Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

PubMed Central

Hu, Yanzhu; Ai, Xinbo

2016-01-01

Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring association network from multivariate time series. The method synthesizes surrogate data, partial symbolic transfer entropy (PSTE) and Granger causality. A proper threshold selection is crucial for common correlation identification methods and it is not easy for users. The proposed method can not only identify the strong correlation without selecting a threshold but also has the ability of correlation quantification, direction identification and temporal relation identification. The method can be divided into three layers, i.e. data layer, model layer and network layer. In the model layer, the method identifies all the possible pair-wise correlation. In the network layer, we introduce a filter algorithm to remove the indirect weak correlation and retain strong correlation. Finally, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pair-wise variables, and then get the weighted directed association network. Two numerical simulated data from linear system and nonlinear system are illustrated to show the steps and performance of the proposed approach. The ability of the proposed method is approved by an application finally. PMID:27832153

BioNSi: A Discrete Biological Network Simulator Tool.

PubMed

Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

2016-08-05

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

Quantized Detector Networks

NASA Astrophysics Data System (ADS)

Jaroszkiewicz, George

2017-12-01

Preface; Acronyms; 1. Introduction; 2. Questions and answers; 3. Classical bits; 4. Quantum bits; 5. Classical and quantum registers; 6. Classical register mechanics; 7. Quantum register dynamics; 8. Partial observations; 9. Mixed states and POVMs; 10. Double-slit experiments; 11. Modules; 12. Computerization and computer algebra; 13. Interferometers; 14. Quantum eraser experiments; 15. Particle decays; 16. Non-locality; 17. Bell inequalities; 18. Change and persistence; 19. Temporal correlations; 20. The Franson experiment; 21. Self-intervening networks; 22. Separability and entanglement; 23. Causal sets; 24. Oscillators; 25. Dynamical theory of observation; 26. Conclusions; Appendix; Index.

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks

NASA Astrophysics Data System (ADS)

Rozdeba, Paul J.

The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems. One of the aims of this dissertation is to simultaneously analyze state and parameter estimation in as a regularized inverse problem, where the introduction of a model makes it possible to reverse the forward problem of partial, noisy observation; and as a statistical inference problem using data assimilation to transfer information from measurements to the model states and parameters. Ultimately these two formulations achieve the same goal. Similar aspects that appear in both are highlighted as a means for better understanding the structure of the nonlinear inference problem. An alternative approach to data assimilation that uses model reduction is then examined as a way to eliminate unresolved nonlinear gating variables from neuron models. In this formulation, only measured variables enter into the model, and the resulting errors are themselves modeled by nonlinear stochastic processes with memory. Finally, variational annealing, a data assimilation method previously applied to dynamical systems, is introduced as a potentially useful tool for understanding deep neural network training in machine learning by exploiting similarities between the two problems.

Estimation of Global Network Statistics from Incomplete Data

PubMed Central

Bliss, Catherine A.; Danforth, Christopher M.; Dodds, Peter Sheridan

2014-01-01

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week. PMID:25338183

Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?

NASA Astrophysics Data System (ADS)

Rings, Thorsten; Lehnertz, Klaus

2016-09-01

We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ≫10 ), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.

Independent saturation of three TrpRS subsites generates a partially assembled state similar to those observed in molecular simulations

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

Laowanapiban, Poramaet; Kapustina, Maryna; Vonrhein, Clemens

2009-03-05

Two new crystal structures of Bacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS) afford evidence that a closed interdomain hinge angle requires a covalent bond between AMP and an occupant of either pyrophosphate or tryptophan subsite. They also are within experimental error of a cluster of structures observed in a nonequilibrium molecular dynamics simulation showing partial active-site assembly. Further, the highest energy structure in a minimum action pathway computed by using elastic network models for Open and Pretransition state (PreTS) conformations for the fully liganded TrpRS monomer is intermediate between that simulated structure and a partially disassembled structure from a nonequilibrium molecular dynamicsmore » trajectory for the unliganded PreTS. These mutual consistencies provide unexpected validation of inferences drawn from molecular simulations.« less

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks

NASA Astrophysics Data System (ADS)

Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay pdelay, whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks.

PubMed

Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay p delay , whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Climate Controls on Tree Growth in the Western Mediterranean

NASA Technical Reports Server (NTRS)

Touchan, Ramzi; Anchukaitis, Kevin J.; Meko, David M.; Kerchouche, Dalila; Slimani, Said; Ilmen, Rachid; Hasnaoui, Fouad; Guibal, Frederic; Canarerim Hesys Hykui; Sanchez-Salguero, Raul;

Observing spatio-temporal dynamics of excitable media using reservoir computing

NASA Astrophysics Data System (ADS)

Zimmermann, Roland S.; Parlitz, Ulrich

2018-04-01

We present a dynamical observer for two dimensional partial differential equation models describing excitable media, where the required cross prediction from observed time series to not measured state variables is provided by Echo State Networks receiving input from local regions in space, only. The efficacy of this approach is demonstrated for (noisy) data from a (cubic) Barkley model and the Bueno-Orovio-Cherry-Fenton model describing chaotic electrical wave propagation in cardiac tissue.

Investigation of the H7 ordinary chondrite, Watson 012: Implications for recognition and classification of Type 7 meteorites

NASA Astrophysics Data System (ADS)

Tait, Alastair W.; Tomkins, Andrew G.; Godel, Bélinda M.; Wilson, Siobhan A.; Hasalova, Pavlina

2014-06-01

Despite the fact that the number of officially classified meteorites is now over 45,000, we lack a clearly defined sequence of samples from a single parent body that records the entire range in metamorphic temperatures from pristine primitive meteorites up to the temperatures required for extensive silicate partial melting. Here, we conduct a detailed analysis of Watson 012, an H7 ordinary chondrite, to generate some clarity on the textural and chemical changes associated with equilibrium-based silicate partial melting in chondritic meteorites. To do this we compare the textures in the meteorite with those preserved in metamorphic contact aureoles on Earth. The most distinctive texture generated by the partial melting that affected Watson 012 is an extensively interconnected plagioclase network, which is clearly observable with a petrographic microscope. Enlarged metal-troilite grains are encapsulated at widenings in this plagioclase network, and this is clearly visible in reflected light. Together with these features, we define a series of other characteristics that can be used to more clearly classify chondritic meteorites as being of petrologic Type 7. To provide comprehensive evidence of silicate partial melting and strengthen the case for using simple petrographic observations to classify similar meteorites, we use high-resolution X-ray computed tomography to demonstrate that the plagioclase network has a high degree of interconnectedness and crystallised as large (cm-scale) skeletal crystals within an olivine-orthopyroxene-clinopyroxene framework, essentially pseudomorphing a melt network. Back-scattered electron imaging and element mapping are used to show that some of the clino- and orthopyroxene in Watson 012 also crystallised from silicate melt, and the order of crystallisation was orthopyroxene → clinopyroxene → plagioclase. X-ray diffraction data, supported by bulk geochemistry, are used to show that plagioclase and ortho- and clinopyroxene were added to the Watson 012 sample by through-flowing basaltic melt. Along with the absence of glass and granophyre, this interconnected network of coarse-grained skeletal plagioclase indicates that the sample cooled slowly at depth within the parent body. The evidence of melt flux indicates that Watson 012 formed in the presence of a gravitational gradient, and thus at significant distance from the centre of the H chondrite parent body (the gravitational gradient at the centre would be zero). Our interpretation is that incipient silicate partial melting in Watson 012 occurred when a region of radiogenically heated H6 material located at considerable depth (possibly at ∼15-20 km from surface) was heated by an additional ca. 200-300 °C in association with a large shock event. Due to insulation at depth within an already hot parent body, the post-shock temperature equilibrated and remained above the solidus long enough for widespread equilibrium-based silicate partial melting, and for melt to migrate. Although the observed melting may have been facilitated by additional heating from an impact event, this is not an example of instantaneous shock melting, which produces thermal disequilibrium at short length scales and distinctly different textures. A small number of H, L and LL chondrites have been previously classified as being of petrologic Type 7; with our new criteria to support that classification, these represent our best opportunity to explore the transition from high temperature sub-solidus metamorphism through the onset of silicate partial melting in three different parent bodies.

