzias latipes a duplicated copy of dmrt1, designated dmrt1bY or DMY, on the Y chromosome was shown to be the master regulator of male development, similar to Sry in mammals. We found that the dmrt1bY gene has acquired a new feedback downregulation of its expression. Additionally, the autosomal dmrt1a gene is also able to regulate transcription of its duplicated paralog by binding to a unique target Dmrt1 site nested within the dmrt1bY proximal promoter region. We could trace back this novel regulatory element to a highly conserved sequence within a new type of TE that inserted into the upstream region of dmrt1bY shortly after the duplication event. Our data provide functional evidence for a role of TEs in transcriptional network rewiring for sub- and/or neo-functionalization of duplicated genes. In the particular case of dmrt1bY, this contributed to create new hierarchies of sex-determining genes.
Krebs cycle rewired for macrophage and dendritic cell effector functions.
Ryan, Dylan Gerard; O'Neill, Luke A J
2017-10-01
The Krebs cycle is an amphibolic pathway operating in the mitochondrial matrix of all eukaryotic organisms. In response to proinflammatory stimuli, macrophages and dendritic cells undergo profound metabolic remodelling to support the biosynthetic and bioenergetic requirements of the cell. Recently, it has been discovered that this metabolic shift also involves the rewiring of the Krebs cycle to regulate cellular metabolic flux and the accumulation of Krebs cycle intermediates, notably, citrate, succinate and fumarate. Interestingly, a new role for Krebs cycle intermediates as signalling molecules and immunomodulators that dictate the inflammatory response has begun to emerge. This review will discuss the latest developments in Krebs cycle rewiring and immune cell effector functions, with a particular focus on the regulation of cytokine production. © 2017 Federation of European Biochemical Societies.
Nonparametric resampling of random walks for spectral network clustering
Fallani, Fabrizio De Vico; Nicosia, Vincenzo; Latora, Vito; Chavez, Mario
2014-01-01
Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.
Nonparametric resampling of random walks for spectral network clustering.
De Vico Fallani, Fabrizio; Nicosia, Vincenzo; Latora, Vito; Chavez, Mario
2014-01-01
Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.
Exponential random graph models for networks with community structure.
Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian
2013-09-01
Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.
Maps of random walks on complex networks reveal community structure.
Rosvall, Martin; Bergstrom, Carl T
2008-01-29
To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
On Distributed Computation in Noisy Random Planar Networks
Kanoria, Y.; Manjunath, D.
2007-01-01
We consider distributed computation of functions of distributed data in random planar networks with noisy wireless links. We present a new algorithm for computation of the maximum value which is order optimal in the number of transmissions and computation time.We also adapt the histogram computation algorithm of Ying et al to make the histogram computation time optimal.
Random Access with Physical-layer Network Coding
Goseling, J.; Gastpar, M.C.; Weber, J.H.
2013-01-01
Leveraging recent progress in compute-and-forward we propose an approach to random access that is based on physical-layer network coding: When packets collide, it is possible to recover a linear combination of the packets at the receiver. Over many rounds of transmission, the receiver can thus
Navigation by anomalous random walks on complex networks.
Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan
2016-11-23
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.
Detection of statistically significant network changes in complex biological networks.
Mall, Raghvendra; Cerulo, Luigi; Bensmail, Halima; Iavarone, Antonio; Ceccarelli, Michele
2017-03-04
Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates. We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In
Computer simulation of randomly cross-linked polymer networks
Williams, T P
2002-01-01
In this work, Monte Carlo and Stochastic Dynamics computer simulations of mesoscale model randomly cross-linked networks were undertaken. Task parallel implementations of the lattice Monte Carlo Bond Fluctuation model and Kremer-Grest Stochastic Dynamics bead-spring continuum model were designed and used for this purpose. Lattice and continuum precursor melt systems were prepared and then cross-linked to varying degrees. The resultant networks were used to study structural changes during deformation and relaxation dynamics. The effects of a random network topology featuring a polydisperse distribution of strand lengths and an abundance of pendant chain ends, were qualitatively compared to recent published work. A preliminary investigation into the effects of temperature on the structural and dynamical properties was also undertaken. Structural changes during isotropic swelling and uniaxial deformation, revealed a pronounced non-affine deformation dependant on the degree of cross-linking. Fractal heterogeneiti...
Domínguez-Andrés, Jorge; Arts, Rob J W; Ter Horst, Rob; Gresnigt, Mark S; Smeekens, Sanne P; Ratter, Jacqueline M; Lachmandas, Ekta; Boutens, Lily; van de Veerdonk, Frank L; Joosten, Leo A B; Notebaart, Richard A; Ardavín, Carlos; Netea, Mihai G
2017-09-01
Monocytes are innate immune cells that play a pivotal role in antifungal immunity, but little is known regarding the cellular metabolic events that regulate their function during infection. Using complementary transcriptomic and immunological studies in human primary monocytes, we show that activation of monocytes by Candida albicans yeast and hyphae was accompanied by metabolic rewiring induced through C-type lectin-signaling pathways. We describe that the innate immune responses against Candida yeast are energy-demanding processes that lead to the mobilization of intracellular metabolite pools and require induction of glucose metabolism, oxidative phosphorylation and glutaminolysis, while responses to hyphae primarily rely on glycolysis. Experimental models of systemic candidiasis models validated a central role for glucose metabolism in anti-Candida immunity, as the impairment of glycolysis led to increased susceptibility in mice. Collectively, these data highlight the importance of understanding the complex network of metabolic responses triggered during infections, and unveil new potential targets for therapeutic approaches against fungal diseases.
Random Deep Belief Networks for Recognizing Emotions from Speech Signals.
Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang
2017-01-01
Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
Finite plateau in spectral gap of polychromatic constrained random networks
Avetisov, V.; Gorsky, A.; Nechaev, S.; Valba, O.
2017-12-01
We consider critical behavior in the ensemble of polychromatic Erdős-Rényi networks and regular random graphs, where network vertices are painted in different colors. The links can be randomly removed and added to the network subject to the condition of the vertex degree conservation. In these constrained graphs we run the Metropolis procedure, which favors the connected unicolor triads of nodes. Changing the chemical potential, μ , of such triads, for some wide region of μ , we find the formation of a finite plateau in the number of intercolor links, which exactly matches the finite plateau in the network algebraic connectivity (the value of the first nonvanishing eigenvalue of the Laplacian matrix, λ2). We claim that at the plateau the spontaneously broken Z2 symmetry is restored by the mechanism of modes collectivization in clusters of different colors. The phenomena of a finite plateau formation holds also for polychromatic networks with M ≥2 colors. The behavior of polychromatic networks is analyzed via the spectral properties of their adjacency and Laplacian matrices.
Wave speed in excitable random networks with spatially constrained connections.
Directory of Open Access Journals (Sweden)
Nikita Vladimirov
Full Text Available Very fast oscillations (VFO in neocortex are widely observed before epileptic seizures, and there is growing evidence that they are caused by networks of pyramidal neurons connected by gap junctions between their axons. We are motivated by the spatio-temporal waves of activity recorded using electrocorticography (ECoG, and study the speed of activity propagation through a network of neurons axonally coupled by gap junctions. We simulate wave propagation by excitable cellular automata (CA on random (Erdös-Rényi networks of special type, with spatially constrained connections. From the cellular automaton model, we derive a mean field theory to predict wave propagation. The governing equation resolved by the Fisher-Kolmogorov PDE fails to describe wave speed. A new (hyperbolic PDE is suggested, which provides adequate wave speed v( that saturates with network degree , in agreement with intuitive expectations and CA simulations. We further show that the maximum length of connection is a much better predictor of the wave speed than the mean length. When tested in networks with various degree distributions, wave speeds are found to strongly depend on the ratio of network moments / rather than on mean degree , which is explained by general network theory. The wave speeds are strikingly similar in a diverse set of networks, including regular, Poisson, exponential and power law distributions, supporting our theory for various network topologies. Our results suggest practical predictions for networks of electrically coupled neurons, and our mean field method can be readily applied for a wide class of similar problems, such as spread of epidemics through spatial networks.
Rewiring neural interactions by micro-stimulation
Directory of Open Access Journals (Sweden)
James M Rebesco
2010-08-01
Full Text Available Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. In vitro, associative pairing of presynaptic spiking and stimulus-induced postsynaptic depolarization causes changes in the synaptic efficacy of the presynaptic neuron, when activated by extrinsic stimulation. In vivo, such paradigms can alter the responses of whole groups of neurons to stimulation. Here, we used in vivo spike-triggered stimulation to drive plastic changes in rat forelimb sensorimotor cortex, which we monitored using a statistical measure of functional connectivity inferred from the spiking statistics of the neurons during normal, spontaneous behavior. These induced plastic changes in inferred functional connectivity depended on the latency between trigger spike and stimulation, and appear to reflect a robust reorganization of the network. Such targeted connectivity changes might provide a tool for rerouting the flow of information through a network, with implications for both rehabilitation and brain-machine interface applications.
Navigation by anomalous random walks on complex networks
Weng, Tongfeng; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan
2016-01-01
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Levy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Levy walks and the underlying network structure. Interestingly, applying our framework to the famous PageRank search, we can explain why its damping factor empirically chosen to be around 0.85. The framework for analyzing anomalous random walks on complex networks offers a new us...
Visual Tracking With Convolutional Random Vector Functional Link Network.
Zhang, Le; Suganthan, Ponnuthurai Nagaratnam
2017-10-01
Deep neural network-based methods have recently achieved excellent performance in visual tracking task. As very few training samples are available in visual tracking task, those approaches rely heavily on extremely large auxiliary dataset such as ImageNet to pretrain the model. In order to address the discrepancy between the source domain (the auxiliary data) and the target domain (the object being tracked), they need to be finetuned during the tracking process. However, those methods suffer from sensitivity to the hyper-parameters such as learning rate, maximum number of epochs, size of mini-batch, and so on. Thus, it is worthy to investigate whether pretraining and fine tuning through conventional back-prop is essential for visual tracking. In this paper, we shed light on this line of research by proposing convolutional random vector functional link (CRVFL) neural network, which can be regarded as a marriage of the convolutional neural network and random vector functional link network, to simplify the visual tracking system. The parameters in the convolutional layer are randomly initialized and kept fixed. Only the parameters in the fully connected layer need to be learned. We further propose an elegant approach to update the tracker. In the widely used visual tracking benchmark, without any auxiliary data, a single CRVFL model achieves 79.0% with a threshold of 20 pixels for the precision plot. Moreover, an ensemble of CRVFL yields comparatively the best result of 86.3%.
Delineating social network data anonymization via random edge perturbation
Xue, Mingqiang
2012-01-01
Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.
Probing the Extent of Randomness in Protein Interaction Networks
2008-07-11
scale-free networks are born equal: the role of the seed graph in PPI network evolution. PLoS Comput Biol 3: e118. doi:10.1371/journal.pcbi.0030118. 57...from seeds [56]. In the degree-conserving degree-weighted (DCDW) model, each node is considered once, in a random order, and a set number of edges are...gene duplication in fungi . Nature 449: 54–61. 63. Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW (2002) Evolutionary rate in the protein
A Random Dot Product Model for Weighted Networks
DeFord, Daryl R
2016-01-01
This paper presents a generalization of the random dot product model for networks whose edge weights are drawn from a parametrized probability distribution. We focus on the case of integer weight edges and show that many previously studied models can be recovered as special cases of this generalization. Our model also determines a dimension--reducing embedding process that gives geometric interpretations of community structure and centrality. The dimension of the embedding has consequences for the derived community structure and we exhibit a stress function for determining appropriate dimensions. We use this approach to analyze a coauthorship network and voting data from the U.S. Senate.
Co-evolutionnary network approach to cultural dynamics controlled by intolerance
Gracia-Lázaro, Carlos; Hernández, Laura; Floría, Luis Mario; Moreno, Yamir
2011-01-01
Starting from Axelrod's model of cultural dissemination, we introduce a rewiring probability, enabling agents to cut the links with their unfriendly neighbors if their cultural similarity is below a tolerance parameter. For low values of tolerance, rewiring promotes the convergence to a frozen monocultural state. However, intermediate tolerance values prevent rewiring once the network is fragmented, resulting in a multicultural society even for values of initial cultural diversity in which the original Axelrod model reaches globalization.
Neuropsychological Definition of Learning: Strategies for Rewiring Neural Networks
Barwegen, Laura
2008-01-01
For many years, most scientists believed that the physical structure of our brains, and by definition the people we had become, was set after the initial developmental period of early childhood and adolescence. New research in the area of neurology and neuropsychology is revealing that our brain is a much more open system than ever thought…
Design and implementation of a random neural network routing engine.
Kocak, T; Seeber, J; Terzioglu, H
2003-01-01
Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n/sup 2/ to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.
Randomly evolving idiotypic networks: modular mean field theory.
Schmidtchen, Holger; Behn, Ulrich
2012-07-01
We develop a modular mean field theory for a minimalistic model of the idiotypic network. The model comprises the random influx of new idiotypes and a deterministic selection. It describes the evolution of the idiotypic network towards complex modular architectures, the building principles of which are known. The nodes of the network can be classified into groups of nodes, the modules, which share statistical properties. Each node experiences only the mean influence of the groups to which it is linked. Given the size of the groups and linking between them the statistical properties such as mean occupation, mean lifetime, and mean number of occupied neighbors are calculated for a variety of patterns and compared with simulations. For a pattern which consists of pairs of occupied nodes correlations are taken into account.
Peer-Assisted Content Distribution with Random Linear Network Coding
DEFF Research Database (Denmark)
Hundebøll, Martin; Ledet-Pedersen, Jeppe; Sluyterman, Georg
2014-01-01
Peer-to-peer networks constitute a widely used, cost-effective and scalable technology to distribute bandwidth-intensive content. The technology forms a great platform to build distributed cloud storage without the need of a central provider. However, the majority of todays peer-to-peer systems...... require complex algorithms to schedule what parts of obtained content to forward to other peers. Random Linear Network Coding can greatly simplify these algorithm by removing the need for coordination between the distributing nodes. In this paper we propose and evaluate the structure of the BRONCO peer-to-peer....... Furthermore, we evaluate the performance of different parameters and suggest a suitable trade-off between CPU utilization and network overhead. Within the limitations of the used test environment, we have shown that networkc coding is usable in peer-assisted content distribution and we suggest further...
Passive random walkers and riverlike networks on growing surfaces.
Chin, Chen-Shan
2002-08-01
Passive random walker dynamics is introduced on a growing surface. The walker is designed to drift upward or downward and then follow specific topological features, such as hill tops or valley bottoms, of the fluctuating surface. The passive random walker can thus be used to directly explore scaling properties of otherwise somewhat hidden topological features. For example, the walker allows us to directly measure the dynamical exponent of the underlying growth dynamics. We use the Kardar-Parisi-Zhang (KPZ) -type surface growth as an example. The world lines of a set of merging passive walkers show nontrivial coalescence behaviors and display the riverlike network structures of surface ridges in space-time. In other dynamics, such as Edwards-Wilkinson growth, this does not happen. The passive random walkers in KPZ-type surface growth are closely related to the shock waves in the noiseless Burgers equation. We also briefly discuss their relations to the passive scalar dynamics in turbulence.
Robustness and information propagation in attractors of Random Boolean Networks.
Lloyd-Price, Jason; Gupta, Abhishekh; Ribeiro, Andre S
2012-01-01
Attractors represent the long-term behaviors of Random Boolean Networks. We study how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information (I(A)), relates to the robustness of the attractor to perturbations (R(A)). We find that the dynamical regime of the network affects the relationship between I(A) and R(A). In the ordered and chaotic regimes, I(A) is anti-correlated with R(A), implying that attractors that are highly robust to perturbations have necessarily limited information propagation. Between order and chaos (for so-called "critical" networks) these quantities are uncorrelated. Finite size effects cause this behavior to be visible for a range of networks, from having a sensitivity of 1 to the point where I(A) is maximized. In this region, the two quantities are weakly correlated and attractors can be almost arbitrarily robust to perturbations without restricting the propagation of information in the network.
Mechanical Behavior of Homogeneous and Composite Random Fiber Networks
Shahsavari, Ali
Random fiber networks are present in many biological and non-biological materials such as paper, cytoskeleton, and tissue scaffolds. Mechanical behavior of networks is controlled by the mechanical properties of the constituent fibers and the architecture of the network. To characterize these two main factors, different parameters such as fiber density, fiber length, average segment length, nature of the cross-links at the fiber intersections, ratio of bending to axial behavior of fibers have been considered. Random fiber networks are usually modeled by representing each fiber as a Timoshenko or an Euler-Bernoulli beam and each cross-link as either a welded or rotating joint. In this dissertation, the effect of these modeling options on the dependence of the overall linear network modulus on microstructural parameters is studied. It is concluded that Timoshenko beams can be used for the whole range of density and fiber stiffness parameters, while the Euler-Bernoulli model can be used only at relatively low densities. In the low density-low bending stiffness range, elastic strain energy is stored in the bending mode of the deformation, while in the other extreme range of parameters, the energy is stored predominantly in the axial and shear deformation modes. It is shown that both rotating and welded joint models give the same rules for scaling of the network modulus with different micromechanical parameters. The elastic modulus of sparsely cross-linked random fiber networks, i.e. networks in which the degree of cross-linking varies, is studied. The relationship between the micromechanical parameters - fiber density, fiber axial and bending stiffness, and degree of cross-linking - and the overall elastic modulus is presented in terms of a master curve. It is shown that the master plot with various degrees of cross-linking can be collapsed to a curve which is also valid for fully cross-linked networks. Random fiber networks in which fibers are bonded to each other are
Optimized evolution of networks for principal eigenvector localization
Pradhan, Priodyuti; Yadav, Alok; Dwivedi, Sanjiv K.; Jalan, Sarika
2017-08-01
Network science is increasingly being developed to get new insights about behavior and properties of complex systems represented in terms of nodes and interactions. One useful approach is investigating the localization properties of eigenvectors having diverse applications including disease-spreading phenomena in underlying networks. In this work, we evolve an initial random network with an edge rewiring optimization technique considering the inverse participation ratio as a fitness function. The evolution process yields a network having a localized principal eigenvector. We analyze various properties of the optimized networks and those obtained at the intermediate stage. Our investigations reveal the existence of a few special structural features of such optimized networks, for instance, the presence of a set of edges which are necessary for localization, and rewiring only one of them leads to complete delocalization of the principal eigenvector. Furthermore, we report that principal eigenvector localization is not a consequence of changes in a single network property and, preferably, requires the collective influence of various distinct structural as well as spectral features.
DNA adenine hypomethylation leads to metabolic rewiring in Deinococcus radiodurans.
Shaiwale, Nayana S; Basu, Bhakti; Deobagkar, Deepti D; Deobagkar, Dileep N; Apte, Shree K
2015-08-03
The protein encoded by DR_0643 gene from Deinococcus radiodurans was shown to be an active N-6 adenine-specific DNA methyltransferase (Dam). Deletion of corresponding protein reduced adenine methylation in the genome by 60% and resulted in slow-growth phenotype. Proteomic changes induced by DNA adenine hypomethylation were mapped by two-dimensional protein electrophoresis coupled with mass spectrometry. As compared to wild type D. radiodurans cells, at least 54 proteins were differentially expressed in Δdam mutant. Among these, 39 metabolic enzymes were differentially expressed in Δdam mutant. The most prominent change was DNA adenine hypomethylation induced de-repression of pyruvate dehydrogenase complex, E1 component (aceE) gene resulting in 10 fold increase in the abundance of corresponding protein. The observed differential expression profile of metabolic enzymes included increased abundance of enzymes involved in fatty acid and amino acid degradation to replenish acetyl Co-A and TCA cycle intermediates and diversion of phosphoenolpyruvate and pyruvate into amino acid biosynthesis, a metabolic rewiring attempt by Δdam mutant to restore energy generation via glycolysis-TCA cycle axis. This is the first report of DNA adenine hypomethylation mediated rewiring of metabolic pathways in prokaryotes. Copyright © 2015 Elsevier B.V. All rights reserved.
Rewiring protein synthesis: From natural to synthetic amino acids.
Fan, Yongqiang; Evans, Christopher R; Ling, Jiqiang
2017-11-01
The protein synthesis machinery uses 22 natural amino acids as building blocks that faithfully decode the genetic information. Such fidelity is controlled at multiple steps and can be compromised in nature and in the laboratory to rewire protein synthesis with natural and synthetic amino acids. This review summarizes the major quality control mechanisms during protein synthesis, including aminoacyl-tRNA synthetases, elongation factors, and the ribosome. We will discuss evolution and engineering of such components that allow incorporation of natural and synthetic amino acids at positions that deviate from the standard genetic code. The protein synthesis machinery is highly selective, yet not fixed, for the correct amino acids that match the mRNA codons. Ambiguous translation of a codon with multiple amino acids or complete reassignment of a codon with a synthetic amino acid diversifies the proteome. Expanding the genetic code with synthetic amino acids through rewiring protein synthesis has broad applications in synthetic biology and chemical biology. Biochemical, structural, and genetic studies of the translational quality control mechanisms are not only crucial to understand the physiological role of translational fidelity and evolution of the genetic code, but also enable us to better design biological parts to expand the proteomes of synthetic organisms. This article is part of a Special Issue entitled "Biochemistry of Synthetic Biology - Recent Developments" Guest Editor: Dr. Ilka Heinemann and Dr. Patrick O'Donoghue. Copyright © 2017 Elsevier B.V. All rights reserved.
Functional brain networks: random, "small world" or deterministic?
Blinowska, Katarzyna J; Kaminski, Maciej
2013-01-01
Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.
Functional brain networks: random, "small world" or deterministic?
Directory of Open Access Journals (Sweden)
Katarzyna J Blinowska
Full Text Available Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.
Predicting genetic interactions with random walks on biological networks
Directory of Open Access Journals (Sweden)
Singh Ambuj K
2009-01-01
Full Text Available Abstract Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree
First Passage Time for Random Walks in Heterogeneous Networks
Hwang, S.; Lee, D.-S.; Kahng, B.
2012-08-01
The first passage time (FPT) for random walks is a key indicator of how fast information diffuses in a given system. Despite the role of FPT as a fundamental feature in transport phenomena, its behavior, particularly in heterogeneous networks, is not yet fully understood. Here, we study, both analytically and numerically, the scaling behavior of the FPT distribution to a given target node, averaged over all starting nodes. We find that random walks arrive quickly at a local hub, and therefore, the FPT distribution shows a crossover with respect to time from fast decay behavior (induced from the attractive effect to the hub) to slow decay behavior (caused by the exploring of the entire system). Moreover, the mean FPT is independent of the degree of the target node in the case of compact exploration. These theoretical results justify the necessity of using a random jump protocol (empirically used in search engines) and provide guidelines for designing an effective network to make information quickly accessible.
Random Deep Belief Networks for Recognizing Emotions from Speech Signals
Directory of Open Access Journals (Sweden)
Guihua Wen
2017-01-01
Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
Exploring MEDLINE space with random indexing and pathfinder networks.
Cohen, Trevor
2008-11-06
The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search.
Comparison of Synchronization in Small World and Random Networks
Bernard, Tess; Miller, Bruce
2008-10-01
There are many models that simulate neuron firing in the brain. These range from the basic integrate-and-fire method to the complex Hodgkin-Huxley model. Eugene Izhikevich (2003) employed the principles of nonlinear dynamics, specifically bifurcation theory, to develop a model that is both simple and powerful, which can be described as an integrate-and-reset model. By changing only a few parameters, this model can simulate all the known types of cortical neuron firing patterns. Using it, we studied the properties of two different types of neural networks. In the first, originally used by Izhikevich, the synaptic connection strengths between the neurons are determined randomly, and each neuron is connected to all of the other neurons in the network. The second is a small world network modeled after the one employed by Alex Roxin, et al. (2004), but expanded to include inhibition. This geometry is an idealized representation of the nervous system. In our investigation we compared the onset of synchronization in each network, as well as its stability in the presence of external currents. We also considered the relevance of these results to real world phenomena such as seizures.
Characterizing the Path Coverage of Random Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Moslem Noori
2010-01-01
Full Text Available Wireless sensor networks are widely used in security monitoring applications to sense and report specific activities in a field. In path coverage, for example, the network is in charge of monitoring a path and discovering any intruder trying to cross it. In this paper, we investigate the path coverage properties of a randomly deployed wireless sensor network when the number of sensors and also the length of the path are finite. As a consequence, Boolean model, which has been widely used previously, is not applicable. Using results from geometric probability, we determine the probability of full path coverage, distribution of the number of uncovered gaps over the path, and the probability of having no uncovered gaps larger than a specific size. We also find the cumulative distribution function (cdf of the covered part of the path. Based on our results on the probability of full path coverage, we derive a tight upper bound for the number of nodes guaranteeing the full path coverage with a desired reliability. Through computer simulations, it is verified that for networks with nonasymptotic size, our analysis is accurate where the Boolean model can be inaccurate.
Network Randomization and Dynamic Defense for Critical Infrastructure Systems
Energy Technology Data Exchange (ETDEWEB)
Chavez, Adrian R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Martin, Mitchell Tyler [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hamlet, Jason [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stout, William M.S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lee, Erik [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-04-01
Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.
Long-Range Navigation on Complex Networks using L\\'evy Random Walks
Riascos, A. P.; Mateos, José L.
2012-01-01
We introduce a strategy of navigation in undirected networks, including regular, random, and complex networks, that is inspired by L\\'evy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time, and the average time to reach a node on the network. We found that the long-range navigation using the L\\'evy random walk strategy, compared with the normal random walk stra...
Random linear network coding for streams with unequally sized packets
DEFF Research Database (Denmark)
Taghouti, Maroua; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk
2016-01-01
State of the art Random Linear Network Coding (RLNC) schemes assume that data streams generate packets with equal sizes. This is an assumption that results in the highest efficiency gains for RLNC. A typical solution for managing unequal packet sizes is to zero-pad the smallest packets. However...... of packets, which are strategies that require additional signalling. Performance is evaluated using CAIDA TCP packets and 4k video traces. Our results show that our mechanisms reduce significantly the padding overhead even for small field sizes. Finally, our strategies provide a natural trade-off between...
Deep recurrent conditional random field network for protein secondary prediction
DEFF Research Database (Denmark)
Johansen, Alexander Rosenberg; Sønderby, Søren Kaae; Sønderby, Casper Kaae
2017-01-01
Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile. Building upon these results, we propose to combine a bi-directional recurrent neural network (biRNN) with a conditional random field (CRF), which...... of the labels for all time-steps. We condition the CRF on the output of biRNN, which learns a distributed representation based on the entire sequence. The biRNN-CRF is therefore close to ideally suited for the secondary structure task because a high degree of cross-talk between neighboring elements can...
Measuring symmetry, asymmetry and randomness in neural network connectivity.
Directory of Open Access Journals (Sweden)
Umberto Esposito
Full Text Available Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.
Patel, Tapan P; Ventre, Scott C; Geddes-Klein, Donna; Singh, Pallab K; Meaney, David F
2014-03-19
Alterations in the activity of neural circuits are a common consequence of traumatic brain injury (TBI), but the relationship between single-neuron properties and the aggregate network behavior is not well understood. We recently reported that the GluN2B-containing NMDA receptors (NMDARs) are key in mediating mechanical forces during TBI, and that TBI produces a complex change in the functional connectivity of neuronal networks. Here, we evaluated whether cell-to-cell heterogeneity in the connectivity and aggregate contribution of GluN2B receptors to [Ca(2+)]i before injury influenced the functional rewiring, spontaneous activity, and network plasticity following injury using primary rat cortical dissociated neurons. We found that the functional connectivity of a neuron to its neighbors, combined with the relative influx of calcium through distinct NMDAR subtypes, together contributed to the individual neuronal response to trauma. Specifically, individual neurons whose [Ca(2+)]i oscillations were largely due to GluN2B NMDAR activation lost many of their functional targets 1 h following injury. In comparison, neurons with large GluN2A contribution or neurons with high functional connectivity both independently protected against injury-induced loss in connectivity. Mechanistically, we found that traumatic injury resulted in increased uncorrelated network activity, an effect linked to reduction of the voltage-sensitive Mg(2+) block of GluN2B-containing NMDARs. This uncorrelated activation of GluN2B subtypes after injury significantly limited the potential for network remodeling in response to a plasticity stimulus. Together, our data suggest that two single-cell characteristics, the aggregate contribution of NMDAR subtypes and the number of functional connections, influence network structure following traumatic injury.
Upscaling of spectral induced polarization response using random tube networks
Maineult, Alexis; Revil, André; Camerlynck, Christian; Florsch, Nicolas; Titov, Konstantin
2017-05-01
In order to upscale the induced polarization (IP) response of porous media, from the pore scale to the sample scale, we implement a procedure to compute the macroscopic complex resistivity response of random tube networks. A network is made of a 2-D square-meshed grid of connected tubes, which obey to a given tube radius distribution. In a simplified approach, the electrical impedance of each tube follows a local Pelton resistivity model, with identical resistivity, chargeability and Cole-Cole exponent values for all the tubes-only the time constant varies, as it depends on the radius of each tube and on a diffusion coefficient also identical for all the tubes. By solving the conservation law for the electrical charge, the macroscopic IP response of the network is obtained. We fit successfully the macroscopic complex resistivity also by a Pelton resistivity model. Simulations on uncorrelated and correlated networks, for which the tube radius distribution is so that the decimal logarithm of the radius is normally distributed, evidence that the local and macroscopic model parameters are the same, except the Cole-Cole exponent: its macroscopic value diminishes with increasing heterogeneity (i.e. with increasing standard deviation of the radius distribution), compared to its local value. The methodology is also applied to six siliciclastic rock samples, for which the pore radius distributions from mercury porosimetry are available. These samples exhibit the same behaviour as synthetic media, that is, the macroscopic Cole-Cole exponent is always lower than the local one. As a conclusion, the pore network method seems to be a promising tool for studying the upscaling of the IP response of porous media.
The Random Walk Model Based on Bipartite Network
Directory of Open Access Journals (Sweden)
Zhang Man-Dun
2016-01-01
Full Text Available With the continuing development of the electronic commerce and growth of network information, there is a growing possibility for citizens to be confused by the information. Though the traditional technology of information retrieval have the ability to relieve the overload of information in some extent, it can not offer a targeted personality service based on user’s interests and activities. In this context, the recommendation algorithm arose. In this paper, on the basis of conventional recommendation, we studied the scheme of random walk based on bipartite network and the application of it. We put forward a similarity measurement based on implicit feedback. In this method, a uneven character vector is imported(the weight of item in the system. We put forward a improved random walk pattern which make use of partial or incomplete neighbor information to create recommendation information. In the end, there is an experiment in the real data set, the recommendation accuracy and practicality are improved. We promise the reality of the result of the experiment
Analysis of complex contagions in random multiplex networks
Yagan, Osman
2012-01-01
We study the diffusion of influence in random multiplex networks where links can be of $r$ different types, and for a given content (e.g., rumor, product, political view), each link type is associated with a content dependent parameter $c_i$ in $[0,\\infty]$ that measures the relative bias type-$i$ links have in spreading this content. In this setting, we propose a linear threshold model of contagion where nodes switch state if their "perceived" proportion of active neighbors exceeds a threshold \\tau. Namely, a node connected to $m_i$ active neighbors and $k_i-m_i$ inactive neighbors via type-$i$ links will turn active if $\\sum{c_i m_i}/\\sum{c_i k_i}$ exceeds its threshold \\tau. Under this model, we obtain the condition, probability and expected size of global spreading events. Our results extend the existing work on complex contagions in several directions by i) providing solutions for coupled random networks whose vertices are neither identical nor disjoint, (ii) highlighting the effect of content on the dyn...
Rewiring food systems to enhance human health and biosphere stewardship
Gordon, Line J.; Bignet, Victoria; Crona, Beatrice; Henriksson, Patrik J. G.; Van Holt, Tracy; Jonell, Malin; Lindahl, Therese; Troell, Max; Barthel, Stephan; Deutsch, Lisa; Folke, Carl; Jamila Haider, L.; Rockström, Johan; Queiroz, Cibele
2017-10-01
Food lies at the heart of both health and sustainability challenges. We use a social-ecological framework to illustrate how major changes to the volume, nutrition and safety of food systems between 1961 and today impact health and sustainability. These changes have almost halved undernutrition while doubling the proportion who are overweight. They have also resulted in reduced resilience of the biosphere, pushing four out of six analysed planetary boundaries across the safe operating space of the biosphere. Our analysis further illustrates that consumers and producers have become more distant from one another, with substantial power consolidated within a small group of key actors. Solutions include a shift from a volume-focused production system to focus on quality, nutrition, resource use efficiency, and reduced antimicrobial use. To achieve this, we need to rewire food systems in ways that enhance transparency between producers and consumers, mobilize key actors to become biosphere stewards, and re-connect people to the biosphere.
Neural circuit rewiring: insights from DD synapse remodeling.
Kurup, Naina; Jin, Yishi
2016-01-01
Nervous systems exhibit many forms of neuronal plasticity during growth, learning and memory consolidation, as well as in response to injury. Such plasticity can occur across entire nervous systems as with the case of insect metamorphosis, in individual classes of neurons, or even at the level of a single neuron. A striking example of neuronal plasticity in C. elegans is the synaptic rewiring of the GABAergic Dorsal D-type motor neurons during larval development, termed DD remodeling. DD remodeling entails multi-step coordination to concurrently eliminate pre-existing synapses and form new synapses on different neurites, without changing the overall morphology of the neuron. This mini-review focuses on recent advances in understanding the cellular and molecular mechanisms driving DD remodeling.
Identification of yeast transcriptional regulation networks using multivariate random forests.
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Yuanyuan Xiao
2009-06-01
Full Text Available The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays to sequence features residing in gene promoters (as derived from DNA motif data and transcription factor binding to gene promoters (as derived from tiling microarrays. We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.
Enhancing the Performance of Random Access Networks with Random Packet CDMA and Joint Detection
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Behrouz Farhang-Boroujeny
2008-09-01
Full Text Available Random packet CDMA (RP-CDMA is a recently proposed random transmission scheme which has been designed from the beginning as a cross-layer method to overcome the restrictive nature of the Aloha protocol. Herein, we more precisely model its performance and investigate throughput and network stability. In contrast to previous works, we adopt the spread Aloha model for header transmission, and the performance of different joint detection methods for the payload data is investigated. Furthermore, we introduce performance measures for multiple access systems based on the diagonal elements of a modified multipacket reception matrix, and show that our measures describe the upper limit of the vector of stable arrival rates for a finite number of users. Finally, we simulate queue sizes and throughput characteristics of RP-CDMA with various receiver structures and compare them to spread Aloha.
Enhancing the Performance of Random Access Networks with Random Packet CDMA and Joint Detection
Kempter, Roland; Amini, Peiman; Farhang-Boroujeny, Behrouz
2008-12-01
Random packet CDMA (RP-CDMA) is a recently proposed random transmission scheme which has been designed from the beginning as a cross-layer method to overcome the restrictive nature of the Aloha protocol. Herein, we more precisely model its performance and investigate throughput and network stability. In contrast to previous works, we adopt the spread Aloha model for header transmission, and the performance of different joint detection methods for the payload data is investigated. Furthermore, we introduce performance measures for multiple access systems based on the diagonal elements of a modified multipacket reception matrix, and show that our measures describe the upper limit of the vector of stable arrival rates for a finite number of users. Finally, we simulate queue sizes and throughput characteristics of RP-CDMA with various receiver structures and compare them to spread Aloha.
Joon-Woo Lee; Won Kim
2015-01-01
This paper reports the design of a randomly deployed heterogeneous wireless sensor network (HWSN) with two types of nodes: a powerful node and an ordinary node. Powerful nodes, such as Cluster Heads (CHs), communicate directly to the data sink of the network, and ordinary nodes sense the desired information and transmit the processed data to powerful nodes. The heterogeneity of HWSNs improves the networks lifetime and coverage. This paper focuses on the design of a random network among HWSNs....
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Jorge Domínguez-Andrés
2017-09-01
Full Text Available Monocytes are innate immune cells that play a pivotal role in antifungal immunity, but little is known regarding the cellular metabolic events that regulate their function during infection. Using complementary transcriptomic and immunological studies in human primary monocytes, we show that activation of monocytes by Candida albicans yeast and hyphae was accompanied by metabolic rewiring induced through C-type lectin-signaling pathways. We describe that the innate immune responses against Candida yeast are energy-demanding processes that lead to the mobilization of intracellular metabolite pools and require induction of glucose metabolism, oxidative phosphorylation and glutaminolysis, while responses to hyphae primarily rely on glycolysis. Experimental models of systemic candidiasis models validated a central role for glucose metabolism in anti-Candida immunity, as the impairment of glycolysis led to increased susceptibility in mice. Collectively, these data highlight the importance of understanding the complex network of metabolic responses triggered during infections, and unveil new potential targets for therapeutic approaches against fungal diseases.
Damage Spreading in Spatial and Small-world Random Boolean Networks
Energy Technology Data Exchange (ETDEWEB)
Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.