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

NASA Astrophysics Data System (ADS)

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

2009-02-01

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

Synchronization of coupled large-scale Boolean networks

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

Li, Fangfei, E-mail: li-fangfei@163.com

2014-03-15

This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

PubMed Central

Wang, Yikai; Kang, Jian; Kemmer, Phebe B.; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods. PMID:27242395

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

PubMed

Wang, Yikai; Kang, Jian; Kemmer, Phebe B; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package "DensParcorr" can be downloaded from CRAN for implementing the proposed statistical methods.

Synchronization invariance under network structural transformations

NASA Astrophysics Data System (ADS)

Arola-Fernández, Lluís; Díaz-Guilera, Albert; Arenas, Alex

2018-06-01

Synchronization processes are ubiquitous despite the many connectivity patterns that complex systems can show. Usually, the emergence of synchrony is a macroscopic observable; however, the microscopic details of the system, as, e.g., the underlying network of interactions, is many times partially or totally unknown. We already know that different interaction structures can give rise to a common functionality, understood as a common macroscopic observable. Building upon this fact, here we propose network transformations that keep the collective behavior of a large system of Kuramoto oscillators invariant. We derive a method based on information theory principles, that allows us to adjust the weights of the structural interactions to map random homogeneous in-degree networks into random heterogeneous networks and vice versa, keeping synchronization values invariant. The results of the proposed transformations reveal an interesting principle; heterogeneous networks can be mapped to homogeneous ones with local information, but the reverse process needs to exploit higher-order information. The formalism provides analytical insight to tackle real complex scenarios when dealing with uncertainty in the measurements of the underlying connectivity structure.

Part mutual information for quantifying direct associations in networks.

PubMed

Zhao, Juan; Zhou, Yiwei; Zhang, Xiujun; Chen, Luonan

2016-05-03

Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

Empirical evaluation of neutral interactions in host-parasite networks.

PubMed

Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

2014-04-01

While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization.

Sparse brain network using penalized linear regression

NASA Astrophysics Data System (ADS)

Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.

2011-03-01

Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.

Qualitative Discovery in Medical Databases

NASA Technical Reports Server (NTRS)

Maluf, David A.

2000-01-01

Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that: (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.

Changes in Fish Assemblages following the Establishment of a Network of No-Take Marine Reserves and Partially-Protected Areas

PubMed Central

Kelaher, Brendan P.; Coleman, Melinda A.; Broad, Allison; Rees, Matthew J.; Jordan, Alan; Davis, Andrew R.

2014-01-01

Networks of no-take marine reserves and partially-protected areas (with limited fishing) are being increasingly promoted as a means of conserving biodiversity. We examined changes in fish assemblages across a network of marine reserves and two different types of partially-protected areas within a marine park over the first 5 years of its establishment. We used Baited Remote Underwater Video (BRUV) to quantify fish communities on rocky reefs at 20–40 m depth between 2008–2011. Each year, we sampled 12 sites in 6 no-take marine reserves and 12 sites in two types of partially-protected areas with contrasting levels of protection (n = 4 BRUV stations per site). Fish abundances were 38% greater across the network of marine reserves compared to the partially-protected areas, although not all individual reserves performed equally. Compliance actions were positively associated with marine reserve responses, while reserve size had no apparent relationship with reserve performance after 5 years. The richness and abundance of fishes did not consistently differ between the two types of partially-protected areas. There was, therefore, no evidence that the more regulated partially-protected areas had additional conservation benefits for reef fish assemblages. Overall, our results demonstrate conservation benefits to fish assemblages from a newly established network of temperate marine reserves. They also show that ecological monitoring can contribute to adaptive management of newly established marine reserve networks, but the extent of this contribution is limited by the rate of change in marine communities in response to protection. PMID:24454934

Characterizing partial AZFc deletions of the Y chromosome with amplicon-specific sequence markers

PubMed Central

Navarro-Costa, Paulo; Pereira, Luísa; Alves, Cíntia; Gusmão, Leonor; Proença, Carmen; Marques-Vidal, Pedro; Rocha, Tiago; Correia, Sónia C; Jorge, Sónia; Neves, António; Soares, Ana P; Nunes, Joaquim; Calhaz-Jorge, Carlos; Amorim, António; Plancha, Carlos E; Gonçalves, João

2007-01-01

Background The AZFc region of the human Y chromosome is a highly recombinogenic locus containing multi-copy male fertility genes located in repeated DNA blocks (amplicons). These AZFc gene families exhibit slight sequence variations between copies which are considered to have functional relevance. Yet, partial AZFc deletions yield phenotypes ranging from normospermia to azoospermia, thwarting definite conclusions on their real impact on fertility. Results The amplicon content of partial AZFc deletion products was characterized with novel amplicon-specific sequence markers. Data indicate that partial AZFc deletions are a male infertility risk [odds ratio: 5.6 (95% CI: 1.6–30.1)] and although high diversity of partial deletion products and sequence conversion profiles were recorded, the AZFc marker profiles detected in fertile men were also observed in infertile men. Additionally, the assessment of rearrangement recurrence by Y-lineage analysis indicated that while partial AZFc deletions occurred in highly diverse samples, haplotype diversity was minimal in fertile men sharing identical marker profiles. Conclusion Although partial AZFc deletion products are highly heterogeneous in terms of amplicon content, this plasticity is not sufficient to account for the observed phenotypical variance. The lack of causative association between the deletion of specific gene copies and infertility suggests that AZFc gene content might be part of a multifactorial network, with Y-lineage evolution emerging as a possible phenotype modulator. PMID:17903263

Simulation of uniaxial deformation of hexagonal crystals (Mg, Be)

NASA Astrophysics Data System (ADS)

Vlasova, A. M.; Kesarev, A. G.

2017-12-01

Molecular dynamics (MD) simulations were performed for the nanocompression loading of nanocrystalline magnesium and beryllium modeled by an interatomic potential of the embedded atom method (EAM). It is shown that the main deformation modes are prismatic slip and twinning for magnesium, and only prismatic slip for beryllium. The formation of stable configurations of dislocation grids in magnesium and beryllium was observed. Dislocation networks are formed in the habit plane of the twin in a magnesium nanocrystall. Some dislocation reactions are suggested to explain the appearance of such networks. Shockley partial dislocations in a beryllium nanocrystall form grids in the slip plane. A strong anisotropy between slip systems was observed, which is in agreement with experimental data.

Navigation systems. [for interplanetary flight

NASA Technical Reports Server (NTRS)

Jordan, J. F.

1985-01-01

The elements of the measurement and communications network comprising the global deep space navigation system (DSN) for NASA missions are described. Among the measurement systems discussed are: VLBI, two-way Doppler and range measurements, and optical measurements carried out on board the spacecraft. Processing of navigation measurement is carried out using two modules: an N-body numerical integration of the trajectory (and state transition partial derivatives) based on pre-guessed initial conditions; and partial derivatives of simulated observables corresponding to each actual observation. Calculations of velocity correction parameters is performed by precise modelling of all physical phenomena influencing the observational measurements, including: planetary motions; tracking station locations, gravity field structure, and transmission media effects. Some of the contributions to earth-relative orbit estimate errors for the Doppler/range system on board Voyager are discussed in detail. A line drawing of the DSN navigation system is provided.

Impact of Partial Time Delay on Temporal Dynamics of Watts-Strogatz Small-World Neuronal Networks

NASA Astrophysics Data System (ADS)

Yan, Hao; Sun, Xiaojuan

2017-06-01

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts-Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay τ and the other is the probability of partial time delay pdelay. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay τ, the probability pdelay could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay τ, temporal coherence and mean firing rate do not have great changes with respect to pdelay. Time delay τ always has great influence on both temporal coherence and mean firing rate no matter what is the value of pdelay. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay τ. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

SIR model on a dynamical network and the endemic state of an infectious disease

NASA Astrophysics Data System (ADS)

Dottori, M.; Fabricius, G.

2015-09-01

In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.

Minimum-Risk Path Finding by an Adaptive Amoebal Network

NASA Astrophysics Data System (ADS)

Nakagaki, Toshiyuki; Iima, Makoto; Ueda, Tetsuo; Nishiura, Yasumasa; Saigusa, Tetsu; Tero, Atsushi; Kobayashi, Ryo; Showalter, Kenneth

2007-08-01

When two food sources are presented to the slime mold Physarum in the dark, a thick tube for absorbing nutrients is formed that connects the food sources through the shortest route. When the light-avoiding organism is partially illuminated, however, the tube connecting the food sources follows a different route. Defining risk as the experimentally measurable rate of light-avoiding movement, the minimum-risk path is exhibited by the organism, determined by integrating along the path. A model for an adaptive-tube network is presented that is in good agreement with the experimental observations.

Title: Chimeras in small, globally coupled networks: Experiments and stability analysis

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Bansal, Kanika; Murphy, Thomas E.; Roy, Rajarshi

Since the initial observation of chimera states, there has been much discussion of the conditions under which these states emerge. The emphasis thus far has mainly been to analyze large networks of coupled oscillators; however, recent studies have begun to focus on the opposite limit: what is the smallest system of coupled oscillators in which chimeras can exist? We experimentally observe chimeras and other partially synchronous patterns in a network of four globally-coupled chaotic opto-electronic oscillators. By examining the equations of motion, we demonstrate that symmetries in the network topology allow a variety of synchronous states to exist, including cluster synchronous states and a chimera state. Using the group theoretical approach recently developed for analyzing cluster synchronization, we show how to derive the variational equations for these synchronous patterns and calculate their linear stability. The stability analysis gives good agreement with our experimental results. Both experiments and simulations suggest that these chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

Network formation of nanofibrillated cellulose in solution blended poly(methyl methacrylate) composites.

PubMed

Littunen, Kuisma; Hippi, Ulla; Saarinen, Tapio; Seppälä, Jukka

2013-01-02

Composites of poly(methyl methacrylate) (PMMA) and nanofibrillated cellulose (NFC) were prepared by solution blending and further processed by injection and compression molding. To improve adhesion at the PMMA/NFC interface, the nanofibrils were covalently grafted with PMMA. Formation of a percolating nanofibril network was observed between 1 and 5 wt.% of NFC by dynamic rotational rheometry in molten state. This observation was further supported by the behavior of glass transition temperature which decreased at low NFC concentrations but recovered above the percolation threshold, indicating a decreased mobility of the matrix polymer. This effect was more pronounced with ungrafted NFC, possibly due to a stronger network. The unmodified NFC induced a minor degradation of the molar mass of PMMA. As thin plates, the composites were transparent at low NFC concentrations but became partially aggregated at the highest NFC concentrations. Despite the continuous NFC network, tensile testing showed no improvement of the mechanical properties. Copyright © 2012 Elsevier Ltd. All rights reserved.

Environment-dependent morphology in plasmodium of true slime mold Physarum polycephalum and a network growth model.

PubMed

Takamatsu, Atsuko; Takaba, Eri; Takizawa, Ginjiro

2009-01-07

Branching network growth patterns, depending on environmental conditions, in plasmodium of true slime mold Physarum polycephalum were investigated. Surprisingly, the patterns resemble those in bacterial colonies even though the biological mechanisms differ greatly. Bacterial colonies are collectives of microorganisms in which individual organisms have motility and interact through nutritious and chemical fields. In contrast, the plasmodium is a giant amoeba-like multinucleated unicellular organism that forms a network of tubular structures through which protoplasm streams. The cell motility of the plasmodium is generated by oscillation phenomena observed in the partial bodies, which interact through the tubular structures. First, we analyze characteristics of the morphology quantitatively, then we abstract local rules governing the growing process to construct a simple network growth model. This model is independent of specific systems, in which only two rules are applied. Finally, we discuss the mechanism of commonly observed biological pattern formations through comparison with the system of bacterial colonies.

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies

PubMed Central

Ochs, Christopher; Geller, James; Perl, Yehoshua; Musen, Mark A.

2016-01-01

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving “live partial-area taxonomies” is demonstrated. PMID:27345947

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.

PubMed

Ochs, Christopher; Geller, James; Perl, Yehoshua; Musen, Mark A

2016-08-01

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving "live partial-area taxonomies" is demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

Chimera states in brain networks: Empirical neural vs. modular fractal connectivity

NASA Astrophysics Data System (ADS)

Chouzouris, Teresa; Omelchenko, Iryna; Zakharova, Anna; Hlinka, Jaroslav; Jiruska, Premysl; Schöll, Eckehard

2018-04-01

Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.

Seeing Eye to Eye: Predicting Teacher-Student Agreement on Classroom Social Networks

PubMed Central

Neal, Jennifer Watling; Cappella, Elise; Wagner, Caroline; Atkins, Marc S.

2010-01-01

This study examines the association between classroom characteristics and teacher-student agreement in perceptions of students’ classroom peer networks. Social network, peer nomination, and observational data were collected from a sample of second through fourth grade teachers (N=33) and students (N=669) in 33 classrooms across five high poverty urban schools. Results demonstrate that variation in teacher-student agreement on the structure of students’ peer networks can be explained, in part, by developmental factors and classroom characteristics. Developmental increases in network density partially mediated the positive relationship between grade level and teacher-student agreement. Larger class sizes and higher levels of normative aggressive behavior resulted in lower levels of teacher-student agreement. Teachers’ levels of classroom organization had mixed influences, with behavior management negatively predicting agreement, and productivity positively predicting agreement. These results underscore the importance of the classroom context in shaping teacher and student perceptions of peer networks. PMID:21666768

Influence maximization based on partial network structure information: A comparative analysis on seed selection heuristics

NASA Astrophysics Data System (ADS)

Erkol, Şirag; Yücel, Gönenç

In this study, the problem of seed selection is investigated. This problem is mainly treated as an optimization problem, which is proved to be NP-hard. There are several heuristic approaches in the literature which mostly use algorithmic heuristics. These approaches mainly focus on the trade-off between computational complexity and accuracy. Although the accuracy of algorithmic heuristics are high, they also have high computational complexity. Furthermore, in the literature, it is generally assumed that complete information on the structure and features of a network is available, which is not the case in most of the times. For the study, a simulation model is constructed, which is capable of creating networks, performing seed selection heuristics, and simulating diffusion models. Novel metric-based seed selection heuristics that rely only on partial information are proposed and tested using the simulation model. These heuristics use local information available from nodes in the synthetically created networks. The performances of heuristics are comparatively analyzed on three different network types. The results clearly show that the performance of a heuristic depends on the structure of a network. A heuristic to be used should be selected after investigating the properties of the network at hand. More importantly, the approach of partial information provided promising results. In certain cases, selection heuristics that rely only on partial network information perform very close to similar heuristics that require complete network data.

Adaptation to sensory input tunes visual cortex to criticality

NASA Astrophysics Data System (ADS)

Shew, Woodrow L.; Clawson, Wesley P.; Pobst, Jeff; Karimipanah, Yahya; Wright, Nathaniel C.; Wessel, Ralf

2015-08-01

A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex, but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis--the self-organization process and how it manifests during strong sensory input--remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality. Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing.