2014-02-18
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Stability and anomalous entropic elasticity of sub isostatic random-bond networks
Wigbers, Manon C.; MacKintosh, Fred C.; Dennison, Matthew
2014-01-01
We study the elasticity of thermalized spring networks under an applied bulk strain. The networks considered are sub-isostatic random-bond networks that, in the athermal limit, are known to have vanishing bulk and linear shear moduli at zero bulk strain. Above a bulk strain threshold, however, these networks become rigid, although surprisingly the shear modulus remains zero until a second, higher, strain threshold. We find that thermal fluctuations stabilize all networks below the rigidity tr...
Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding
DEFF Research Database (Denmark)
Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani
2014-01-01
This work proposes a new protocol applying on– the–fly random linear network coding in wireless mesh net- works. The protocol provides increased reliability, low delay, and high throughput to the upper layers, while being oblivious to their specific requirements. This seemingly conflicting goals ...... and evaluated in a real test bed with Raspberry Pi devices. We show that order of magnitude gains in throughput over plain TCP are possible with moderate losses and up to two fold improvement in per packet delay in our results....
Collective dynamics of `small-world' networks
Watts, Duncan J.; Strogatz, Steven H.
1998-06-01
Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks `rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them `small-world' networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
The data-driven null models for information dissemination tree in social networks
Zhang, Zhiwei; Wang, Zhenyu
2017-10-01
For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.
Complementary feeding: a Global Network cluster randomized controlled trial
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Pasha Omrana
2011-01-01
Full Text Available Abstract Background Inadequate and inappropriate complementary feeding are major factors contributing to excess morbidity and mortality in young children in low resource settings. Animal source foods in particular are cited as essential to achieve micronutrient requirements. The efficacy of the recommendation for regular meat consumption, however, has not been systematically evaluated. Methods/Design A cluster randomized efficacy trial was designed to test the hypothesis that 12 months of daily intake of beef added as a complementary food would result in greater linear growth velocity than a micronutrient fortified equi-caloric rice-soy cereal supplement. The study is being conducted in 4 sites of the Global Network for Women's and Children's Health Research located in Guatemala, Pakistan, Democratic Republic of the Congo (DRC and Zambia in communities with toddler stunting rates of at least 20%. Five clusters per country were randomized to each of the food arms, with 30 infants in each cluster. The daily meat or cereal supplement was delivered to the home by community coordinators, starting when the infants were 6 months of age and continuing through 18 months. All participating mothers received nutrition education messages to enhance complementary feeding practices delivered by study coordinators and through posters at the local health center. Outcome measures, obtained at 6, 9, 12, and 18 months by a separate assessment team, included anthropometry; dietary variety and diversity scores; biomarkers of iron, zinc and Vitamin B12 status (18 months; neurocognitive development (12 and 18 months; and incidence of infectious morbidity throughout the trial. The trial was supervised by a trial steering committee, and an independent data monitoring committee provided oversight for the safety and conduct of the trial. Discussion Findings from this trial will test the efficacy of daily intake of meat commencing at age 6 months and, if beneficial, will
Modelling opinion formation driven communities in social networks
Iñiguez, Gerardo; Kertész, János; Kaski, Kimmo K
2010-01-01
In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter $\\alpha$, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realized by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter $\\alpha$ and its influence on the conformation of opinion and the size of the resulting communities. We present numer...
The investigation of social networks based on multi-component random graphs
Zadorozhnyi, V. N.; Yudin, E. B.
2018-01-01
The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.
Regulatory Rewiring in a Cross Causes Extensive Genetic Heterogeneity.
Matsui, Takeshi; Linder, Robert; Phan, Joann; Seidl, Fabian; Ehrenreich, Ian M
2015-10-01
Genetic heterogeneity occurs when individuals express similar phenotypes as a result of different underlying mechanisms. Although such heterogeneity is known to be a potential source of unexplained heritability in genetic mapping studies, its prevalence and molecular basis are not fully understood. Here we show that substantial genetic heterogeneity underlies a model phenotype--the ability to grow invasively--in a cross of two Saccharomyces cerevisiae strains. The heterogeneous basis of this trait across genotypes and environments makes it difficult to detect causal loci with standard genetic mapping techniques. However, using selective genotyping in the original cross, as well as in targeted backcrosses, we detected four loci that contribute to differences in the ability to grow invasively. Identification of causal genes at these loci suggests that they act by changing the underlying regulatory architecture of invasion. We verified this point by deleting many of the known transcriptional activators of invasion, as well as the gene encoding the cell surface protein Flo11 from five relevant segregants and showing that these individuals differ in the genes they require for invasion. Our work illustrates the extensive genetic heterogeneity that can underlie a trait and suggests that regulatory rewiring is a basic mechanism that gives rise to this heterogeneity. Copyright © 2015 by the Genetics Society of America.
Stability and dynamical properties of material flow systems on random networks
Anand, K.; Galla, T.
2009-04-01
The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.
Long-range navigation on complex networks using Lévy random walks
Riascos, A. P.; Mateos, José L.
2012-11-01
We introduce a strategy of navigation in undirected networks, including regular, random, and complex networks, that is inspired by Lévy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time, and the average time to reach a node on the network. We found that the long-range navigation using the Lévy random walk strategy, compared with the normal random walk strategy, is more efficient at reducing the time to cover the network. The dynamical effect of using the Lévy walk strategy is to transform a large-world network into a small world. Our exact results provide a general framework that connects two important fields: Lévy navigation strategies and dynamics on complex networks.
Smith, Laramie R; Strathdee, Steffanie A; Metzger, David; Latkin, Carl
2017-06-01
Little is known about ways network-level factors that may influence the adoption of combination prevention behaviors among injection networks, or how network-oriented interventions might moderate this behavior change process. A total of 232 unique injection risk networks in Philadelphia, PA, were randomized to a peer educator network-oriented intervention or standard of care control arm. Network-level aggregates reflecting the injection networks' baseline substance use dynamics, social interactions, and the networks exposure to gender- and structural-related vulnerabilities were calculated and used to predict changes in the proportion of network members adopting safer injection practices at 6-month follow-up. At follow-up, safer injection practices were observed among 46.31% of a network's members on average. In contrast, 25.7% of networks observed no change. Controlling for the effects of the intervention, significant network-level factors influencing network-level behavior change reflected larger sized injection networks (b=2.20, p=0.013) with a greater proportion of members who shared needles (b=0.29, pnetwork's safer injection practices were also observed for networks with fewer new network members (b=-0.31, p=0.008), and for networks whose members were proportionally less likely to have experienced incarceration (b=-0.20, p=0.012) or more likely to have been exposed to drug treatment (b=0.17, p=0.034) in the 6-months prior to baseline. A significant interaction suggested the intervention uniquely facilitated change in safer injection practices among female-only networks (b=-0.32, p=0.046). Network-level factors offer insights into ways injection networks might be leveraged to promote combination prevention efforts. Copyright © 2017 Elsevier B.V. All rights reserved.
Statistical mechanics of scale-free gene expression networks
Gross, Eitan
2012-12-01
The gene co-expression networks of many organisms including bacteria, mice and man exhibit scale-free distribution. This heterogeneous distribution of connections decreases the vulnerability of the network to random attacks and thus may confer the genetic replication machinery an intrinsic resilience to such attacks, triggered by changing environmental conditions that the organism may be subject to during evolution. This resilience to random attacks comes at an energetic cost, however, reflected by the lower entropy of the scale-free distribution compared to the more homogenous, random network. In this study we found that the cell cycle-regulated gene expression pattern of the yeast Saccharomyces cerevisiae obeys a power-law distribution with an exponent α = 2.1 and an entropy of 1.58. The latter is very close to the maximal value of 1.65 obtained from linear optimization of the entropy function under the constraint of a constant cost function, determined by the average degree connectivity . We further show that the yeast's gene expression network can achieve scale-free distribution in a process that does not involve growth but rather via re-wiring of the connections between nodes of an ordered network. Our results support the idea of an evolutionary selection, which acts at the level of the protein sequence, and is compatible with the notion of greater biological importance of highly connected nodes in the protein interaction network. Our constrained re-wiring model provides a theoretical framework for a putative thermodynamically driven evolutionary selection process.
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks
Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli
2016-01-01
In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423
Mission-Aware Medium Access Control in Random Access Networks
Park, Jaeok; Van Der Schaar, Mihaela
2009-01-01
We study mission-critical networking in wireless communication networks, where network users are subject to critical events such as emergencies and crises. If a critical event occurs to a user, the user needs to send necessary information for help as early as possible. However, most existing medium access control (MAC) protocols are not adequate to meet the urgent need for information transmission by users in a critical situation. In this paer, we propose a novel class of MAC protocols that u...
Electrospun dye-doped fiber networks: lasing emission from randomly distributed cavities
DEFF Research Database (Denmark)
Krammer, Sarah; Vannahme, Christoph; Smith, Cameron
2015-01-01
Dye-doped polymer fiber networks fabricated with electrospinning exhibit comb-like laser emission. We identify randomly distributed ring resonators being responsible for lasing emission by making use of spatially resolved spectroscopy. Numerical simulations confirm this result quantitatively....
Completely random measures for modelling block-structured sparse networks
DEFF Research Database (Denmark)
Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten
2016-01-01
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re......-introduce the use of block-structure for network models obeying allenberg’s notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model...
Effective trapping of random walkers in complex networks.
Hwang, S; Lee, D-S; Kahng, B
2012-04-01
Exploring the World Wide Web has become one of the key issues in information science, specifically in view of its application to the PageRank-like algorithms used in search engines. The random walk approach has been employed to study such a problem. The probability of return to the origin (RTO) of random walks is inversely related to how information can be accessed during random surfing. We find analytically that the RTO probability for a given starting node shows a crossover from a slow to a fast decay behavior with time and the crossover time increases with the degree of the starting node. We remark that the RTO probability becomes almost constant in the early-time regime as the degree exponent approaches two. This result indicates that a random surfer can be effectively trapped at the hub and supports the necessity of the random jump strategy empirically used in the Google's search engine.
Throughput Analysis of Fading Sensor Networks with Regular and Random Topologies
Directory of Open Access Journals (Sweden)
Liu Xiaowen
2005-01-01
Full Text Available We present closed-form expressions of the average link throughput for sensor networks with a slotted ALOHA MAC protocol in Rayleigh fading channels. We compare networks with three regular topologies in terms of throughput, transmit efficiency, and transport capacity. In particular, for square lattice networks, we present a sensitivity analysis of the maximum throughput and the optimum transmit probability with respect to the signal-to-interference ratio threshold. For random networks with nodes distributed according to a two-dimensional Poisson point process, the average throughput is analytically characterized and numerically evaluated. It turns out that although regular networks have an only slightly higher average link throughput than random networks for the same link distance, regular topologies have a significant benefit when the end-to-end throughput in multihop connections is considered.
Capitanio, Daniele; Fania, Chiara; Torretta, Enrica; Viganò, Agnese; Moriggi, Manuela; Bravatà, Valentina; Caretti, Anna; Levett, Denny Z H; Grocott, Michael P W; Samaja, Michele; Cerretelli, Paolo; Gelfi, Cecilia
2017-08-29
In mammals, hypoxic stress management is under the control of the Hypoxia Inducible Factors, whose activity depends on the stabilization of their labile α subunit. In particular, the skeletal muscle appears to be able to react to changes in substrates and O2 delivery by tuning its metabolism. The present study provides a comprehensive overview of skeletal muscle metabolic adaptation to hypoxia in mice and in human subjects exposed for 7/9 and 19 days to high altitude levels. The investigation was carried out combining proteomics, qRT-PCR mRNA transcripts analysis, and enzyme activities assessment in rodents, and protein detection by antigen antibody reactions in humans and rodents. Results indicate that the skeletal muscle react to a decreased O2 delivery by rewiring the TCA cycle. The first TCA rewiring occurs in mice in 2-day hypoxia and is mediated by cytosolic malate whereas in 10-day hypoxia the rewiring is mediated by Idh1 and Fasn, supported by glutamine and HIF-2α increments. The combination of these specific anaplerotic steps can support energy demand despite HIFs degradation. These results were confirmed in human subjects, demonstrating that the TCA double rewiring represents an essential factor for the maintenance of muscle homeostasis during adaptation to hypoxia.
The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.
Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang
2017-10-13
Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.
Boussalis, Dhemetrios; Wang, Shyh J.
1992-01-01
This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.
Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting
2016-10-01
Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.
$k$-core percolation on complex networks: Comparing random, localized and targeted attacks
Yuan, Xin; Stanley, H Eugene; Havlin, Shlomo
2016-01-01
The type of malicious attack inflicting on networks greatly influences their stability under ordinary percolation in which a node fails when it becomes disconnected from the giant component. Here we study its generalization, $k$-core percolation, in which a node fails when it loses connection to a threshold $k$ number of neighbors. We study and compare analytically and by numerical simulations of $k$-core percolation the stability of networks under random attacks (RA), localized attacks (LA) and targeted attacks (TA), respectively. By mapping a network under LA or TA into an equivalent network under RA, we find that in both single and interdependent networks, TA exerts the greatest damage to the core structure of a network. We also find that for Erd\\H{o}s-R\\'{e}nyi (ER) networks, LA and RA exert equal damage to the core structure whereas for scale-free (SF) networks, LA exerts much more damage than RA does to the core structure.
Multitarget search on complex networks: A logarithmic growth of global mean random cover time
Weng, Tongfeng; Zhang, Jie; Small, Michael; Yang, Ji; Bijarbooneh, Farshid Hassani; Hui, Pan
2017-09-01
We investigate multitarget search on complex networks and derive an exact expression for the mean random cover time that quantifies the expected time a walker needs to visit multiple targets. Based on this, we recover and extend some interesting results of multitarget search on networks. Specifically, we observe the logarithmic increase of the global mean random cover time with the target number for a broad range of random search processes, including generic random walks, biased random walks, and maximal entropy random walks. We show that the logarithmic growth pattern is a universal feature of multi-target search on networks by using the annealed network approach and the Sherman-Morrison formula. Moreover, we find that for biased random walks, the global mean random cover time can be minimized, and that the corresponding optimal parameter also minimizes the global mean first passage time, pointing towards its robustness. Our findings further confirm that the logarithmic growth pattern is a universal law governing multitarget search in confined media.
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks
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Jie Wang
2016-01-01
(ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T
2015-07-21
Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.
CUFID-query: accurate network querying through random walk based network flow estimation.
Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun
2017-12-28
Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive
Random Walker Coverage Analysis for Information Dissemination in Wireless Sensor Networks
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Konstantinos Skiadopoulos
2017-06-01
Full Text Available The increasing technological progress in electronics provides network nodes with new and enhanced capabilities that allow the revisit of the traditional information dissemination (and collection problem. The probabilistic nature of information dissemination using random walkers is exploited here to deal with challenges imposed by unconventional modern environments. In such systems, node operation is not deterministic (e.g., does not depend only on network nodes’ battery, but it rather depends on the particulars of the ambient environment (e.g., in the case of energy harvesting: sunshine, wind. The mechanism of information dissemination using one random walker is studied and analyzed in this paper under a different and novel perspective. In particular, it takes into account the stochastic nature of random walks, enabling further understanding of network coverage. A novel and original analysis is presented, which reveals the evolution network coverage by a random walker with respect to time. The derived analytical results reveal certain additional interesting aspects regarding network coverage, thus shedding more light on the random walker mechanism. Further analytical results, regarding the walker’s spatial movement and its associated neighborhood, are also confirmed through experimentation. Finally, simulation results considering random geometric graph topologies, which are suitable for modeling mobile environments, support and confirm the analytical findings.
Random Walks on Directed Networks: Inference and Respondent-driven Sampling
Malmros, Jens; Britton, Tom
2013-01-01
Respondent driven sampling (RDS) is a method often used to estimate population properties (e.g. sexual risk behavior) in hard-to-reach populations. It combines an effective modified snowball sampling methodology with an estimation procedure that yields unbiased population estimates under the assumption that the sampling process behaves like a random walk on the social network of the population. Current RDS estimation methodology assumes that the social network is undirected, i.e. that all edges are reciprocal. However, empirical social networks in general also have non-reciprocated edges. To account for this fact, we develop a new estimation method for RDS in the presence of directed edges on the basis of random walks on directed networks. We distinguish directed and undirected edges and consider the possibility that the random walk returns to its current position in two steps through an undirected edge. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing...
USING THE RANDOM OF QUANTIZATION IN THE SIMULATION OF NETWORKED CONTROL SYSTEMS
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V. K. Bitiukov
2014-01-01
Full Text Available Network control systems using a network channel for communication between the elements. This approach has several advantages: lower installation costs, ease of configuration, ease of diagnostics and maintenance. The use of networks in control systems poses new problems. The network characteristics make the analysis, modeling, and control of networked control systems more complex and challenging. In the simulation must consider the following factors: packet loss, packet random time over the network, the need for location records in a channel simultaneously multiple data packets with sequential transmission. Attempts to account at the same time all of these factors lead to a significant increase in the dimension of the mathematical model and, as a con-sequence, a significant computational challenges. Such models tend to have a wide application in research. However, for engineering calculations required mathematical models of small dimension, but at the same time having sufficient accuracy. Considered the networks channels with random delays and packet loss. Random delay modeled by appropriate distribution the Erlang. The probability of packet loss depends on the arrival rate of data packets in the transmission channel, and the parameters of the distribution Erlang. We propose a model of the channel in the form of a serial connection of discrete elements. Discrete elements produce independents quantization of the input signal. To change the probability of packet loss is proposed to use a random quantization input signal. Obtained a formula to determine the probability of packet loss during transmission.
Evolution of vocabulary on scale-free and random networks
Kalampokis, Alkiviadis; Kosmidis, Kosmas; Argyrakis, Panos
2007-06-01
We examine the evolution of the vocabulary of a group of individuals (linguistic agents) on a scale-free network, using Monte Carlo simulations and assumptions from evolutionary game theory. It is known that when the agents are arranged in a two-dimensional lattice structure and interact by diffusion and encounter, then their final vocabulary size is the maximum possible. Knowing all available words is essential in order to increase the probability to “survive” by effective reproduction. On scale-free networks we find a different result. It is not necessary to learn the entire vocabulary available. Survival chances are increased by using the vocabulary of the “hubs” (nodes with high degree). The existence of the “hubs” in a scale-free network is the source of an additional important fitness generating mechanism.
Eigentime identities for random walks on a family of treelike networks and polymer networks
Dai, Meifeng; Wang, Xiaoqian; Sun, Yanqiu; Sun, Yu; Su, Weiyi
2017-10-01
In this paper, we investigate the eigentime identities quantifying as the sum of reciprocals of all nonzero normalized Laplacian eigenvalues on a family of treelike networks and the polymer networks. Firstly, for a family of treelike networks, it is shown that all their eigenvalues can be obtained by computing the roots of several small-degree polynomials defined recursively. We obtain the scalings of the eigentime identity on a family of treelike with network size Nn is Nn lnNn. Then, for the polymer networks, we apply the spectral decimation approach to determine analytically all the eigenvalues and their corresponding multiplicities. Using the relationship between the generation and the next generation of eigenvalues we obtain the scalings of the eigentime identity on the polymer networks with network size Nn is Nn lnNn. By comparing the eigentime identities on these two kinds of networks, their scalings with network size Nn are all Nn lnNn.
Analysis of Greedy Decision Making for Geographic Routing for Networks of Randomly Moving Objects
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Amber Israr
2016-04-01
Full Text Available Autonomous and self-organizing wireless ad-hoc communication networks for moving objects consist of nodes, which use no centralized network infrastructure. Examples of moving object networks are networks of flying objects, networks of vehicles, networks of moving people or robots. Moving object networks have to face many critical challenges in terms of routing because of dynamic topological changes and asymmetric networks links. A suitable and effective routing mechanism helps to extend the deployment of moving nodes. In this paper an attempt has been made to analyze the performance of the Greedy Decision method (position aware distance based algorithm for geographic routing for network nodes moving according to the random waypoint mobility model. The widely used GPSR (Greedy Packet Stateless Routing protocol utilizes geographic distance and position based data of nodes to transmit packets towards destination nodes. In this paper different scenarios have been tested to develop a concrete set of recommendations for optimum deployment of distance based Greedy Decision of Geographic Routing in randomly moving objects network
A random walk evolution model of wireless sensor networks and virus spreading
Wang, Ya-Qi; Yang, Xiao-Yuan
2013-01-01
In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations.
Evaluation of geocast routing trees on random and actual networks
Meijerink, Berend Jan; Baratchi, Mitra; Heijenk, Geert; Koucheryavy, Yevgeni; Mamatas, Lefteris; Matta, Ibrahim; Ometov, Aleksandr; Papadimitriou, Panagiotis
2017-01-01
Efficient geocast routing schemes are needed to transmit messages to mobile networked devices in geographically scoped areas. To design an efficient geocast routing algorithm a comprehensive evaluation of different routing tree approaches is needed. In this paper, we present an analytical study
Network motif identification and structure detection with exponential random graph models
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Munni Begum
2014-12-01
Full Text Available Local regulatory motifs are identified in the transcription regulatory network of the most studied model organism Escherichia coli (E. coli through graphical models. Network motifs are small structures in a network that appear more frequently than expected by chance alone. We apply social network methodologies such as p* models, also known as Exponential Random Graph Models (ERGMs, to identify statistically significant network motifs. In particular, we generate directed graphical models that can be applied to study interaction networks in a broad range of databases. The Markov Chain Monte Carlo (MCMC computational algorithms are implemented to obtain the estimates of model parameters to the corresponding network statistics. A variety of ERGMs are fitted to identify statistically significant network motifs in transcription regulatory networks of E. coli. A total of nine ERGMs are fitted to study the transcription factor - transcription factor interactions and eleven ERGMs are fitted for the transcription factor-operon interactions. For both of these interaction networks, arc (a directed edge in a directed network and k-istar (or incoming star structures, for values of k between 2 and 10, are found to be statistically significant local structures or network motifs. The goodness of fit statistics are provided to determine the quality of these models.
Application of Poisson random effect models for highway network screening.
Jiang, Ximiao; Abdel-Aty, Mohamed; Alamili, Samer
2014-02-01
In recent years, Bayesian random effect models that account for the temporal and spatial correlations of crash data became popular in traffic safety research. This study employs random effect Poisson Log-Normal models for crash risk hotspot identification. Both the temporal and spatial correlations of crash data were considered. Potential for Safety Improvement (PSI) were adopted as a measure of the crash risk. Using the fatal and injury crashes that occurred on urban 4-lane divided arterials from 2006 to 2009 in the Central Florida area, the random effect approaches were compared to the traditional Empirical Bayesian (EB) method and the conventional Bayesian Poisson Log-Normal model. A series of method examination tests were conducted to evaluate the performance of different approaches. These tests include the previously developed site consistence test, method consistence test, total rank difference test, and the modified total score test, as well as the newly proposed total safety performance measure difference test. Results show that the Bayesian Poisson model accounting for both temporal and spatial random effects (PTSRE) outperforms the model that with only temporal random effect, and both are superior to the conventional Poisson Log-Normal model (PLN) and the EB model in the fitting of crash data. Additionally, the method evaluation tests indicate that the PTSRE model is significantly superior to the PLN model and the EB model in consistently identifying hotspots during successive time periods. The results suggest that the PTSRE model is a superior alternative for road site crash risk hotspot identification. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Martin Rosvall
Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.
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Davide De Lucrezia
Full Text Available Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity. The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random.
Learning random networks for compression of still and moving images
Gelenbe, Erol; Sungur, Mert; Cramer, Christopher
1994-01-01
Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.
Variability of Fiber Elastic Moduli in Composite Random Fiber Networks Makes the Network Softer
Ban, Ehsan; Picu, Catalin
2015-03-01
Athermal fiber networks are assemblies of beams or trusses. They have been used to model mechanics of fibrous materials such as biopolymer gels and synthetic nonwovens. Elasticity of these networks has been studied in terms of various microstructural parameters such as the stiffness of their constituent fibers. In this work we investigate the elasticity of composite fiber networks made from fibers with moduli sampled from a distribution function. We use finite elements simulations to study networks made by 3D Voronoi and Delaunay tessellations. The resulting data collapse to power laws showing that variability in fiber stiffness makes fiber networks softer. We also support the findings by analytical arguments. Finally, we apply these results to a network with curved fibers to explain the dependence of the network's modulus on the variation of its structural parameters.
Fully-distributed randomized cooperation in wireless sensor networks
Bader, Ahmed
2015-01-07
When marrying randomized distributed space-time coding (RDSTC) to geographical routing, new performance horizons can be created. In order to reach those horizons however, routing protocols must evolve to operate in a fully distributed fashion. In this letter, we expose a technique to construct a fully distributed geographical routing scheme in conjunction with RDSTC. We then demonstrate the performance gains of this novel scheme by comparing it to one of the prominent classical schemes.
Effect of inhibitory firing pattern on coherence resonance in random neural networks
Yu, Haitao; Zhang, Lianghao; Guo, Xinmeng; Wang, Jiang; Cao, Yibin; Liu, Jing
2018-01-01
The effect of inhibitory firing patterns on coherence resonance (CR) in random neuronal network is systematically studied. Spiking and bursting are two main types of firing pattern considered in this work. Numerical results show that, irrespective of the inhibitory firing patterns, the regularity of network is maximized by an optimal intensity of external noise, indicating the occurrence of coherence resonance. Moreover, the firing pattern of inhibitory neuron indeed has a significant influence on coherence resonance, but the efficacy is determined by network property. In the network with strong coupling strength but weak inhibition, bursting neurons largely increase the amplitude of resonance, while they can decrease the noise intensity that induced coherence resonance within the neural system of strong inhibition. Different temporal windows of inhibition induced by different inhibitory neurons may account for the above observations. The network structure also plays a constructive role in the coherence resonance. There exists an optimal network topology to maximize the regularity of the neural systems.
Role Analysis in Networks using Mixtures of Exponential Random Graph Models.
Salter-Townshend, Michael; Murphy, Thomas Brendan
2015-06-01
A novel and flexible framework for investigating the roles of actors within a network is introduced. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes into roles. We refer to this model as the ego-ERGM. An Expectation-Maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation. The flexibility and utility of the method are demonstrated on examples of simulated and real networks.
Shortest loops are pacemakers in random networks of electrically coupled axons
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Nikita eVladimirov
2012-04-01
Full Text Available High-frequency oscillations (HFOs are an important part of brain activity in health and disease. However, their origins remain obscure and controversial. One possible mechanism depends on the presence of sparsely distributed gap junctions that electrically couple the axons of principal cells. A plexus of electrically coupled axons is modeled as a random network with bidirectional connections between its nodes. Under certain conditions the network can demonstrate one of two types of oscillatory activity. Type I oscillations (100-200 Hz are predicted to be caused by spontaneously spiking axons in a network with strong (high-conductance gap junctions. Type II oscillations (200-300 Hz require no spontaneous spiking and relatively weak (low-conductance gap junctions, across which spike propagation failures occur. The type II oscillations are reentrant and self-sustained. Here we examine what determines the frequency of type II oscillations. Using simulations we show that the distribution of loop lengths is the key factor for determining frequency in type II network oscillations. We first analyze spike failure between two electrically coupled cells using a model of anatomically reconstructed CA1 pyramidal neuron. Then network oscillations are studied by a cellular automaton model with random network connectivity, in which we control loop statistics. We show that oscillation periods can be predicted from the network's loop statistics. The shortest loop, around which a spike can travel, is the most likely pacemaker candidate.The principle of one loop as a pacemaker is remarkable, because random networks contain a large number of loops juxtaposed and superimposed, and their number rapidly grows with network size. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We finally propose that type I oscillations may correspond to ripples, while type II oscillations correspond to so-called fast ripples.
Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs
Yasin Yazicioǧlu, A.
2015-11-25
Multi-Agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-Agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m ϵ [k+k ] 2. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges. © 2015 IEEE.
Decentralized formation of random regular graphs for robust multi-agent networks
Yazicioglu, A. Yasin
2014-12-15
Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs have significant impact on the robustness of networked systems. One family of robust graphs is the random regular graphs. In this paper, we present a locally applicable reconfiguration scheme to build random regular graphs through self-organization. For any connected initial graph, the proposed scheme maintains connectivity and the average degree while minimizing the degree differences and randomizing the links. As such, if the average degree of the initial graph is an integer, then connected regular graphs are realized uniformly at random as time goes to infinity.
Risk Assessment of Distribution Network Based on Random set Theory and Sensitivity Analysis
Zhang, Sh; Bai, C. X.; Liang, J.; Jiao, L.; Hou, Z.; Liu, B. Zh
2017-05-01
Considering the complexity and uncertainty of operating information in distribution network, this paper introduces the use of random set for risk assessment. The proposed method is based on the operating conditions defined in the random set framework to obtain the upper and lower cumulative probability functions of risk indices. Moreover, the sensitivity of risk indices can effectually reflect information about system reliability and operating conditions, and by use of these information the bottlenecks that suppress system reliability can be found. The analysis about a typical radial distribution network shows that the proposed method is reasonable and effective.
Trend-driven information cascades on random networks.
Kobayashi, Teruyoshi
2015-12-01
Threshold models of global cascades have been extensively used to model real-world collective behavior, such as the contagious spread of fads and the adoption of new technologies. A common property of those cascade models is that a vanishingly small seed fraction can spread to a finite fraction of an infinitely large network through local infections. In social and economic networks, however, individuals' behavior is often influenced not only by what their direct neighbors are doing, but also by what the majority of people are doing as a trend. A trend affects individuals' behavior while individuals' behavior creates a trend. To analyze such a complex interplay between local- and global-scale phenomena, I generalize the standard threshold model by introducing a type of node called global nodes (or trend followers), whose activation probability depends on a global-scale trend, specifically the percentage of activated nodes in the population. The model shows that global nodes play a role as accelerating cascades once a trend emerges while reducing the probability of a trend emerging. Global nodes thus either facilitate or inhibit cascades, suggesting that a moderate share of trend followers may maximize the average size of cascades.
Mean First Passage Time of Preferential Random Walks on Complex Networks with Applications
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Zhongtuan Zheng
2017-01-01
Full Text Available This paper investigates, both theoretically and numerically, preferential random walks (PRW on weighted complex networks. By using two different analytical methods, two exact expressions are derived for the mean first passage time (MFPT between two nodes. On one hand, the MFPT is got explicitly in terms of the eigenvalues and eigenvectors of a matrix associated with the transition matrix of PRW. On the other hand, the center-product-degree (CPD is introduced as one measure of node strength and it plays a main role in determining the scaling of the MFPT for the PRW. Comparative studies are also performed on PRW and simple random walks (SRW. Numerical simulations of random walks on paradigmatic network models confirm analytical predictions and deepen discussions in different aspects. The work may provide a comprehensive approach for exploring random walks on complex networks, especially biased random walks, which may also help to better understand and tackle some practical problems such as search and routing on networks.
Cascading failures in interdependent modular networks with partial random coupling preference
Tian, Meng; Wang, Xianpei; Dong, Zhengcheng; Zhu, Guowei; Long, Jiachuang; Dai, Dangdang; Zhang, Qilin
2017-10-01
Cascading failures have been widely analyzed in interdependent networks with different coupling preferences from microscopic and macroscopic perspectives in recent years. Plenty of real-world interdependent infrastructures, representing as interdependent networks, exhibit community structure, one of the most important mesoscopic structures, and partial coupling preferences, which can affect cascading failures in interdependent networks. In this paper, we propose the partial random coupling in communities, investigating cascading failures in interdependent modular scale-free networks under inner attacks and hub attacks. We mainly analyze the effects of the discoupling probability and the intermodular connection probability on cascading failures in interdependent networks. We find that increasing either the dicoupling probability or the intermodular connection probability can enhance the network robustness under both hub attacks and inner attacks. We also note that the community structure can prevent cascading failures spreading globally in entire interdependent networks. Finally, we obtain the result that if we want to efficiently improve the robustness of interdependent networks and reduce the protection cost, the intermodular connection probability should be protected preferentially, implying that improving the robustness of a single network is the fundamental method to enhance the robustness of the entire interdependent networks.
Ban, Ehsan; Barocas, Victor H; Shephard, Mark S; Picu, Catalin R
2016-04-01
Fiber networks are assemblies of one-dimensional elements representative of materials with fibrous microstructures such as collagen networks and synthetic nonwovens. The mechanics of random fiber networks has been the focus of numerous studies. However, fiber crimp has been explicitly represented only in few cases. In the present work, the mechanics of cross-linked networks with crimped athermal fibers, with and without an embedding elastic matrix, is studied. The dependence of the effective network stiffness on the fraction of nonstraight fibers and the relative crimp amplitude (or tortuosity) is studied using finite element simulations of networks with sinusoidally curved fibers. A semi-analytic model is developed to predict the dependence of network modulus on the crimp amplitude and the bounds of the stiffness reduction associated with the presence of crimp. The transition from the linear to the nonlinear elastic response of the network is rendered more gradual by the presence of crimp, and the effect of crimp on the network tangent stiffness decreases as strain increases. If the network is embedded in an elastic matrix, the effect of crimp becomes negligible even for very small, biologically relevant matrix stiffness values. However, the distribution of the maximum principal stress in the matrix becomes broader in the presence of crimp relative to the similar system with straight fibers, which indicates an increased probability of matrix failure.
Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.
2013-01-01
The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071
Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan
2016-11-01
Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.
Dynamics of comb-of-comb-network polymers in random layered flows.
Katyal, Divya; Kant, Rama
2016-12-01
We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength W_{α}. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν=2-α/2. Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t^{-α/2}. We show that the network with greater total mass moves faster.
A Markov model for the temporal dynamics of balanced random networks of finite size
Lagzi, Fereshteh; Rotter, Stefan
2014-01-01
The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between
Non-random food-web assembly at habitat edges increases connectivity and functional redundancy.
Peralta, Guadalupe; Frost, Carol M; Didham, Raphael K; Rand, Tatyana A; Tylianakis, Jason M
2017-04-01
Habitat fragmentation dramatically alters the spatial configuration of landscapes, with the creation of artificial edges affecting community structure and dynamics. Despite this, it is not known how the different food webs in adjacent habitats assemble at their boundaries. Here we demonstrate that the composition and structure of herbivore-parasitoid food webs across edges between native and plantation forests are not randomly assembled from those of the adjacent communities. Rather, elevated proportions of abundant, interaction-generalist parasitoid species at habitat edges allowed considerable interaction rewiring, which led to higher linkage density and less modular networks, with higher parasitoid functional redundancy. This was despite high overlap in host composition between edges and interiors. We also provide testable hypotheses for how food webs may assemble between habitats with lower species overlap. In an increasingly fragmented world, non-random assembly of food webs at edges may increasingly affect community dynamics at the landscape level. © 2016 by the Ecological Society of America.
Basic concepts and principles of stoichiometric modeling of metabolic networks
T.R. Maarleveld (Timo); R.A. Khandelwal; B.E. Olivier; B. Teusink (Bas); F.J. Bruggeman (Frank)
2013-01-01
htmlabstractMetabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into
DEFF Research Database (Denmark)
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani
2014-01-01
This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...
Topological plasticity increases robustness of mutualistic networks.
Ramos-Jiliberto, Rodrigo; Valdovinos, Fernanda S; Moisset de Espanés, Pablo; Flores, José D
2012-07-01
1. Earlier studies used static models to evaluate the responses of mutualistic networks to external perturbations. Two classes of dynamics can be distinguished in ecological networks; population dynamics, represented mainly by changes in species abundances, and topological dynamics, represented by changes in the architecture of the web. 2. In this study, we model the temporal evolution of three empirical plant-pollination networks incorporating both population and topological dynamics. We test the hypothesis that topological plasticity, realized through the ability of animals to rewire their connections after depletion of host abundances, enhances tolerance of mutualistic networks to species loss. We also compared the performance of various rewiring rules in affecting robustness. 3. The results show that topological plasticity markedly increased the robustness of mutualistic networks. Our analyses also revealed that network robustness reached maximum levels when animals with less host plant availability were more likely to rewire. Also, preferential attachment to richer host plants, that is, to plants exhibiting higher abundance and few exploiters, enhances robustness more than other rewiring alternatives. 4. Our results highlight the potential role of topological plasticity in the robustness of mutualistic networks to species extinctions and suggest some plausible mechanisms by which the decisions of foragers may shape the collective dynamics of plant-pollinator systems. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.
Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong
2007-01-01
Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the
Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong
2007-08-14
Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene
Microscopic Evaluation of Electrical and Thermal Conduction in Random Metal Wire Networks.
Gupta, Ritu; Kumar, Ankush; Sadasivam, Sridhar; Walia, Sunil; Kulkarni, Giridhar U; Fisher, Timothy S; Marconnet, Amy
2017-04-19
Ideally, transparent heaters exhibit uniform temperature, fast response time, high achievable temperatures, low operating voltage, stability across a range of temperatures, and high optical transmittance. For metal network heaters, unlike for uniform thin-film heaters, all of these parameters are directly or indirectly related to the network geometry. In the past, at equilibrium, the temperature distributions within metal networks have primarily been studied using either a physical temperature probe or direct infrared (IR) thermography, but there are limits to the spatial resolution of these cameras and probes, and thus, only average regional temperatures have typically been measured. However, knowledge of local temperatures within the network with a very high spatial resolution is required for ensuring a safe and stable operation. Here, we examine the thermal properties of random metal network thin-film heaters fabricated from crack templates using high-resolution IR microscopy. Importantly, the heaters achieve predominantly uniform temperatures throughout the substrate despite the random crack network structure (e.g., unequal sized polygons created by metal wires), but the temperatures of the wires in the network are observed to be significantly higher than the substrate because of the significant thermal contact resistance at the interface between the metal and the substrate. Last, the electrical breakdown mechanisms within the network are examined through transient IR imaging. In addition to experimental measurements of temperatures, an analytical model of the thermal properties of the network is developed in terms of geometrical parameters and material properties, providing insights into key design rules for such transparent heaters. Beyond this work, the methods and the understanding developed here extend to other network-based heaters and conducting films, including those that are not transparent.
Energy Technology Data Exchange (ETDEWEB)
Shi, Cindy
2015-07-17
The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.
Scaling laws for file dissemination in P2P networks with random contacts
Núñez-Queija, R.; Prabhu, B.
2008-01-01
In this paper we obtain the scaling law for the mean broadcast time of a file in a P2P network with an initial population of N nodes. In the model, at Poisson rate lambda a node initiates a contact with another node chosen uniformly at random. This contact is said to be successful if the contacted
Relay-aided multi-cell broadcasting with random network coding
DEFF Research Database (Denmark)
Lu, Lu; Sun, Fan; Xiao, Ming
2010-01-01
We investigate a relay-aided multi-cell broadcasting system using random network codes, where the focus is on devising efficient scheduling algorithms between relay and base stations. Two scheduling algorithms are proposed based on different feedback strategies; namely, a one-step scheduling...
Theoretical solutions for degree distribution of decreasing random birth-and-death networks
Long, Yin; Zhang, Xiao-Jun; Wang, Kui
2017-05-01
In this paper, theoretical solutions for degree distribution of decreasing random birth-and-death networks (0 probability generating function approach are employed. Then, based on the form of Poisson summation, we further confirm the tail characteristic of degree distribution is Poisson tail. Finally, simulations are carried out to verify these results by comparing the theoretical solutions with computer simulations.
High Performance Ambipolar Field-Effect Transistor of Random Network Carbon Nanotubes
Bisri, Satria Zulkarnaen; Gao, Jia; Derenskyi, Vladimir; Gomulya, Widianta; Iezhokin, Igor; Gordiichuk, Pavlo; Herrmann, Andreas; Loi, Maria Antonietta
2012-01-01
Ambipolar field-effect transistors of random network carbon nanotubes are fabricated from an enriched dispersion utilizing a conjugated polymer as the selective purifying medium. The devices exhibit high mobility values for both holes and electrons (3 cm(2)/V.s) with a high on/off ratio (10(6)). The
Active random noise control using adaptive learning rate neural networks with an immune feedback law
Sasaki, Minoru; Kuribayashi, Takumi; Ito, Satoshi
2005-12-01
In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. In the proposed method, because of the immune feedback law change a learning rate of the neural networks individually and adaptively, it is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks with the immune feedback law. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.
Xiaojia He; Natarajan Meghanathan
2016-01-01
In this paper, we thoroughly investigate correlations of eigenvector centrality to five centrality measures, including degree centrality, betweenness centrality, clustering coefficient centrality, closeness centrality, and farness centrality, of various types of network (random network, small world network, and real-world network). For each network, we compute those six centrality measures, from which the correlation coefficient is determined. Our analysis suggests that the degree centrali...
Observer-based H(infinity) control for networked nonlinear systems with random packet losses.
Li, Jian Guo; Yuan, Jing Qi; Lu, Jun Guo
2010-01-01
This paper investigates the observer-based H(infinity) control problem of networked nonlinear systems with global Lipschitz nonlinearities and random communication packet losses. The random packet loss is modelled as a Bernoulli distributed white sequence with a known conditional probability distribution. In the presence of random packet losses, sufficient conditions for the existence of an observer-based feedback controller are derived, such that the closed-loop networked nonlinear system is exponentially stable in the mean-square sense, and a prescribed H(infinity) disturbance-rejection-attenuation performance is also achieved. Then a linear matrix inequality (LMI) approach for designing such an observer-based H(infinity) controller is presented. Finally, a simulation example is used to demonstrate the effectiveness of the proposed method. 2009. Published by Elsevier Ltd.
Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial
Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.
2016-01-01
Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724
Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.
Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N
2016-12-01
Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).
Numerical simulation of fibrous biomaterials with randomly distributed fiber network structure.
Jin, Tao; Stanciulescu, Ilinca
2016-08-01
This paper presents a computational framework to simulate the mechanical behavior of fibrous biomaterials with randomly distributed fiber networks. A random walk algorithm is implemented to generate the synthetic fiber network in 2D used in simulations. The embedded fiber approach is then adopted to model the fibers as embedded truss elements in the ground matrix, which is essentially equivalent to the affine fiber kinematics. The fiber-matrix interaction is partially considered in the sense that the two material components deform together, but no relative movement is considered. A variational approach is carried out to derive the element residual and stiffness matrices for finite element method (FEM), in which material and geometric nonlinearities are both included. Using a data structure proposed to record the network geometric information, the fiber network is directly incorporated into the FEM simulation without significantly increasing the computational cost. A mesh sensitivity analysis is conducted to show the influence of mesh size on various simulation results. The proposed method can be easily combined with Monte Carlo (MC) simulations to include the influence of the stochastic nature of the network and capture the material behavior in an average sense. The computational framework proposed in this work goes midway between homogenizing the fiber network into the surrounding matrix and accounting for the fully coupled fiber-matrix interaction at the segment length scale, and can be used to study the connection between the microscopic structure and the macro-mechanical behavior of fibrous biomaterials with a reasonable computational cost.
Randomness in the evolution of cooperation.
Hadzibeganovic, Tarik; Stauffer, Dietrich; Han, Xiao-Pu
2015-04-01
Tag-based ethnocentric cooperation is a highly robust behavior which can evolve and prevail under a wide variety of conditions. Recent studies have demonstrated, however, that ethnocentrism can temporarily be suppressed by other competing strategies, especially in its early evolutionary stages. In a series of computational experiments, conducted with an agent-based evolutionary model of tag-mediated cooperation, we addressed the question of whether a stochastically established and once dominant non-ethnocentric strategy such as indiscriminate altruism can stably persist and permanently outweigh ethnocentrism. Our model, simulated on various complex network topologies, employs simple haploid genetics and asexual reproduction of computational agents equipped with memory and heritable phenotypic traits. We find that in combination with an implemented memory mechanism and tags, random bias acting in favor of altruists can lead to their long-lasting victory over all other types of strategists. The difference in density between altruistic and ethnocentric cooperators increases with greater rewiring of the underlying network, but decreases with growing population size. These findings suggest that randomness plays an important role in promoting non-ethnocentric cooperation and contributes to our understanding of how other than adaptive mechanisms can initiate the design of novel behavioral phenotypes, thereby shaping surprisingly new evolutionary pathways. Copyright © 2015 Elsevier B.V. All rights reserved.
Degree-correlation, omniscience, and randomized immunization protocols in finite networks
Alm, Jeremy F
2016-01-01
Many naturally occurring networks have a power-law degree distribution as well as a non-zero degree correlation. Despite this, most studies analyzing the efficiency of immunization strategies in networks have concentrated only on power-law degree distribution and ignored degree correlation. This study looks specifically at the effect degree-correlation has on the efficiency of several immunization strategies in scale-free networks. Generally, we found that positive degree correlation raises the number of immunized individuals needed to stop the spread of the infection. Importantly, we found that in networks with positive degree correlation, immunization strategies that utilize knowledge of initial popularity actually perform worse on average than random immunization strategies.
H∞ Networked Cascade Control System Design for Turboshaft Engines with Random Packet Dropouts
Directory of Open Access Journals (Sweden)
Xiaofeng Liu
2017-01-01
Full Text Available The distributed control architecture becomes more and more important in future gas turbine engine control systems, in which the sensors and actuators will be connected to the controllers via a network. Therefore, the control problem of network-enabled high-performance distributed engine control (DEC has come to play an important role in modern gas turbine control systems, while, due to the properties of the network, the packet dropouts must be considered. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS. Typical turboshaft engine distributed controllers are designed based on the NCCS framework with H∞ state feedback under random packet dropouts. The sufficient robust stable conditions are derived via the Lyapunov stability theory and linear matrix inequality approach. Simulations illustrate the effectiveness of the presented method.
LCN: a random graph mixture model for community detection in functional brain networks.
Bryant, Christopher; Zhu, Hongtu; Ahn, Mihye; Ibrahim, Joseph
2017-01-01
The aim of this article is to develop a Bayesian random graph mixture model (RGMM) to detect the latent class network (LCN) structure of brain connectivity networks and estimate the parameters governing this structure. The use of conjugate priors for unknown parameters leads to efficient estimation, and a well-known nonidentifiability issue is avoided by a particular parameterization of the stochastic block model (SBM). Posterior computation proceeds via an efficient Markov Chain Monte Carlo algorithm. Simulations demonstrate that LCN outperforms several other competing methods for community detection in weighted networks, and we apply our RGMM to estimate the latent community structures in the functional resting brain networks of 185 subjects from the ADHD-200 sample. We find overlap in the estimated community structure across subjects, but also heterogeneity even within a given diagnosis group.
Diffusion in random networks: Asymptotic properties, and numerical and engineering approximations
Padrino, Juan C.; Zhang, Duan Z.
2016-11-01
The ensemble phase averaging technique is applied to model mass transport by diffusion in random networks. The system consists of an ensemble of random networks, where each network is made of a set of pockets connected by tortuous channels. Inside a channel, we assume that fluid transport is governed by the one-dimensional diffusion equation. Mass balance leads to an integro-differential equation for the pores mass density. The so-called dual porosity model is found to be equivalent to the leading order approximation of the integration kernel when the diffusion time scale inside the channels is small compared to the macroscopic time scale. As a test problem, we consider the one-dimensional mass diffusion in a semi-infinite domain, whose solution is sought numerically. Because of the required time to establish the linear concentration profile inside a channel, for early times the similarity variable is xt- 1 / 4 rather than xt- 1 / 2 as in the traditional theory. This early time sub-diffusive similarity can be explained by random walk theory through the network. In addition, by applying concepts of fractional calculus, we show that, for small time, the governing equation reduces to a fractional diffusion equation with known solution. We recast this solution in terms of special functions easier to compute. Comparison of the numerical and exact solutions shows excellent agreement.
Random sampling of elementary flux modes in large-scale metabolic networks.
Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel
2012-09-15
The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.
Multiple random walks on complex networks: A harmonic law predicts search time
Weng, Tongfeng; Zhang, Jie; Small, Michael; Hui, Pan
2017-05-01
We investigate multiple random walks traversing independently and concurrently on complex networks and introduce the concept of mean first parallel passage time (MFPPT) to quantify their search efficiency. The mean first parallel passage time represents the expected time required to find a given target by one or some of the multiple walkers. We develop a general theory that allows us to calculate the MFPPT analytically. Interestingly, we find that the global MFPPT follows a harmonic law with respect to the global mean first passage times of the associated walkers. Remarkably, when the properties of multiple walkers are identical, the global MFPPT decays in a power law manner with an exponent of unity, irrespective of network structure. These findings are confirmed by numerical and theoretical results on various synthetic and real networks. The harmonic law reveals a universal principle governing multiple random walks on networks that uncovers the contribution and role of the combined walkers in a target search. Our paradigm is also applicable to a broad range of random search processes.
Directory of Open Access Journals (Sweden)
Paul eMiller
2013-05-01
Full Text Available Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be destabilized with increasing input. This leads to an itinerancy, where with either repeated transient stimuli, or increasing duration of a single stimulus, the network activity advances through sequences of attractor states. We find that the resulting network state, which persists beyond stimulus offset, can encode the number of stimuli presented via a distributed representation of neural activity with non-monotonic tuning curves for most neurons. Increased duration of a single stimulus is encoded via different distributed representations, so unlike an integrator, the network distinguishes separate successive presentations of a short stimulus from a single presentation of a longer stimulus with equal total duration. Moreover, different amplitudes of stimulus cause new, distinct activity patterns, such that changes in stimulus number, duration and amplitude can be distinguished from each other. These properties of the network depend on dynamic depressing synapses, as they disappear if synapses are static. Thus short-term synaptic depression allows a network to store separately the different dynamic properties of a spatially constant stimulus.
Pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks
Wu, Shaochuan; Tan, Xuezhi
2007-11-01
By analyzing all kinds of address configuration algorithms, this paper provides a new pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks. Based on PRDAC, the first node that initials this network randomly chooses a nonlinear shift register that can generates an m-sequence. When another node joins this network, the initial node will act as an IP address configuration sever to compute an IP address according to this nonlinear shift register, and then allocates this address and tell the generator polynomial of this shift register to this new node. By this means, when other node joins this network, any node that has obtained an IP address can act as a server to allocate address to this new node. PRDAC can also efficiently avoid IP conflicts and deal with network partition and merge as same as prophet address (PA) allocation and dynamic configuration and distribution protocol (DCDP). Furthermore, PRDAC has less algorithm complexity, less computational complexity and more sufficient assumption than PA. In addition, PRDAC radically avoids address conflicts and maximizes the utilization rate of IP addresses. Analysis and simulation results show that PRDAC has rapid convergence, low overhead and immune from topological structures.
Directory of Open Access Journals (Sweden)
Géraud Thierry
2004-01-01
Full Text Available We present a fast method for road network extraction in satellite images. It can be seen as a transposition of the segmentation scheme "watershed transform region adjacency graph Markov random fields" to the extraction of curvilinear objects. Many road extractors which are composed of two stages can be found in the literature. The first one acts like a filter that can decide from a local analysis, at every image point, if there is a road or not. The second stage aims at obtaining the road network structure. In the method we propose to rely on a "potential" image, that is, unstructured image data that can be derived from any road extractor filter. In such a potential image, the value assigned to a point is a measure of its likelihood to be located in the middle of a road. A filtering step applied on the potential image relies on the area closing operator followed by the watershed transform to obtain a connected line which encloses the road network. Then a graph describing adjacency relationships between watershed lines is built. Defining Markov random fields upon this graph, associated with an energetic model of road networks, leads to the expression of road network extraction as a global energy minimization problem. This method can easily be adapted to other image processing fields, where the recognition of curvilinear structures is involved.
Estimating the Size of a Large Network and its Communities from a Random Sample.
Chen, Lin; Karbasi, Amin; Crawford, Forrest W
2016-01-01
Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K, and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios.
An adaptive random search for short term generation scheduling with network constraints.
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J A Marmolejo
Full Text Available This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.
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Tingjian Chen
Full Text Available Cell reprogramming for microorganisms via engineered or artificial transcription factors and RNA polymerase mutants has presented a powerful tool for eliciting complex traits that are practically useful particularly for industrial strains, and for understanding at the global level the regulatory network of gene transcription. We previously further showed that an exogenous global regulator IrrE (derived from the extreme radiation-resistant bacterium Deinococcus radiodurans can be tailored to confer Escherichia coli (E. coli with significantly enhanced tolerances to different stresses. In this work, based on comparative transcriptomic and proteomic analyses of the representative strains E1 and E0, harboring the ethanol-tolerant IrrE mutant E1 and the ethanol-intolerant wild type IrrE, respectively, we found that the transcriptome and proteome of E. coli were extensively rewired by the tailored IrrE protein. Overall, 1196 genes (or approximately 27% of E. coli genes were significantly altered at the transcriptomic level, including notably genes in the nitrate-nitrite-nitric oxide (NO pathway, and genes for non-coding RNAs. The proteomic profile revealed significant up- or downregulation of several proteins associated with syntheses of the cell membrane and cell wall. Analyses of the intracellular NO level and cell growth under reduced temperature supported a close correlation between NO and ethanol tolerance, and also suggests a role for membrane fluidity. The significantly different omic profiles of strain E1 indicate that IrrE functions as a global regulator in E. coli, and that IrrE may be evolved for other cellular tolerances. In this sense, it will provide synthetic biology with a practical and evolvable regulatory "part" that operates at a higher level of complexity than local regulators. This work also suggests a possibility of introducing and engineering other exogenous global regulators to rewire the genomes of microorganism cells.
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Jayaweera SudharmanK
2010-01-01
Full Text Available Performance gain achieved by adding mobile nodes to a stationary sensor network for target detection depends on factors such as the number of mobile nodes deployed, mobility patterns, speed and energy constraints of mobile nodes, and the nature of the target locations (deterministic or random. In this paper, we address the problem of distributed detection of a randomly located target by a hybrid sensor network. Specifically, we develop two decision-fusion architectures for detection where in the first one, impact of node mobility is taken into account for decisions updating at the fusion center, while in the second model the impact of node mobility is taken at the node level decision updating. The cost of deploying mobile nodes is analyzed in terms of the minimum fraction of mobile nodes required to achieve the desired performance level within a desired delay constraint. Moreover, we consider managing node mobility under given constraints.
Randomized gradient-free method for multiagent optimization over time-varying networks.
Yuan, Deming; Ho, Daniel W C
2015-06-01
In this brief, we consider the multiagent optimization over a network where multiple agents try to minimize a sum of nonsmooth but Lipschitz continuous functions, subject to a convex state constraint set. The underlying network topology is modeled as time varying. We propose a randomized derivative-free method, where in each update, the random gradient-free oracles are utilized instead of the subgradients (SGs). In contrast to the existing work, we do not require that agents are able to compute the SGs of their objective functions. We establish the convergence of the method to an approximate solution of the multiagent optimization problem within the error level depending on the smoothing parameter and the Lipschitz constant of each agent's objective function. Finally, a numerical example is provided to demonstrate the effectiveness of the method.
Stability Analysis of Recurrent Neural Networks with Random Delay and Markovian Switching
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Enwen Zhu
2010-01-01
Full Text Available In this paper, the exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs with random delay and Markovian switching. The evolution of the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed recurrent neural network with Markovian switching is exponentially stable. The analysis is based on the Lyapunov-Krasovskii functional and stochastic analysis approach, and the conditions are expressed in terms of linear matrix inequalities, which can be readily checked by using some standard numerical packages such as the Matlab LMI Toolbox. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.
Ambient awareness: From random noise to digital closeness in online social networks
Levordashka, Ana; Utz, Sonja
2016-01-01
Ambient awareness refers to the awareness social media users develop of their online network in result of being constantly exposed to social information, such as microblogging updates. Although each individual bit of information can seem like random noise, their incessant reception can amass to a coherent representation of social others. Despite its growing popularity and important implications for social media research, ambient awareness on public social media has not been studied empiricall...
Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks
Jia, Tao; Barab?si, Albert-L?szl?
2013-01-01
Controlling complex systems is a fundamental challenge of network science. Recent advances indicate that control over the system can be achieved through a minimum driver node set (MDS). The existence of multiple MDS's suggests that nodes do not participate in control equally, prompting us to quantify their participations. Here we introduce control capacity quantifying the likelihood that a node is a driver node. To efficiently measure this quantity, we develop a random sampling algorithm. Thi...
Network Medicine Strikes a Blow against Breast Cancer
DEFF Research Database (Denmark)
Erler, Janine Terra; Linding, Rune
2012-01-01
Drug development for complex diseases is shifting from targeting individual proteins or genes to systems-based attacks targeting dynamic network states. Lee et al. now reveal how the progressive rewiring of a signaling network over time following EGF receptor inhibition leaves triple-negative bre...
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Raquel Caballero-Águila
2015-01-01
Full Text Available The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be one-step autocorrelated and different sensor noises are one-step cross-correlated; also, the process noise and each sensor measurement noise are two-step cross-correlated. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.
Naming games in two-dimensional and small-world-connected random geometric networks
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.
Naming games in two-dimensional and small-world-connected random geometric networks.
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.
A hybrid network intrusion detection framework based on random forests and weighted k-means
Directory of Open Access Journals (Sweden)
Reda M. Elbasiony
2013-12-01
Full Text Available Many current NIDSs are rule-based systems, which are very difficult in encoding rules, and cannot detect novel intrusions. Therefore, a hybrid detection framework that depends on data mining classification and clustering techniques is proposed. In misuse detection, random forests classification algorithm is used to build intrusion patterns automatically from a training dataset, and then matches network connections to these intrusion patterns to detect network intrusions. In anomaly detection, the k-means clustering algorithm is used to detect novel intrusions by clustering the network connections’ data to collect the most of intrusions together in one or more clusters. In the proposed hybrid framework, the anomaly part is improved by replacing the k-means algorithm with another one called weighted k-means algorithm, moreover, it uses a proposed method in choosing the anomalous clusters by injecting known attacks into uncertain connections data. Our approaches are evaluated over the Knowledge Discovery and Data Mining (KDD’99 datasets.
Properties of a new small-world network with spatially biased random shortcuts
Matsuzawa, Ryo; Tanimoto, Jun; Fukuda, Eriko
2017-11-01
This paper introduces a small-world (SW) network with a power-law distance distribution that differs from conventional models in that it uses completely random shortcuts. By incorporating spatial constraints, we analyze the divergence of the proposed model from conventional models in terms of fundamental network properties such as clustering coefficient, average path length, and degree distribution. We find that when the spatial constraint more strongly prohibits a long shortcut, the clustering coefficient is improved and the average path length increases. We also analyze the spatial prisoner's dilemma (SPD) games played on our new SW network in order to understand its dynamical characteristics. Depending on the basis graph, i.e., whether it is a one-dimensional ring or a two-dimensional lattice, and the parameter controlling the prohibition of long-distance shortcuts, the emergent results can vastly differ.
Edge-based SEIR dynamics with or without infectious force in latent period on random networks
Wang, Yi; Cao, Jinde; Alsaedi, Ahmed; Ahmad, Bashir
2017-04-01
In nature, most of the diseases have latent periods, and most of the networks look as if they were spun randomly at the first glance. Hence, we consider SEIR dynamics with or without infectious force in latent period on random networks with arbitrary degree distributions. Both of these models are governed by intrinsically three dimensional nonlinear systems of ordinary differential equations, which are the same as classical SEIR models. The basic reproduction numbers and the final size formulae are explicitly derived. Predictions of the models agree well with the large-scale stochastic SEIR simulations on contact networks. In particular, for SEIR model without infectious force in latent period, although the length of latent period has no effect on the basic reproduction number and the final epidemic size, it affects the arrival time of the peak and the peak size; while for SEIR model with infectious force in latent period it also affects the basic reproduction number and the final epidemic size. These accurate model predictions, may provide guidance for the control of network infectious diseases with latent periods.
Throughput-Delay Analysis of Random Linear Network Coding for Wireless Broadcasting
Swapna, B T; Shroff, Ness B
2011-01-01
In an unreliable single-hop broadcast network setting, we investigate the throughput and decoding-delay performance of random linear network coding as a function of the coding window size and the network size. Our model consists of a source transmitting packets of a single flow to a set of $n$ users over independent erasure channels. The source performs random linear network coding (RLNC) over $k$ (coding window size) packets and broadcasts them to the users. We note that the broadcast throughput of RLNC must vanish with increasing $n$, for any fixed $k.$ Hence, in contrast to other works in the literature, we investigate how the coding window size $k$ must scale for increasing $n$. Our analysis reveals that the coding window size of $\\Theta(\\ln(n))$ represents a phase transition rate, below which the throughput converges to zero, and above which it converges to the broadcast capacity. Further, we characterize the asymptotic distribution of decoding delay and provide approximate expressions for the mean and v...
Estimating the Size of a Large Network and its Communities from a Random Sample
Chen, Lin; Crawford, Forrest W
2016-01-01
Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V;E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that correctly estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhausti...
Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.
Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B
2014-11-01
Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks. Copyright © 2014. Published by Elsevier Ltd.
Prezel, Elea; Elie, Auréliane; Delaroche, Julie; Stoppin-Mellet, Virginie; Bosc, Christophe; Serre, Laurence; Fourest-Lieuvin, Anne; Andrieux, Annie; Vantard, Marylin; Arnal, Isabelle
2017-11-22
In neurons, microtubule networks alternate between single filaments and bundled arrays under the influence of effectors controlling their dynamics and organization. Tau is a microtubule bundler which stabilizes microtubules by stimulating growth and inhibiting shrinkage. The mechanisms by which tau organizes microtubule networks remain poorly understood. Here, we studied the self-organization of microtubules growing in the presence of tau isoforms and mutants. The results show that tau's ability to induce stable microtubule bundles requires two hexapeptides located in its microtubule-binding domain, and is modulated by its projection domain. Site-specific pseudo-phosphorylation of tau promotes distinct microtubule organizations: stable single microtubules, stable bundles or dynamic bundles. Disease-related tau mutations increase the formation of highly dynamic bundles. Finally, cryo-electron microscopy experiments indicate that tau and its variants similarly change the microtubule lattice structure by increasing both the protofilament number and lattice defects. Overall, our results uncover novel phospho-dependent mechanisms governing tau's ability to trigger microtubule organization and reveal that disease-related modifications of tau promote specific microtubule organizations which may have a deleterious impact during neurodegeneration. © 2017 by The American Society for Cell Biology.
Optimal system size for complex dynamics in random neural networks near criticality
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Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)
2013-12-15
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.
Capturing the Flatness of a peer-to-peer lending network through random and selected perturbations
Karampourniotis, Panagiotis D.; Singh, Pramesh; Uparna, Jayaram; Horvat, Emoke-Agnes; Szymanski, Boleslaw K.; Korniss, Gyorgy; Bakdash, Jonathan Z.; Uzzi, Brian
Null models are established tools that have been used in network analysis to uncover various structural patterns. They quantify the deviance of an observed network measure to that given by the null model. We construct a null model for weighted, directed networks to identify biased links (carrying significantly different weights than expected according to the null model) and thus quantify the flatness of the system. Using this model, we study the flatness of Kiva, a large international crownfinancing network of borrowers and lenders, aggregated to the country level. The dataset spans the years from 2006 to 2013. Our longitudinal analysis shows that flatness of the system is reducing over time, meaning the proportion of biased inter-country links is growing. We extend our analysis by testing the robustness of the flatness of the network in perturbations on the links' weights or the nodes themselves. Examples of such perturbations are event shocks (e.g. erecting walls) or regulatory shocks (e.g. Brexit). We find that flatness is unaffected by random shocks, but changes after shocks target links with a large weight or bias. The methods we use to capture the flatness are based on analytics, simulations, and numerical computations using Shannon's maximum entropy. Supported by ARL NS-CTA.
Light scattering optimization of chitin random network in ultrawhite beetle scales
Utel, Francesco; Cortese, Lorenzo; Pattelli, Lorenzo; Burresi, Matteo; Vignolini, Silvia; Wiersma, Diederik
2017-09-01
Among the natural white colored photonics structures, a bio-system has become of great interest in the field of disordered optical media: the scale of the white beetle Chyphochilus. Despite its low thickness, on average 7 μm, and low refractive index, this beetle exhibits extreme high brightness and unique whiteness. These properties arise from the interaction of light with a complex network of chitin nano filaments embedded in the interior of the scales. As it's been recently claimed, this could be a consequence of the peculiar morphology of the filaments network that, by means of high filling fraction (0.61) and structural anisotropy, optimizes the multiple scattering of light. We therefore performed a numerical analysis on the structural properties of the chitin network in order to understand their role in the enhancement of the scale scattering intensity. Modeling the filaments as interconnected rod shaped scattering centers, we numerically generated the spatial coordinates of the network components. Controlling the quantities that are claimed to play a fundamental role in the brightness and whiteness properties of the investigated system (filling fraction and average rods orientation, i.e. the anisotropy of the ensemble of scattering centers), we obtained a set of customized random networks. FDTD simulations of light transport have been performed on these systems, observing high reflectance for all the visible frequencies and proving the implemented algorithm to numerically generate the structures is suitable to investigate the dependence of reflectance by anisotropy.
Wang, Na; Zeng, Jiwen
2017-03-17
Wireless sensor networks are deployed to monitor the surrounding physical environments and they also act as the physical environments of parasitic sensor networks, whose purpose is analyzing the contextual privacy and obtaining valuable information from the original wireless sensor networks. Recently, contextual privacy issues associated with wireless communication in open spaces have not been thoroughly addressed and one of the most important challenges is protecting the source locations of the valuable packages. In this paper, we design an all-direction random routing algorithm (ARR) for source-location protecting against parasitic sensor networks. For each package, the routing process of ARR is divided into three stages, i.e., selecting a proper agent node, delivering the package to the agent node from the source node, and sending it to the final destination from the agent node. In ARR, the agent nodes are randomly chosen in all directions by the source nodes using only local decisions, rather than knowing the whole topology of the networks. ARR can control the distributions of the routing paths in a very flexible way and it can guarantee that the routing paths with the same source and destination are totally different from each other. Therefore, it is extremely difficult for the parasitic sensor nodes to trace the packages back to the source nodes. Simulation results illustrate that ARR perfectly confuses the parasitic nodes and obviously outperforms traditional routing-based schemes in protecting source-location privacy, with a marginal increase in the communication overhead and energy consumption. In addition, ARR also requires much less energy than the cloud-based source-location privacy protection schemes.
Network Centrality Analysis in Fungi Reveals Complex Regulation of Lost and Gained Genes.
Coulombe-Huntington, Jasmin; Xia, Yu
2017-01-01
Gene gain and loss shape both proteomes and the networks they form. The increasing availability of closely related sequenced genomes and of genome-wide network data should enable a better understanding of the evolutionary forces driving gene gain, gene loss and evolutionary network rewiring. Using orthology mappings across 23 ascomycete fungi genomes, we identified proteins that were lost, gained or universally conserved across the tree, enabling us to compare genes across all stages of their life-cycle. Based on a collection of genome-wide network and gene expression datasets from baker's yeast, as well as a few from fission yeast, we found that gene loss is more strongly associated with network and expression features of closely related species than that of distant species, consistent with the evolutionary modulation of gene loss propensity through network rewiring. We also discovered that lost and gained genes, as compared to universally conserved "core" genes, have more regulators, more complex expression patterns and are much more likely to encode for transcription factors. Finally, we found that the relative rate of network integration of new genes into the different types of networks agrees with experimentally measured rates of network rewiring. This systems-level view of the life-cycle of eukaryotic genes suggests that the gain and loss of genes is tightly coupled to the gain and loss of network interactions, that lineage-specific adaptations drive regulatory complexity and that the relative rates of integration of new genes are consistent with network rewiring rates.
Synchrony-optimized networks of Kuramoto oscillators with inertia
Pinto, Rafael S.; Saa, Alberto
2016-12-01
We investigate synchronization in networks of Kuramoto oscillators with inertia. More specifically, we introduce a rewiring algorithm consisting basically in a hill climb scheme in which the edges of the network are swapped in order to enhance its synchronization capacity. We show that the synchrony-optimized networks generated by our algorithm have some interesting topological and dynamical properties. In particular, they typically exhibit an anticipation of the synchronization onset and are more robust against certain types of perturbations. We consider synthetic random networks and also a network with a topology based on an approximated model of the (high voltage) power grid of Spain, since networks of Kuramoto oscillators with inertia have been used recently as simplified models for power grids, for which synchronization is obviously a crucial issue. Despite the extreme simplifications adopted in these models, our results, among others recently obtained in the literature, may provide interesting principles to guide the future growth and development of real-world grids, specially in the case of a change of the current paradigm of centralized towards distributed generation power grids.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting
Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo
2015-01-01
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825
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Zhuo Qi Lee
Full Text Available Biased random walk has been studied extensively over the past decade especially in the transport and communication networks communities. The mean first passage time (MFPT of a biased random walk is an important performance indicator in those domains. While the fundamental matrix approach gives precise solution to MFPT, the computation is expensive and the solution lacks interpretability. Other approaches based on the Mean Field Theory relate MFPT to the node degree alone. However, nodes with the same degree may have very different local weight distribution, which may result in vastly different MFPT. We derive an approximate bound to the MFPT of biased random walk with short relaxation time on complex network where the biases are controlled by arbitrarily assigned node weights. We show that the MFPT of a node in this general case is closely related to not only its node degree, but also its local weight distribution. The MFPTs obtained from computer simulations also agree with the new theoretical analysis. Our result enables fast estimation of MFPT, which is useful especially to differentiate between nodes that have very different local node weight distribution even though they share the same node degrees.
Ramakrishnan, Navneeth; Lai, Ying Tong; Lara, Silvia; Parish, Meera M.; Adam, Shaffique
2017-12-01
A linear unsaturating magnetoresistance at high perpendicular magnetic fields, together with a quadratic positive magnetoresistance at low fields, has been seen in many different experimental materials, ranging from silver chalcogenides and thin films of InSb to topological materials like graphene and Dirac semimetals. In the literature, two very different theoretical approaches have been used to explain this classical magnetoresistance as a consequence of sample disorder. The phenomenological random resistor network model constructs a grid of four terminal resistors, each with a varying random resistance. The effective medium theory model imagines a smoothly varying disorder potential that causes a continuous variation of the local conductivity. Here, we demonstrate numerically that both models belong to the same universality class and that a restricted class of the random resistor network is actually equivalent to the effective medium theory. Both models are also in good agreement with experiments on a diverse range of materials. Moreover, we show that in both cases, a single parameter, i.e., the ratio of the fluctuations in the carrier density to the average carrier density, completely determines the magnetoresistance profile.
German, D.; Sutcliffe, C. G.; Sirirojn, B.; Sherman, S. G.; Latkin, C. A.; Aramrattana, A.; Celentano, D. D.
2012-01-01
We examined the effect on depressive symptoms of a peer network-oriented intervention effective in reducing sexual risk behavior and methamphetamine (MA) use. Current Thai MA users aged 18-25 years and their drug and/or sex network members enrolled in a randomized controlled trial with 4 follow-ups over 12 months. A total of 415 index participants…
Rolls, David A.; Wang, Peng; McBryde, Emma; Pattison, Philippa; Robins, Garry
2015-01-01
We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a “hidden population”. In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model) and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure. PMID:26555701
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Guitao Zhang
2014-01-01
Full Text Available The advertisement can increase the consumers demand; therefore it is one of the most important marketing strategies in the operations management of enterprises. This paper aims to analyze the impact of advertising investment on a discrete dynamic supply chain network which consists of suppliers, manufactures, retailers, and demand markets associated at different tiers under random demand. The impact of advertising investment will last several planning periods besides the current period due to delay effect. Based on noncooperative game theory, variational inequality, and Lagrange dual theory, the optimal economic behaviors of the suppliers, the manufactures, the retailers, and the consumers in the demand markets are modeled. In turn, the supply chain network equilibrium model is proposed and computed by modified project contraction algorithm with fixed step. The effectiveness of the model is illustrated by numerical examples, and managerial insights are obtained through the analysis of advertising investment in multiple periods and advertising delay effect among different periods.
Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services
DEFF Research Database (Denmark)
Tassi, Andrea; Chatzigeorgiou, Ioannis; Roetter, Daniel Enrique Lucani
2016-01-01
techniques, and without any assumption on the implementation of the RLNC decoder in use, we provide an efficient way to characterize the performance of users targeted by ultra-reliable layered multicast services. The proposed modeling allows to efficiently derive the average number of coded packet...... transmissions needed to recover one or more service layers. We design a convex resource allocation framework that allows to minimize the complexity of the RLNC decoder by jointly optimizing the transmission parameters and the sparsity of the code. The designed optimization framework also ensures service......Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliability of communications is ensured via different random linear network coding (RLNC...
Ball, Frank
2016-01-01
This paper is concerned with the analysis of vaccination strategies in a stochastic SIR (susceptible $\\to$ infected $\\to$ removed) model for the spread of an epidemic amongst a population of individuals with a random network of social contacts that is also partitioned into households. Under various vaccine action models, we consider both household-based vaccination schemes, in which the way in which individuals are chosen for vaccination depends on the size of the households in which they reside, and acquaintance vaccination, which targets individuals of high degree in the social network. For both types of vaccination scheme, assuming a large population with few initial infectives, we derive a threshold parameter which determines whether or not a large outbreak can occur and also the probability and fraction of the population infected by such an outbreak. The performance of these schemes is studied numerically, focusing on the influence of the household size distribution and the degree distribution of the soc...
Directory of Open Access Journals (Sweden)
Junlong Zhu
2017-01-01
Full Text Available We consider a distributed constrained optimization problem over a time-varying network, where each agent only knows its own cost functions and its constraint set. However, the local constraint set may not be known in advance or consists of huge number of components in some applications. To deal with such cases, we propose a distributed stochastic subgradient algorithm over time-varying networks, where the estimate of each agent projects onto its constraint set by using random projection technique and the implement of information exchange between agents by employing asynchronous broadcast communication protocol. We show that our proposed algorithm is convergent with probability 1 by choosing suitable learning rate. For constant learning rate, we obtain an error bound, which is defined as the expected distance between the estimates of agent and the optimal solution. We also establish an asymptotic upper bound between the global objective function value at the average of the estimates and the optimal value.
Control capacity and a random sampling method in exploring controllability of complex networks.