Decentralized Opportunistic Spectrum Resources Access Model and Algorithm toward Cooperative Ad-Hoc Networks.

PubMed

Liu, Ming; Xu, Yang; Mohammed, Abdul-Wahid

2016-01-01

Limited communication resources have gradually become a critical factor toward efficiency of decentralized large scale multi-agent coordination when both system scales up and tasks become more complex. In current researches, due to the agent's limited communication and observational capability, an agent in a decentralized setting can only choose a part of channels to access, but cannot perceive or share global information. Each agent's cooperative decision is based on the partial observation of the system state, and as such, uncertainty in the communication network is unavoidable. In this situation, it is a major challenge working out cooperative decision-making under uncertainty with only a partial observation of the environment. In this paper, we propose a decentralized approach that allows agents cooperatively search and independently choose channels. The key to our design is to build an up-to-date observation for each agent's view so that a local decision model is achievable in a large scale team coordination. We simplify the Dec-POMDP model problem, and each agent can jointly work out its communication policy in order to improve its local decision utilities for the choice of communication resources. Finally, we discuss an implicate resource competition game, and show that, there exists an approximate resources access tradeoff balance between agents. Based on this discovery, the tradeoff between real-time decision-making and the efficiency of cooperation using these channels can be well improved.

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

PubMed Central

Wang, Chenhao; Ong, Ju Lynn; Patanaik, Amiya; Chee, Michael W. L.

2016-01-01

Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants’ eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. PMID:27512040

Specialization and interaction strength in a tropical plant-frugivore network differ among forest strata.

PubMed

Schleuning, Matthias; Blüthgen, Nico; Flörchinger, Martina; Braun, Julius; Schaefer, H Martin; Böhning-Gaese, Katrin

2011-01-01

The degree of interdependence and potential for shared coevolutionary history of frugivorous animals and fleshy-fruited plants are contentious topics. Recently, network analyses revealed that mutualistic relationships between fleshy-fruited plants and frugivores are mostly built upon generalized associations. However, little is known about the determinants of network structure, especially from tropical forests where plants' dependence on animal seed dispersal is particularly high. Here, we present an in-depth analysis of specialization and interaction strength in a plant-frugivore network from a Kenyan rain forest. We recorded fruit removal from 33 plant species in different forest strata (canopy, midstory, understory) and habitats (primary and secondary forest) with a standardized sampling design (3447 interactions in 924 observation hours). We classified the 88 frugivore species into guilds according to dietary specialization (14 obligate, 28 partial, 46 opportunistic frugivores) and forest dependence (50 forest species, 38 visitors). Overall, complementary specialization was similar to that in other plant-frugivore networks. However, the plant-frugivore interactions in the canopy stratum were less specialized than in the mid- and understory, whereas primary and secondary forest did not differ. Plant specialization on frugivores decreased with plant height, and obligate and partial frugivores were less specialized than opportunistic frugivores. The overall impact of a frugivore increased with the number of visits and the specialization on specific plants. Moreover, interaction strength of frugivores differed among forest strata. Obligate frugivores foraged in the canopy where fruit resources were abundant, whereas partial and opportunistic frugivores were more common on mid- and understory plants, respectively. We conclude that the vertical stratification of the frugivore community into obligate and opportunistic feeding guilds structures this plant-frugivore network. The canopy stratum comprises stronger links and generalized associations, whereas the lower strata are composed of weaker links and more specialized interactions. Our results suggest that seed-dispersal relationships of plants in lower forest strata are more prone to disruption than those of canopy trees.

New Abstraction Networks and a New Visualization Tool in Support of Auditing the SNOMED CT Content

PubMed Central

Geller, James; Ochs, Christopher; Perl, Yehoshua; Xu, Junchuan

2012-01-01

Medical terminologies are large and complex. Frequently, errors are hidden in this complexity. Our objective is to find such errors, which can be aided by deriving abstraction networks from a large terminology. Abstraction networks preserve important features but eliminate many minor details, which are often not useful for identifying errors. Providing visualizations for such abstraction networks aids auditors by allowing them to quickly focus on elements of interest within a terminology. Previously we introduced area taxonomies and partial area taxonomies for SNOMED CT. In this paper, two advanced, novel kinds of abstraction networks, the relationship-constrained partial area subtaxonomy and the root-constrained partial area subtaxonomy are defined and their benefits are demonstrated. We also describe BLUSNO, an innovative software tool for quickly generating and visualizing these SNOMED CT abstraction networks. BLUSNO is a dynamic, interactive system that provides quick access to well organized information about SNOMED CT. PMID:23304293

New abstraction networks and a new visualization tool in support of auditing the SNOMED CT content.

PubMed

Geller, James; Ochs, Christopher; Perl, Yehoshua; Xu, Junchuan

2012-01-01

Medical terminologies are large and complex. Frequently, errors are hidden in this complexity. Our objective is to find such errors, which can be aided by deriving abstraction networks from a large terminology. Abstraction networks preserve important features but eliminate many minor details, which are often not useful for identifying errors. Providing visualizations for such abstraction networks aids auditors by allowing them to quickly focus on elements of interest within a terminology. Previously we introduced area taxonomies and partial area taxonomies for SNOMED CT. In this paper, two advanced, novel kinds of abstraction networks, the relationship-constrained partial area subtaxonomy and the root-constrained partial area subtaxonomy are defined and their benefits are demonstrated. We also describe BLUSNO, an innovative software tool for quickly generating and visualizing these SNOMED CT abstraction networks. BLUSNO is a dynamic, interactive system that provides quick access to well organized information about SNOMED CT.

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

PubMed

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

2012-01-01

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

76 FR 25310 - Notice of Intent To Grant a Partially Exclusive Patent License to videoNEXT Network Solutions, Inc.

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

... DEPARTMENT OF DEFENSE Department of the Army Notice of Intent To Grant a Partially Exclusive Patent License to videoNEXT Network Solutions, Inc. AGENCY: Department of the Army, DoD. ACTION: Notice... notice of its intent to grant to videoNEXT Network Solutions, Inc., a corporation having its principle...

Comparison of universal approximators incorporating partial monotonicity by structure.

PubMed

Minin, Alexey; Velikova, Marina; Lang, Bernhard; Daniels, Hennie

2010-05-01

Neural networks applied in control loops and safety-critical domains have to meet more requirements than just the overall best function approximation. On the one hand, a small approximation error is required; on the other hand, the smoothness and the monotonicity of selected input-output relations have to be guaranteed. Otherwise, the stability of most of the control laws is lost. In this article we compare two neural network-based approaches incorporating partial monotonicity by structure, namely the Monotonic Multi-Layer Perceptron (MONMLP) network and the Monotonic MIN-MAX (MONMM) network. We show the universal approximation capabilities of both types of network for partially monotone functions. On a number of datasets, we investigate the advantages and disadvantages of these approaches related to approximation performance, training of the model and convergence. 2009 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Čech, Radek; Mačutek, Ján; Žabokrtský, Zdeněk

2011-10-01

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

Coexistence of stable stationary behavior and partial synchrony in an all-to-all coupled spiking neural network

NASA Astrophysics Data System (ADS)

de Smet, Filip; Aeyels, Dirk

2010-12-01

We consider the stationary and the partially synchronous regimes in an all-to-all coupled neural network consisting of an infinite number of leaky integrate-and-fire neurons. Using analytical tools as well as simulation results, we show that two threshold values for the coupling strength may be distinguished. Below the lower threshold, no synchronization is possible; above the upper threshold, the stationary regime is unstable and partial synchrony prevails. In between there is a range of values for the coupling strength where both regimes may be observed. The assumption of an infinite number of neurons is crucial: simulations with a finite number of neurons indicate that above the lower threshold partial synchrony always prevails—but with a transient time that may be unbounded with increasing system size. For values of the coupling strength in a neighborhood of the lower threshold, the finite model repeatedly builds up toward synchronous behavior, followed by a sudden breakdown, after which the synchronization is slowly built up again. The “transient” time needed to build up synchronization again increases with increasing system size, and in the limit of an infinite number of neurons we retrieve stationary behavior. Similarly, within some range for the coupling strength in this neighborhood, a stable synchronous solution may exist for an infinite number of neurons.