Jia, Tao; Barabási, Albert-László
2013-01-01
Controlling complex systems is a fundamental challenge of network science. Recent advances indicate that control over the system can be achieved through a minimum driver node set (MDS). The existence of multiple MDS's suggests that nodes do not participate in control equally, prompting us to quantify their participations. Here we introduce control capacity quantifying the likelihood that a node is a driver node. To efficiently measure this quantity, we develop a random sampling algorithm. This algorithm not only provides a statistical estimate of the control capacity, but also bridges the gap between multiple microscopic control configurations and macroscopic properties of the network under control. We demonstrate that the possibility of being a driver node decreases with a node's in-degree and is independent of its out-degree. Given the inherent multiplicity of MDS's, our findings offer tools to explore control in various complex systems.
DEFF Research Database (Denmark)
Veenstra, Frank; Struck, Alexander; Krauledat, Matthias
2015-01-01
The acquisition and optimization of dynamically stable locomotion is important to engender fast and energy efficient locomotion in animals. Conventional optimization strategies tend to have difficulties in acquiring dynamically stable gaits in legged robots. In this paper, an evolving neural...... network (ENN) was implemented with the aim to optimize the locomotive behavior of a four-legged simulated robot. In the initial generation, individuals had neural networks (NNs) that were either predefined or randomly initialized. Additional investigations show that the efficiency of applying additional...... sensors to the simulated quadruped improved the performance of the ENN slightly. Promising results were seen in the evolutionary runs where the initial predefined NNs of the population contributed to slight movements of the limbs. This paper shows how a predefined ENNs linked to bio-inspired sensors can...
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Elli Kartsakli
2014-03-01
Full Text Available Relay sensor networks are often employed in end-to-end healthcare applications to facilitate the information flow between patient worn sensors and the medical data center. Medium access control (MAC protocols, based on random linear network coding (RLNC, are a novel and suitable approach to efficiently handle data dissemination. However, several challenges arise, such as additional delays introduced by the intermediate relay nodes and decoding failures, due to channel errors. In this paper, we tackle these issues by adopting a cloud architecture where the set of relays is connected to a coordinating entity, called cloud manager. We propose a cloud-assisted RLNC-based MAC protocol (CLNC-MAC and develop a mathematical model for the calculation of the key performance metrics, namely the system throughput, the mean completion time for data delivery and the energy efficiency. We show the importance of central coordination in fully exploiting the gain of RLNC under error-prone channels.
Balankin, Alexander S.; Susarrey Huerta, Orlando; Tapia, Viktor
2013-09-01
We study stress relaxation in hand folded aluminum foils subjected to the uniaxial compression force F(λ). We found that once the compression ratio is fixed (λ=const) the compression force decreases in time as F∝F0P(t), where P(t) is the survival probability time distribution belonging to the domain of attraction of max-stable distribution of the Fréchet type. This finding provides a general physical picture of energy dissipation in the crumpling network of a crushed elastoplastic foil. The difference between energy dissipation statistics in crushed viscoelastic papers and elastoplastic foils is outlined. Specifically, we argue that the dissipation of elastic energy in crushed aluminum foils is ruled by a multiplicative Poisson process governed by the maximum waiting time distribution. The mapping of this process into the problem of transient random walk on a fractal crumpling network is suggested.
Ambient awareness: From random noise to digital closeness in online social networks.
Levordashka, Ana; Utz, Sonja
2016-07-01
Ambient awareness refers to the awareness social media users develop of their online network in result of being constantly exposed to social information, such as microblogging updates. Although each individual bit of information can seem like random noise, their incessant reception can amass to a coherent representation of social others. Despite its growing popularity and important implications for social media research, ambient awareness on public social media has not been studied empirically. We provide evidence for the occurrence of ambient awareness and examine key questions related to its content and functions. A diverse sample of participants reported experiencing awareness, both as a general feeling towards their network as a whole, and as knowledge of individual members of the network, whom they had not met in real life. Our results indicate that ambient awareness can develop peripherally, from fragmented information and in the relative absence of extensive one-to-one communication. We report the effects of demographics, media use, and network variables and discuss the implications of ambient awareness for relational and informational processes online.
A novel root-index based prioritized random access scheme for 5G cellular networks
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Taehoon Kim
2015-12-01
Full Text Available Cellular networks will play an important role in realizing the newly emerging Internet-of-Everything (IoE. One of the challenging issues is to support the quality of service (QoS during the access phase, while accommodating a massive number of machine nodes. In this paper, we show a new paradigm of multiple access priorities in random access (RA procedure and propose a novel root-index based prioritized random access (RIPRA scheme that implicitly embeds the access priority in the root index of the RA preambles. The performance evaluation shows that the proposed RIPRA scheme can successfully support differentiated performance for different access priority levels, even though there exist a massive number of machine nodes.
Variances as order parameter and complexity measure for random Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Luque, Bartolo [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain); Ballesteros, Fernando J [Observatori Astronomic, Universitat de Valencia, Ed. Instituts d' Investigacio, Pol. La Coma s/n, E-46980 Paterna, Valencia (Spain); Fernandez, Manuel [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain)
2005-02-04
Several order parameters have been considered to predict and characterize the transition between ordered and disordered phases in random Boolean networks, such as the Hamming distance between replicas or the stable core, which have been successfully used. In this work, we propose a natural and clear new order parameter: the temporal variance. We compute its value analytically and compare it with the results of numerical experiments. Finally, we propose a complexity measure based on the compromise between temporal and spatial variances. This new order parameter and its related complexity measure can be easily applied to other complex systems.
Flexible sampling large-scale social networks by self-adjustable random walk
Xu, Xiao-Ke; Zhu, Jonathan J. H.
2016-12-01
Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.
Social networking technologies as an emerging tool for HIV prevention: a cluster randomized trial.
Young, Sean D; Cumberland, William G; Lee, Sung-Jae; Jaganath, Devan; Szekeres, Greg; Coates, Thomas
2013-09-03
Social networking technologies are an emerging tool for HIV prevention. To determine whether social networking communities can increase HIV testing among African American and Latino men who have sex with men (MSM). Randomized, controlled trial with concealed allocation. (ClinicalTrials.gov: NCT01701206). Online. 112 MSM based in Los Angeles, more than 85% of whom were African American or Latino. Sixteen peer leaders were randomly assigned to deliver information about HIV or general health to participants via Facebook groups over 12 weeks. After participants accepted a request to join the group, participation was voluntary. Group participation and engagement were monitored. Participants could request a free, home-based HIV testing kit and completed questionnaires at baseline and 12-week follow-up. Participant acceptance of and engagement in the intervention and social network participation, rates of home-based HIV testing, and sexual risk behaviors. Almost 95% of intervention participants and 73% of control participants voluntarily communicated using the social platform. Twenty-five of 57 intervention participants (44%) requested home-based HIV testing kits compared with 11 of 55 control participants (20%) (difference, 24 percentage points [95% CI, 8 to 41 percentage points]). Nine of the 25 intervention participants (36%) who requested the test took it and mailed it back compared with 2 of the 11 control participants (18%) who requested the test. Retention at study follow-up was more than 93%. Only 2 Facebook communities were included for each group. Social networking communities are acceptable and effective tools to increase home-based HIV testing among at-risk populations. National Institute of Mental Health.
Bayesian Markov Random Field analysis for protein function prediction based on network data.
Kourmpetis, Yiannis A I; van Dijk, Aalt D J; Bink, Marco C A M; van Ham, Roeland C H J; ter Braak, Cajo J F
2010-02-24
Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S. cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.
Richter, Erik; Ventz, Katharina; Harms, Manuela; Mostertz, Jörg; Hochgräfe, Falko
2016-01-01
Macrophages represent the primary human host response to pathogen infection and link the immediate defense to the adaptive immune system. Mature tissue macrophages convert from circulating monocyte precursor cells by terminal differentiation in a process that is not fully understood. Here, we analyzed the protein kinases of the human monocytic cell line THP-1 before and after induction of macrophage differentiation by using kinomics and phosphoproteomics. When comparing the macrophage-like state with the monocytic precursor, 50% of the kinome was altered in expression and even 71% of covered kinase phosphorylation sites were affected. Kinome rearrangements are for example characterized by a shift of overrepresented cyclin-dependent kinases associated with cell cycle control in monocytes to calmodulin-dependent kinases and kinases involved in proinflammatory signaling. Eventually, we show that monocyte-to-macrophage differentiation is associated with major rewiring of mitogen-activated protein kinase signaling networks and demonstrate that protein kinase MAP3K7 (TAK1) acts as the key signaling hub in bacterial killing, chemokine production and differentiation. Our study proves the fundamental role of protein kinases and cellular signaling as major drivers of macrophage differentiation and function. The finding that MAP3K7 is central to macrophage function suggests MAP3K7 and its networking partners as promising targets in host-directed therapy for macrophage-associated disease.
Li, Yi; Moore, Richard; Guinn, Michael; Bleris, Leonidas
2012-01-01
The ability to conditionally rewire pathways in human cells holds great therapeutic potential. Transcription activator-like effectors (TALEs) are a class of naturally occurring specific DNA binding proteins that can be used to introduce targeted genome modifications or control gene expression. Here we present TALE hybrids engineered to respond to endogenous signals and capable of controlling transgenes by applying a predetermined and tunable action at the single-cell level. Specifically, we first demonstrate that combinations of TALEs can be used to modulate the expression of stably integrated genes in kidney cells. We then introduce a general purpose two-hybrid approach that can be customized to regulate the function of any TALE either using effector molecules or a heterodimerization reaction. Finally, we demonstrate the successful interface of TALEs to specific endogenous signals, namely hypoxia signaling and microRNAs, essentially closing the loop between cellular information and chromosomal transgene expression.
Synaptic rewiring of stress-sensitive neurons by early-life experience: a mechanism for resilience?
Singh-Taylor, Akanksha; Korosi, Aniko; Molet, Jenny; Gunn, Benjamin G; Baram, Tallie Z
2015-01-01
Genes and environment interact to influence cognitive and emotional functions throughout life. Early-life experiences in particular contribute to vulnerability or resilience to a number of emotional and cognitive illnesses in humans. In rodents, early-life experiences directly lead to resilience or vulnerability to stress later in life, and influence the development of cognitive and emotional deficits. The mechanisms for the enduring effects of early-life experiences on cognitive and emotional outcomes are not completely understood. Here, we present emerging information supporting experience-dependent modulation of the number and efficacy of synaptic inputs onto stress-sensitive neurons. This synaptic 'rewiring', in turn, may influence the expression of crucial neuronal genes. The persistent changes in gene expression in resilient versus vulnerable rodent models are likely maintained via epigenetic mechanisms. Thus, early-life experience may generate resilience by altering synaptic input to neurons, which informs them to modulate their epigenetic machinery.
Collaborative rewiring of the pluripotency network by chromatin and signalling modulating pathways
Tran, Khoa A.; Jackson, Steven?A.; Olufs, Zachariah P.G.; Zaidan, Nur Zafirah; Leng, Ning; Kendziorski, Christina; Roy, Sushmita; Sridharan, Rupa
2015-01-01
Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) represents a profound change in cell fate. Here, we show that combining ascorbic acid (AA) and 2i (MAP kinase and GSK inhibitors) increases the efficiency of reprogramming from fibroblasts and synergistically enhances conversion of partially reprogrammed intermediates to the iPSC state. AA and 2i induce differential transcriptional responses, each leading to the activation of specific pluripotency loci. A unique cohort o...
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Dongfang Li
2015-10-01
Full Text Available Random number generators (RNG play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST randomness tests and is resilient to a wide range of security attacks.
Li, Dongfang; Lu, Zhaojun; Zou, Xuecheng; Liu, Zhenglin
2015-10-16
Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks.
Zhan, Yiqiang; Karlsson, Ida K; Karlsson, Robert; Tillander, Annika; Reynolds, Chandra A; Pedersen, Nancy L; Hägg, Sara
2017-07-21
Observational studies have found shorter leukocyte telomere length (TL) to be a risk factor for coronary heart disease (CHD), and recently the association was suggested to be causal. However, the relationship between TL and common metabolic risk factors for CHD is not well understood. Whether these risk factors could explain pathways from TL to CHD warrants further attention. To examine whether metabolic risk factors for CHD mediate the causal pathway from short TL to increased risk of CHD using a network Mendelian randomization design. Summary statistics from several genome-wide association studies were used in a 2-sample Mendelian randomization study design. Network Mendelian randomization analysis-an approach using genetic variants as the instrumental variables for both the exposure and mediator to infer causality-was performed to examine the causal association between telomeres and CHD and metabolic risk factors. Summary statistics from the ENGAGE Telomere Consortium were used (n=37 684) as a TL genetic instrument, CARDIoGRAMplusC4D Consortium data were used (case=22 233 and control=64 762) for CHD, and other consortia data were used for metabolic traits (fasting insulin, triglyceride, total cholesterol, low-density lipoprotein cholesterol, fasting glucose, diabetes mellitus, glycohemoglobin, body mass index, waist circumference, and waist:hip ratio). One-unit increase of genetically determined TL was associated with -0.07 (95% confidence interval, -0.01 to -0.12; P=0.01) lower log-transformed fasting insulin (pmol/L) and 21% lower odds (95% confidence interval, 3-35; P=0.02) of CHD. Higher genetically determined log-transformed fasting insulin level was associated with higher CHD risk (odds ratio, 1.86; 95% confidence interval, 1.01-3.41; P=0.04). Overall, our findings support a role of insulin as a mediator on the causal pathway from shorter telomeres to CHD pathogenesis. © 2017 American Heart Association, Inc.
Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.
Yin, Yuyu; Yu, Fangzheng; Xu, Yueshen; Yu, Lifeng; Mu, Jinglong
2017-09-08
Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.
Asymptotic Analysis of Large Cooperative Relay Networks Using Random Matrix Theory
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H. Poor
2008-04-01
Full Text Available Cooperative transmission is an emerging communication technology that takes advantage of the broadcast nature of wireless channels. In cooperative transmission, the use of relays can create a virtual antenna array so that multiple-input/multiple-output (MIMO techniques can be employed. Most existing work in this area has focused on the situation in which there are a small number of sources and relays and a destination. In this paper, cooperative relay networks with large numbers of nodes are analyzed, and in particular the asymptotic performance improvement of cooperative transmission over direction transmission and relay transmission is analyzed using random matrix theory. The key idea is to investigate the eigenvalue distributions related to channel capacity and to analyze the moments of this distribution in large wireless networks. A performance upper bound is derived, the performance in the low signal-to-noise-ratio regime is analyzed, and two approximations are obtained for high and low relay-to-destination link qualities, respectively. Finally, simulations are provided to validate the accuracy of the analytical results. The analysis in this paper provides important tools for the understanding and the design of large cooperative wireless networks.
Lian, Cheng; Zeng, Zhigang; Yao, Wei; Tang, Huiming; Chen, Chun Lung Philip
2016-12-01
In this paper, we propose a new approach to establish a landslide displacement forecasting model based on artificial neural networks (ANNs) with random hidden weights. To quantify the uncertainty associated with the predictions, a framework for probabilistic forecasting of landslide displacement is developed. The aim of this paper is to construct prediction intervals (PIs) instead of deterministic forecasting. A lower-upper bound estimation (LUBE) method is adopted to construct ANN-based PIs, while a new single hidden layer feedforward ANN with random hidden weights for LUBE is proposed. Unlike the original implementation of LUBE, the input weights and hidden biases of the ANN are randomly chosen, and only the output weights need to be adjusted. Combining particle swarm optimization (PSO) and gravitational search algorithm (GSA), a hybrid evolutionary algorithm, PSOGSA, is utilized to optimize the output weights. Furthermore, a new ANN objective function, which combines a modified combinational coverage width-based criterion with one-norm regularization, is proposed. Two benchmark data sets and two real-world landslide data sets are presented to illustrate the capability and merit of our method. Experimental results reveal that the proposed method can construct high-quality PIs.
Maslennikov, O. V.; Nekorkin, V. I.
2017-10-01
Dynamical networks are systems of active elements (nodes) interacting with each other through links. Examples are power grids, neural structures, coupled chemical oscillators, and communications networks, all of which are characterized by a networked structure and intrinsic dynamics of their interacting components. If the coupling structure of a dynamical network can change over time due to nodal dynamics, then such a system is called an adaptive dynamical network. The term ‘adaptive’ implies that the coupling topology can be rewired; the term ‘dynamical’ implies the presence of internal node and link dynamics. The main results of research on adaptive dynamical networks are reviewed. Key notions and definitions of the theory of complex networks are given, and major collective effects that emerge in adaptive dynamical networks are described.
Social dilemmas in an online social network: The structure and evolution of cooperation
Fu, Feng; Chen, Xiaojie; Liu, Lianghuan; Wang, Long
2007-11-01
We investigate two paradigms for studying the evolution of cooperation—Prisoner's Dilemma and Snowdrift game in an online friendship network, obtained from a social networking site. By structural analysis, it is revealed that the empirical social network has small-world and scale-free properties. Besides, it exhibits assortative mixing pattern. Then, we study the evolutionary version of the two types of games on it. It is found that cooperation is substantially promoted with small values of game matrix parameters in both games. Whereas the competent cooperators induced by the underlying network of contacts will be dramatically inhibited with increasing values of the game parameters. Further, we explore the role of assortativity in evolution of cooperation by random edge rewiring. We find that increasing amount of assortativity will to a certain extent diminish the cooperation level. We also show that connected large hubs are capable of maintaining cooperation. The evolution of cooperation on empirical networks is influenced by various network effects in a combined manner, compared with that on model networks. Our results can help understand the cooperative behaviors in human groups and society.
Dynamic fair node spectrum allocation for ad hoc networks using random matrices
Rahmes, Mark; Lemieux, George; Chester, Dave; Sonnenberg, Jerry
2015-05-01
Dynamic Spectrum Access (DSA) is widely seen as a solution to the problem of limited spectrum, because of its ability to adapt the operating frequency of a radio. Mobile Ad Hoc Networks (MANETs) can extend high-capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact, cognitive radio employs spectrum sensing to facilitate the identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, while secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate fair path use of the DSA-discovered links. Utilization of high-resolution geospatial data layers in RF propagation analysis is directly applicable. Random matrix theory (RMT) is useful in simulating network layer usage in nodes by a Wishart adjacency matrix. We use the Dijkstra algorithm for discovering ad hoc network node connection patterns. We present a method for analysts to dynamically allocate node-node path and link resources using fair division. User allocation of limited resources as a function of time must be dynamic and based on system fairness policies. The context of fair means that first available request for an asset is not envied as long as it is not yet allocated or tasked in order to prevent cycling of the system. This solution may also save money by offering a Pareto efficient repeatable process. We use a water fill queue algorithm to include Shapley value marginal contributions for allocation.
Taren, Adrienne A; Gianaros, Peter J; Greco, Carol M; Lindsay, Emily K; Fairgrieve, April; Brown, Kirk Warren; Rosen, Rhonda K; Ferris, Jennifer L; Julson, Erica; Marsland, Anna L; Creswell, J David
Mindfulness meditation training has been previously shown to enhance behavioral measures of executive control (e.g., attention, working memory, cognitive control), but the neural mechanisms underlying these improvements are largely unknown. Here, we test whether mindfulness training interventions foster executive control by strengthening functional connections between dorsolateral prefrontal cortex (dlPFC)-a hub of the executive control network-and frontoparietal regions that coordinate executive function. Thirty-five adults with elevated levels of psychological distress participated in a 3-day randomized controlled trial of intensive mindfulness meditation or relaxation training. Participants completed a resting state functional magnetic resonance imaging scan before and after the intervention. We tested whether mindfulness meditation training increased resting state functional connectivity (rsFC) between dlPFC and frontoparietal control network regions. Left dlPFC showed increased connectivity to the right inferior frontal gyrus (T = 3.74), right middle frontal gyrus (MFG) (T = 3.98), right supplementary eye field (T = 4.29), right parietal cortex (T = 4.44), and left middle temporal gyrus (T = 3.97, all p < .05) after mindfulness training relative to the relaxation control. Right dlPFC showed increased connectivity to right MFG (T = 4.97, p < .05). We report that mindfulness training increases rsFC between dlPFC and dorsal network (superior parietal lobule, supplementary eye field, MFG) and ventral network (right IFG, middle temporal/angular gyrus) regions. These findings extend previous work showing increased functional connectivity among brain regions associated with executive function during active meditation by identifying specific neural circuits in which rsFC is enhanced by a mindfulness intervention in individuals with high levels of psychological distress. Clinicaltrials.gov,NCT01628809.
Randomized Trial of a Social Networking Intervention for Cancer-Related Distress.
Owen, Jason E; O'Carroll Bantum, Erin; Pagano, Ian S; Stanton, Annette
2017-10-01
Web and mobile technologies appear to hold promise for delivering evidence-informed and evidence-based intervention to cancer survivors and others living with trauma and other psychological concerns. Health-space.net was developed as a comprehensive online social networking and coping skills training program for cancer survivors living with distress. The purpose of this study was to evaluate the effects of a 12-week social networking intervention on distress, depression, anxiety, vigor, and fatigue in cancer survivors reporting high levels of cancer-related distress. We recruited 347 participants from a local cancer registry and internet, and all were randomized to either a 12-week waiting list control group or to immediate access to the intervention. Intervention participants received secure access to the study website, which provided extensive social networking capabilities and coping skills training exercises facilitated by a professional facilitator. Across time, the prevalence of clinically significant depression symptoms declined from 67 to 34 % in both conditions. The health-space.net intervention had greater declines in fatigue than the waitlist control group, but the intervention did not improve outcomes for depression, trauma-related anxiety symptoms, or overall mood disturbance. For those with more severe levels of anxiety at baseline, greater engagement with the intervention was associated with higher levels of symptom reduction over time. The intervention resulted in small but significant effects on fatigue but not other primary or secondary outcomes. Results suggest that this social networking intervention may be most effective for those who have distress that is not associated with high levels of anxiety symptoms or very poor overall psychological functioning. The trial was registered with the ClinicalTrials.gov database ( ClinicalTrials.gov #NCT01976949).
DEFF Research Database (Denmark)
Kiilerich Pratas, Nuno; Thomsen, Henning; Popovski, Petar
2015-01-01
In this chapter, we describe and discuss the current LTE random access procedure and the Radio Access Network Load Control solution within LTE/LTE-A. We provide an overview of the several considered load control solutions and give a detailed description of the standardized Extended Access Class B...
Quorum system and random based asynchronous rendezvous protocol for cognitive radio ad hoc networks
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Sylwia Romaszko
2013-12-01
Full Text Available This paper proposes a rendezvous protocol for cognitive radio ad hoc networks, RAC2E-gQS, which utilizes (1 the asynchronous and randomness properties of the RAC2E protocol, and (2 channel mapping protocol, based on a grid Quorum System (gQS, and taking into account channel heterogeneity and asymmetric channel views. We show that the combination of the RAC2E protocol with the grid-quorum based channel mapping can yield a powerful RAC2E-gQS rendezvous protocol for asynchronous operation in a distributed environment assuring a rapid rendezvous between the cognitive radio nodes having available both symmetric and asymmetric channel views. We also propose an enhancement of the protocol, which uses a torus QS for a slot allocation, dealing with the worst case scenario, a large number of channels with opposite ranking lists.
Flexible, Transparent, and Conductive Film Based on Random Networks of Ag Nanowires
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Shunhua Wang
2013-01-01
Full Text Available Flexible, transparent, and conductive films based on random networks of Ag nanowires were prepared by vacuum-filtrating method. The size of Ag nanowires prepared by hydrothermal method is uniform, with a relatively smaller diameter and a longer length, thereby achieving a high aspect ratio (>1000. The films fabricated by Ag nanowires exhibit the excellent transparency with a 92% optical transmittance and a low surface resistivity of 11 Ωsq−1. Importantly, both the transmittance and sheet resistance decrease with the increasing of the Ag nanowires contents. When the contents of Ag nanowires are up to 200 mg/m2 especially, the surface resistivity quickly falls below 5 Ωsq−1. Also, these films are robust, which have almost no change in sheet resistance after the repeating bends over 200 cycles. These encouraging results may have a potential application in flexible and transparent electronics and other heating systems.
Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo
2016-01-01
This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...
A Chemical Reaction Network to Generate Random, Power-Law-Distributed Time Intervals.
Krauss, Patrick; Schulze, Holger; Metzner, Claus
2017-10-06
In Lévy walks (LWs), particles move with a fixed speed along straight line segments and turn in new directions after random time intervals that are distributed according to a power law. Such LWs are thought to be an advantageous foraging and search strategy for organisms. While complex nervous systems are certainly capable of producing such behavior, it is not clear at present how single-cell organisms can generate the long-term correlated control signals required for a LW. Here, we construct a biochemical reaction system that generates long-time correlated concentration fluctuations of a signaling substance, with a tunable fractional exponent of the autocorrelation function. The network is based on well-known modules, and its basic function is highly robust with respect to the parameter settings.
Stability and Stabilization of Networked Control System with Forward and Backward Random Time Delays
Directory of Open Access Journals (Sweden)
Ye-Guo Sun
2012-01-01
Full Text Available This paper deals with the problem of stabilization for a class of networked control systems (NCSs with random time delay via the state feedback control. Both sensor-to-controller and controller-to-actuator delays are modeled as Markov processes, and the resulting closed-loop system is modeled as a Markovian jump linear system (MJLS. Based on Lyapunov stability theorem combined with Razumikhin-based technique, a new delay-dependent stochastic stability criterion in terms of bilinear matrix inequalities (BMIs for the system is derived. A state feedback controller that makes the closed-loop system stochastically stable is designed, which can be solved by the proposed algorithm. Simulations are included to demonstrate the theoretical result.
Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm
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Qing Han
2013-10-01
Full Text Available Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.
Modelling opinion formation driven communities in social networks
Iñiguez, Gerardo; Barrio, Rafael A.; Kertész, János; Kaski, Kimmo K.
2011-09-01
In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter α, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realised by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter α and its influence on the conformation of opinion and the size of the resulting communities. We present numerical studies and extract interesting features of the model that can be interpreted in terms of social behaviour.
Chandrasekar, A; Rakkiyappan, R; Cao, Jinde
2015-10-01
This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Maher, Carol; Ferguson, Monika; Vandelanotte, Corneel; Plotnikoff, Ron; de Bourdeaudhuij, Ilse; Thomas, Samantha; Nelson-Field, Karen; Olds, Tim
2015-01-01
Background Online social networks offer considerable potential for delivery of socially influential health behavior change interventions. Objective To determine the efficacy, engagement, and feasibility of an online social networking physical activity intervention with pedometers delivered via Facebook app. Methods A total of 110 adults with a mean age of 35.6 years (SD 12.4) were recruited online in teams of 3 to 8 friends. Teams were randomly allocated to receive access to a 50-day online s...
Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces
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Hossein Bashashati
2017-07-01
Full Text Available Classification of EEG signals in self-paced Brain Computer Interfaces (BCI is an extremely challenging task. The main diﬃculty stems from the fact that start time of a control task is not defined. Therefore it is imperative to exploit the characteristics of the EEG data to the extent possible. In sensory motor self-paced BCIs, while performing the mental task, the user’s brain goes through several well-defined internal state changes. Applying appropriate classifiers that can capture these state changes and exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an ensemble learning approach for self-paced BCIs. We use Bayesian optimization to train several different classifiers on different parts of the BCI hyper- parameter space. We call each of these classifiers Neural Network Conditional Random Field (NNCRF. NNCRF is a combination of a neural network and conditional random field (CRF. As in the standard CRF, NNCRF is able to model the correlation between adjacent EEG samples. However, NNCRF can also model the nonlinear dependencies between the input and the output, which makes it more powerful than the standard CRF. We compare the performance of our algorithm to those of three popular sequence labeling algorithms (Hidden Markov Models, Hidden Markov Support Vector Machines and CRF, and to two classical classifiers (Logistic Regression and Support Vector Machines. The classifiers are compared for the two cases: when the ensemble learning approach is not used and when it is. The data used in our studies are those from the BCI competition IV and the SM2 dataset. We show that our algorithm is considerably superior to the other approaches in terms of the Area Under the Curve (AUC of the BCI system.
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Jia-Guo Zhao
Full Text Available There are three main surgical techniques to treat humeral shaft fractures: open reduction and plate fixation (ORPF, intramedullary nail (IMN fixation, and minimally invasive percutaneous osteosynthesis (MIPO. We performed a network meta-analysis to compare three surgical procedures, including ORPF, IMN fixation, and MIPO, to provide the optimum treatment for humerus shaft fractures.MEDLINE, EMBASE, Cochrane Bone, Joint and Muscle Trauma Group Specialised Register, and Cochrane library were researched for reports published up to May 2016. We only included randomized controlled trials (RCTs comparing two or more of the three surgical procedures, including the ORPF, IMN, and MIPO techniques, for humeral shaft fractures in adults. The methodological quality was evaluated based on the Cochrane risk of bias tool. We used WinBUGS1.4 to conduct this Bayesian network meta-analysis. We used the odd ratios (ORs with 95% confidence intervals (CIs to calculate the dichotomous outcomes and analyzed the percentages of the surface under the cumulative ranking curve.Seventeen eligible publications reporting 16 RCTs were included in this study. Eight hundred and thirty-two participants were randomized to receive one of three surgical procedures. The results showed that shoulder impingement occurred more commonly in the IMN group than with either ORPF (OR, 0.13; 95% CI, 0.03-0.37 or MIPO fixation (OR, 0.08; 95% CI, 0.00-0.69. Iatrogenic radial nerve injury occurred more commonly in the ORPF group than in the MIPO group (OR, 11.09; 95% CI, 1.80-124.20. There were no significant differences among the three procedures in nonunion, delayed union, and infection.Compared with IMN and ORPF, MIPO technique is the preferred treatment method for humeral shaft fractures.
Burgess, Stephen; Daniel, Rhian M; Butterworth, Adam S; Thompson, Simon G
2015-04-01
Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes. © The Author 2014. Published by Oxford University Press on behalf of the International Epidemiological Association.
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Jingwen Zhang, PhD
2016-12-01
Full Text Available To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2 at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2 in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2 (p = 0.003. Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives.
Scaling of peak flows with constant flow velocity in random self-similar networks
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R. Mantilla
2011-07-01
Full Text Available A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs with geometrically distributed interior and exterior generators having parameters p_{i} and p_{e}, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β^{(E} and φ^{(E} that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β^{(E} and φ^{(E} and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ^{(E} and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit
The distribution of first hitting times of random walks on directed Erdős-Rényi networks
Tishby, Ido; Biham, Ofer; Katzav, Eytan
2017-04-01
We present analytical results for the distribution of first hitting times of random walkers (RWs) on directed Erdős-Rényi (ER) networks. Starting from a random initial node, a random walker hops randomly along directed edges between adjacent nodes in the network. The path terminates either by the retracing scenario, when the walker enters a node which it has already visited before, or by the trapping scenario, when it becomes trapped in a dead-end node from which it cannot exit. The path length, namely the number of steps, d, pursued by the random walker from the initial node up to its termination, is called the first hitting time. Using recursion equations, we obtain analytical results for the tail distribution of first hitting times, P≤ft(d>\\ell \\right) . The results are found to be in excellent agreement with numerical simulations. It turns out that the distribution P≤ft(d>\\ell \\right) can be expressed as a product of an exponential distribution and a Rayleigh distribution. We obtain expressions for the mean, median and standard deviation of this distribution in terms of the network size and its mean degree. We also calculate the distribution of last hitting times, namely the path lengths of self-avoiding walks on directed ER networks, which do not retrace their paths. The last hitting times are found to be much longer than the first hitting times. The results are compared to those obtained for undirected ER networks. It is found that the first hitting times of RWs in a directed ER network are much longer than in the corresponding undirected network. This is due to the fact that RWs on directed networks do not exhibit the backtracking scenario, which is a dominant termination mechanism of RWs on undirected networks. It is shown that our approach also applies to a broader class of networks, referred to as semi-ER networks, in which the distribution of in-degrees is Poisson, while the out-degrees may follow any desired distribution with the same mean as
Randomly biased investments and the evolution of public goods on interdependent networks
Chen, Wei; Wu, Te; Li, Zhiwu; Wang, Long
2017-08-01
Deciding how to allocate resources between interdependent systems is significant to optimize efficiency. We study the effects of heterogeneous contribution, induced by such interdependency, on the evolution of cooperation, through implementing the public goods games on two-layer networks. The corresponding players on different layers try to share a fixed amount of resources as the initial investment properly. The symmetry breaking of investments between players located on different layers is able to either prevent investments from, or extract them out of the deadlock. Results show that a moderate investment heterogeneity is best favorable for the evolution of cooperation, and random allocation of investment bias suppresses the cooperators at a wide range of the investment bias and the enhancement effect. Further studies on time evolution with different initial strategy configurations show that the non-interdependent cooperators along the interface of interdependent cooperators also are an indispensable factor in facilitating cooperative behavior. Our main results are qualitatively unchanged even diversifying investment bias that is subject to uniform distribution. Our study may shed light on the understanding of the origin of cooperative behavior on interdependent networks.
Heave motion prediction of a large barge in random seas by using artificial neural network
Lee, Hsiu Eik; Liew, Mohd Shahir; Zawawi, Noor Amila Wan Abdullah; Toloue, Iraj
2017-11-01
This paper describes the development of a multi-layer feed forward artificial neural network (ANN) to predict rigid heave body motions of a large catenary moored barge subjected to multi-directional irregular waves. The barge is idealized as a rigid plate of finite draft with planar dimensions 160m (length) and 100m (width) which is held on station using a six point chain catenary mooring in 50m water depth. Hydroelastic effects are neglected from the physical model as the chief intent of this study is focused on large plate rigid body hydrodynamics modelling using ANN. Even with this assumption, the computational requirements for time domain coupled hydrodynamic simulations of a moored floating body is considerably costly, particularly if a large number of simulations are required such as in the case of response based design (RBD) methods. As an alternative to time consuming numerical hydrodynamics, a regression-type ANN model has been developed for efficient prediction of the barge's heave responses to random waves from various directions. It was determined that a network comprising of 3 input features, 2 hidden layers with 5 neurons each and 1 output was sufficient to produce acceptable predictions within 0.02 mean squared error. By benchmarking results from the ANN with those generated by a fully coupled dynamic model in OrcaFlex, it is demonstrated that the ANN is capable of predicting the barge's heave responses with acceptable accuracy.
Estrada, Ernesto
2016-01-01
We propose a new model to account for the main structural characteristics of rock fracture networks (RFNs). The model is based on a generalization of the random neighborhood graphs to consider fractures embedded into rectangular spaces. We study a series of 29 real-world RFNs and find the best fit with the random rectangular neighborhood graphs (RRNGs) proposed here. We show that this model captures most of the structural characteristics of the RFNs and allows a distinction between small and more spherical rocks and large and more elongated ones. We use a diffusion equation on the graphs in order to model diffusive processes taking place through the channels of the RFNs. We find a small set of structural parameters that highly correlates with the average diffusion time in the RFNs. In particular, the second smallest eigenvalue of the Laplacian matrix is a good predictor of the average diffusion time on RFNs, showing a Pearson correlation coefficient larger than $0.99$ with the average diffusion time on RFNs. ...
Lin, Yu-Ping; Kao, Ying-Jer; Chen, Pochung; Lin, Yu-Cheng
2017-08-01
The antiferromagnetic Ising chain in both transverse and longitudinal magnetic fields is one of the paradigmatic models of a quantum phase transition. The antiferromagnetic system exhibits a zero-temperature critical line separating an antiferromagnetic phase and a paramagnetic phase; the critical line connects an integrable quantum critical point at zero longitudinal field and a classical first-order transition point at zero transverse field. Using a strong-disorder renormalization group method formulated as a tree tensor network, we study the zero-temperature phase of the quantum Ising chain with bond randomness. We introduce a new matrix product operator representation of high-order moments, which provides an efficient and accurate tool for determining quantum phase transitions via the Binder cumulant of the order parameter. Our results demonstrate an infinite-randomness quantum critical point in zero longitudinal field accompanied by pronounced quantum Griffiths singularities, arising from rare ordered regions with anomalously slow fluctuations inside the paramagnetic phase. The strong Griffiths effects are signaled by a large dynamical exponent z >1 , which characterizes a power-law density of low-energy states of the localized rare regions and becomes infinite at the quantum critical point. Upon application of a longitudinal field, the quantum phase transition between the paramagnetic phase and the antiferromagnetic phase is completely destroyed. Furthermore, quantum Griffiths effects are suppressed, showing z <1 , when the dynamics of the rare regions is hampered by the longitudinal field.
Mason, Michael; Mennis, Jeremy; Way, Thomas; Zaharakis, Nikola; Campbell, Leah Floyd; Benotsch, Eric G; Keyser-Marcus, Lori; King, Laura
2016-10-01
Although adolescent tobacco use has declined in the last 10 years, African American high school seniors' past 30-day use has increased by 12 %, and as they age they are more likely to report lifetime use of tobacco. Very few urban youth are enrolled in evidenced-based smoking prevention and cessation programming. Therefore, we tested a text messaging smoking cessation intervention designed to engage urban youth through an automated texting program utilizing motivational interviewing-based peer network counseling. We recruited 200 adolescents (90.5 % African American) into a randomized controlled trial that delivered either the experimental intervention of 30 personalized motivational interviewing-based peer network counseling messages, or the attention control intervention, consisting of text messages covering general (non-smoking related) health habits. All adolescents were provided smart phones for the study and were assessed at baseline, and at 1, 3, and 6 months post intervention. Utilizing repeated measures general linear models we examined the effects of the intervention while controlling for race, gender, age, presence of a smoker in the home, and mental health counseling. At 6 months, participants in the experimental condition significantly decreased the number of days they smoked cigarettes and the number of cigarettes they smoked per day; they significantly increased their intentions not to smoke in the future; and significantly increased peer social support among girls. For boys, participants in the experimental condition significantly reduced the number of close friends in their networks who smoke daily compared to those in the control condition. Effect sizes ranged from small to large. These results provide encouraging evidence of the efficacy of text messaging interventions to reduce smoking among adolescents and our intervention holds promise as a large-scale public health preventive intervention platform.
Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.
Fechter, Tobias; Adebahr, Sonja; Baltas, Dimos; Ben Ayed, Ismail; Desrosiers, Christian; Dolz, Jose
2017-12-01
Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. We show that a CNN can yield accurate estimations of esophagus location, and that
Sethi, Isha; Gluck, Christian; Zhou, Huiqing
2017-01-01
Abstract Although epidermal keratinocyte development and differentiation proceeds in similar fashion between humans and mice, evolutionary pressures have also wrought significant species-specific physiological differences. These differences between species could arise in part, by the rewiring of regulatory network due to changes in the global targets of lineage-specific transcriptional master regulators such as p63. Here we have performed a systematic and comparative analysis of the p63 target gene network within the integrated framework of the transcriptomic and epigenomic landscape of mouse and human keratinocytes. We determined that there exists a core set of ∼1600 genomic regions distributed among enhancers and super-enhancers, which are conserved and occupied by p63 in keratinocytes from both species. Notably, these DNA segments are typified by consensus p63 binding motifs under purifying selection and are associated with genes involved in key keratinocyte and skin-centric biological processes. However, the majority of the p63-bound mouse target regions consist of either murine-specific DNA elements that are not alignable to the human genome or exhibit no p63 binding in the orthologous syntenic regions, typifying an occupancy lost subset. Our results suggest that these evolutionarily divergent regions have undergone significant turnover of p63 binding sites and are associated with an underlying inactive and inaccessible chromatin state, indicative of their selective functional activity in the transcriptional regulatory network in mouse but not human. Furthermore, we demonstrate that this selective targeting of genes by p63 correlates with subtle, but measurable transcriptional differences in mouse and human keratinocytes that converges on major metabolic processes, which often exhibit species-specific trends. Collectively our study offers possible molecular explanation for the observable phenotypic differences between the mouse and human skin and broadly
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Wanxing Sheng
2016-05-01
Full Text Available In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS is defined to measure the degree of similarity between the loads in different days and the calculation method of the load similarity of load random matrix (LRM is presented. By calculating the load similarity between the forecasting random matrix and the random matrix of historical load, the historical reactive power optimization dispatching scheme that most matches the forecasting load can be found for reactive power control usage. The differences of daily load curves between working days and weekends in different seasons are considered in the proposed method. The proposed method is tested on a standard 14 nodes distribution network with three different types of load. The computational result demonstrates that the proposed method for reactive power optimization is fast, feasible and effective in distribution network.
Kullgren, Jeffrey T.; Harkins, Kristin A.; Bellamy, Scarlett L.; Gonzales, Amy; Tao, Yuanyuan; Zhu, Jingsan; Volpp, Kevin G.; Asch, David A.; Heisler, Michele; Karlawish, Jason
2014-01-01
Background: Financial incentives and peer networks could be delivered through eHealth technologies to encourage older adults to walk more. Methods: We conducted a 24-week randomized trial in which 92 older adults with a computer and Internet access received a pedometer, daily walking goals, and weekly feedback on goal achievement. Participants…
RANK rewires energy homeostasis in lung cancer cells and drives primary lung cancer.
Rao, Shuan; Sigl, Verena; Wimmer, Reiner Alois; Novatchkova, Maria; Jais, Alexander; Wagner, Gabriel; Handschuh, Stephan; Uribesalgo, Iris; Hagelkruys, Astrid; Kozieradzki, Ivona; Tortola, Luigi; Nitsch, Roberto; Cronin, Shane J; Orthofer, Michael; Branstetter, Daniel; Canon, Jude; Rossi, John; D'Arcangelo, Manolo; Botling, Johan; Micke, Patrick; Fleur, Linnea La; Edlund, Karolina; Bergqvist, Michael; Ekman, Simon; Lendl, Thomas; Popper, Helmut; Takayanagi, Hiroshi; Kenner, Lukas; Hirsch, Fred R; Dougall, William; Penninger, Josef M
2017-10-15
Lung cancer is the leading cause of cancer deaths. Besides smoking, epidemiological studies have linked female sex hormones to lung cancer in women; however, the underlying mechanisms remain unclear. Here we report that the receptor activator of nuclear factor-kB (RANK), the key regulator of osteoclastogenesis, is frequently expressed in primary lung tumors, an active RANK pathway correlates with decreased survival, and pharmacologic RANK inhibition reduces tumor growth in patient-derived lung cancer xenografts. Clonal genetic inactivation of KRasG12D in mouse lung epithelial cells markedly impairs the progression of KRasG12D -driven lung cancer, resulting in a significant survival advantage. Mechanistically, RANK rewires energy homeostasis in human and murine lung cancer cells and promotes expansion of lung cancer stem-like cells, which is blocked by inhibiting mitochondrial respiration. Our data also indicate survival differences in KRasG12D -driven lung cancer between male and female mice, and we show that female sex hormones can promote lung cancer progression via the RANK pathway. These data uncover a direct role for RANK in lung cancer and may explain why female sex hormones accelerate lung cancer development. Inhibition of RANK using the approved drug denosumab may be a therapeutic drug candidate for primary lung cancer. © 2017 Rao et al.; Published by Cold Spring Harbor Laboratory Press.
Bacterial virulence proteins as tools to rewire kinase pathways in yeast and immune cells.
Wei, Ping; Wong, Wilson W; Park, Jason S; Corcoran, Ethan E; Peisajovich, Sergio G; Onuffer, James J; Weiss, Arthur; Lim, Wendell A
2012-08-16
Bacterial pathogens have evolved specific effector proteins that, by interfacing with host kinase signalling pathways, provide a mechanism to evade immune responses during infection. Although these effectors contribute to pathogen virulence, we realized that they might also serve as valuable synthetic biology reagents for engineering cellular behaviour. Here we exploit two effector proteins, the Shigella flexneri OspF protein and Yersinia pestis YopH protein, to rewire kinase-mediated responses systematically both in yeast and mammalian immune cells. Bacterial effector proteins can be directed to inhibit specific mitogen-activated protein kinase pathways selectively in yeast by artificially targeting them to pathway-specific complexes. Moreover, we show that unique properties of the effectors generate new pathway behaviours: OspF, which irreversibly inactivates mitogen-activated protein kinases, was used to construct a synthetic feedback circuit that shows novel frequency-dependent input filtering. Finally, we show that effectors can be used in T cells, either as feedback modulators to tune the T-cell response amplitude precisely, or as an inducible pause switch that can temporarily disable T-cell activation. These studies demonstrate how pathogens could provide a rich toolkit of parts to engineer cells for therapeutic or biotechnological applications.
Re-wiring of energy metabolism promotes viability during hyperreplication stress in E. coli.
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Godefroid Charbon
2017-01-01
Full Text Available Chromosome replication in Escherichia coli is initiated by DnaA. DnaA binds ATP which is essential for formation of a DnaA-oriC nucleoprotein complex that promotes strand opening, helicase loading and replisome assembly. Following initiation, DnaAATP is converted to DnaAADP primarily by the Regulatory Inactivation of DnaA process (RIDA. In RIDA deficient cells, DnaAATP accumulates leading to uncontrolled initiation of replication and cell death by accumulation of DNA strand breaks. Mutations that suppress RIDA deficiency either dampen overinitiation or permit growth despite overinitiation. We characterize mutations of the last group that have in common that distinct metabolic routes are rewired resulting in the redirection of electron flow towards the cytochrome bd-1. We propose a model where cytochrome bd-1 lowers the formation of reactive oxygen species and hence oxidative damage to the DNA in general. This increases the processivity of replication forks generated by overinitiation to a level that sustains viability.
DEFF Research Database (Denmark)
Kulkarni, Nandkumar P.; Prasad, Ramjee; Cornean, Horia
2011-01-01
Sensor deployment is one of the key topics addressed in Wireless Sensor Network (WSN). This paper proposes a new deployment technique of sensor nodes for WSN called as Quasi Random Deployment (QRD). The novel approach to deploy sensor nodes in QRD fashion is to improve the energy efficiency...... of the WSN in order to increase the network life time and coverage. The QRD produces highly uniform coordinates and it systematically fills the specified area. Along with Random Deployment (RD) pattern of wireless sensor node QRD is analysed in this study. The network is simulated using NS-2 simulator...... energy consumption, coverage area. The simulation results show that the conventional routing protocols like DSR have a best performance for both RD and QRD of the sensor nodes when there is no mobility of the sensor nodes as compared to AODV and DSDV. Among AODV and DSDV, AODV performs better as compared...
Litt, Mark D; Kadden, Ronald M; Tennen, Howard; Kabela-Cormier, Elise
2016-08-01
The social network of those treated for alcohol use disorder can play a significant role in subsequent drinking behavior, both for better and worse. Network Support treatment was devised to teach ways to reconstruct social networks so that they are more supportive of abstinence and less supportive of drinking. For many patients this may involve engagement with AA, but other strategies are also used. The current trial of Network Support treatment, building on our previous work, was intended to further enhance the ability of patients to construct abstinence-supportive social networks, and to test this approach against a strong control treatment. Patients were 193 men and women with alcohol use disorder recruited from the community and assigned to either 12 weeks of Network Support (NS) or Packaged Cognitive-Behavioral Treatment (PCBT), and followed for 27 months. Results of multilevel analyses indicated that NS yielded better posttreatment results in terms of both proportion of days abstinent and drinking consequences, and equivalent improvements in 90-day abstinence, heavy drinking days and drinks per drinking day. Mediation analyses revealed that NS treatment effects were mediated by pre-post changes in abstinence self-efficacy and in social network variables, especially proportion of non-drinkers in the social network and attendance at Alcoholics Anonymous. It was concluded that helping patients enhance their abstinent social network can be effective, and may provide a useful alternative or adjunctive approach to treatment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Li, Xiuxia; Wang, Rong; Xing, Xin; Shi, Xiue; Tian, Jinhui; Zhang, Jun; Ge, Long; Zhang, Jingyun; Li, Lun; Yang, Kehu
2017-09-01
Acupuncture techniques are commonly used as initial treatments for myofascial pain syndrome. This study aimed to assess and compare the efficacy and safety of different techniques of acupuncture for myofascial pain syndrome. Network meta-analysis. All selected studies were randomized controlled trials (RCTs). The Cochrane Central Register of Controlled Trials, PubMed, Web of Science, EMBASE, and Chinese Biomedical Literature Database were searched from their inceptions to February 2016. Only full texts of RCTs comparing acupuncture therapies with any other therapies or placebo-sham acupuncture were included. Two reviewers independently assessed eligibility and extracted data. The primary outcomes included pain intensity, PPT, and adverse events. Secondary outcome was physical function. Thirty-three trials with 1,692 patients were included. Patients were allocated to 22 kinds of interventions, of which dry needling and manual acupuncture was the most frequently investigated intervention. Compared with placebo-sham acupuncture, scraping combined with warming acupuncture and moxibustion was found to be more effective for decreasing pain intensity (standardized mean difference (SMD) = -3.6, 95% confidence interval (CI) ranging from -5.2 to -2.1); miniscalpel-needle was more effective for increasing the PPT (SMD = 2.2, 95% CI ranging from 1.2 to 3.1); trigger points injection with bupivacaine was associated with the highest risk of adverse event (odds ratio = 557.2, 95% CI ranging from 3.6 to 86867.3); and only EA showed a significant difference in the ROM (SMD = -4.4, 95% CI ranging from -7.5 to -1.3). Lack of clarity concerning treatment periods, repetitive RCTs, and other valuable outcome measurements. The potential bias might affect the judgment of efficacy and safety. The existing evidence suggests that most acupuncture therapies, including acupuncture combined with other therapies, are effective in decreasing pain and in improving physical function, but additional
Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks
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Alberto eCapurro
2012-04-01
Full Text Available Neural responses to odor blends often interact at different stages of the olfactory pathway. The first olfactory processing center in insects, the antennal lobe (AL, exhibits a complex network connectivity. We attempt to determine if non-linear blend interactions can arise purely as a function of the AL network connectivity itself, without necessitating additional factors such as competitive ligand binding at the periphery or intrinsic cellular properties. To assess this, we compared blend interactions among responses from single neurons recorded intracellularly in the AL of the moth M. sexta with those generated using a population-based computational model constructed from the morphologically-based connectivity pattern of projection neurons (PNs and local interneurons (LNs with randomized connection probabilities, from which we excluded detailed intrinsic neuronal properties. The model accurately predicted most of the proportions of blend interaction types observed in the physiological data. Our simulations also indicate that input from LNs is important in establishing both the type of blend interaction and the nature of the neuronal response (excitation or inhibition exhibited by AL neurons. For LNs, the only input that significantly impacted the blend interaction type was received from other LNs, while for PNs the input from olfactory sensory neurons (OSNs and other PNs contributed agonistically with the LN input to shape the AL output. Our results demonstrate that non-linear blend interactions can be a natural consequence of AL connectivity, and highlight the importance of lateral inhibition as a key feature of blend coding to be addressed in future experimental and computational studies.
Gallagher, H. Colin; Robins, Garry
2015-01-01
As part of the shift within second language acquisition (SLA) research toward complex systems thinking, researchers have called for investigations of social network structure. One strand of social network analysis yet to receive attention in SLA is network statistical models, whereby networks are explained in terms of smaller substructures of…
Analyzing self-similar and fractal properties of the C. elegans neural network.
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Tyler M Reese
Full Text Available The brain is one of the most studied and highly complex systems in the biological world. While much research has concentrated on studying the brain directly, our focus is the structure of the brain itself: at its core an interconnected network of nodes (neurons. A better understanding of the structural connectivity of the brain should elucidate some of its functional properties. In this paper we analyze the connectome of the nematode Caenorhabditis elegans. Consisting of only 302 neurons, it is one of the better-understood neural networks. Using a Laplacian Matrix of the 279-neuron "giant component" of the network, we use an eigenvalue counting function to look for fractal-like self similarity. This matrix representation is also used to plot visualizations of the neural network in eigenfunction coordinates. Small-world properties of the system are examined, including average path length and clustering coefficient. We test for localization of eigenfunctions, using graph energy and spacial variance on these functions. To better understand results, all calculations are also performed on random networks, branching trees, and known fractals, as well as fractals which have been "rewired" to have small-world properties. We propose algorithms for generating Laplacian matrices of each of these graphs.
The distribution of first hitting times of non-backtracking random walks on Erdős-Rényi networks
Tishby, Ido; Biham, Ofer; Katzav, Eytan
2017-05-01
We present analytical results for the distribution of first hitting times of non-backtracking random walks on finite Erdős-Rényi networks of N nodes. The walkers hop randomly between adjacent nodes on the network, without stepping back to the previous node, until they hit a node which they have already visited before or get trapped in a dead-end node. At this point, the path is terminated. The length, d, of the resulting path, is called the first hitting time. Using recursion equations, we obtain analytical results for the tail distribution of first hitting times, P(d > \\ell) , \\ell=0, 1, 2, \\dots , of non-backtracking random walks starting from a random initial node. It turns out that the distribution P(d > \\ell) is given by a product of a discrete Rayleigh distribution and an exponential distribution. We obtain analytical expressions for central measures (mean and median) and a dispersion measure (standard deviation) of this distribution. It is found that the paths of non-backtracking random walks, up to their termination at the first hitting time, are longer, on average, than those of the corresponding simple random walks. However, they are shorter than those of self avoiding walks on the same network, which terminate at the last hitting time. We obtain analytical results for the probabilities, p ret and p trap, that a path will terminate by retracing, namely stepping into an already visited node, or by trapping, namely entering a node of degree k = 1, which has no exit link, respectively. It is shown that in dilute networks the dominant termination scenario is trapping while in dense networks most paths terminate by retracing. We obtain expressions for the conditional tail distributions of path lengths, P(d> \\ell \\vert ret) and P(d> \\ell \\vert {trap}) , for those paths which terminate by retracing or by trapping, respectively. We also study a class of generalized non-backtracking random walk models which not only avoid the backtracking step
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Huifa Lin
Full Text Available We study secondary random access in multi-input multi-output cognitive radio networks, where a slotted ALOHA-type protocol and successive interference cancellation are used. We first introduce three types of transmit beamforming performed by secondary users, where multiple antennas are used to suppress the interference at the primary base station and/or to increase the received signal power at the secondary base station. Then, we show a simple decentralized power allocation along with the equivalent single-antenna conversion. To exploit the multiuser diversity gain, an opportunistic transmission protocol is proposed, where the secondary users generating less interference are opportunistically selected, resulting in a further reduction of the interference temperature. The proposed methods are validated via computer simulations. Numerical results show that increasing the number of transmit antennas can greatly reduce the interference temperature, while increasing the number of receive antennas leads to a reduction of the total transmit power. Optimal parameter values of the opportunistic transmission protocol are examined according to three types of beamforming and different antenna configurations, in terms of maximizing the cognitive transmission capacity. All the beamforming, decentralized power allocation, and opportunistic transmission protocol are performed by the secondary users in a decentralized manner, thus resulting in an easy implementation in practice.
Random-network simulation of an ultracapacitor based on metal-solid-electrolyte composite
Abel, J.; Kornyshev, A. A.
1996-09-01
A random-network model of a dense (pore-free) metal-solid-electrolyte composite is developed. Real and imaginary parts of admittance are simulated as a function of frequency and composition by means of the transfer matrix algorithm on a cubic lattice. For a composite without a solid-electrolyte membrane in the middle (insulating with respect to electronic current) the results predict the capacity maximum at the percolation threshold in three dimensions and two maxima in two dimensions as a function of composition; they are compared with the predictions of the effective medium theory. For a composite with an insulating membrane in the middle, typical for ultracapacitors, the maximum of capacitance in three dimensions is at equal portion of metal and solid-electrolyte particles. In contrast to metal dielectric mixtures there are no giant enhancement effects in static capacitance as a function of composition: the upper estimates of the enhancement factor are proportional to the ratio of the size of the sample to the size of the grains.
Ohdaira, Tetsushi
2014-07-01
Previous studies discussing cooperation employ the best decision that every player knows all information regarding the payoff matrix and selects the strategy of the highest payoff. Therefore, they do not discuss cooperation based on the altruistic decision with limited information (bounded rational altruistic decision). In addition, they do not cover the case where every player can submit his/her strategy several times in a match of the game. This paper is based on Ohdaira's reconsideration of the bounded rational altruistic decision, and also employs the framework of the prisoner's dilemma game (PDG) with sequential strategy. The distinction between this study and the Ohdaira's reconsideration is that the former covers the model of multiple groups, but the latter deals with the model of only two groups. Ohdaira's reconsideration shows that the bounded rational altruistic decision facilitates much more cooperation in the PDG with sequential strategy than Ohdaira and Terano's bounded rational second-best decision does. However, the detail of cooperation of multiple groups based on the bounded rational altruistic decision has not been resolved yet. This study, therefore, shows how randomness in the network composed of multiple groups affects the increase of the average frequency of mutual cooperation (cooperation between groups) based on the bounded rational altruistic decision of multiple groups. We also discuss the results of the model in comparison with related studies which employ the best decision.
Lin, Huifa; Shin, Won-Yong
2017-01-01
We study secondary random access in multi-input multi-output cognitive radio networks, where a slotted ALOHA-type protocol and successive interference cancellation are used. We first introduce three types of transmit beamforming performed by secondary users, where multiple antennas are used to suppress the interference at the primary base station and/or to increase the received signal power at the secondary base station. Then, we show a simple decentralized power allocation along with the equivalent single-antenna conversion. To exploit the multiuser diversity gain, an opportunistic transmission protocol is proposed, where the secondary users generating less interference are opportunistically selected, resulting in a further reduction of the interference temperature. The proposed methods are validated via computer simulations. Numerical results show that increasing the number of transmit antennas can greatly reduce the interference temperature, while increasing the number of receive antennas leads to a reduction of the total transmit power. Optimal parameter values of the opportunistic transmission protocol are examined according to three types of beamforming and different antenna configurations, in terms of maximizing the cognitive transmission capacity. All the beamforming, decentralized power allocation, and opportunistic transmission protocol are performed by the secondary users in a decentralized manner, thus resulting in an easy implementation in practice.
Wei, Qikang; Chen, Tao; Xu, Ruifeng; He, Yulan; Gui, Lin
2016-01-01
The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V. Database URL: http://219.223.252.210:8080/SS/cdr.html PMID:27777244
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Xiaomei Qi
2012-01-01
Full Text Available A robust fault-tolerant controller design problem for networked control system (NCS with random packet dropout in both sensor-to-controller link and controller-to-actuator link is investigated. A novel stochastic NCS model with state-delay, model uncertainty, disturbance, probabilistic sensor failure, and actuator failure is proposed. The random packet dropout, sensor failures, and actuator failures are characterized by a binary random variable. The sufficient condition for asymptotical mean-square stability of NCS is derived and the closed-loop NCS satisfies H∞ performance constraints caused by the random packet dropout and disturbance. The fault-tolerant controller is designed by solving a linear matrix inequality. A numerical example is presented to illustrate the effectiveness of the proposed method.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-01-01
An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Regulatory Factor X (RFX)-mediated transcriptional rewiring of ciliary genes in animals.
Piasecki, Brian P; Burghoorn, Jan; Swoboda, Peter
2010-07-20
Cilia were present in the last eukaryotic common ancestor (LECA) and were retained by most organisms spanning all extant eukaryotic lineages, including organisms in the Unikonta (Amoebozoa, fungi, choanoflagellates, and animals), Archaeplastida, Excavata, Chromalveolata, and Rhizaria. In certain animals, including humans, ciliary gene regulation is mediated by Regulatory Factor X (RFX) transcription factors (TFs). RFX TFs bind X-box promoter motifs and thereby positively regulate >50 ciliary genes. Though RFX-mediated ciliary gene regulation has been studied in several bilaterian animals, little is known about the evolutionary conservation of ciliary gene regulation. Here, we explore the evolutionary relationships between RFX TFs and cilia. By sampling the genome sequences of >120 eukaryotic organisms, we show that RFX TFs are exclusively found in unikont organisms (whether ciliated or not), but are completely absent from the genome sequences of all nonunikont organisms (again, whether ciliated or not). Sampling the promoter sequences of 12 highly conserved ciliary genes from 23 diverse unikont and nonunikont organisms further revealed that phylogenetic footprints of X-box promoter motif sequences are found exclusively in ciliary genes of certain animals. Thus, there is no correlation between cilia/ciliary genes and the presence or absence of RFX TFs and X-box promoter motifs in nonanimal unikont and in nonunikont organisms. These data suggest that RFX TFs originated early in the unikont lineage, distinctly after cilia evolved. The evolutionary model that best explains these observations indicates that the transcriptional rewiring of many ciliary genes by RFX TFs occurred early in the animal lineage.
Miao, Yiming; Tian, Yuanwen; Cheng, Jingjing; Hossain, M. Shamim; Ghoneim, Ahmed
2018-01-01
With the development of LPWA (Low Power Wide Area) technology, the emerging NB-IoT (Narrowband Internet of Things) technology is becoming popular with wide area and low-data-rate services. In order to achieve objectives such as huge amount of connection and wide area coverage within NB-IoT, the problem of network congestion generated by random access of numerous devices should be solved. In this paper, we first introduce the background of NB-IoT and investigate the research on random access o...
Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network
Wang, Zhen-yu; Zhang, Li-jie
2017-10-01
Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s
Bayesian exponential random graph modeling of whole-brain structural networks across lifespan
Sinke, Michel R T; Dijkhuizen, Rick M; Caimo, Alberto; Stam, Cornelis J; Otte, Wim
2016-01-01
Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as
Fitting a geometric graph to a protein-protein interaction network.
Higham, Desmond J; Rasajski, Marija; Przulj, Natasa
2008-04-15
Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs. We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N(2)) where N is the number of proteins. The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a
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Gao XF
2017-05-01
Full Text Available Xiao-Fei Gao,1 Jun-Jie Zhang,1,2 Xiao-Min Jiang,1 Zhen Ge,1,2 Zhi-Mei Wang,1 Bing Li,1 Wen-Xing Mao,1 Shao-Liang Chen1,2 1Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 2Department of Cardiology, Nanjing Heart Center, Nanjing, People’s Republic of China Background: Pulmonary arterial hypertension (PAH is a devastating disease and ultimately leads to right heart failure and premature death. A total of four classical targeted drugs, prostanoids, endothelin receptor antagonists (ERAs, phosphodiesterase 5 inhibitors (PDE-5Is, and soluble guanylate cyclase stimulator (sGCS, have been proved to improve exercise capacity and hemodynamics compared to placebo; however, direct head-to-head comparisons of these drugs are lacking. This network meta-analysis was conducted to comprehensively compare the efficacy of these targeted drugs for PAH.Methods: Medline, the Cochrane Library, and other Internet sources were searched for randomized clinical trials exploring the efficacy of targeted drugs for patients with PAH. The primary effective end point of this network meta-analysis was a 6-minute walk distance (6MWD.Results: Thirty-two eligible trials including 6,758 patients were identified. There was a statistically significant improvement in 6MWD, mean pulmonary arterial pressure, pulmonary vascular resistance, and clinical worsening events associated with each of the four targeted drugs compared with placebo. Combination therapy improved 6MWD by 20.94 m (95% confidence interval [CI]: 6.94, 34.94; P=0.003 vs prostanoids, and 16.94 m (95% CI: 4.41, 29.47; P=0.008 vs ERAs. PDE-5Is improved 6MWD by 17.28 m (95% CI: 1.91, 32.65; P=0.028 vs prostanoids, with a similar result with combination therapy. In addition, combination therapy reduced mean pulmonary artery pressure by 3.97 mmHg (95% CI: -6.06, -1.88; P<0.001 vs prostanoids, 8.24 mmHg (95% CI: -10.71, -5.76; P<0.001 vs ERAs, 3.38 mmHg (95% CI: -6.30, -0.47; P=0.023 vs
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Suresh Gopalan
2011-01-01
Full Text Available We previously demonstrated that in a metasystem consisting of Arabidopsis seedlings growing in liquid medium (in 96 well plates even microbes considered to be innocuous such as laboratory strains of E. coli and B. subtilis can cause potent damage to the host. We further posited that such environment-induced adaptations are brought about by 'system status changes' (rewiring of pre-existing cellular signaling networks and components of the host and the microbe, and that prolongation of such a situation could lead to the emergence of pathogenic states in real-life. Here, using this infection model, we show that the master regulator GacA of the human opportunistic pathogen P. aeruginosa (strain PA14 is dispensable for pathogenesis, as evidenced by three independent read-outs. The gene expression profile of the host after infection with wild type PA14 or the gacA mutant are also identical. GacA normally acts upstream of the quorum sensing regulatory circuit (that includes the regulator LasR that controls a subset of virulence factors. Double mutants in gacA and lasR behave similar to the lasR mutant, as seen by abrogation of a characteristic cell type specific host cell damage caused by PA14 or the gacA mutant. This indicates that a previously unrecognized regulatory mechanism is operative under these conditions upstream of LasR. In addition, the detrimental effect of PA14 on Arabidopsis seedlings is resistant to high concentrations of the aminoglycoside antibiotic gentamicin. These data suggest that the Arabidopsis seedling infection system could be used to identify anti-infectives with potentially novel modes of action.
Gopalan, Suresh; Ausubel, Frederick M
2011-01-21
We previously demonstrated that in a metasystem consisting of Arabidopsis seedlings growing in liquid medium (in 96 well plates) even microbes considered to be innocuous such as laboratory strains of E. coli and B. subtilis can cause potent damage to the host. We further posited that such environment-induced adaptations are brought about by 'system status changes' (rewiring of pre-existing cellular signaling networks and components) of the host and the microbe, and that prolongation of such a situation could lead to the emergence of pathogenic states in real-life. Here, using this infection model, we show that the master regulator GacA of the human opportunistic pathogen P. aeruginosa (strain PA14) is dispensable for pathogenesis, as evidenced by three independent read-outs. The gene expression profile of the host after infection with wild type PA14 or the gacA mutant are also identical. GacA normally acts upstream of the quorum sensing regulatory circuit (that includes the regulator LasR) that controls a subset of virulence factors. Double mutants in gacA and lasR behave similar to the lasR mutant, as seen by abrogation of a characteristic cell type specific host cell damage caused by PA14 or the gacA mutant. This indicates that a previously unrecognized regulatory mechanism is operative under these conditions upstream of LasR. In addition, the detrimental effect of PA14 on Arabidopsis seedlings is resistant to high concentrations of the aminoglycoside antibiotic gentamicin. These data suggest that the Arabidopsis seedling infection system could be used to identify anti-infectives with potentially novel modes of action.
Schießl, Stefan P.; Rother, Marcel; Lüttgens, Jan; Zaumseil, Jana
2017-11-01
The field-effect mobility is an important figure of merit for semiconductors such as random networks of single-walled carbon nanotubes (SWNTs). However, owing to their network properties and quantum capacitance, the standard models for field-effect transistors cannot be applied without modifications. Several different methods are used to determine the mobility with often very different results. We fabricated and characterized field-effect transistors with different polymer-sorted, semiconducting SWNT network densities ranging from low (≈6 μm-1) to densely packed quasi-monolayers (≈26 μm-1) with a maximum on-conductance of 0.24 μS μm-1 and compared four different techniques to evaluate the field-effect mobility. We demonstrate the limits and requirements for each method with regard to device layout and carrier accumulation. We find that techniques that take into account the measured capacitance on the active device give the most reliable mobility values. Finally, we compare our experimental results to a random-resistor-network model.
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Wu Weili
2006-01-01
Full Text Available Secure communication is a necessity for some wireless sensor network (WSN applications. However, the resource constraints of a sensor render existing cryptographic systems for traditional network systems impractical for a WSN. Random key predistribution scheme has been proposed to overcome these limits. In this scheme, a ring of keys is randomly drawn from a large key pool and assigned to a sensor. Nodes sharing common keys can communicate securely using a shared key, while a path-key is established for those nodes that do not share any common keys. This scheme requires moderate memory and processing power, thus it is considered suitable for WSN applications. However, since the shared key is not exclusively owned by the two end entities, the established path-key may be revealed to other nodes just by eavesdropping. Based on the random-key predistribution scheme, we present a framework that utilizes multiple proxies to secure the path-key establishment. Our scheme is resilient against node capture, collusive attack, and random dropping, while only incurring a small amount of overhead. Furthermore, the scheme ensures that, with high probability, all path-keys are exclusively known by the two end nodes involved in the communication along the path.
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Harkaitz Bengoetxea
2012-01-01
Full Text Available During postnatal development, sensory experience modulates cortical development, inducing numerous changes in all of the components of the cortex. Most of the cortical changes thus induced occur during the critical period, when the functional and structural properties of cortical neurons are particularly susceptible to alterations. Although the time course for experience-mediated sensory development is specific for each system, postnatal development acts as a whole, and if one cortical area is deprived of its normal sensory inputs during early stages, it will be reorganized by the nondeprived senses in a process of cross-modal plasticity that not only increases performance in the remaining senses when one is deprived, but also rewires the brain allowing the deprived cortex to process inputs from other senses and cortices, maintaining the modular configuration. This paper summarizes our current understanding of sensory systems development, focused specially in the visual system. It delineates sensory enhancement and sensory deprivation effects at both physiological and anatomical levels and describes the use of enriched environment as a tool to rewire loss of brain areas to enhance other active senses. Finally, strategies to apply restorative features in human-deprived senses are studied, discussing the beneficial and detrimental effects of cross-modal plasticity in prostheses and sensory substitution devices implantation.
Rewiring a secondary metabolite pathway towards itaconic acid production in Aspergillus niger.
Hossain, Abeer H; Li, An; Brickwedde, Anja; Wilms, Lars; Caspers, Martien; Overkamp, Karin; Punt, Peter J
2016-07-28
The industrially relevant filamentous fungus Aspergillus niger is widely used in industry for its secretion capabilities of enzymes and organic acids. Biotechnologically produced organic acids promise to be an attractive alternative for the chemical industry to replace petrochemicals. Itaconic acid (IA) has been identified as one of the top twelve building block chemicals which have high potential to be produced by biotechnological means. The IA biosynthesis cluster (cadA, mttA and mfsA) has been elucidated in its natural producer Aspergillus terreus and transferred to A. niger to enable IA production. Here we report the rewiring of a secondary metabolite pathway towards further improved IA production through the overexpression of a putative cytosolic citrate synthase citB in a A. niger strain carrying the IA biosynthesis cluster. We have previously shown that expression of cadA from A. terreus results in itaconic acid production in A. niger AB1.13, albeit at low levels. This low-level production is boosted fivefold by the overexpression of mttA and mfsA in itaconic acid producing AB1.13 CAD background strains. Controlled batch cultivations with AB1.13 CAD + MFS + MTT strains showed increased production of itaconic acid compared with AB1.13 CAD strain. Moreover, preliminary RNA-Seq analysis of an itaconic acid producing AB1.13 CAD strain has led to the identification of the putative cytosolic citrate synthase citB which was induced in an IA producing strain. We have overexpressed citB in a AB1.13 CAD + MFS + MTT strain and by doing so hypothesize to have targeted itaconic acid production to the cytosolic compartment. By overexpressing citB in AB1.13 CAD + MFS + MTT strains in controlled batch cultivations we have achieved highly increased titers of up to 26.2 g/L IA with a productivity of 0.35 g/L/h while no CA was produced. Expression of the IA biosynthesis cluster in Aspergillus niger AB1.13 strain enables IA production. Moreover, in the AB1.13 CAD
MATIN: a random network coding based framework for high quality peer-to-peer live video streaming.
Barekatain, Behrang; Khezrimotlagh, Dariush; Aizaini Maarof, Mohd; Ghaeini, Hamid Reza; Salleh, Shaharuddin; Quintana, Alfonso Ariza; Akbari, Behzad; Cabrera, Alicia Triviño
2013-01-01
In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.
MATIN: a random network coding based framework for high quality peer-to-peer live video streaming.
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Behrang Barekatain
Full Text Available In recent years, Random Network Coding (RNC has emerged as a promising solution for efficient Peer-to-Peer (P2P video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.
Graph Theoretical Analysis of Developmental Patterns of the White Matter Network
Directory of Open Access Journals (Sweden)
Zhang eChen
2013-11-01
Full Text Available Understanding the development of human brain organization is critical for gaining insight into how the enhancement of cognitive processes is related to the fine-tuning of the brain network. However, the developmental trajectory of the large-scale white matter (WM network is not fully understood. Here, using graph theory, we examine developmental changes in the organization of WM networks in 180 typically-developing participants. WM networks were constructed using whole brain tractography and 78 cortical regions of interest were extracted from each participant. The subjects were first divided into 5 equal sample size (n=36 groups (early childhood: 6.0-9.7 years; late childhood: 9.8-12.7 years; adolescence: 12.9-17.5 years; young adult: 17.6-21.8 years; adult: 21.9-29.6 years. Most prominent changes in the topological properties of developing brain networks occur at late childhood and adolescence. During late childhood period, the structural brain network showed significant increase in the global efficiency but decrease in modularity, suggesting a shift of topological organization toward a more randomized configuration. However, while preserving most topological features, there was a significant increase in the local efficiency at adolescence, suggesting the dynamic process of rewiring and rebalancing brain connections at different growth stages. In addition, several pivotal hubs were identified that are vital for the global coordination of information flow over the whole brain network across all age groups. Significant increases of nodal efficiency were present in several regions such as precuneus at late childhood. Finally, a stable and functionally/anatomically related modular organization was identified throughout the development of the WM network. This study used network analysis to elucidate the topological changes in brain maturation, paving the way for developing novel methods for analyzing disrupted brain connectivity in
Construction of citrus gene coexpression networks from microarray data using random matrix theory
Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G.
2015-01-01
After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus. PMID:26504573
Zhou, Jian; Wang, Lusheng; Wang, Weidong; Zhou, Qingfeng
2017-11-06
In future scenarios of heterogeneous and dense networks, randomly-deployed small star networks (SSNs) become a key paradigm, whose system performance is restricted to inter-SSN interference and requires an efficient resource allocation scheme for interference coordination. Traditional resource allocation schemes do not specifically focus on this paradigm and are usually too time consuming in dense networks. In this article, a very efficient graph-based scheme is proposed, which applies the maximal independent set (MIS) concept in graph theory to help divide SSNs into almost interference-free groups. We first construct an interference graph for the system based on a derived distance threshold indicating for any pair of SSNs whether there is intolerable inter-SSN interference or not. Then, SSNs are divided into MISs, and the same resource can be repetitively used by all the SSNs in each MIS. Empirical parameters and equations are set in the scheme to guarantee high performance. Finally, extensive scenarios both dense and nondense are randomly generated and simulated to demonstrate the performance of our scheme, indicating that it outperforms the classical max K-cut-based scheme in terms of system capacity, utility and especially time cost. Its achieved system capacity, utility and fairness can be close to the near-optimal strategy obtained by a time-consuming simulated annealing search.