Water resources data for Pennsylvania, water year 1995. Volume 2. Susquehanna and Potomac River basins. Water-data report (Annual), 1 October 1994-30 September 1995

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

Durlin, R.R.; Schaffstall, W.P.

1997-02-01

This report, Volume, 2, includes record from the Susquehanna and Potomac River Basins. Specifically, it contains: (1) discharge records for 90 continuous-record streamflow-gaging stations and 41 partial-record stations; (2) elevation and contents record for 12 lakes and reservoirs; (3) water-quality records for 13 streamflow-gaging stations and 189 partial-record and project stations; and (4) water-level records for 25 network observation wells. Site locations are shown in figures throughout the report. Additional water data collected at various sites not involved in the systematic data-collection program are also presented.

Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output.

PubMed

Nochomovitz, Yigal D; Li, Hao

2006-03-14

Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three- and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like certain protein structures can be designed by an unusually broad spectrum of sequences. The network topologies that encode a highly designable dynamical phenotype possess two classes of connections: a fully conserved core of dedicated connections that encodes the stable dynamical phenotype and a partially conserved set of variable connections that controls the transient dynamical flow. By comparing the topologies and dynamics of the three- and four-node network ensembles, we observe a large number of instances of the phenomenon of "mutational buffering," whereby addition of a fourth node suppresses phenotypic variation amongst a set of three-node networks.

Structure and properties of semi-interpenetrating network hydrogel based on starch.

PubMed

Zhu, Baodong; Ma, Dongzhuo; Wang, Jian; Zhang, Shuang

2015-11-20

Starch-g-P(acrylic acid-co-acrylamide)/PVA semi-interpenetrating network (semi-IPN) hydrogels were prepared by aqueous solution polymerization method. Starch grafting copolymerization reaction, semi-IPN structure and crystal morphology were characterized by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The PVA in the form of partial crystallization distributing in the gel matrix uniformly were observed by Field emission scanning electron microscope (FESEM). The space network structure, finer microstructure and pore size in the interior of hydrogel were presented by biomicroscope. The results demonstrated that absorption ratio of water and salt generated different degree changes with the effect of PVA. In addition, the mechanical strength of hydrogel was improved. Copyright © 2015 Elsevier Ltd. All rights reserved.

Method and system for mesh network embedded devices

NASA Technical Reports Server (NTRS)

Wang, Ray (Inventor)

2009-01-01

A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.

Networked dynamical systems with linear coupling: synchronisation patterns, coherence and other behaviours.

PubMed

Judd, Kevin

2013-12-01

Many physical and biochemical systems are well modelled as a network of identical non-linear dynamical elements with linear coupling between them. An important question is how network structure affects chaotic dynamics, for example, by patterns of synchronisation and coherence. It is shown that small networks can be characterised precisely into patterns of exact synchronisation and large networks characterised by partial synchronisation at the local and global scale. Exact synchronisation modes are explained using tools of symmetry groups and invariance, and partial synchronisation is explained by finite-time shadowing of exact synchronisation modes.

A Mechanistic Model of Human Recall of Social Network Structure and Relationship Affect.

PubMed

Omodei, Elisa; Brashears, Matthew E; Arenas, Alex

2017-12-07

The social brain hypothesis argues that the need to deal with social challenges was key to our evolution of high intelligence. Research with non-human primates as well as experimental and fMRI studies in humans produce results consistent with this claim, leading to an estimate that human primary groups should consist of roughly 150 individuals. Gaps between this prediction and empirical observations can be partially accounted for using "compression heuristics", or schemata that simplify the encoding and recall of social information. However, little is known about the specific algorithmic processes used by humans to store and recall social information. We describe a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior observed in experimental contexts. We find that human recall is predicated on accurate recall of a small number of high degree network nodes and the application of heuristics for both structural and affective information. This provides new insight into human memory, social network evolution, and demonstrates a novel approach to uncovering human cognitive operations.

Quantum communication for satellite-to-ground networks with partially entangled states

NASA Astrophysics Data System (ADS)

Chen, Na; Quan, Dong-Xiao; Pei, Chang-Xing; Yang-Hong

2015-02-01

To realize practical wide-area quantum communication, a satellite-to-ground network with partially entangled states is developed in this paper. For efficiency and security reasons, the existing method of quantum communication in distributed wireless quantum networks with partially entangled states cannot be applied directly to the proposed quantum network. Based on this point, an efficient and secure quantum communication scheme with partially entangled states is presented. In our scheme, the source node performs teleportation only after an end-to-end entangled state has been established by entanglement swapping with partially entangled states. Thus, the security of quantum communication is guaranteed. The destination node recovers the transmitted quantum bit with the help of an auxiliary quantum bit and specially defined unitary matrices. Detailed calculations and simulation analyses show that the probability of successfully transferring a quantum bit in the presented scheme is high. In addition, the auxiliary quantum bit provides a heralded mechanism for successful communication. Based on the critical components that are presented in this article an efficient, secure, and practical wide-area quantum communication can be achieved. Project supported by the National Natural Science Foundation of China (Grant Nos. 61072067 and 61372076), the 111 Project (Grant No. B08038), the Fund from the State Key Laboratory of Integrated Services Networks (Grant No. ISN 1001004), and the Fundamental Research Funds for the Central Universities (Grant Nos. K5051301059 and K5051201021).

International and Domestic Business Cycles as Dynamics of a Network of Networks

NASA Astrophysics Data System (ADS)

Ikeda, Yuichi; Iyetomi, Hiroshi; Aoyama, Hideaki; Yoshikawa, Hiroshi

2014-03-01

Synchronization in business cycles has attracted economists and physicists as self-organization in the time domain. From a different point of view, international and domestic business cycles are also interesting as dynamics of a network of networks or a multi-level network. In this paper, we analyze the Indices of Industrial Production monthly time-series in Japan from January 1988 to December 2007 to develop a deeper understanding of domestic business cycles. The frequency entrainment and the partial phase locking were observed for the 16 sectors to be direct evidence of synchronization. We also showed that the information of the economic shock is carried by the phase time-series. The common shock and individual shocks are separated using phase time-series. The former dominates the economic recession in all of 1992, 1998 and 2001. In addition to the above analysis, we analyze the quarterly GDP time series for Australia, Canada, France, Italy, the United Kingdom, and the United States from Q2 1960 to Q1 2010 in order to clarify its origin. We find frequency entrainment and partial phase locking. Furthermore, a coupled limit-cycle oscillator model is developed to explain the mechanism of synchronization. In this model, the interaction due to international trade is interpreted as the origin of the synchronization. The obtained results suggest that the business cycle may be described as a dynamics of the multi-level coupled oscillators exposed to random individual shocks.

Matching-centrality decomposition and the forecasting of new links in networks.

PubMed

Rohr, Rudolf P; Naisbit, Russell E; Mazza, Christian; Bersier, Louis-Félix

2016-02-10

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching-centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network. © 2016 The Author(s).