Sun, Xuejun; Deng, Linghui; Qiu, Shi; Tu, Xiang; Wang, Deren; Liu, Ming
2017-02-01
Poststroke depression (PSD) constitutes an important complication of stroke, leading to great disability as well as increased mortality. Since which treatment for PSD should be preferred are still matters of controversy, we are aiming to compare and rank these pharmacological and nonpharmacological interventions. We will employ a network meta-analysis to incorporate both direct and indirect evidence from relevant trials. We will search PubMed, the Cochrane Library Central Register of Controlled Trials, Embase, and the reference lists of relevant articles for randomized controlled trials (RCT) of different PSD treatment strategies. The characteristics of each RCT will be summarized, including the study characteristics, the participant characteristics, the outcome measurements, and adverse events. The risk of bias will be assessed by means of the Cochrane Collaboration's risk of bias tool. The primary outcome was change in Hamilton Depression Scale (HAMD) score. Secondary outcomes involve patient response rate (defined as at least a 50% score reduction on HAMD), and remission rate (defined as no longer meeting baseline criteria for depression). Moreover, we will assess the acceptability of treatments according to treatment discontinuation. We will perform pairwise meta-analyses by random effects model and network meta-analysis by Bayesian random effects model. Formal ethical approval is not required as primary data will not be collected. Our results will help to reduce the uncertainty about the effectiveness and safety of PSD management, which will encourage further research for other therapeutic options. The review will be disseminated in peer-reviewed publications and conference presentations. CRD42016049049.
Explaining the structure of inter-organizational networks using exponential random graph models
Broekel, T.; Hartog, M.
2013-01-01
A key question raised in recent years is what factors determine the structure of interorganizational networks. Most research so far has focused on different forms of proximity between organizations, namely geographical, cognitive, social, institutional and organizational proximity, which are
Guo, Zhenyuan; Yang, Shaofu; Wang, Jun
2016-12-01
This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Maihemuti Maimaiti
2017-11-01
Full Text Available Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF, long short-term memory (LSTM, bidirectional LSTM networks (BI-LSTM, LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels and 0.48% (64 labels. The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages.
Enhanced modularity-based community detection by random walk network preprocessing.
Lai, Darong; Lu, Hongtao; Nardini, Christine
2010-06-01
The representation of real systems with network models is becoming increasingly common and critical to both capture and simplify systems' complexity, notably, via the partitioning of networks into communities. In this respect, the definition of modularity, a common and broadly used quality measure for networks partitioning, has induced a surge of efficient modularity-based community detection algorithms. However, recently, the optimization of modularity has been found to show a resolution limit, which reduces its effectiveness and range of applications. Therefore, one recent trend in this area of research has been related to the definition of novel quality functions, alternative to modularity. In this paper, however, instead of laying aside the important body of knowledge developed so far for modularity-based algorithms, we propose to use a strategy to preprocess networks before feeding them into modularity-based algorithms. This approach is based on the observation that dynamic processes triggered on vertices in the same community possess similar behavior patterns but dissimilar on vertices in different communities. Validations on real-world and synthetic networks demonstrate that network preprocessing can enhance the modularity-based community detection algorithms to find more natural clusters and effectively alleviates the problem of resolution limit.
Veksler, Boris A.; Maksimova, Irina L.; Meglinski, Igor V.
2007-07-01
Nowadays the artificial neural network (ANN), an effective powerful technique that is able denoting complex input and output relationships, is widely used in different biomedical applications. In present study the applying of ANN for the determination of characteristics of random highly scattering medium (like bio-tissue) is considered. Spatial distribution of the backscattered light calculated by Monte Carlo method is used to train ANN for multiply scattering regimes. The potential opportunities of use of ANN for image reconstruction of an absorbing macro inhomogeneity located in topical layers of random scattering medium are presented. This is especially of high priority because of new diagnostics/treatment developing that is based on the applying gold nano-particles for labeling cancer cells.
Maher, Carol; Ferguson, Monika; Vandelanotte, Corneel; Plotnikoff, Ron; De Bourdeaudhuij, Ilse; Thomas, Samantha; Nelson-Field, Karen; Olds, Tim
2015-07-13
Online social networks offer considerable potential for delivery of socially influential health behavior change interventions. To determine the efficacy, engagement, and feasibility of an online social networking physical activity intervention with pedometers delivered via Facebook app. A total of 110 adults with a mean age of 35.6 years (SD 12.4) were recruited online in teams of 3 to 8 friends. Teams were randomly allocated to receive access to a 50-day online social networking physical activity intervention which included self-monitoring, social elements, and pedometers ("Active Team" Facebook app; n=51 individuals, 12 teams) or a wait-listed control condition (n=59 individuals, 13 teams). Assessments were undertaken online at baseline, 8 weeks, and 20 weeks. The primary outcome measure was self-reported weekly moderate-to-vigorous physical activity (MVPA). Secondary outcomes were weekly walking, vigorous physical activity time, moderate physical activity time, overall quality of life, and mental health quality of life. Analyses were undertaken using random-effects mixed modeling, accounting for potential clustering at the team level. Usage statistics were reported descriptively to determine engagement and feasibility. At the 8-week follow-up, the intervention participants had significantly increased their total weekly MVPA by 135 minutes relative to the control group (P=.03), due primarily to increases in walking time (155 min/week increase relative to controls, Pself-monitoring features, were observed. An online, social networking physical activity intervention with pedometers can produce sizable short-term physical activity changes. Future work is needed to determine how to maintain behavior change in the longer term, how to reach at-need populations, and how to disseminate such interventions on a mass scale. Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12614000488606; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366239
Du, Qinghe; Zhao, Weidong; Li, Weimin; Zhang, Xuelin; Sun, Bo; Song, Houbing; Ren, Pinyi; Sun, Li; Wang, Yichen
2016-07-01
The prosperity of e-health is boosted by fast development of medical devices with wireless communications capability such as wearable devices, tiny sensors, monitoring equipments, etc., which are randomly distributed in clinic environments. The drastically-increasing population of such devices imposes new challenges on the limited wireless resources. To relieve this problem, key knowledge needs to be extracted from massive connection attempts dispersed in the air towards efficient access control. In this paper, a hybrid periodic-random massive access (HPRMA) scheme for wireless clinical networks employing ultra-narrow band (UNB) techniques is proposed. In particular, the proposed scheme towards accommodating a large population of devices include the following new features. On one hand, it can dynamically adjust the resource allocated for coexisting periodic and random services based on the traffic load learned from signal collision status. On the other hand, the resource allocation within periodic services is thoroughly designed to simultaneously align with the timing requests of differentiated services. Abundant simulation results are also presented to demonstrate the superiority of the proposed HPRMA scheme over baseline schemes including time-division multiple access (TDMA) and random access approach, in terms of channel utilization efficiency, packet drop ratio, etc., for the support of massive devices' services.
Moisset de Espanés, P; Osses, A; Rapaport, I
2016-12-01
Fixed points are fundamental states in any dynamical system. In the case of gene regulatory networks (GRNs) they correspond to stable genes profiles associated to the various cell types. We use Kauffman's approach to model GRNs with random Boolean networks (RBNs). In this paper we explore how the topology affects the distribution of the number of fixed points in randomly generated networks. We also study the size of the basins of attraction of these fixed points if we assume the α-asynchronous dynamics (where every node is updated independently with probability 0≤α≤1). It is well-known that asynchrony avoids the cyclic attractors into which parallel dynamics tends to fall. We observe the remarkable property that, in all our simulations, if for a given RBN with Barabási-Albert topology and α-asynchronous dynamics an initial configuration reaches a fixed point, then every configuration also reaches a fixed point. By contrast, in the parallel regime, the percentage of initial configurations reaching a fixed point (for the same networks) is dramatically smaller. We contrast the results of the simulations on Barabási-Albert networks with the classical Erdös-Rényi model of random networks. Everything indicates that Barabási-Albert networks are extremely robust. Finally, we study the mean and maximum time/work needed to reach a fixed point when starting from randomly chosen initial configurations. Copyright Â© 2016 Elsevier Ireland Ltd. All rights reserved.
Kim, Ha Yeon; Kim, Eun Joo; You, Joshua Sung H
2017-07-20
An improved understanding of the mechanisms underlying locomotor networks has the potential to benefit the neurorehabilitation of patients with neurological locomotor deficits. However, the specific locomotor networks that mediate adaptive locomotor performance and changes in gait speed remain unknown. The aim of the present study was to examine patterns of cortical activation associated with the walking speeds of 1.5, 2.0, 2.5, and 3.0 km/h on a treadmill. Functional near-infrared spectroscopy (fNIRS) was performed on a 30-year-old right-handed healthy female subject, and cerebral hemodynamic changes were observed in cortical locomotor network areas including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), prefrontal cortex (PFC), and sensory association cortex (SAC). The software package NIRS-statistical parametric mapping (NIRS-SPM) was utilized to analyze fNIRS data in the MATLAB environment. SPM t-statistic maps were computed at an uncorrected threshold of pglobalized locomotor network activation of the SMC, PMC, SMA, and PMC; additionally, the site with the highest cortical activation ratio shifted from the SMC to the SMA. Global locomotor network recruitment, in particular PFC activation indicated by OxyHb in our study, may indicate a response to increased cognitive-locomotor demand due to simultaneous postural maintenance and leg movement coordination.
Analytic treatment of tipping points for social consensus in large random networks
Zhang, W.; Lim, C.; Szymanski, B. K.
2012-12-01
We introduce a homogeneous pair approximation to the naming game (NG) model by deriving a six-dimensional Open Dynamics Engine (ODE) for the two-word naming game. Our ODE reveals the change in dynamical behavior of the naming game as a function of the average degree of an uncorrelated network. This result is in good agreement with the numerical results. We also analyze the extended NG model that allows for presence of committed nodes and show that there is a shift of the tipping point for social consensus in sparse networks.
Catalá-López, Ferrán; Alonso-Arroyo, Adolfo; Hutton, Brian; Aleixandre-Benavent, Rafael; Moher, David
2014-01-29
Research collaboration contributes to the advancement of knowledge by exploiting the results of scientific efforts more efficiently, but the global patterns of collaboration on meta-analysis are unknown. The purpose of this research was to describe and characterize the global collaborative patterns in meta-analyses of randomized trials published in high impact factor medical journals over the past three decades. This was a cross-sectional, social network analysis. We searched PubMed for relevant meta-analyses of randomized trials published up to December 2012. We selected meta-analyses (including at least randomized trials as primary evidence source) published in the top seven high impact factor general medical journals (according to Journal Citation Reports 2011): The New England Journal of Medicine, The Lancet, the BMJ, JAMA, Annals of Internal Medicine, Archives of Internal Medicine (now renamed JAMA Internal Medicine), and PLoS Medicine. Opinion articles, conceptual papers, narrative reviews, reviews without meta-analysis, reviews of reviews, and other study designs were excluded. Overall, we included 736 meta-analyses, in which 3,178 authors, 891 institutions, and 51 countries participated. The BMJ was the journal that published the greatest number of articles (39%), followed by The Lancet (18%), JAMA (15%) and the Archives of Internal Medicine (15%). The USA, the UK, and Canada headed the absolute global productivity ranking in number of papers. The 64 authors and the 39 institutions with the highest publication rates were identified. We also found 82 clusters of authors (one group with 55 members and one group with 54 members) and 19 clusters of institutions (one major group with 76 members). The most prolific authors were mainly affiliated with the University of Oxford (UK), McMaster University (Canada), and the University of Bern (Switzerland). Our analysis identified networks of authors, institutions and countries publishing meta-analyses of randomized
Vandelanotte, Corneel; Maher, Carol A
2015-01-01
Despite their popularity and potential to promote health in large populations, the effectiveness of online social networks (e.g., Facebook) to improve health behaviors has been somewhat disappointing. Most of the research examining the effectiveness of such interventions has used randomized controlled trials (RCTs). It is asserted that the modest outcomes may be due to characteristics specific to both online social networks and RCTs. The highly controlled nature of RCTs stifles the dynamic nature of online social networks. Alternative and ecologically valid research designs that evaluate online social networks in real-life conditions are needed to advance the science in this area.
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Ahmet Kuzu
2014-01-01
Full Text Available This paper proposes two novel master-slave configurations that provide improvements in both control and communication aspects of teleoperation systems to achieve an overall improved performance in position control. The proposed novel master-slave configurations integrate modular control and communication approaches, consisting of a delay regulator to address problems related to variable network delay common to such systems, and a model tracking control that runs on the slave side for the compensation of uncertainties and model mismatch on the slave side. One of the configurations uses a sliding mode observer and the other one uses a modified Smith predictor scheme on the master side to ensure position transparency between the master and slave, while reference tracking of the slave is ensured by a proportional-differentiator type controller in both configurations. Experiments conducted for the networked position control of a single-link arm under system uncertainties and randomly varying network delays demonstrate significant performance improvements with both configurations over the past literature.
Distributed Detection of Binary Decisions with Collisions in a Large, Random Network
2013-09-01
each sensor with a detection attempts to transmit a single detection message via Slotted ALOHA (S- ALOHA ). The communicating period is broken into 1...asynchronous (or pure) ALOHA to reduce energy consumption in the sensor network. S- ALOHA requires time synchronization between all the sensor nodes and... ALOHA reduces the probability of a collision over pure ALOHA due to halving of the vulnerability period. Thus, it is reasonable to conclude that the
2017-08-01
help us understand how a social environment evolves so that we are able to make predictions about its impact. Moreover, we might even learn how to...Mika, B. Schlkopf, and R. Williamson, “ Regularized principal manifolds,” Journal of Machine Learning Research,, vol. 1, pp. 179–209, June 2001. [74] H...Architecture Against Random Threats in WMD Environments : Theoretical Limits and Recovery Strategies Distribution Statement A. Approved for public
Bashor, Caleb J; Horwitz, Andrew A; Peisajovich, Sergio G; Lim, Wendell A
2010-01-01
The living cell is an incredibly complex entity, and the goal of predictively and quantitatively understanding its function is one of the next great challenges in biology. Much of what we know about the cell concerns its constituent parts, but to a great extent we have yet to decode how these parts are organized to yield complex physiological function. Classically, we have learned about the organization of cellular networks by disrupting them through genetic or chemical means. The emerging discipline of synthetic biology offers an additional, powerful approach to study systems. By rearranging the parts that comprise existing networks, we can gain valuable insight into the hierarchical logic of the networks and identify the modular building blocks that evolution uses to generate innovative function. In addition, by building minimal toy networks, one can systematically explore the relationship between network structure and function. Here, we outline recent work that uses synthetic biology approaches to investigate the organization and function of cellular networks, and describe a vision for a synthetic biology toolkit that could be used to interrogate the design principles of diverse systems.
El-Melegy, Moumen T
2013-07-01
This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network. Although it is of high importance in practical applications, this problem has not received careful attention from the neural network research community. One recent approach to solving this problem is to use a neural network training algorithm based on the random sample consensus (RANSAC) framework. This paper proposes a new algorithm that offers two enhancements over the original RANSAC algorithm. The first one improves the algorithm accuracy and robustness by employing an M-estimator cost function to decide on the best estimated model from the randomly selected samples. The other one improves the time performance of the algorithm by utilizing a statistical pretest based on Wald's sequential probability ratio test. The proposed algorithm is successfully evaluated on synthetic and real data, contaminated with varying degrees of outliers, and compared with existing neural network training algorithms.
Management of frailty: a protocol of a network meta-analysis of randomized controlled trials.
Negm, Ahmed M; Kennedy, Courtney C; Thabane, Lehana; Veroniki, Areti-Angeliki; Adachi, Jonathan D; Richardson, Julie; Cameron, Ian D; Giangregorio, Aidan; Papaioannou, Alexandra
2017-07-05
Frailty is a common syndrome affecting 5-17% of community-dwelling older adults. Various interventions are used to prevent or treat frailty. Given the diversity of singular and multi-faceted frailty interventions, not all of them have been compared in head-to-head studies. Network meta-analyses provide an approach to simultaneous consideration of the relative effectiveness of multiple treatment alternatives. This systematic review and network meta-analysis of RCTs aims to determine the comparative effect of interventions targeting the prevention or treatment of frailty. We will identify relevant RCTs, in any language and publication date, by a systematic search of databases including MEDLINE, EMBASE, CINAHL, AMED, the Cochrane Central Registry of Controlled Trials (CENTRAL), HealthSTAR, DARE, PsychINFO, PEDro, SCOPUS, and Scielo. Duplicate title and abstract and full-text screening will be performed. Authors will extract data and assess risk of bias (using the Cochrane Risk of Bias tool) of eligible studies. The review interventions will include (1) physical activity only, (2) physical activity with protein supplementation or other nutritional supplementation, (3) psychosocial intervention, (4) medication management, (5) pharmacotherapy, and (6) multi-faceted intervention (defined as an intervention that combine physical activity and/or nutrition with any of the following: (1) psychosocial intervention, (2) medication management, and (3) pharmacotherapy). Our primary outcome is difference in change of physical frailty from baseline measured by a reliable and valid frailty measure. Secondary outcomes and the assessments are (1) cognition, (2) short physical performance battery, (3) any other physical performance measure, (4) treatment cost, (5) quality of life, and (6) any adverse outcome. We will conduct a network meta-analysis using a Bayesian hierarchical model. We will also estimate the ranking probabilities for all treatments at each possible rank for each
Jiao, Can; Wang, Ting; Liu, Jianxin; Wu, Huanjie; Cui, Fang; Peng, Xiaozhe
2017-01-01
The influences of peer relationships on adolescent subjective well-being were investigated within the framework of social network analysis, using exponential random graph models as a methodological tool. The participants in the study were 1,279 students (678 boys and 601 girls) from nine junior middle schools in Shenzhen, China. The initial stage of the research used a peer nomination questionnaire and a subjective well-being scale (used in previous studies) to collect data on the peer relationship networks and the subjective well-being of the students. Exponential random graph models were then used to explore the relationships between students with the aim of clarifying the character of the peer relationship networks and the influence of peer relationships on subjective well being. The results showed that all the adolescent peer relationship networks in our investigation had positive reciprocal effects, positive transitivity effects and negative expansiveness effects. However, none of the relationship networks had obvious receiver effects or leaders. The adolescents in partial peer relationship networks presented similar levels of subjective well-being on three dimensions (satisfaction with life, positive affects and negative affects) though not all network friends presented these similarities. The study shows that peer networks can affect an individual's subjective well-being. However, whether similarities among adolescents are the result of social influences or social choices needs further exploration, including longitudinal studies that investigate the potential processes of subjective well-being similarities among adolescents.
Cortés, J.-C.; Colmenar, J.-M.; Hidalgo, J.-I.; Sánchez-Sánchez, A.; Santonja, F.-J.; Villanueva, R.-J.
2016-01-01
Academic performance is a concern of paramount importance in Spain, where around of 30 % of the students in the last two courses in high school, before to access to the labor market or to the university, do not achieve the minimum knowledge required according to the Spanish educational law in force. In order to analyze this problem, we propose a random network model to study the dynamics of the academic performance in Spain. Our approach is based on the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. Moreover, in order to consider the uncertainty in the estimation of model parameters, we perform a lot of simulations taking as the model parameters the ones that best fit data returned by the Differential Evolution algorithm. This technique permits to forecast model trends in the next few years using confidence intervals.
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Elena Tutubalina
2017-01-01
Full Text Available Adverse drug reactions (ADRs are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.
Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.
Lindsay, Grace W; Rigotti, Mattia; Warden, Melissa R; Miller, Earl K; Fusi, Stefano
2017-11-08
Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear "mixed" selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli-and in particular, to combinations of stimuli ("mixed
Yeh, Mei-Ling; Ko, Shu-Hua; Wang, Mei-Hua; Chi, Ching-Chi; Chung, Yu-Chu
2017-12-01
There has be a large body of evidence on the pharmacological treatments for psoriasis, but whether nonpharmacological interventions are effective in managing psoriasis remains largely unclear. This systematic review conducted pairwise and network meta-analyses to determine the effects of acupuncture-related techniques on acupoint stimulation for the treatment of psoriasis and to determine the order of effectiveness of these remedies. This study searched the following databases from inception to March 15, 2016: Medline, PubMed, Cochrane Central Register of Controlled Trials, EBSCO (including Academic Search Premier, American Doctoral Dissertations, and CINAHL), Airiti Library, and China National Knowledge Infrastructure. Randomized controlled trials (RCTs) on the effects of acupuncture-related techniques on acupoint stimulation as intervention for psoriasis were independently reviewed by two researchers. A total of 13 RCTs with 1,060 participants were included. The methodological quality of included studies was not rigorous. Acupoint stimulation, compared with nonacupoint stimulation, had a significant treatment for psoriasis. However, the most common adverse events were thirst and dry mouth. Subgroup analysis was further done to confirm that the short-term treatment effect was superior to that of the long-term effect in treating psoriasis. Network meta-analysis identified acupressure or acupoint catgut embedding, compared with medication, and had a significant effect for improving psoriasis. It was noted that acupressure was the most effective treatment. Acupuncture-related techniques could be considered as an alternative or adjuvant therapy for psoriasis in short term, especially of acupressure and acupoint catgut embedding. This study recommends further well-designed, methodologically rigorous, and more head-to-head randomized trials to explore the effects of acupuncture-related techniques for treating psoriasis.
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Yuichi eYamashita
2011-04-01
Full Text Available How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC, a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf-HVC interaction.
Sridharan, Kannan; Sivaramakrishnan, Gowri
2017-10-01
Medical expulsive therapy (MET) using alpha blockers, calcium channel blockers (CCB), phosphodiesterase inhibitors (PDEI) and spasmolytics have been shown to be effective in clinical trials on urolithiasis. The present study is a network meta-analysis comparing the above mentioned drug classes. Electronic databases were searched for randomized controlled trials comparing the above mentioned drug classes in patients with urolithiasis using appropriate search strategy. Inverse variance heterogeneity model was used for the mixed treatment comparisons. Stone expulsion rate (SER) was the primary and stone expulsion time (SET) was the main secondary outcome measure. We included a total of 114 studies for systematic review and 108 studies for the network meta-analysis. Alpha blockers, PDEI, and combined alpha blockers and corticosteroids had significantly increased SER and shorter SET than placebo or standard of care. Alpha blockers have the highest probability of being the 'best' in the pool with regard to SER. This effect persisted in patients with stones ≥ 5 mm, children, after shockwave lithotripsy, proximal ureteric stones and distal ureteric stones. To conclude, we observed a statistically significant increase in the expulsion rate and shorter expulsion time with alpha blockers, PDEI and combined alpha blockers with corticosteroids. Of these interventions, alpha blockers have the high probability of being the 'best'.
Directory of Open Access Journals (Sweden)
Teerapong Panboonyuen
2017-07-01
Full Text Available Object segmentation of remotely-sensed aerial (or very-high resolution, VHS images and satellite (or high-resolution, HR images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs and conditional random fields (CRFs. To improve the DCNN, a modern activation function called the exponential linear unit (ELU, is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F 1 .
Directory of Open Access Journals (Sweden)
Jean-François Feller
2014-01-01
Full Text Available Different grades of chemically functionalized carbon nanotubes (CNT have been processed by spraying layer-by-layer (sLbL to obtain an array of chemoresistive transducers for volatile organic compound (VOC detection. The sLbL process led to random networks of CNT less conductive, but more sensitive to vapors than filtration under vacuum (bucky papers. Shorter CNT were also found to be more sensitive due to the less entangled and more easily disconnectable conducting networks they are making. Chemical functionalization of the CNT’ surface is changing their selectivity towards VOC, which makes it possible to easily discriminate methanol, chloroform and tetrahydrofuran (THF from toluene vapors after the assembly of CNT transducers into an array to make an e-nose. Interestingly, the amplitude of the CNT transducers’ responses can be enhanced by a factor of five (methanol to 100 (chloroform by dispersing them into a polymer matrix, such as poly(styrene (PS, poly(carbonate (PC or poly(methyl methacrylate (PMMA. COOH functionalization of CNT was found to penalize their dispersion in polymers and to decrease the sensors’ sensitivity. The resulting conductive polymer nanocomposites (CPCs not only allow for a more easy tuning of the sensors’ selectivity by changing the chemical nature of the matrix, but they also allow them to adjust their sensitivity by changing the average gap between CNT (acting on quantum tunneling in the CNT network. Quantum resistive sensors (QRSs appear promising for environmental monitoring and anticipated disease diagnostics that are both based on VOC analysis.
Node-based learning of differential networks from multi-platform gene expression data.
Ou-Yang, Le; Zhang, Xiao-Fei; Wu, Min; Li, Xiao-Li
2017-10-01
Recovering gene regulatory networks and exploring the network rewiring between two different disease states are important for revealing the mechanisms behind disease progression. The advent of high-throughput experimental techniques has enabled the possibility of inferring gene regulatory networks and differential networks using computational methods. However, most of existing differential network analysis methods are designed for single-platform data analysis and assume that differences between networks are driven by individual edges. Therefore, they cannot take into account the common information shared across different data platforms and may fail in identifying driver genes that lead to the change of network. In this study, we develop a node-based multi-view differential network analysis model to simultaneously estimate multiple gene regulatory networks and their differences from multi-platform gene expression data. Our model can leverage the strength across multiple data platforms to improve the accuracy of network inference and differential network estimation. Simulation studies demonstrate that our model can obtain more accurate estimations of gene regulatory networks and differential networks than other existing state-of-the-art models. We apply our model on TCGA ovarian cancer samples to identify network rewiring associated with drug resistance. We observe from our experiments that the hub nodes of our identified differential networks include known drug resistance-related genes and potential targets that are useful to improve the treatment of drug resistant tumors. Copyright © 2017 Elsevier Inc. All rights reserved.
DEFF Research Database (Denmark)
Fitzek, Frank H. P.; Toth, Tamas; Szabados, Aron
2014-01-01
Distributed storage is usually considered within a cloud provider to ensure availability and reliability of the data. However, the user is still directly dependent on the quality of a single system. It is also entrusting the service provider with large amounts of private data, which may be accessed...... by a successful attack to that cloud system or even be inspected by government agencies in some countries. This paper advocates a general framework for network coding enabled distributed storage over multiple commercial cloud solutions, such as, Dropbox, Box, Skydrive, and Google Drive, as a way to address...... these reliability and privacy issues. By means of theoretical analysis and real– life implementations, we show not only that our framework constitutes a viable solution to increase the reliability of stored data and to ensure data privacy, but it also provides a way to reduce the storage costs and to increase...
Coevolving complex networks in the model of social interactions
Raducha, Tomasz; Gubiec, Tomasz
2017-04-01
We analyze Axelrod's model of social interactions on coevolving complex networks. We introduce four extensions with different mechanisms of edge rewiring. The models are intended to catch two kinds of interactions-preferential attachment, which can be observed in scientists or actors collaborations, and local rewiring, which can be observed in friendship formation in everyday relations. Numerical simulations show that proposed dynamics can lead to the power-law distribution of nodes' degree and high value of the clustering coefficient, while still retaining the small-world effect in three models. All models are characterized by two phase transitions of a different nature. In case of local rewiring we obtain order-disorder discontinuous phase transition even in the thermodynamic limit, while in case of long-distance switching discontinuity disappears in the thermodynamic limit, leaving one continuous phase transition. In addition, we discover a new and universal characteristic of the second transition point-an abrupt increase of the clustering coefficient, due to formation of many small complete subgraphs inside the network.
Network reorganization driven by temporal interdependence of its elements
Waddell, Jack; Zochowski, Michal
2006-06-01
We employ an adaptive parameter control technique based on detection of phase/lag synchrony between the elements of the system to dynamically modify the structure of a network of nonidentical, coupled Rössler oscillators. Two processes are simulated: adaptation, under which the initially different properties of the units converge, and rewiring, in which clusters of interconnected elements are formed based on the temporal correlations. We show how those processes lead to different network structures and investigate their optimal characteristics from the point of view of resulting network properties.
Directory of Open Access Journals (Sweden)
Ahmed N. U.
2005-01-01
Full Text Available We consider a dynamic model that simulates the interaction of TCP sources with active queue management system (AQM. We propose a modified version of an earlier dynamic model called RED. This is governed by a system of stochastic differential equations driven by a doubly stochastic point process with intensity as the control. The feedback control law proposed observes the router (queue status and controls the intensity by sending congestion signals (warnings to the sources for adjustment of their transmission rates. The (feedback control laws used are of polynomial type (including linear with adjustable coefficients. They are optimized by use of genetic algorithm (GA and random recursive search (RRS technique. The numerical results demonstrate that the proposed model and the method can improve the system performance significantly.
Reich, K; Burden, A D; Eaton, J N; Hawkins, N S
2012-01-01
Ustekinumab, a novel monoclonal antibody for the treatment of moderate to severe plaque-type psoriasis, has recently received regulatory approval in Europe, bringing the total number of biologic agents licensed in this indication to five. To assist treatment selection in daily practice it is essential to understand the benefit/risk profile of these agents and in the absence of a clinical trial comparing all available biologics a number of reviews have used statistical techniques to generate estimates of the comparative effectiveness of these therapies through the available network of randomized clinical trials. These estimates have previously been published for a limited range of psoriasis biologic treatments, although, to date no review has compared all the currently available agents in Europe. To estimate the comparative effectiveness of all biologic agents indicated in the treatment of moderate to severe psoriasis currently available in Europe based on the primary trial endpoints. A number of databases were searched for details of randomized controlled trials of available biologics in the treatment of plaque-type psoriasis in adults. Comparative effectiveness was estimated based on the reported Psoriasis Area and Severity Index (PASI) 50, 75 and 90 response rates. A network meta-analysis conducted on the ordered probit scale and implemented as a Bayesian hierarchical model provided estimates for the probability of response and relative risk vs. placebo, based on all observed comparisons. Twenty trials were included in the meta-analysis including patients with a mean disease duration of 18-22years. Based on the indirect comparison and given a placebo PASI 50 response of 13%, infliximab had the highest predicted mean probability of response at PASI levels 50 (93%), 75 (80%) and 90 (54%), followed by ustekinumab 90 mg at 90%, 74% and 46%, respectively, and then ustekinumab 45mg, adalimumab, etanercept and efalizumab. The ordered probit model allowed a
Wang, Xiaojie; Zheng, Hong; Shou, Tao; Tang, Chunming; Miao, Kun; Wang, Ping
2017-03-29
Osteosarcoma is the most common malignant bone tumour. Due to the high metastasis rate and drug resistance of this disease, multi-drug regimens are necessary to control tumour cells at various stages of the cell cycle, eliminate local or distant micrometastases, and reduce the emergence of drug-resistant cells. Many adjuvant chemotherapy protocols have shown different efficacies and controversial results. Therefore, we classified the types of drugs used for adjuvant chemotherapy and evaluated the differences between single- and multi-drug chemotherapy regimens using network meta-analysis. We searched electronic databases, including PubMed (MEDLINE), EmBase, and the Cochrane Library, through November 2016 using the keywords "osteosarcoma", "osteogenic sarcoma", "chemotherapy", and "random*" without language restrictions. The major outcome in the present analysis was progression-free survival (PFS), and the secondary outcome was overall survival (OS). We used a random effect network meta-analysis for mixed multiple treatment comparisons. We included 23 articles assessing a total of 5742 patients in the present systematic review. The analysis of PFS indicated that the T12 protocol (including adriamycin, bleomycin, cyclophosphamide, dactinomycin, methotrexate, cisplatin) plays a more critical role in osteosarcoma treatment (surface under the cumulative ranking (SUCRA) probability 76.9%), with a better effect on prolonging the PFS of patients when combined with ifosfamide (94.1%) or vincristine (81.9%). For the analysis of OS, we separated the regimens to two groups, reflecting the disconnection. The T12 protocol plus vincristine (94.7%) or the removal of cisplatinum (89.4%) is most likely the best regimen. We concluded that multi-drug regimens have a better effect on prolonging the PFS and OS of osteosarcoma patients, and the T12 protocol has a better effect on prolonging the PFS of osteosarcoma patients, particularly in combination with ifosfamide or vincristine
Michelitsch, T. M.; Collet, B. A.; Riascos, A. P.; Nowakowski, A. F.; Nicolleau, F. C. G. A.
2017-12-01
We analyze a Markovian random walk strategy on undirected regular networks involving power matrix functions of the type L\\frac{α{2}} where L indicates a ‘simple’ Laplacian matrix. We refer to such walks as ‘fractional random walks’ with admissible interval 0 α (recurrent for d≤slantα ) of the lattice. As a consequence, for 0global mean first passage times (Kemeny constant) for the fractional random walk. For an infinite 1D lattice (infinite ring) we obtain for the transient regime 0world properties with the emergence of Lévy flights on large (infinite) lattices.
Modified Penna bit-string network evolution model for scale-free networks with assortative mixing
Kim, Yup; Choi, Woosik; Yook, Soon-Hyung
2012-02-01
Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.
Computational modeling of electrokinetic transport in random networks of micro-pores and nano-pores
Alizadeh, Shima; Mani, Ali
2014-11-01
A reduced order model has been developed to study the nonlinear electrokinetic behaviors emerging in the transport of ionic species through micro-scale and nano-scale porous media. In this approach a porous structure is modeled as a network of long and thin pores. By assuming transport equilibrium in the thin dimensions for each pore, a 1D transport equation is developed in the longitudinal direction covering a wide range of conditions including extreme limits of thick and thin electric double layers. This 1D model includes transport via diffusion, electromigration and wide range of advection mechanisms including pressure driven flow, electroosmosis, and diffusion osmosis. The area-averaged equations governing the axial transport from different pores are coupled at the pore intersections using the proper conservation laws. Moreover, an asymptotic treatment has been included in order to remove singularities in the limit of small concentration. The proposed method provides an efficient framework for insightful simulations of porous electrokinetic systems with applications in water desalination and energy storage. PhD student in Mechanical Engineering, Stanford University. She received her Master's degree in Mechanical Engineering from Stanford at 2013. Her research interests include CFD, high performance computing, and optimization.
Random Network Models to Predict the Long-Term Impact of HPV Vaccination on Genital Warts
Díez-Domingo, Javier; Sánchez-Alonso, Víctor; Acedo, Luis; Villanueva-Oller, Javier
2017-01-01
The Human papillomaviruses (HPV) vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. This aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore, it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model, we simulated the scenario that occurred in Australia in 2007, where 12–13 year-old girls were vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, which protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73% in girls with three doses and with coverage rates decreasing with age until 52% for 20–26 year-olds. A fast 59% reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature. PMID:29035332
Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.
2016-01-01
A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566
Directory of Open Access Journals (Sweden)
Huiqing Chong
Full Text Available The presence of acetate exceeding 5 g/L is a major concern during E. coli fermentation due to its inhibitory effect on cell growth, thereby limiting high-density cell culture and recombinant protein production. Hence, engineered E. coli strains with enhanced acetate tolerance would be valuable for these bioprocesses. In this work, the acetate tolerance of E. coli was much improved by rewiring its global regulator cAMP receptor protein (CRP, which is reported to regulate 444 genes. Error-prone PCR method was employed to modify crp and the mutagenesis libraries (~3×10(6 were subjected to M9 minimal medium supplemented with 5-10 g/L sodium acetate for selection. Mutant A2 (D138Y was isolated and its growth rate in 15 g/L sodium acetate was found to be 0.083 h(-1, much higher than that of the control (0.016 h(-1. Real-time PCR analysis via OpenArray(® system revealed that over 400 CRP-regulated genes were differentially expressed in A2 with or without acetate stress, including those involved in the TCA cycle, phosphotransferase system, etc. Eight genes were chosen for overexpression and the overexpression of uxaB was found to lead to E. coli acetate sensitivity.
Huang, Sui; Ingber, Donald E
It is commonly assumed that somatic evolution drives the multi-step process that produces metastatic cancer. But it is difficult to reconcile the inexorable progression towards metastasis in virtually all carcinomas and the associated complex change of cancer cell phenotype, characterized by an epithelial-to-mesenchymal transition, with the random nature of gene mutations. Given their irreversible nature, it is also difficult to explain why certain metastatic carcinomas can reform normal tissue boundaries and remain dormant for years at distant sites. Here we propose an encompassing conceptual framework based on system-level dynamics of gene regulatory networks that may help reconcile these inconsistencies. The concepts of gene expression state space and attractors are introduced which provide a mathematical and molecular basis for an "epigenetic landscape". We then describe how cancer cells are trapped in "embryonic attractors" because of distortions of this landscape caused by mutational rewiring of the regulatory network. The implications of this concept for a new integrative understanding of tumor formation and metastatic progression are discussed. This formal framework of cancer progression unites mainstream genetic determinism with alternative ideas that emphasize non-genetic influences, including chronic growth stimulation,extracellular matrix remodeling, alteration of cell mechanics and disruption of tissue architecture.
Latkin, Carl A.; Kukhareva, Polina V.; Malov, Sergey V.; Batluk, Julia V.; Shaboltas, Alla V.; Skochilov, Roman V.; Sokolov, Nicolay V.; Verevochkin, Sergei V.; Hudgens, Michael G.; Kozlov, Andrei P.
2014-01-01
We evaluated the efficacy of a peer-educator network intervention as a strategy to reduce HIV acquisition among injection drug users (IDUs) and their drug and/or sexual networks. A randomized controlled trial was conducted in St. Petersburg, Russia among IDU index participants and their risk network participants. Network units were randomized to the control or experimental intervention. Only the experimental index participants received training sessions to communicate risk reduction techniques to their network members. Analysis includes 76 index and 84 network participants who were HIV uninfected. The main outcome measure was HIV sero-conversion. The incidence rates in the control and experimental groups were 19.57 (95 % CI 10.74–35.65) and 7.76 (95 % CI 3.51–17.19) cases per 100 p/y, respectively. The IRR was 0.41 (95 % CI 0.15–1.08) without a statistically significant difference between the two groups (log rank test statistic X2 = 2.73, permutation p value = 0.16). Retention rate was 67 % with a third of the loss due to incarceration or death. The results show a promising trend that this strategy would be successful in reducing the acquisition of HIV among IDUs. PMID:23881187
Majority-vote model with a bimodal distribution of noises in small-world networks
Vilela, André L. M.; de Souza, Adauto J. F.
2017-12-01
We consider a generalized version of the majority-vote model in small-world networks. In our model, each site of the network has noise q = 0 and q ≠ 0 with probability f and 1 - f, respectively. The connections of the two-dimensional square lattice are rewired with probability p. We performed Monte Carlo simulations to characterize the order-disorder phase transition of the system. Through finite-size scaling analysis, we calculated the critical noise value qc and the standard critical exponents β / ν, γ / ν, 1 / ν. Our results suggest that these exponents are different from those of the isotropic majority-vote model. We concluded that the zero noise fraction f when combined with the rewiring probability p drive the system to a different universality class from that of the isotropic majority-vote model.
Hibert, Clement; Stumpf, André; Provost, Floriane; Malet, Jean-Philippe
2017-04-01
In the past decades, the increasing quality of seismic sensors and capability to transfer remotely large quantity of data led to a fast densification of local, regional and global seismic networks for near real-time monitoring of crustal and surface processes. This technological advance permits the use of seismology to document geological and natural/anthropogenic processes (volcanoes, ice-calving, landslides, snow and rock avalanches, geothermal fields), but also led to an ever-growing quantity of seismic data. This wealth of seismic data makes the construction of complete seismicity catalogs, which include earthquakes but also other sources of seismic waves, more challenging and very time-consuming as this critical pre-processing stage is classically done by human operators and because hundreds of thousands of seismic signals have to be processed. To overcome this issue, the development of automatic methods for the processing of continuous seismic data appears to be a necessity. The classification algorithm should satisfy the need of a method that is robust, precise and versatile enough to be deployed to monitor the seismicity in very different contexts. In this study, we evaluate the ability of machine learning algorithms for the analysis of seismic sources at the Piton de la Fournaise volcano being Random Forest and Deep Neural Network classifiers. We gather a catalog of more than 20,000 events, belonging to 8 classes of seismic sources. We define 60 attributes, based on the waveform, the frequency content and the polarization of the seismic waves, to parameterize the seismic signals recorded. We show that both algorithms provide similar positive classification rates, with values exceeding 90% of the events. When trained with a sufficient number of events, the rate of positive identification can reach 99%. These very high rates of positive identification open the perspective of an operational implementation of these algorithms for near-real time monitoring of
Aimola, Lina; Jasim, Sarah; Tripathi, Neeraj; Tucker, Sarah; Worrall, Adrian; Quirk, Alan; Crawford, Mike J
2016-09-21
Quality improvement networks are peer-led programmes in which members of the network assess the quality of care colleagues provide according to agreed standards of practice. These networks aim to help members identify areas of service provision that could be improved and share good practice. Despite the widespread use of peer-led quality improvement networks, there is very little information about their impact. We are conducting a cluster randomized controlled trial of a quality improvement network for low-secure mental health wards to examine the impact of membership on the process and outcomes of care over a 12 month period. Standalone low secure units in England and Wales that expressed an interest in joining the quality improvement network were recruited for the study from 2012 to 2014. Thirty-eight units were randomly allocated to either the active intervention (participation in the network n = 18) or a control arm (delayed participation in the network n = 20). Using a 5 % significance level and 90 % power, it was calculated that a sample size of 60 wards was required taking into account a 10 % drop out. A total of 75 wards were assessed at baseline and 8 wards dropped out the study before the data collection at follow up. Researchers masked to the allocation status of the units assessed all study outcomes at baseline and follow-up 12 months later. The primary outcome is the quality of the physical environment and facilities on the wards. The secondary outcomes are: safety of the ward, patient-rated satisfaction with care and mental well-being, staff burnout, training and supervision. Relative to control wards, it is hypothesized that the quality of the physical environment and facilities will be higher on wards in the active arm of the trial 12 months after randomization. To our knowledge, this is the first randomized evaluation of a peer-led quality improvement network that has examined the impact of participation on both patient-level and
Sahu, Sandeep; Yadav, Prabhat Chand; Shekhar, Shashank
2018-02-01
In this investigation, Inconel 600 alloy was thermomechanically processed to different strains via hot rolling followed by a short-time annealing treatment to determine an appropriate thermomechanical process to achieve a high fraction of low-Σ CSL boundaries. Experimental results demonstrate that a certain level of deformation is necessary to obtain effective "grain boundary engineering"; i.e., the deformation must be sufficiently high to provide the required driving force for postdeformation static recrystallization, yet it should be low enough to retain a large fraction of original twin boundaries. Samples processed in such a fashion exhibited 77 pct length fraction of low-Σ CSL boundaries, a dominant fraction of which was from Σ3 ( 64 pct), the latter with very low deviation from its theoretical misorientation. The application of hot rolling also resulted in a very low fraction of Σ1 ( 1 pct) boundaries, as desired. The process also leads to so-called "triple junction engineering" with the generation of special triple junctions, which are very effective in disrupting the connectivity of the random grain boundary network.
Sahu, Sandeep; Yadav, Prabhat Chand; Shekhar, Shashank
2017-12-01
In this investigation, Inconel 600 alloy was thermomechanically processed to different strains via hot rolling followed by a short-time annealing treatment to determine an appropriate thermomechanical process to achieve a high fraction of low-Σ CSL boundaries. Experimental results demonstrate that a certain level of deformation is necessary to obtain effective "grain boundary engineering"; i.e., the deformation must be sufficiently high to provide the required driving force for postdeformation static recrystallization, yet it should be low enough to retain a large fraction of original twin boundaries. Samples processed in such a fashion exhibited 77 pct length fraction of low-Σ CSL boundaries, a dominant fraction of which was from Σ3 ( 64 pct), the latter with very low deviation from its theoretical misorientation. The application of hot rolling also resulted in a very low fraction of Σ1 ( 1 pct) boundaries, as desired. The process also leads to so-called "triple junction engineering" with the generation of special triple junctions, which are very effective in disrupting the connectivity of the random grain boundary network.
Magdesian, Margaret H; Anthonisen, Madeleine; Lopez-Ayon, G Monserratt; Chua, Xue Ying; Rigby, Matthew; Grütter, Peter
2017-06-13
Brain and spinal cord injury may lead to permanent disability and death because it is still not possible to regenerate neurons over long distances and accurately reconnect them with an appropriate target. Here a procedure is described to rapidly initiate, elongate, and precisely connect new functional neuronal circuits over long distances. The extension rates achieved reach over 1.2 mm/h, 30-60 times faster than the in vivo rates of the fastest growing axons from the peripheral nervous system (0.02 to 0.04 mm/h)(28) and 10 times faster than previously reported for the same neuronal type at an earlier stage of development(4). First, isolated populations of rat hippocampal neurons are grown for 2-3 weeks in microfluidic devices to precisely position the cells, enabling easy micromanipulation and experimental reproducibility. Next, beads coated with poly-D-lysine (PDL) are placed on neurites to form adhesive contacts and pipette micromanipulation is used to move the resulting bead-neurite complex. As the bead is moved, it pulls out a new neurite that can be extended over hundreds of micrometers and functionally connected to a target cell in less than 1 h. This process enables experimental reproducibility and ease of manipulation while bypassing slower chemical strategies to induce neurite growth. Preliminary measurements presented here demonstrate a neuronal growth rate far exceeding physiological ones. Combining these innovations allows for the precise establishment of neuronal networks in culture with an unprecedented degree of control. It is a novel method that opens the door to a plethora of information and insights into signal transmission and communication within the neuronal network as well as being a playground in which to explore the limits of neuronal growth. The potential applications and experiments are widespread with direct implications for therapies that aim to reconnect neuronal circuits after trauma or in neurodegenerative diseases.
Information transmission on hybrid networks
Chen, Rongbin; Cui, Wei; Pu, Cunlai; Li, Jie; Ji, Bo; Gakis, Konstantinos; Pardalos, Panos M.
2018-01-01
Many real-world communication networks often have hybrid nature with both fixed nodes and moving modes, such as the mobile phone networks mainly composed of fixed base stations and mobile phones. In this paper, we discuss the information transmission process on the hybrid networks with both fixed and mobile nodes. The fixed nodes (base stations) are connected as a spatial lattice on the plane forming the information-carrying backbone, while the mobile nodes (users), which are the sources and destinations of information packets, connect to their current nearest fixed nodes respectively to deliver and receive information packets. We observe the phase transition of traffic load in the hybrid network when the packet generation rate goes from below and then above a critical value, which measures the network capacity of packets delivery. We obtain the optimal speed of moving nodes leading to the maximum network capacity. We further improve the network capacity by rewiring the fixed nodes and by considering the current load of fixed nodes during packets transmission. Our purpose is to optimize the network capacity of hybrid networks from the perspective of network science, and provide some insights for the construction of future communication infrastructures.
Peng, Bao-Yu; Pan, Yue; Li, Ru-Jiao; Wei, Jin-Wei; Liang, Fang; Wang, Li; Wang, Fang-Fang; Qian, Wei
2017-08-01
How essential, regulatory genes originate and evolve is intriguing because mutations of these genes not only lead to lethality in organisms, but also have pleiotropic effects since they control the expression of multiple downstream genes. Therefore, the evolution of essential, regulatory genes is not only determined by genetic variations of their own sequences, but also by the biological function of downstream genes and molecular mechanisms of regulation. To understand the origin of essential, regulatory genes, experimental dissection of the complete regulatory cascade is needed. Here, we provide genetic evidences to reveal that PhoP-PhoQ is an essential two-component signal transduction system in the gram-negative bacterium Xanthomonas campestris, but that its orthologs in other bacteria belonging to Proteobacteria are nonessential. Mutational, biochemical, and chromatin immunoprecipitation together with high-throughput sequencing analyses revealed that phoP and phoQ of X. campestris and its close relative Pseudomonas aeruginosa are replaceable, and that the consensus binding motifs of the transcription factor PhoP are also highly conserved. PhoP Xcc in X. campestris regulates the transcription of a number of essential, structural genes by directly binding to cis-regulatory elements (CREs); however, these CREs are lacking in the orthologous essential, structural genes in P. aeruginosa, and thus the regulatory relationships between PhoP Pae and these downstream essential genes are disassociated. Our findings suggested that the recruitment of regulatory proteins by critical structural genes via transcription factor-CRE rewiring is a driving force in the origin and functional divergence of essential, regulatory genes. Copyright © 2017 by the Genetics Society of America.
Aiello, Allison E; Simanek, Amanda M; Eisenberg, Marisa C; Walsh, Alison R; Davis, Brian; Volz, Erik; Cheng, Caroline; Rainey, Jeanette J; Uzicanin, Amra; Gao, Hongjiang; Osgood, Nathaniel; Knowles, Dylan; Stanley, Kevin; Tarter, Kara; Monto, Arnold S
2016-06-01
Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period. 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as "seed" participants, who nominated their social contacts to join the study, of which 328 "nominees" enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N=103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all Pimpact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project, including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers
Role of social environment and social clustering in spread of opinions in coevolving networks.
Malik, Nishant; Mucha, Peter J
2013-12-01
Taking a pragmatic approach to the processes involved in the phenomena of collective opinion formation, we investigate two specific modifications to the coevolving network voter model of opinion formation studied by Holme and Newman [Phys. Rev. E 74, 056108 (2006)]. First, we replace the rewiring probability parameter by a distribution of probability of accepting or rejecting opinions between individuals, accounting for heterogeneity and asymmetric influences in relationships between individuals. Second, we modify the rewiring step by a path-length-based preference for rewiring that reinforces local clustering. We have investigated the influences of these modifications on the outcomes of simulations of this model. We found that varying the shape of the distribution of probability of accepting or rejecting opinions can lead to the emergence of two qualitatively distinct final states, one having several isolated connected components each in internal consensus, allowing for the existence of diverse opinions, and the other having a single dominant connected component with each node within that dominant component having the same opinion. Furthermore, more importantly, we found that the initial clustering in the network can also induce similar transitions. Our investigation also indicates that these transitions are governed by a weak and complex dependence on system size. We found that the networks in the final states of the model have rich structural properties including the small world property for some parameter regimes.
Towards an evolutionary model of transcription networks.
Directory of Open Access Journals (Sweden)
Dan Xie
2011-06-01
Full Text Available DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs, we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns, the interspecies difference in TF-target relationship is much smaller. The data showed increasing conservation levels from genomic sequences to TF-DNA interaction, gene expression, TN, and finally to morphology, suggesting that evolutionary changes are larger at molecular levels and smaller at functional levels. The data also showed that evolutionarily older TFs are more likely to have conserved target genes, whereas younger TFs tend to have larger re-wiring rates.
Unraveling protein networks with power graph analysis.
Royer, Loïc; Reimann, Matthias; Andreopoulos, Bill; Schroeder, Michael
2008-07-11
Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.
P-body proteins regulate transcriptional rewiring to promote DNA replication stress resistance.
Loll-Krippleber, Raphael; Brown, Grant W
2017-09-15
mRNA-processing (P-) bodies are cytoplasmic granules that form in eukaryotic cells in response to numerous stresses to serve as sites of degradation and storage of mRNAs. Functional P-bodies are critical for the DNA replication stress response in yeast, yet the repertoire of P-body targets and the mechanisms by which P-bodies promote replication stress resistance are unknown. In this study we identify the complete complement of mRNA targets of P-bodies during replication stress induced by hydroxyurea treatment. The key P-body protein Lsm1 controls the abundance of HHT1, ACF4, ARL3, TMA16, RRS1 and YOX1 mRNAs to prevent their toxic accumulation during replication stress. Accumulation of YOX1 mRNA causes aberrant downregulation of a network of genes critical for DNA replication stress resistance and leads to toxic acetaldehyde accumulation. Our data reveal the scope and the targets of regulation by P-body proteins during the DNA replication stress response.P-bodies form in response to stress and act as sites of mRNA storage and degradation. Here the authors identify the mRNA targets of P-bodies during DNA replication stress, and show that P-body proteins act to prevent toxic accumulation of these target transcripts.
Smit, Crystal R; de Leeuw, Rebecca N H; Bevelander, Kirsten E; Burk, William J; Buijzen, Moniek
2016-08-01
The current pilot study examined the effectiveness of a social network-based intervention using peer influence on self-reported water consumption. A total of 210 children (52% girls; M age = 10.75 ± SD = 0.80) were randomly assigned to either the intervention (n = 106; 52% girls) or control condition (n = 104; 52% girls). In the intervention condition, the most influential children in each classroom were trained to promote water consumption among their peers for eight weeks. The schools in the control condition did not receive any intervention. Water consumption, sugar-sweetened beverage (SSB) consumption, and intentions to drink more water in the near future were assessed by self-report measures before and immediately after the intervention. A repeated measure MANCOVA showed a significant multivariate interaction effect between condition and time (V = 0.07, F(3, 204) = 5.18, p = 0.002, pη(2) = 0.07) on the dependent variables. Further examination revealed significant univariate interaction effects between condition and time on water (p = 0.021) and SSB consumption (p = 0.015) as well as water drinking intentions (p = 0.049). Posthoc analyses showed that children in the intervention condition reported a significant increase in their water consumption (p = 0.018) and a decrease in their SSB consumption (p 0.05). The children who were exposed to the intervention did not report a change in their water drinking intentions over time (p = 0.576) whereas the nonexposed children decreased their intentions (p = 0.026). These findings show promise for a social network-based intervention using peer influence to positively alter consumption behaviors. This RCT was registered in the Australian New Zealand Clinical Trials Registry (ACTRN12614001179628). Study procedures were approved by the Ethics Committee of the Faculty of Social Sciences at Radboud University (ECSW2014-1003-203). Copyright © 2016 Elsevier Ltd. All rights reserved.
Martinez, V; Beloeil, H; Marret, E; Fletcher, D; Ravaud, P; Trinquart, L
2017-01-01
Morphine, and analgesics other than morphine (AOM), are commonly used to treat postoperative pain after major surgery. However, which AOM provides the best efficacy-safety profile remains unclear. Randomized trials of any AOM alone or any combination of AOM compared with placebo or another AOM in adults undergoing major surgery and receiving morphine patient-controlled analgesia were included in a network meta-analysis. The outcomes were morphine consumption, pain, incidence of nausea, vomiting at 24 h and severe adverse effects. 135 trials (13,287 patients) assessing 14 AOM alone or in combination were included. For all outcomes, comparisons with placebo were over-represented. Few trials assessed combinations of two AOM and none the combination of three or more. Network meta-analysis found morphine consumption reduction was greatest with the combination of two AOM (acetaminophen + nefopam, acetaminophen + NSAID, and tramadol + metamizol): -23.9 (95% CI -40;-7.7), -22.8 (-31.5;-14) and -19.8 (35.4;-4.2) mg per 24 h, respectively. For AOM used alone, morphine consumption reduction was greatest with α-2 agonists, NSAIDs, and COX-2 inhibitors. When considering the risk of nausea, NSAIDs, corticosteroids and α-2 agonists used alone were the most efficacious (OR 0.7 [95% CI: 0.6-0.8], 0.36 [0.18-0.79], 0.41 [0.15-.64], respectively). The paucity of severe adverse effects data did not allow assessment of efficacy-safety balance. A combination of aetaminophen with either an NSAID or nefopam was superior to most AOM used alone, in reducing morphine consumption. Efficacy was best with three AOM used alone (α-2 agonists, NSAIDs and COX-2 inhibitors) and least with tramadol and acetaminophen. There is insufficient trial data reporting adverse events. PROSPERO: CRD42013003912. © The Author 2016. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang
2016-01-01
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.
Phan, Kevin; Xie, Ashleigh; Kumar, Narendra; Wong, Sophia; Medi, Caroline; La Meir, Mark; Yan, Tristan D
2015-08-01
Simplified maze procedures involving radiofrequency, cryoenergy and microwave energy sources have been increasingly utilized for surgical treatment of atrial fibrillation as an alternative to the traditional cut-and-sew approach. In the absence of direct comparisons, a Bayesian network meta-analysis is another alternative to assess the relative effect of different treatments, using indirect evidence. A Bayesian meta-analysis of indirect evidence was performed using 16 published randomized trials identified from 6 databases. Rank probability analysis was used to rank each intervention in terms of their probability of having the best outcome. Sinus rhythm prevalence beyond the 12-month follow-up was similar between the cut-and-sew, microwave and radiofrequency approaches, which were all ranked better than cryoablation (respectively, 39, 36, and 25 vs 1%). The cut-and-sew maze was ranked worst in terms of mortality outcomes compared with microwave, radiofrequency and cryoenergy (2 vs 19, 34, and 24%, respectively). The cut-and-sew maze procedure was associated with significantly lower stroke rates compared with microwave ablation [odds ratio <0.01; 95% confidence interval 0.00, 0.82], and ranked the best in terms of pacemaker requirements compared with microwave, radiofrequency and cryoenergy (81 vs 14, and 1, <0.01% respectively). Bayesian rank probability analysis shows that the cut-and-sew approach is associated with the best outcomes in terms of sinus rhythm prevalence and stroke outcomes, and remains the gold standard approach for AF treatment. Given the limitations of indirect comparison analysis, these results should be viewed with caution and not over-interpreted. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
Directory of Open Access Journals (Sweden)
B. S. Daya Sagar
2001-01-01
Full Text Available This letter presents a brief framework based on nonlinear morphological transformations to generate a self organized critical connectivity network map (SOCCNM in 2-dimensional space. This simple and elegant framework is implemented on a section that contains a few simulated water bodies to generate SOCCNM. This is based on a postulate that the randomly situated surface water bodies of various sizes and shapes self organize during flooding process.
Kleinnijenhuis, J.; de Nooy, W.
2013-01-01
During election campaigns, political parties deliver statements on salient issues in the news media, which are called issue positions. This article conceptualizes issue positions as a valued and longitudinal two-mode network of parties by issues. The network is valued because parties pronounce pro
Kleinnijenhuis, J.; de Nooy, W.
2013-01-01
During election campaigns, political parties deliver statements on salient issues in the news media, which are called issue positions. This article conceptualizes issue positions as a valued and longitudinal two-mode network of parties by issues. The network is valued because parties pronounce pro
Energy Technology Data Exchange (ETDEWEB)
Conte, Viviane Cristhyne Bini; Arruda, Lucia Valeria Ramos de; Yamamoto, Lia [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil)
2008-07-01
Planning and scheduling of the pipeline network operations aim the most efficient use of the resources resulting in a better performance of the network. A petroleum distribution pipeline network is composed by refineries, sources and/or storage parks, connected by a set of pipelines, which operate the transportation of petroleum and derivatives among adjacent areas. In real scenes, this problem is considered a combinatorial problem, which has difficult solution, which makes necessary methodologies of the resolution that present low computational time. This work aims to get solutions that attempt the demands and minimize the number of batch fragmentations on the sent operations of products for the pipelines in a simplified model of a real network, through by application of the local search metaheuristic GRASP. GRASP does not depend of solutions of previous iterations and works in a random way so it allows the search for the solution in an ampler and diversified search space. GRASP utilization does not demand complex calculation, even the construction stage that requires more computational effort, which provides relative rapidity in the attainment of good solutions. GRASP application on the scheduling of the operations of this network presented feasible solutions in a low computational time. (author)
Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias
2008-12-01
We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
Gu, Yang; Deng, Jieying; Liu, Yanfeng; Li, Jianghua; Shin, Hyun-Dong; Du, Guocheng; Chen, Jian; Liu, Long
2017-10-01
N-acetylglucosamine (GlcNAc) is an important amino sugar extensively used in the healthcare field. In a previous study, the recombinant Bacillus subtilis strain BSGN6-PxylA -glmS-pP43NMK-GNA1 (BN0-GNA1) had been constructed for microbial production of GlcNAc by pathway design and modular optimization. Here, the production of GlcNAc is further improved by rewiring both the glucose transportation and central metabolic pathways. First, the phosphotransferase system (PTS) is blocked by deletion of three genes, yyzE (encoding the PTS system transporter subunit IIA YyzE), ypqE (encoding the PTS system transporter subunit IIA YpqE), and ptsG (encoding the PTS system glucose-specific EIICBA component), resulting in 47.6% increase in the GlcNAc titer (from 6.5 ± 0.25 to 9.6 ± 0.16 g L-1 ) in shake flasks. Then, reinforcement of the expression of the glcP and glcK genes and optimization of glucose facilitator proteins are performed to promote glucose import and phosphorylation. Next, the competitive pathways for GlcNAc synthesis, namely glycolysis, peptidoglycan synthesis pathway, pentose phosphate pathway, and tricarboxylic acid cycle, are repressed by initiation codon-optimization strategies, and the GlcNAc titer in shake flasks is improved from 10.8 ± 0.25 to 13.2 ± 0.31 g L-1 . Finally, the GlcNAc titer is further increased to 42.1 ± 1.1 g L-1 in a 3-L fed-batch bioreactor, which is 1.72-fold that of the original strain, BN0-GNA1. This study shows considerably enhanced GlcNAc production, and the metabolic engineering strategy described here will be useful for engineering other prokaryotic microorganisms for the production of GlcNAc and related molecules. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Computational design of genomic transcriptional networks with adaptation to varying environments
Carrera, Javier; Elena, Santiago F.; Jaramillo, Alfonso
2012-01-01
Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes. PMID:22927389
Lee, Hojoon; Macpherson, Lindsey J; Parada, Camilo A; Zuker, Charles S; Ryba, Nicholas J P
2017-08-17
In mammals, taste buds typically contain 50-100 tightly packed taste-receptor cells (TRCs), representing all five basic qualities: sweet, sour, bitter, salty and umami. Notably, mature taste cells have life spans of only 5-20 days and, consequently, are constantly replenished by differentiation of taste stem cells. Given the importance of establishing and maintaining appropriate connectivity between TRCs and their partner ganglion neurons (that is, ensuring that a labelled line from sweet TRCs connects to sweet neurons, bitter TRCs to bitter neurons, sour to sour, and so on), we examined how new connections are specified to retain fidelity of signal transmission. Here we show that bitter and sweet TRCs provide instructive signals to bitter and sweet target neurons via different guidance molecules (SEMA3A and SEMA7A). We demonstrate that targeted expression of SEMA3A or SEMA7A in different classes of TRCs produces peripheral taste systems with miswired sweet or bitter cells. Indeed, we engineered mice with bitter neurons that now responded to sweet tastants, sweet neurons that responded to bitter or sweet neurons responding to sour stimuli. Together, these results uncover the basic logic of the wiring of the taste system at the periphery, and illustrate how a labelled-line sensory circuit preserves signalling integrity despite rapid and stochastic turnover of receptor cells.
National Research Council Canada - National Science Library
Elena Tutubalina; Sergey Nikolenko
2017-01-01
.... Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural...
Wu, Yi-Cheng; Tsai, Wen-Chung; Tu, Yu-Kung; Yu, Tung-Yang
2017-08-01
To investigate the effectiveness of various nonoperative treatments for chronic calcific tendinitis of the shoulder, a systematic review and network meta-analysis of randomized trials was performed to evaluate changes in pain reduction, functional improvements in patients with calcific tendinitis, and the ratio of complete resolution of calcific deposition. Studies were comprehensively searched, without language restrictions, on PubMed, Embase, Cochrane Controlled Trials Register, the Cochrane, and other databases. The reference lists of articles and reviews were cross-checked for possible studies. Randomized controlled trials from before August 2016 were included. Study selection was conducted by 2 reviewers independently. The quality of studies was assessed and data extracted by 2 independent reviewers. Disagreements were settled by consulting a third reviewer to reach a consensus. Fourteen studies with 1105 participants were included in the network meta-analysis that used a random-effect model to investigate the mean difference of pooled effect sizes of the visual analog scale, Constant-Murley score, and the ratio of complete resolution of calcific deposition on native radiographs. The present network meta-analysis demonstrates that ultrasound-guided needling (UGN), radial extracorporeal shockwave therapy (RSW), and high-energy focused extracorporeal shockwave therapy (H-FSW) alleviate pain and achieve complete resolution of calcium deposition. Compared with low-energy focused extracorporeal shockwave therapy, transcutaneous electrical nerve stimulation, and ultrasound therapy, H-FSW is the best therapy for providing functional recovery. Physicians should consider UGN, RSW, and H-FSW as alternative effective therapies for chronic calcific tendinitis of the shoulder when initial conservative treatment fails. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Zhou Sheng Jie
2016-01-01
Full Text Available A MAC protocol for public bus networks, called Bus MAC protocol, designed to provide high quality Internet service for bus passengers. The paper proposed a multi-channel dual clocks three-demission probability random multiple access protocol based on RTS/CTS mechanism, decreasing collisions caused by multiple access from multiple passengers. Use the RTS/CTS mechanism increases the reliability and stability of the system, reducing the collision possibility of the information packets to a certain extent, improves the channel utilization; use the multi-channel mechanism, not only enables the channel load balancing, but also solves the problem of the hidden terminal and exposed terminal. Use the dual clocks mechanism, reducing the system idle time. At last, the different selection of the three-dimensional probabilities can make the system throughput adapt to the network load which could realize the maximum of the system throughput.
DEFF Research Database (Denmark)
Kamchevska, Valerija; Cristofori, Valentina; Da Ros, Francesco
2016-01-01
We propose and demonstrate an algorithm that allows for automatic synchronization of SDN-controlled all-optical TDM switching nodes connected in a ring network. We experimentally show successful WDM-SDM transmission of data bursts between all ring nodes.......We propose and demonstrate an algorithm that allows for automatic synchronization of SDN-controlled all-optical TDM switching nodes connected in a ring network. We experimentally show successful WDM-SDM transmission of data bursts between all ring nodes....
Network Inoculation: Heteroclinics and phase transitions in an epidemic model
Yang, Hui; Gross, Thilo
2016-01-01
In epidemiological modelling, dynamics on networks, and in particular adaptive and heterogeneous networks have recently received much interest. Here we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description one of these corresponds to a local bifurcation whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region exposure of the system to a pathogen will lead to an outbreak that collapses, but leaves the network in a configuration wher...
Day, Edward; Copello, Alex; Seddon, Jennifer L; Christie, Marilyn; Bamber, Deborah; Powell, Charlotte; George, Sanju; Ball, Andrew; Frew, Emma; Freemantle, Nicholas
2013-08-19
Research indicates that 3% of people receiving opiate substitution treatment (OST) in the UK manage to achieve abstinence from all prescribed and illicit drugs within 3 years of commencing treatment, and there is concern that treatment services have become skilled at engaging people but not at helping them to enter a stage of recovery and drug abstinence. The National Treatment Agency for Substance Misuse recommends the involvement of families and wider social networks in supporting drug users' psychological treatment, and this pilot randomized controlled trial aims to evaluate the impact of a social network-focused intervention for patients receiving OST. In this two-site, early phase, randomized controlled trial, a total of 120 patients receiving OST will be recruited and randomized to receive one of three treatments: 1) Brief Social Behavior and Network Therapy (B-SBNT), 2) Personal Goal Setting (PGS) or 3) treatment as usual. Randomization will take place following baseline assessment. Participants allocated to receive B-SBNT or PGS will continue to receive the same treatment that is routinely provided by drug treatment services, plus four additional sessions of either intervention. Outcomes will be assessed at baseline, 3 and 12 months. The primary outcome will be assessment of illicit heroin use, measured by both urinary analysis and self-report. Secondary outcomes involve assessment of dependence, psychological symptoms, social satisfaction, motivation to change, quality of life and therapeutic engagement. Family members (n = 120) of patients involved in the trial will also be assessed to measure the level of symptoms, coping and the impact of the addiction problem on the family member at baseline, 3 and 12 months. This study will provide experimental data regarding the feasibility and efficacy of implementing a social network intervention within routine drug treatment services in the UK National Health Service. The study will explore the impact of the
Lubbers, Miranda J.; Snijders, Tom A. B.
2007-01-01
This paper describes an empirical comparison of four specifications of the exponential family of random graph models (ERGM), distinguished by model specification (dyadic independence, Markov, partial conditional dependence) and, for the Markov model, by estimation method (Maximum Pseudolikelihood,
IT Department
2009-01-01
A site wide network maintenance has been scheduled for Saturday 28 February. Most of the network devices of the General Purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites along this day. This upgrade will not affect: the Computer centre itself, building 613, the Technical Network and the LHC experiments dedicated networks at the pits. Should you need more details on this intervention, please contact Netops by phone 74927 or email mailto:Netops@cern.ch. IT/CS Group
GS Department
2009-01-01
A site-wide network maintenance operation has been scheduled for Saturday 28 February. Most of the network devices of the general purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites throughout the day. This upgrade will not affect the Computer Centre itself, Building 613, the Technical Network and the LHC experiments, dedicated networks at the pits. For further details of this intervention, please contact Netops by phone 74927 or e-mail mailto:Netops@cern.ch. IT/CS Group
Small-world networks exhibit pronounced intermittent synchronization
Choudhary, Anshul; Mitra, Chiranjit; Kohar, Vivek; Sinha, Sudeshna; Kurths, Jürgen
2017-11-01
We report the phenomenon of temporally intermittently synchronized and desynchronized dynamics in Watts-Strogatz networks of chaotic Rössler oscillators. We consider topologies for which the master stability function (MSF) predicts stable synchronized behaviour, as the rewiring probability (p) is tuned from 0 to 1. MSF essentially utilizes the largest non-zero Lyapunov exponent transversal to the synchronization manifold in making stability considerations, thereby ignoring the other Lyapunov exponents. However, for an N-node networked dynamical system, we observe that the difference in its Lyapunov spectra (corresponding to the N - 1 directions transversal to the synchronization manifold) is crucial and serves as an indicator of the presence of intermittently synchronized behaviour. In addition to the linear stability-based (MSF) analysis, we further provide global stability estimate in terms of the fraction of state-space volume shared by the intermittently synchronized state, as p is varied from 0 to 1. This fraction becomes appreciably large in the small-world regime, which is surprising, since this limit has been otherwise considered optimal for synchronized dynamics. Finally, we characterize the nature of the observed intermittency and its dominance in state-space as network rewiring probability (p) is varied.
Higher-order structure and epidemic dynamics in clustered networks.
Ritchie, Martin; Berthouze, Luc; House, Thomas; Kiss, Istvan Z
2014-05-07
Clustering is typically measured by the ratio of triangles to all triples regardless of whether open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks (Volz et al., 2011; Karrer and Newman, 2010), e.g. networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network׳s topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
DEFF Research Database (Denmark)
Peruzzi, Mariangela; De Luca, Leonardo; Thomsen, Henrik S
2014-01-01
Contrast-induced nephropathy is a common complication of iodinated contrast administration. Statins may reduce the risk of contrast-induced nephropathy, but data remain inconclusive. We summarized the evidence based on statins for the prevention of contrast-induced nephropathy with a network meta...
DEFF Research Database (Denmark)
Tömösközi, Máté; Fitzek, Frank; Roetter, Daniel Enrique Lucani
2015-01-01
decoding of the packets on the receiver side while playing out the video recording contained in the payload. Our solutions are implemented and evaluated on serially connected Raspberry Pi devices and a network (de)coding enabled software running on a regular PC. We find that the recoding relays work...
Percolation in clustered networks
Miller, Joel C
2009-01-01
The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied clustered networks, but the networks often contain preferential mixing between high degree nodes. We introduce a class of random clustered networks and another class of random unclustered networks with the same prefer...
Faggion, Clovis M; Listl, Stefan; Frühauf, Nadine; Chang, Huei-Ju; Tu, Yu-Kang
2014-10-01
It remains unclear which type of non-surgical treatment is most appropriate as first-line intervention against peri-implantitis. This systematic review and Bayesian network meta-analysis aimed to compare the clinical effect of various non-surgical peri-implantitis therapies. The PubMed, SCOPUS, CINAHL, DARE and Web of Knowledge databases were searched in duplicate for randomized controlled trials (RCTs) up to and including 01 January 2014. Additional relevant literature was identified using handsearching of reference lists within published systematic reviews, and screenings of OpenGrey, ClinicalTrials.gov and Controlled-Trials.com. Probing pocket depth (PPD) was the outcome measure assessed. Multilevel mixed modelling was used to perform the network meta-analysis, and Markov Chain Monte Carlo simulation to obtain random effects. Eleven studies were included in the network meta-analysis. Debridement in conjunction with antibiotics achieved the greatest additional PPD reduction in comparison to debridement only (0.490 mm; 95% credible interval: -0.647;1.252). The highest probabilities of being the most effective interventions were achieved by Vector system (p = 20.60%), debridement plus periochip (p = 20.00%) and photodynamic therapy (p = 18.90%). The differences between various non-surgical treatments were relatively small with large credible intervals. On the basis of currently available RCTs, there is insufficient evidence to support that any particular non-surgical treatment for peri-implantitis showed better performance than debridement alone. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Dias, Sofia; Sutton, Alex J; Ades, A E; Welton, Nicky J
2013-07-01
We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pairwise meta-analysis, indirect comparisons, synthesis of multiarm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction. We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats.
Image Informatics Strategies for Deciphering Neuronal Network Connectivity.
Detrez, Jan R; Verstraelen, Peter; Gebuis, Titia; Verschuuren, Marlies; Kuijlaars, Jacobine; Langlois, Xavier; Nuydens, Rony; Timmermans, Jean-Pierre; De Vos, Winnok H
2016-01-01
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.
A distance constrained synaptic plasticity model of C. elegans neuronal network
Badhwar, Rahul; Bagler, Ganesh
2017-03-01
Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.
De Vivo, B.; Lamberti, P.; Spinelli, G.; Tucci, V.
2014-04-01
Small quantities of carbon nanotubes (CNTs) in polymer resins allow to obtain new lightweight nanocomposites suitable for microwave applications, such as efficient electromagnetic shielding or radar absorbing materials. The availability of appropriate simulation models taking into account the morphological and physical features of such very interesting composites is very important for design and performance optimization of devices and systems. In this study, a 3-dimensional (3D) numerical structure modeling the morphology of a CNT-based composite is considered in order to carry out a computational analysis of their electromagnetic performances. The main innovative features of the proposed model consists in the identification of a resistance and capacitance network whose values depend on the filler geometry and loading and whose complexity is associated with the percolation paths. Tunneling effect and capacitive interactions between the individual conductive particles are properly taken into account. The obtained network allows an easy calculation in a wide frequency range of the complex permittivity and others electromagnetic parameters. Moreover, a reliable sensitivity analysis concerning the impact of some crucial parameters, such as the CNTs properties and the dielectric permittivity of the neat resin, on the electromagnetic features of the resulting composites can be carried out. The model predictions are in good agreement with existing experimental data, suggesting that the proposed model can be a useful tool for their design and performance optimization in the microwave range.