Matching–centrality decomposition and the forecasting of new links in networks

PubMed Central

Rohr, Rudolf P.; Naisbit, Russell E.; Mazza, Christian; Bersier, Louis-Félix

2016-01-01

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching–centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network. PMID:26842568

Negative differential resistance in partially fluorinated graphene films

NASA Astrophysics Data System (ADS)

Antonova, I. V.; Shojaei, S.; Sattari-Esfahlan, S. M.; Kurkina, Irina I.

2017-07-01

Partially fluorinated graphene films were created by chemical functionalization of graphene layers in an aqueous solution of hydrofluoric acid. The formation of graphene islands or graphene quantum dots (GQDs) and a fluorinated graphene network is demonstrated in such films. Negative differential resistance (NDR) resulting from the formation of the potential barrier system in the films was observed for different fluorination degrees of suspension. The origin of the NDR varies with an increase in the fluorination degree of the suspension. Numerical calculations were performed to elucidate the tunneling between adjacent energy levels and creation of NDR. It was found that in the case of films with smaller flake and smaller GQD sizes, multi-peak NDR appears in the I-V curve. We predict that the NDR peak position shifts towards lower voltage with a decrease in the GQD size. Surprisingly, we observed a negative step-like valley for positive biases in the I-V curve of samples. Our findings with detailed analysis shed light on understanding the mechanisms of the NDR phenomenon in a partially fluorinated graphene system.

Deformation Measurement In The Hayward Fault Zone Using Partially Correlated Persistent Scatterers

NASA Astrophysics Data System (ADS)

Lien, J.; Zebker, H. A.

2013-12-01

Interferometric synthetic aperture radar (InSAR) is an effective tool for measuring temporal changes in the Earth's surface. By combining SAR phase data collected at varying times and orbit geometries, with InSAR we can produce high accuracy, wide coverage images of crustal deformation fields. Changes in the radar imaging geometry, scatterer positions, or scattering behavior between radar passes causes the measured radar return to differ, leading to a decorrelation phase term that obscures the deformation signal and prevents the use of large baseline data. Here we present a new physically-based method of modeling decorrelation from the subset of pixels with the highest intrinsic signal-to-noise ratio, the so-called persistent scatters (PS). This more complete formulation, which includes both phase and amplitude scintillations, better describes the scattering behavior of partially correlated PS pixels and leads to a more reliable selection algorithm. The new method identifies PS pixels using maximum likelihood signal-to-clutter ratio (SCR) estimation based on the joint interferometric stack phase-amplitude distribution. Our PS selection method is unique in that it considers both phase and amplitude; accounts for correlation between all possible pairs of interferometric observations; and models the effect of spatial and temporal baselines on the stack. We use the resulting maximum likelihood SCR estimate as a criterion for PS selection. We implement the partially correlated persistent scatterer technique to analyze a stack of C-band European Remote Sensing (ERS-1/2) interferometric radar data imaging the Hayward Fault Zone from 1995 to 2000. We show that our technique achieves a better trade-off between PS pixel selection accuracy and network density compared to other PS identification methods, particularly in areas of natural terrain. We then present deformation measurements obtained by the selected PS network. Our results demonstrate that the partially correlated persistent scatterer technique can attain accurate deformation measurements even in areas that suffer decorrelation due to natural terrain. The accuracy of phase unwrapping and subsequent deformation estimation on the spatially sparse PS network depends on both pixel selection accuracy and the density of the network. We find that many additional pixels can be added to the PS list if we are able to correctly identify and add those in which the scattering mechanism exhibits partial, rather than complete, correlation across all radar scenes.

Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study.

PubMed

Luo, Cheng; Qiu, Chuan; Guo, Zhiwei; Fang, Jiajia; Li, Qifu; Lei, Xu; Xia, Yang; Lai, Yongxiu; Gong, Qiyong; Zhou, Dong; Yao, Dezhong

2011-01-01

Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy.

Network connectivity value.

PubMed

Dragicevic, Arnaud; Boulanger, Vincent; Bruciamacchie, Max; Chauchard, Sandrine; Dupouey, Jean-Luc; Stenger, Anne

2017-04-21

In order to unveil the value of network connectivity, we formalize the construction of ecological networks in forest environments as an optimal control dynamic graph-theoretic problem. The network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological network is fully connected, and a case of incomplete graph, where the ecological network is partially connected. The results show that the network equilibrium depends on the size of the reception zone, while the network connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing connectivity in partially connected networks is more expensive than in fully connected networks, but should be undertaken when the opportunity costs are significant. Copyright © 2017 Elsevier Ltd. All rights reserved.

Event-triggered output feedback control for distributed networked systems.

PubMed

Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

2016-01-01

This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Deep conservation of cis-regulatory elements in metazoans

PubMed Central

Maeso, Ignacio; Irimia, Manuel; Tena, Juan J.; Casares, Fernando; Gómez-Skarmeta, José Luis

2013-01-01

Despite the vast morphological variation observed across phyla, animals share multiple basic developmental processes orchestrated by a common ancestral gene toolkit. These genes interact with each other building complex gene regulatory networks (GRNs), which are encoded in the genome by cis-regulatory elements (CREs) that serve as computational units of the network. Although GRN subcircuits involved in ancient developmental processes are expected to be at least partially conserved, identification of CREs that are conserved across phyla has remained elusive. Here, we review recent studies that revealed such deeply conserved CREs do exist, discuss the difficulties associated with their identification and describe new approaches that will facilitate this search. PMID:24218633

The adaptive safety analysis and monitoring system

NASA Astrophysics Data System (ADS)

Tu, Haiying; Allanach, Jeffrey; Singh, Satnam; Pattipati, Krishna R.; Willett, Peter

2004-09-01

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

When structure affects function--the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies.

PubMed

Dukart, Juergen; Bertolino, Alessandro

2014-01-01

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality--an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.

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

PubMed Central

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

2012-01-01

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

A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks

PubMed Central

Zaikin, Alexey; Míguez, Joaquín

2017-01-01

We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency. PMID:28797087

Ground-state coding in partially connected neural networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1989-01-01

Patterns over (-1,0,1) define, by their outer products, partially connected neural networks, consisting of internally strongly connected, externally weakly connected subnetworks. The connectivity patterns may have highly organized structures, such as lattices and fractal trees or nests. Subpatterns over (-1,1) define the subcodes stored in the subnetwork, that agree in their common bits. It is first shown that the code words are locally stable stares of the network, provided that each of the subcodes consists of mutually orthogonal words or of, at most, two words. Then it is shown that if each of the subcodes consists of two orthogonal words, the code words are the unique ground states (absolute minima) of the Hamiltonian associated with the network. The regions of attraction associated with the code words are shown to grow with the number of subnetworks sharing each of the neurons. Depending on the particular network architecture, the code sizes of partially connected networks can be vastly greater than those of fully connected ones and their error correction capabilities can be significantly greater than those of the disconnected subnetworks. The codes associated with lattice-structured and hierarchical networks are discussed in some detail.

An efficient and reliable geographic routing protocol based on partial network coding for underwater sensor networks.

PubMed

Hao, Kun; Jin, Zhigang; Shen, Haifeng; Wang, Ying

2015-05-28

Efficient routing protocols for data packet delivery are crucial to underwater sensor networks (UWSNs). However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. Network coding is a promising technique for efficient data packet delivery thanks to the broadcast nature of acoustic channels and the relatively high computation capabilities of the sensor nodes. In this work, we present GPNC, a novel geographic routing protocol for UWSNs that incorporates partial network coding to encode data packets and uses sensor nodes' location information to greedily forward data packets to sink nodes. GPNC can effectively reduce network delays and retransmissions of redundant packets causing additional network energy consumption. Simulation results show that GPNC can significantly improve network throughput and packet delivery ratio, while reducing energy consumption and network latency when compared with other routing protocols.