Liu, Jingjing; Xie, Zhipeng; Shin, Hyun-Dong; Li, Jianghua; Du, Guocheng; Chen, Jian; Liu, Long
2017-07-10
Aspergillus oryzae finds wide application in the food, feed, and wine industries, and is an excellent cell factory platform for production of organic acids. In this work, we achieved the overproduction of L-malate by rewiring the reductive tricarboxylic acid (rTCA) pathway and L-malate transport pathway of A. oryzae NRRL 3488. First, overexpression of native pyruvate carboxylase and malate dehydrogenase in the rTCA pathway improved the L-malate titer from 26.1gL-1 to 42.3gL-1 in shake flask culture. Then, the oxaloacetate anaplerotic reaction was constructed by heterologous expression of phosphoenolpyruvate carboxykinase and phosphoenolpyruvate carboxylase from Escherichia coli, increasing the L-malate titer to 58.5gL-1. Next, the export of L-malate from the cytoplasm to the external medium was strengthened by overexpression of a C4-dicarboxylate transporter gene from A. oryzae and an L-malate permease gene from Schizosaccharomyces pombe, improving the L-malate titer from 58.5gL-1 to 89.5gL-1. Lastly, guided by transcription analysis of the expression profile of key genes related to L-malate synthesis, the 6-phosphofructokinase encoded by the pfk gene was identified as a potential limiting step for L-malate synthesis. Overexpression of pfk with the strong sodM promoter increased the L-malate titer to 93.2gL-1. The final engineered A. oryzae strain produced 165gL-1 L-malate with a productivity of 1.38gL-1h-1 in 3-L fed-batch culture. Overall, we constructed an efficient L-malate producer by rewiring the rTCA pathway and L-malate transport pathway of A. oryzae NRRL 3488, and the engineering strategy adopted here may be useful for the construction of A. oryzae cell factories to produce other organic acids. Copyright © 2017 Elsevier B.V. All rights reserved.
Li, Lin; Wang, YanShu; An, Lifeng; Kong, XiangYin; Huang, Tao
2017-01-01
As a chronic illness derived from hair cells of the inner ear, Menière's disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.
Avetisov, V; Nechaev, S; Valba, O
2016-01-01
We consider from the localization perspective the new critical behavior discovered recently for the regular random graphs (RRG) and constrained Erd\\H{o}s-Renyi networks (CERN). The diagonal disorder for standard models, we replace by the fugacity $\\mu$ of triads in the RRG and CERN. At some critical value of $\\mu$ the network decays into the maximally possible number of almost full graphs, and the adjacency matrix acquires the two-gapped structure. We find that the eigenvalue statistics corresponds to delocalized states in the central zone, and to the localized states in the side one. The mobility edge lies between zones. We apply these findings to the many-body localization assuming the approximation of the hierarchical structure of the Fock space (for some interacting many-body system) by the RGG and by CERN with some vertex degree. We allow the 3-cycles in the Fock space and identify particles in the many-body system above the phase transition with clusters in the RRG. We discuss the controversial issue of...
Maineult, Alexis; Jougnot, Damien; Revil, André
2018-02-01
We implement a procedure to simulate the drainage and imbibition in random, 2-D, square networks. We compute the resistivity index, the relative permeability and the characteristic lengths of a correlated network at various saturation states, under the assumption that the surface conductivity can be neglected. These parameters exhibit a hysteretic behaviour. Then, we calculate the spectral induced polarization (SIP) response of the medium, under the assumption that the electrical impedance of each tube follows a local Warburg conductivity model, with identical DC conductivity and chargeability for all the tubes. We evidence that the shape of the SIP spectra depends on the saturation state. The analysis of the evolution of the macroscopic Cole-Cole parameters of the spectra in function of the saturation also behaves hysteretically, except for the Cole-Cole exponent. We also observe a power-law relationship between the macroscopic DC conductivity and time constant and the relative permeability. We also show that the frequency peak of the phase spectra is directly related to the characteristic length and to the relative permeability, underlining the potential interest of SIP measurements for the estimation of the permeability of unsaturated media.
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Seung Jun Baek
2017-01-01
Full Text Available We consider a scheduling problem for a two-hop queueing network where the queues have randomly varying connectivity. Customers arrive at the source queue and are later routed to multiple relay queues. A relay queue can be served only if it is in connected state, and the state changes randomly over time. The source queue and relay queues are served in a time-sharing manner; that is, only one customer can be served at any instant. We propose Join the Shortest Queue-Longest Connected Queue (JSQ-LCQ policy as follows: (1 if there exist nonempty relay queues in connected state, serve the longest queue among them; (2 if there are no relay queues to serve, route a customer from the source queue to the shortest relay queue. For symmetric systems in which the connectivity has symmetric statistics across the relay queues, we show that JSQ-LCQ is strongly optimal, that is, minimizes the delay in the stochastic ordering sense. We use stochastic coupling and show that the systems under coupling exist in two distinct phases, due to dynamic interactions among source and relay queues. By careful construction of coupling in both phases, we establish the stochastic dominance in delay between JSQ-LCQ and any arbitrary policy.
Lee, Shaun Wen Huey; Lee, Jun Yang; Tan, Christina San San; Wong, Chee Piau
2016-01-01
Ramadan is the holy month for Muslims whereby they fast from predawn to after sunset and is observed by all healthy Muslim adults as well as a large population of type 2 diabetic Muslims.To determine the comparative effectiveness of various strategies that have been used for type 2 diabetic Muslim who fast during Ramadan.A systematic review and network meta-analysis of randomized controlled studies (RCT) as well as observational studies for patients with type 2 diabetes who fasted during Ramadan was conducted. Eight databases were searched from January 1980 through October 2015 for relevant studies. Two reviewers independently screened and assessed study for eligibility, assessed the risk of bias, and extracted relevant data. A network meta-analysis for each outcome was fitted separately, combining direct and indirect evidence for each comparison.Twenty-nine studies, 16 RCTs and 13 observational studies each met the inclusion criteria. The most common strategy used was drug changes during the Ramadan period, which found that the use of DPP-4 (Dipeptidyl peptidase inhibitor -4) inhibitors were associated with a reduction in incidence of experiencing hypoglycemia during Ramadan in both RCTs (pooled relative risk: 0.56; 95% confidence interval: 0.44-0.72) as well as in observational studies (pooled relative risk: 0.27; 0.09-0.75). Ramadan-focused education was shown to be beneficial in reducing hypoglycemia in observational studies but not RCTs (0.25 versus 1.00). Network meta-analyses suggest that incretin mimetics can reduce the risk of hypoglycemia by nearly 1.5 times.The newer antidiabetic agents appear to lower the risk of hypoglycemia and improved glycemic control when compared with sulfonylureas. Ramadan-focused education shows to be a promising strategy but more rigorous examination from RCTs are required.
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Hongli Dong
Full Text Available Supplementation with B vitamins for stroke prevention has been evaluated over the years, but which combination of B vitamins is optimal for stroke prevention is unclear. We performed a network meta-analysis to assess the impact of different combinations of B vitamins on risk of stroke.A total of 17 trials (86 393 patients comparing 7 treatment strategies and placebo were included. A network meta-analysis combined all available direct and indirect treatment comparisons to evaluate the efficacy of B vitamin supplementation for all interventions.B vitamin supplementation was associated with reduced risk of stroke and cerebral hemorrhage. The risk of stroke was lower with folic acid plus vitamin B6 as compared with folic acid plus vitamin B12 and was lower with folic acid plus vitamin B6 plus vitamin B12 as compared with placebo or folic acid plus vitamin B12. The treatments ranked in order of efficacy for stroke, from higher to lower, were folic acid plus vitamin B6 > folic acid > folic acid plus vitamin B6 plus vitamin B12 > vitamin B6 plus vitamin B12 > niacin > vitamin B6 > placebo > folic acid plus vitamin B12.B vitamin supplementation was associated with reduced risk of stroke; different B vitamins and their combined treatments had different efficacy on stroke prevention. Folic acid plus vitamin B6 might be the optimal therapy for stroke prevention. Folic acid and vitamin B6 were both valuable for stroke prevention. The efficacy of vitamin B12 remains to be studied.
Komócsi, András; Kehl, Dániel; d'Ascenso, Fabrizio; DiNicolantonio, James; Vorobcsuk, András
2017-03-01
In ST-segment elevation myocardial infarction (STEMI), current guidelines discourage treatment of the non-culprit lesions at the time of the primary intervention. Latest trials have challenged this strategy suggesting benefit of early complete revascularization. We performed a Bayesian multiple treatment network meta-analysis of randomized clinical trials (RCTs) in STEMI on culprit-only intervention (CO) versus different timing multivessel revascularization, including immediate (IM), same hospitalization (SH) or later staged (ST). Outcome parameters were pooled with a random-effects model. For multiple-treatment meta-analysis, a Bayesian Markov chain Monte Carlo method was used. Eight RCTs involving 2077 patients were identified. ST and IM revascularization was associated with a decrease in major adverse cardiac events (MACEs) compared to culprit-only approach (risk ratio [RR]: 0.43 credible interval [CrI]: 0.22-0.77 and RR: 0.36 CrI: 0.24-0.54, respectively). IM was superior to SH (RR: 0.49 CrI: 0.29-0.80). With regards to myocardial infarction IM was superior to SH (RR: 0.18 CrI: 0.02-0.99). The posterior probability of being the best choice of treatment regarding the frequency of MACEs was 71.2% for IM, 28.5% for ST, 0.3% for SH and 0.05% for culprit-only approach. Results from RCTs indicate that immediate or staged revascularization of non-culprit lesions reduces major adverse events in patients after primary percutaneous coronary intervention. Differences in MACEs suggest superiority of the immediate or staged intervention; however, further randomized trials are needed to determine the optimal timing of revascularization of the non-culprit lesions.
Bachschmid-Romano, L.; Battistin, C.; Opper, M.; Roudi, Y.
2016-10-01
We describe and analyze some novel approaches for studying the dynamics of Ising spin glass models. We first briefly consider the variational approach based on minimizing the Kullback-Leibler divergence between independent trajectories and the real ones and note that this approach only coincides with the mean field equations from the saddle point approximation to the generating functional when the dynamics is defined through a logistic link function, which is the case for the kinetic Ising model with parallel update. We then spend the rest of the paper developing two ways of going beyond the saddle point approximation to the generating functional. In the first one, we develop a variational perturbative approximation to the generating functional by expanding the action around a quadratic function of the local fields and conjugate local fields whose parameters are optimized. We derive analytical expressions for the optimal parameters and show that when the optimization is suitably restricted, we recover the mean field equations that are exact for the fully asymmetric random couplings (Mézard and Sakellariou 2011 J. Stat. Mech. 2011 L07001). However, without this restriction the results are different. We also describe an extended Plefka expansion in which in addition to the magnetization, we also fix the correlation and response functions. Finally, we numerically study the performance of these approximations for Sherrington-Kirkpatrick type couplings for various coupling strengths and the degrees of coupling symmetry, for both temporally constant but random, as well as time varying external fields. We show that the dynamical equations derived from the extended Plefka expansion outperform the others in all regimes, although it is computationally more demanding. The unconstrained variational approach does not perform well in the small coupling regime, while it approaches dynamical TAP equations of (Roudi and Hertz 2011 J. Stat. Mech. 2011 P03031) for strong couplings.
Generating confidence intervals on biological networks
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Stumpf Michael PH
2007-11-01
Full Text Available Abstract Background In the analysis of networks we frequently require the statistical significance of some network statistic, such as measures of similarity for the properties of interacting nodes. The structure of the network may introduce dependencies among the nodes and it will in general be necessary to account for these dependencies in the statistical analysis. To this end we require some form of Null model of the network: generally rewired replicates of the network are generated which preserve only the degree (number of interactions of each node. We show that this can fail to capture important features of network structure, and may result in unrealistic significance levels, when potentially confounding additional information is available. Methods We present a new network resampling Null model which takes into account the degree sequence as well as available biological annotations. Using gene ontology information as an illustration we show how this information can be accounted for in the resampling approach, and the impact such information has on the assessment of statistical significance of correlations and motif-abundances in the Saccharomyces cerevisiae protein interaction network. An algorithm, GOcardShuffle, is introduced to allow for the efficient construction of an improved Null model for network data. Results We use the protein interaction network of S. cerevisiae; correlations between the evolutionary rates and expression levels of interacting proteins and their statistical significance were assessed for Null models which condition on different aspects of the available data. The novel GOcardShuffle approach results in a Null model for annotated network data which appears better to describe the properties of real biological networks. Conclusion An improved statistical approach for the statistical analysis of biological network data, which conditions on the available biological information, leads to qualitatively different results
Elgendy, Islam Y; Mahmoud, Ahmed N; Kumbhani, Dharam J; Bhatt, Deepak L; Bavry, Anthony A
2017-02-27
The authors sought to compare the effectiveness of the different revascularization strategies in ST-segment elevation myocardial infarction (STEMI) patients with multivessel coronary artery disease undergoing primary percutaneous coronary intervention (PCI). Recent randomized trials have suggested that multivessel complete revascularization at the time of primary percutaneous coronary intervention (PCI) is associated with better outcomes, however; the optimum timing for nonculprit PCI is unknown. Trials that randomized STEMI patients with multivessel disease to any combination of the 4 different revascularization strategies (i.e., complete revascularization at the index procedure, staged procedure during the hospitalization, staged procedure after discharge or culprit-only revascularization) were included. Random effect risk ratios (RRs) were conducted. Network meta-analysis was constructed using mixed treatment comparison models, and the 4 revascularization strategies were compared. A total of 10 trials with 2,285 patients were included. In the pairwise meta-analysis, complete revascularization (i.e., at the index procedure or as a staged procedure) was associated with a lower risk of major adverse cardiac events (MACE) (RR: 0.57; 95% confidence interval [CI]: 0.42 to 0.77), due to lower risk of urgent revascularization (RR: 0.44; 95% CI: 0.30 to 0.66). The risk of all-cause mortality (RR: 0.76; 95% CI: 0.52 to 1.12), and spontaneous reinfarction (RR: 0.54; 95% CI: 0.23 to 1.27) was similar. The reduction in the risk of MACE was observed irrespective of the timing of nonculprit artery revascularization in the mixed treatment model. Current evidence from randomized trials suggests that the risk of all-cause mortality and spontaneous reinfarction is not different among the various revascularization strategies for multivessel disease. Complete revascularization at the index procedure or as a staged procedure (either during the hospitalization or after discharge
Barabasi, Albert-Laszlo
2016-01-01
Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network sci...
Tarazona-Santabalbina, Francisco José; Gómez-Cabrera, Mari Carmen; Pérez-Ros, Pilar; Martínez-Arnau, Francisco Miguel; Cabo, Helena; Tsaparas, Konstantina; Salvador-Pascual, Andrea; Rodriguez-Mañas, Leocadio; Viña, José
2016-05-01
Frailty can be an important clinical target to reduce rates of disability. To ascertain if a supervised-facility multicomponent exercise program (MEP) when performed by frail older persons can reverse frailty and improve functionality; cognitive, emotional, and social networking; as well as biological biomarkers of frailty, when compared with a controlled population that received no training. This is an interventional, controlled, simple randomized study. Researchers responsible for data gathering were blinded for this study. Participants from 2 primary rural care centers (Sollana and Carcaixent) of the same health department in Spain were enrolled in the study between December 2013 and September 2014. We randomized a volunteer sample of 100 men and women who were sedentary, with a gait speed lower than 0.8 meters per second and frail (met at least 3 of the frailty phenotype criteria). Participants were randomized to a supervised-facility MEP (n = 51, age = 79.5, SD 3.9) that included proprioception, aerobic, strength, and stretching exercises for 65 minutes, 5 days per week, 24 weeks, or to a control group (n = 49, age = 80.3, SD 3.7). The intervention was performed by 8 experienced physiotherapists or nurses. Protein-calorie and vitamin D supplementation were controlled in both groups. Our MEP reverses frailty (number needed to treat to recover robustness in subjects with attendance to ≥50% of the training sessions was 3.2) and improves functional measurements: Barthel (trained group 91.6 SD 8.0 vs 82.0 SD 11.0 control group), Lawton and Brody (trained group 6.9 SD 0.9 vs 5.7 SD 2.0 control group), Tinetti (trained group 24.5 SD 4.4 vs 21.7 SD 4.5 control group), Short Physical Performance Battery (trained group 9.5 SD 1.8 vs 7.1 SD 2.8 control group), and physical performance test (trained group 23.5 SD 5.9 vs 16.5 SD 5.1 control group) as well as cognitive, emotional, and social networking determinations: Mini-Mental State Examination (trained
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Jing Sun
2016-07-01
Full Text Available Abstract Background Families with children under age six participating in the Temporary Assistance for Needy Families Program (TANF must participate in work-related activities for 20 h per week. However, due to financial hardship, poor health, and exposure to violence and adversity, families may experience great difficulty in reaching self-sufficiency. The purpose of this report is to describe study design and baseline findings of a trauma-informed financial empowerment and peer support intervention meant to mitigate these hardships. Methods We conducted a randomized controlled trial of a 28-week intervention called Building Wealth and Health Network to improve financial security and maternal and child health among caregivers participating in TANF. Participants, recruited from County Assistance offices in Philadelphia, PA, were randomized into two intervention groups (partial and full and one control group. Participants completed questionnaires at baseline to assess career readiness, economic hardship, health and wellbeing, exposure to adversity and violence, and interaction with criminal justice systems. Results Baseline characteristics demonstrate that among 103 participants, there were no significant differences by group. Mean age of participants was 25 years, and youngest child was 30 months. The majority of participants were women (94.2 %, never married (83.5 %, unemployed (94.2 %, and without a bank account (66.0 %. Many reported economic hardship (32.0 % very low household food secure, 65.0 % housing insecure, and 31.1 % severe energy insecure, and depression (57.3 %. Exposure to adversity was prevalent, where 38.8 % reported four or more Adverse Childhood Experiences including abuse, neglect and household dysfunction. In terms of community violence, 64.7 % saw a seriously wounded person after an incident of violence, and 27.2 % had seen someone killed. Finally, 14.6 % spent time in an adult correctional institution, and 48
Looyestyn, Jemma; Kernot, Jocelyn; Boshoff, Kobie; Maher, Carol
2018-02-26
Online social networks continue to grow in popularity, with 1.7 billion users worldwide accessing Facebook each month. The use of social networking sites such as Facebook for the delivery of health behavior programs is relatively new. The primary aim of this study was to determine the effectiveness of a Web-based beginners' running program for adults aged 18 to 50 years, delivered via a Facebook group, in increasing physical activity (PA) and cardiorespiratory fitness. A total of 89 adults with a mean age of 35.2 years (SD 10.9) were recruited online and via print media. Participants were randomly allocated to receive the UniSA Run Free program, an 8-week Web-based beginners' running intervention, delivered via a closed Facebook group (n=41) that included daily interactive posts (information with links, motivational quotes, opinion polls, or questions) and details of the running sessions; or to the control group who received a hard copy of the running program (n=48). Assessments were completed online at baseline, 2 months, and 5 months. The primary outcome measures were self-reported weekly moderate to vigorous physical activity (MVPA) and objectively measured cardiorespiratory fitness. Secondary outcomes were social support, exercise attitudes, and self-efficacy. Analyses were undertaken using random effects mixed modeling. Compliance with the running program and engagement with the Facebook group were analyzed descriptively. Both groups significantly increased MVPA across the study period (P=.004); however, this was significantly higher in the Facebook group (P=.04). The Facebook group increased their MVPA from baseline by 140 min/week versus 91 min for the control at 2 months. MVPA remained elevated for the Facebook group (from baseline) by 129 min/week versus a 50 min/week decrease for the control at 5 months. Both groups had significant increases in social support scores at 2 months (P=.02); however, there were no group by time differences (P=.16). There were
Sun, Jing; Patel, Falguni; Kirzner, Rachel; Newton-Famous, Nijah; Owens, Constance; Welles, Seth L; Chilton, Mariana
2016-07-16
Families with children under age six participating in the Temporary Assistance for Needy Families Program (TANF) must participate in work-related activities for 20 h per week. However, due to financial hardship, poor health, and exposure to violence and adversity, families may experience great difficulty in reaching self-sufficiency. The purpose of this report is to describe study design and baseline findings of a trauma-informed financial empowerment and peer support intervention meant to mitigate these hardships. We conducted a randomized controlled trial of a 28-week intervention called Building Wealth and Health Network to improve financial security and maternal and child health among caregivers participating in TANF. Participants, recruited from County Assistance offices in Philadelphia, PA, were randomized into two intervention groups (partial and full) and one control group. Participants completed questionnaires at baseline to assess career readiness, economic hardship, health and wellbeing, exposure to adversity and violence, and interaction with criminal justice systems. Baseline characteristics demonstrate that among 103 participants, there were no significant differences by group. Mean age of participants was 25 years, and youngest child was 30 months. The majority of participants were women (94.2 %), never married (83.5 %), unemployed (94.2 %), and without a bank account (66.0 %). Many reported economic hardship (32.0 % very low household food secure, 65.0 % housing insecure, and 31.1 % severe energy insecure), and depression (57.3 %). Exposure to adversity was prevalent, where 38.8 % reported four or more Adverse Childhood Experiences including abuse, neglect and household dysfunction. In terms of community violence, 64.7 % saw a seriously wounded person after an incident of violence, and 27.2 % had seen someone killed. Finally, 14.6 % spent time in an adult correctional institution, and 48.5 % of the fathers of the youngest child spent
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Chun L. Hsu
2017-06-01
Full Text Available Impaired mobility is a major concern for older adults and has significant consequences. While the widely accepted belief is that improved physical function underlies the effectiveness of targeted exercise training in improving mobility and reducing falls, recent evidence suggests cognitive and neural benefits gained through exercise may also play an important role in promoting mobility. However, the underlying neural mechanisms of this relationship are currently unclear. Thus, we hypothesize that 6 months of progressive aerobic exercise training would alter frontoparietal network (FPN connectivity during a motor task among older adults with mild subcortical ischemic vascular cognitive impairment (SIVCI—and exercise-induced changes in FPN connectivity would correlate with changes in mobility. We focused on the FPN as it is involved in top-down attentional control as well as motor planning and motor execution. Participants were randomized either to usual-care (CON, which included monthly educational materials about VCI and healthy diet; or thrice-weekly aerobic training (AT, which was walking outdoors with progressive intensity. Functional magnetic resonance imaging was acquired at baseline and trial completion, where the participants were instructed to perform bilateral finger tapping task. At trial completion, compared with AT, CON showed significantly increased FPN connectivity strength during right finger tapping (p < 0.05. Across the participants, reduced FPN connectivity was associated with greater cardiovascular capacity (p = 0.05. In the AT group, reduced FPN connectivity was significantly associated with improved mobility performance, as measured by the Timed-Up-and-Go test (r = 0.67, p = 0.02. These results suggest progressive AT may improve mobility in older adults with SIVCI via maintaining intra-network connectivity of the FPN.
Goldin, Philippe; Ziv, Michal; Jazaieri, Hooria; Gross, James J
2012-01-01
Social anxiety disorder (SAD) is characterized by distorted self-views. The goal of this study was to examine whether mindfulness-based stress reduction (MBSR) alters behavioral and brain measures of negative and positive self-views. Fifty-six adult patients with generalized SAD were randomly assigned to MBSR or a comparison aerobic exercise (AE) program. A self-referential encoding task was administered at baseline and post-intervention to examine changes in behavioral and neural responses in the self-referential brain network during functional magnetic resonance imaging. Patients were cued to decide whether positive and negative social trait adjectives were self-descriptive or in upper case font. Behaviorally, compared to AE, MBSR produced greater decreases in negative self-views, and equivalent increases in positive self-views. Neurally, during negative self versus case, compared to AE, MBSR led to increased brain responses in the posterior cingulate cortex (PCC). There were no differential changes for positive self versus case. Secondary analyses showed that changes in endorsement of negative and positive self-views were associated with decreased social anxiety symptom severity for MBSR, but not AE. Additionally, MBSR-related increases in dorsomedial prefrontal cortex (DMPFC) activity during negative self-view versus case were associated with decreased social anxiety related disability and increased mindfulness. Analysis of neural temporal dynamics revealed MBSR-related changes in the timing of neural responses in the DMPFC and PCC for negative self-view versus case. These findings suggest that MBSR attenuates maladaptive habitual self-views by facilitating automatic (i.e., uninstructed) recruitment of cognitive and attention regulation neural networks. This highlights potentially important links between self-referential and cognitive-attention regulation systems and suggests that MBSR may enhance more adaptive social self-referential processes in patients with
Directory of Open Access Journals (Sweden)
Philippe eGoldin
2012-11-01
Full Text Available Background: Social Anxiety Disorder (SAD is characterized by distorted self-views. The goal of this study was to examine whether Mindfulness-Based Stress Reduction (MBSR alters behavioral and brain measures of negative and positive self-views. Methods: 56 adult patients with generalized SAD were randomly assigned to MBSR or a comparison aerobic exercise (AE program. A self-referential encoding task was administered at baseline and post-intervention to examine changes in behavioral and neural responses in the self-referential brain network during functional magnetic resonance imaging. Patients were cued to decide whether positive and negative social trait adjectives were self-descriptive or in upper case font. Results: Behaviorally, compared to AE, MBSR produced greater decreases in negative self-views, and equivalent increases in positive self-views. Neurally, during negative self vs. case, compared to AE, MBSR led to increased brain responses in the posterior cingulate cortex (PCC. There were no differential changes for positive self vs. case. Secondary analyses showed that changes in endorsement of negative and positive self-views were associated with decreased social anxiety symptom severity for MBSR, but not AE. Additionally, MBSR-related increases in DMPFC activity during negative self-view vs. case were associated with decreased social anxiety-related disability and increased mindfulness. Analysis of neural temporal dynamics revealed MBSR-related changes in the timing of neural responses in the DMPFC and PCC for negative self-view vs. case.Conclusions: These findings suggest that MBSR attenuates maladaptive habitual self-views by facilitating automatic (i.e., uninstructed recruitment of cognitive and attention regulation neural networks. This highlights potentially important links between self-referential and cognitive-attention regulation systems and suggests that MBSR may enhance more adaptive social self-referential processes in
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Zeng ZH
2017-07-01
Full Text Available Zi-Hang Zeng,1,2 Jia-Feng Chen,1,2 Yi-Xuan Li,1,2 Ran Zhang,1,2 Ling-Fei Xiao,1,2 Xiang-Yu Meng1,2 1Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, 2Department of Evidence-Based Medicine and Clinical Epidemiology, Second Clinical College of Wuhan University, Wuhan, People’s Republic of China Objective: The aim of this study was to compare the early efficacy and survivals of induction regimens for transplant-eligible patients with untreated multiple myeloma. Materials and methods: A comprehensive literature search in electronic databases was conducted for relevant randomized controlled trials (RCTs. Eligible studies were selected according to the predefined selection criteria, before they were evaluated for methodological quality. Basic characteristics and data for network meta-analysis (NMA were extracted from included trials and pooled in our meta-analysis. The end points were the overall response rate (ORR, progression-free survival (PFS, and overall survival (OS. Results: A total of 14 RCTs that included 4,763 patients were analyzed. The post-induction ORR was higher with bortezomib plus thalidomide plus dexamethasone (VTD regimens, and VTD was better than the majority of other regimens. For OS, VTD plus cyclophosphamide (VTDC regimens showed potential superiority over other regimens, but the difference was not statistically significant. The PFS was longer with thalidomide plus doxorubicin plus dexamethasone (TAD regimens for transplant-eligible patients with newly diagnosed multiple myeloma (NDMM. Conclusion: The NMA demonstrated that the VTD, VTDC, and TAD regimens are most beneficial in terms of ORR, OS, and PFS for transplant-eligible patients with NDMM, respectively. Keywords: multiple myeloma, newly diagnosed, transplant-eligible, induction therapies, network meta-analysis
Wang, Qinxue; Huang, Haobin; Zeng, Xiaoning; Ma, Yuan; Zhao, Xin; Huang, Mao
2016-01-01
The benefit of maintenance therapy has been confirmed in patients with non-progressing non-small cell lung cancer (NSCLC) after first-line therapy by many trials and meta-analyses. However, since few head-to-head trials between different regimens have been reported, clinicians still have little guidance on how to select the most efficacious single-agent regimen. Hence, we present a network meta-analysis to assess the comparative treatment efficacy of several single-agent maintenance therapy regimens for stage III/IV NSCLC. A comprehensive literature search of public databases and conference proceedings was performed. Randomized clinical trials (RCTs) meeting the eligible criteria were integrated into a Bayesian network meta-analysis. The primary outcome was overall survival (OS) and the secondary outcome was progression free survival (PFS). A total of 26 trials covering 7,839 patients were identified, of which 24 trials were included in the OS analysis, while 23 trials were included in the PFS analysis. Switch-racotumomab-alum vaccine and switch-pemetrexed were identified as the most efficacious regimens based on OS (HR, 0.64; 95% CrI, 0.45-0.92) and PFS (HR, 0.54; 95% CrI, 0.26-1.04) separately. According to the rank order based on OS, switch-racotumomab-alum vaccine had the highest probability as the most effective regimen (52%), while switch-pemetrexed ranked first (34%) based on PFS. Several single-agent maintenance therapy regimens can prolong OS and PFS for stage III/IV NSCLC. Switch-racotumomab-alum vaccine maintenance therapy may be the most optimal regimen, but should be confirmed by additional evidence.
Yount, Boyd; Roberts, Rhonda S.; Lindesmith, Lisa; Baric, Ralph S.
2006-08-01
Live virus vaccines provide significant protection against many detrimental human and animal diseases, but reversion to virulence by mutation and recombination has reduced appeal. Using severe acute respiratory syndrome coronavirus as a model, we engineered a different transcription regulatory circuit and isolated recombinant viruses. The transcription network allowed for efficient expression of the viral transcripts and proteins, and the recombinant viruses replicated to WT levels. Recombinant genomes were then constructed that contained mixtures of the WT and mutant regulatory circuits, reflecting recombinant viruses that might occur in nature. Although viable viruses could readily be isolated from WT and recombinant genomes containing homogeneous transcription circuits, chimeras that contained mixed regulatory networks were invariantly lethal, because viable chimeric viruses were not isolated. Mechanistically, mixed regulatory circuits promoted inefficient subgenomic transcription from inappropriate start sites, resulting in truncated ORFs and effectively minimize viral structural protein expression. Engineering regulatory transcription circuits of intercommunicating alleles successfully introduces genetic traps into a viral genome that are lethal in RNA recombinant progeny viruses. regulation | systems biology | vaccine design
Remonti, Luciana R; Dias, Sofia; Leitão, Cristiane B; Kramer, Caroline K; Klassman, Lucas P; Welton, Nicky J; Ades, A E; Gross, Jorge L
2016-08-01
The aim of this study was to evaluate the effects of antihypertensive drug classes in mortality in patients with type 2 diabetes. MEDLINE, EMBASE, Clinical Trials and Cochrane Library were searched for randomized trials comparing thiazides, beta-blockers, calcium channel blockers (CCBs), angiotensin-converting inhibitors (ACEi) and angiotensin-receptor blockers (ARBs), alone or in combination for hypertension treatment in patients with type 2 diabetes. Outcomes were overall and cardiovascular mortality. Network meta-analysis was used to obtain pooled effect estimate. A total of 27 studies, comprising 49,418 participants, 5647 total and 1306 cardiovascular deaths were included. No differences in total or cardiovascular mortality were observed with isolated antihypertensive drug classes compared to each other or placebo. The ACEi and CCB combination showed evidence of reduction in cardiovascular mortality comparing to placebo [median HR, 95% credibility intervals: 0.16, 0.01-0.82], betablockers (0.20, 0.02-0.98), CCBs (0.21, 0.02-0.97) and ARBs (0.18, 0.02-0.91). In included trials, this combination was the treatment that most consistently achieved both lower systolic and diastolic end of study blood pressure. There is no benefit of a single antihypertensive class in reduction of mortality in hypertensive patients with type 2 diabetes. Reduction of cardiovascular mortality observed in patients treated with ACEi and CCB combination may be related to lower blood pressure levels. Copyright © 2016 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Veettil SK
2017-05-01
Full Text Available Sajesh K Veettil,1 Nattawat Teerawattanapong,2 Siew Mooi Ching,3,4 Kean Ghee Lim,5 Surasak Saokaew,6–9 Pochamana Phisalprapa,10 Nathorn Chaiyakunapruk7,8,11,12 1School of Pharmacy/School of Postgraduate Studies, International Medical University, Kuala Lumpur, Malaysia; 2Division of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani, Thailand; 3Department of Family Medicine, Faculty of Medicine and Health Sciences, 4Malaysian Research Institute on Ageing, Universiti Putra Malaysia, Serdang, 5Clinical School, Department of Surgery, International Medical University, Seremban, Negeri Sembilan, 6Center of Health Outcomes Research and Therapeutic Safety (Cohorts, School of Pharmaceutical Sciences, University of Phayao, Phayao, 7School of Pharmacy, Monash University Malaysia, Selangor, Malaysia; 8Centre of Pharmaceutical Outcomes Research, Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand; 9Unit of Excellence on Herbal Medicine, School of Pharmaceutical Sciences, University of Phayao, Thailand; 10Division of Ambulatory Medicine, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 11School of Pharmacy, University of Wisconsin, Madison, USA; 12Health and Well-being Cluster, Global Asia Platform in the 21st Century (GA21 Platform, Monash University Malaysia, Selangor, Malaysia Background: Protective effects of several chemopreventive agents (CPAs against colorectal adenomas have been well documented in randomized controlled trials (RCTs; however, there is uncertainty regarding which agents are the most effective.Methods: We searched for RCTs published up until September 2016. Retrieved trials were evaluated using risk of bias. We performed both pairwise analysis and network meta-analysis (NMA of RCTs to compare the effects of CPAs on the recurrence of colorectal adenomas (primary outcome. Using NMA, we
Connection adaption for control of networked mobile chaotic agents.
Zhou, Jie; Zou, Yong; Guan, Shuguang; Liu, Zonghua; Xiao, Gaoxi; Boccaletti, S
2017-11-22
In this paper, we propose a strategy for the control of mobile chaotic oscillators by adaptively rewiring connections between nearby agents with local information. In contrast to the dominant adaptive control schemes where coupling strength is adjusted continuously according to the states of the oscillators, our method does not request adaption of coupling strength. As the resulting interaction structure generated by this proposed strategy is strongly related to unidirectional chains, by investigating synchronization property of unidirectional chains, we reveal that there exists a certain coupling range in which the agents could be controlled regardless of the length of the chain. This feature enables the adaptive strategy to control the mobile oscillators regardless of their moving speed. Compared with existing adaptive control strategies for networked mobile agents, our proposed strategy is simpler for implementation where the resulting interaction networks are kept unweighted at all time.
Macroscopic description of complex adaptive networks co-evolving with dynamic node states
Wiedermann, Marc; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen
2015-01-01
In many real-world complex systems, the time-evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here, we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the co-evolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we show that in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability play a crucial role for the sustainability of the system's equilibrium state. We derive a macroscopic description of the system which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network and is applicable to many fields of study, such as epidemic spreading or social modeling.
Kriener, Birgit; Helias, Moritz; Rotter, Stefan; Diesmann, Markus; Einevoll, Gaute T.
2014-01-01
Pattern formation, i.e., the generation of an inhomogeneous spatial activity distribution in a dynamical system with translation invariant structure, is a well-studied phenomenon in neuronal network dynamics, specifically in neural field models. These are population models to describe the spatio-temporal dynamics of large groups of neurons in terms of macroscopic variables such as population firing rates. Though neural field models are often deduced from and equipped with biophysically meaningful properties, a direct mapping to simulations of individual spiking neuron populations is rarely considered. Neurons have a distinct identity defined by their action on their postsynaptic targets. In its simplest form they act either excitatorily or inhibitorily. When the distribution of neuron identities is assumed to be periodic, pattern formation can be observed, given the coupling strength is supracritical, i.e., larger than a critical weight. We find that this critical weight is strongly dependent on the characteristics of the neuronal input, i.e., depends on whether neurons are mean- or fluctuation driven, and different limits in linearizing the full non-linear system apply in order to assess stability. In particular, if neurons are mean-driven, the linearization has a very simple form and becomes independent of both the fixed point firing rate and the variance of the input current, while in the very strongly fluctuation-driven regime the fixed point rate, as well as the input mean and variance are important parameters in the determination of the critical weight. We demonstrate that interestingly even in “intermediate” regimes, when the system is technically fluctuation-driven, the simple linearization neglecting the variance of the input can yield the better prediction of the critical coupling strength. We moreover analyze the effects of structural randomness by rewiring individual synapses or redistributing weights, as well as coarse-graining on the formation of