Correlations between Community Structure and Link Formation in Complex Networks

PubMed Central

Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

2013-01-01

Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

Blood glucose prediction using neural network

NASA Astrophysics Data System (ADS)

Soh, Chit Siang; Zhang, Xiqin; Chen, Jianhong; Raveendran, P.; Soh, Phey Hong; Yeo, Joon Hock

2008-02-01

We used neural network for blood glucose level determination in this study. The data set used in this study was collected using a non-invasive blood glucose monitoring system with six laser diodes, each laser diode operating at distinct near infrared wavelength between 1500nm and 1800nm. The neural network is specifically used to determine blood glucose level of one individual who participated in an oral glucose tolerance test (OGTT) session. Partial least squares regression is also used for blood glucose level determination for the purpose of comparison with the neural network model. The neural network model performs better in the prediction of blood glucose level as compared with the partial least squares model.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

An electric-analog simulation of elliptic partial differential equations using finite element theory

USGS Publications Warehouse

Franke, O.L.; Pinder, G.F.; Patten, E.P.

1982-01-01

Elliptic partial differential equations can be solved using the Galerkin-finite element method to generate the approximating algebraic equations, and an electrical network to solve the resulting matrices. Some element configurations require the use of networks containing negative resistances which, while physically realizable, are more expensive and time-consuming to construct. ?? 1982.

Fermi GBM Observations of Terrestrial Gamma-ray Flashes

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

Briggs, Michael S.

2011-09-21

Terrestrial Gamma-ray Flashes are short pulses of energetic radiation associated with thunderstorms and lightning. While the Gamma-ray Burst Monitor (GBM) on Fermi was designed to observe gamma-ray bursts, its large BGO detectors are excellent for observing TGFs. Using GBM, TGF pulses are seen to either be symmetrical or have faster rise time than fall times. Some TGFs are resolved into double, partially overlapping pulses. Using ground-based radio observations of lightning from the World Wide Lightning Location Network (WWLLN), TGFs and their associated lightning are found to be simultaneous to {approx_equal}40 {mu} s. The lightning locations are typically within 300 kmmore » of the sub-spacecraft point.« less

Continuum Modeling and Control of Large Nonuniform Wireless Networks via Nonlinear Partial Differential Equations

DOE PAGES

Zhang, Yang; Chong, Edwin K. P.; Hannig, Jan; ...

2013-01-01

We inmore » troduce a continuum modeling method to approximate a class of large wireless networks by nonlinear partial differential equations (PDEs). This method is based on the convergence of a sequence of underlying Markov chains of the network indexed by N , the number of nodes in the network. As N goes to infinity, the sequence converges to a continuum limit, which is the solution of a certain nonlinear PDE. We first describe PDE models for networks with uniformly located nodes and then generalize to networks with nonuniformly located, and possibly mobile, nodes. Based on the PDE models, we develop a method to control the transmissions in nonuniform networks so that the continuum limit is invariant under perturbations in node locations. This enables the networks to maintain stable global characteristics in the presence of varying node locations.« less

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

NASA Astrophysics Data System (ADS)

Oświȩcimka, Paweł; Livi, Lorenzo; Drożdż, Stanisław

2018-04-01

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

Stream-groundwater exchange and hydrologic turnover at the network scale

NASA Astrophysics Data System (ADS)

Covino, Tim; McGlynn, Brian; Mallard, John

2011-12-01

The exchange of water between streams and groundwater can influence stream water quality, hydrologic mass balances, and attenuate solute export from watersheds. We used conservative tracer injections (chloride, Cl-) across 10 stream reaches to investigate stream water gains and losses from and to groundwater at larger spatial and temporal scales than typically associated with hyporheic exchanges. We found strong relationships between reach discharge, median tracer velocity, and gross hydrologic loss across a range of stream morphologies and sizes in the 11.4 km2 Bull Trout Watershed of central ID. We implemented these empirical relationships in a numerical network model and simulated stream water gains and losses and subsequent fractional hydrologic turnover across the stream network. We found that stream gains and losses from and to groundwater can influence source water contributions and stream water compositions across stream networks. Quantifying proportional influences of source water contributions from runoff generation locations across the network on stream water composition can provide insight into the internal mechanisms that partially control the hydrologic and biogeochemical signatures observed along networks and at watershed outlets.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Internal epithelia in Drosophila display rudimentary competence to form cytoplasmic networks of transgenic human vimentin.

PubMed

Gullmets, Josef; Torvaldson, Elin; Lindqvist, Julia; Imanishi, Susumu Y; Taimen, Pekka; Meinander, Annika; Eriksson, John E

2017-12-01

Cytoplasmic intermediate filaments (cIFs) are found in all eumetazoans, except arthropods. To investigate the compatibility of cIFs in arthropods, we expressed human vimentin (hVim), a cIF with filament-forming capacity in vertebrate cells and tissues, transgenically in Drosophila Transgenic hVim could be recovered from whole-fly lysates by using a standard procedure for intermediate filament (IF) extraction. When this procedure was used to test for the possible presence of IF-like proteins in flies, only lamins and tropomyosin were observed in IF-enriched extracts, thereby providing biochemical reinforcement to the paradigm that arthropods lack cIFs. In Drosophila , transgenic hVim was unable to form filament networks in S2 cells and mesenchymal tissues; however, cage-like vimentin structures could be observed around the nuclei in internal epithelia, which suggests that Drosophila retains selective competence for filament formation. Taken together, our results imply that although the filament network formation competence is partially lost in Drosophila , a rudimentary filament network formation ability remains in epithelial cells. As a result of the observed selective competence for cIF assembly in Drosophila , we hypothesize that internal epithelial cIFs were the last cIFs to disappear from arthropods.-Gullmets, J., Torvaldson, E., Lindqvist, J., Imanishi, S. Y., Taimen, P., Meinander, A., Eriksson, J. E. Internal epithelia in Drosophila display rudimentary competence to form cytoplasmic networks of transgenic human vimentin. © FASEB.

Network Terminations: A Compilation of Possible Answers.

ERIC Educational Resources Information Center

Wilson, John S.

An examination of 20 library network terminations reveals five major reasons for termination: lack of adequate funding, absorption by larger networks, loosely structured governance, partial termination of services, and networks programmed for short durations. Two tables present survey data. (RAA)

Network marketing with bounded rationality and partial information

NASA Astrophysics Data System (ADS)

Kiet, Hoang Anh Tuan; Kim, Beom Jun

2008-08-01

Network marketing has been proposed and used as a way to spread the product information to consumers through social connections. We extend the previous game model of the network marketing on a small-world tree network and propose two games: In the first model with the bounded rationality, each consumer makes purchase decision stochastically, while in the second model, consumers get only partial information due to the finite length of social connections. Via extensive numerical simulations, we find that as the rationality is enhanced not only the consumer surplus but also the firm’s profit is increased. The implication of our results is also discussed.

Friendship Network and Dental Brushing Behavior among Middle School Students: An Agent Based Modeling Approach.

PubMed

Sadeghipour, Maryam; Khoshnevisan, Mohammad Hossein; Jafari, Afshin; Shariatpanahi, Seyed Peyman

2017-01-01

By using a standard questionnaire, the level of dental brushing frequency was assessed among 201 adolescent female middle school students in Tehran. The initial assessment was repeated after 5 months, in order to observe the dynamics in dental health behavior level. Logistic Regression model was used to evaluate the correlation among individuals' dental health behavior in their social network. A significant correlation on dental brushing habits was detected among groups of friends. This correlation was further spread over the network within the 5 months period. Moreover, it was identified that the average brushing level was improved within the 5 months period. Given that there was a significant correlation between social network's nodes' in-degree value, and brushing level, it was suggested that the observed improvement was partially due to more popularity of individuals with better tooth brushing habit. Agent Based Modeling (ABM) was used to demonstrate the dynamics of dental brushing frequency within a sample of friendship network. Two models with static and dynamic assumptions for the network structure were proposed. The model with dynamic network structure successfully described the dynamics of dental health behavior. Based on this model, on average, every 43 weeks a student changes her brushing habit due to learning from her friends. Finally, three training scenarios were tested by these models in order to evaluate their effectiveness. When training more popular students, considerable improvement in total students' brushing frequency was demonstrated by simulation results.

Effect of surface on the dissociation of perfect dislocations into Shockley partials describing the herringbone Au(1\\xA01\\xA01) surface reconstruction

NASA Astrophysics Data System (ADS)

Ait-Oubba, A.; Coupeau, C.; Durinck, J.; Talea, M.; Grilhé, J.

2018-06-01

In the framework of the continuum elastic theory, the equilibrium positions of Shockley partial dislocations have been determined as a function of their distance from the free surface. It is found that the dissociation width decreases with the decreasing depth, except for a depth range very close to the free surface for which the dissociation width is enlarged. A similar behaviour is also predicted when Shockley dislocation pairs are regularly arranged, whatever the wavelength. These results derived from the elastic theory are compared to STM observations of the reconstructed (1 1 1) surface in gold, which is usually described by a Shockley dislocations network.

Modeling of chromosome intermingling by partially overlapping uniform random polygons.

PubMed

Blackstone, T; Scharein, R; Borgo, B; Varela, R; Diao, Y; Arsuaga, J

2011-03-01

During the early phase of the cell cycle the eukaryotic genome is organized into chromosome territories. The geometry of the interface between any two chromosomes remains a matter of debate and may have important functional consequences. The Interchromosomal Network model (introduced by Branco and Pombo) proposes that territories intermingle along their periphery. In order to partially quantify this concept we here investigate the probability that two chromosomes form an unsplittable link. We use the uniform random polygon as a crude model for chromosome territories and we model the interchromosomal network as the common spatial region of two overlapping uniform random polygons. This simple model allows us to derive some rigorous mathematical results as well as to perform computer simulations easily. We find that the probability that one uniform random polygon of length n that partially overlaps a fixed polygon is bounded below by 1 − O(1/√n). We use numerical simulations to estimate the dependence of the linking probability of two uniform random polygons (of lengths n and m, respectively) on the amount of overlapping. The degree of overlapping is parametrized by a parameter [Formula: see text] such that [Formula: see text] indicates no overlapping and [Formula: see text] indicates total overlapping. We propose that this dependence relation may be modeled as f (ε, m, n) = [Formula: see text]. Numerical evidence shows that this model works well when [Formula: see text] is relatively large (ε ≥ 0.5). We then use these results to model the data published by Branco and Pombo and observe that for the amount of overlapping observed experimentally the URPs have a non-zero probability of forming an unsplittable link.

Heat Shock Partially Dissociates the Overlapping Modules of the Yeast Protein-Protein Interaction Network: A Systems Level Model of Adaptation

PubMed Central

Mihalik, Ágoston; Csermely, Peter

2011-01-01

Network analysis became a powerful tool giving new insights to the understanding of cellular behavior. Heat shock, the archetype of stress responses, is a well-characterized and simple model of cellular dynamics. S. cerevisiae is an appropriate model organism, since both its protein-protein interaction network (interactome) and stress response at the gene expression level have been well characterized. However, the analysis of the reorganization of the yeast interactome during stress has not been investigated yet. We calculated the changes of the interaction-weights of the yeast interactome from the changes of mRNA expression levels upon heat shock. The major finding of our study is that heat shock induced a significant decrease in both the overlaps and connections of yeast interactome modules. In agreement with this the weighted diameter of the yeast interactome had a 4.9-fold increase in heat shock. Several key proteins of the heat shock response became centers of heat shock-induced local communities, as well as bridges providing a residual connection of modules after heat shock. The observed changes resemble to a ‘stratus-cumulus’ type transition of the interactome structure, since the unstressed yeast interactome had a globally connected organization, similar to that of stratus clouds, whereas the heat shocked interactome had a multifocal organization, similar to that of cumulus clouds. Our results showed that heat shock induces a partial disintegration of the global organization of the yeast interactome. This change may be rather general occurring in many types of stresses. Moreover, other complex systems, such as single proteins, social networks and ecosystems may also decrease their inter-modular links, thus develop more compact modules, and display a partial disintegration of their global structure in the initial phase of crisis. Thus, our work may provide a model of a general, system-level adaptation mechanism to environmental changes. PMID:22022244

Interference Alignment With Partial CSI Feedback in MIMO Cellular Networks

NASA Astrophysics Data System (ADS)

Rao, Xiongbin; Lau, Vincent K. N.

2014-04-01

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. However, most existing IA designs require full channel state information (CSI) at the transmitters, which would lead to significant CSI signaling overhead. There are two techniques, namely CSI quantization and CSI feedback filtering, to reduce the CSI feedback overhead. In this paper, we consider IA processing with CSI feedback filtering in MIMO cellular networks. We introduce a novel metric, namely the feedback dimension, to quantify the first order CSI feedback cost associated with the CSI feedback filtering. The CSI feedback filtering poses several important challenges in IA processing. First, there is a hidden partial CSI knowledge constraint in IA precoder design which cannot be handled using conventional IA design methodology. Furthermore, existing results on the feasibility conditions of IA cannot be applied due to the partial CSI knowledge. Finally, it is very challenging to find out how much CSI feedback is actually needed to support IA processing. We shall address the above challenges and propose a new IA feasibility condition under partial CSIT knowledge in MIMO cellular networks. Based on this, we consider the CSI feedback profile design subject to the degrees of freedom requirements, and we derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks.

Phase dynamics of coupled oscillators reconstructed from data

NASA Astrophysics Data System (ADS)

Rosenblum, Michael; Kralemann, Bjoern; Pikovsky, Arkady

2013-03-01

We present a technique for invariant reconstruction of the phase dynamics equations for coupled oscillators from data. The invariant description is achieved by means of a transformation of phase estimates (protophases) obtained from general scalar observables to genuine phases. Staring from the bivariate data, we obtain the coupling functions in terms of these phases. We discuss the importance of the protophase-to-phase transformation for characterization of strength and directionality of interaction. To illustrate the technique we analyse the cardio-respiratory interaction on healthy humans. Our invariant approach is confirmed by high similarity of the coupling functions obtained from different observables of the cardiac system. Next, we generalize the technique to cover the case of small networks of coupled periodic units. We use the partial norms of the reconstructed coupling functions to quantify directed coupling between the oscillators. We illustrate the method by different network motifs for three coupled oscillators. We also discuss nonlinear effects in coupling.

The formation mechanism of defects, spiral wave in the network of neurons.

PubMed

Wu, Xinyi; Ma, Jun

2013-01-01

A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as 'defects' on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system.

The Formation Mechanism of Defects, Spiral Wave in the Network of Neurons

PubMed Central

Wu, Xinyi; Ma, Jun

2013-01-01

A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as ‘defects’ on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system. PMID:23383179

Against the grain: The physical properties of anisotropic partially molten rocks

NASA Astrophysics Data System (ADS)

Ghanbarzadeh, S.; Hesse, M. A.; Prodanovic, M.

2014-12-01

Partially molten rocks commonly develop textures that appear close to textural equilibrium, where the melt network evolves to minimize the energy of the melt-solid interfaces, while maintaining the dihedral angle θ at solid-solid-melt contact lines. Textural equilibrium provides a powerful model for the melt distribution that controls the petro-physical properties of partially molten rocks, e.g., permeability, elastic moduli, and electrical resistivity. We present the first level-set computations of three-dimensional texturally equilibrated melt networks in rocks with an anisotropic fabric. Our results show that anisotropy induces wetting of smaller grain boundary faces for θ > 0 at realistic porosities ϕ < 3%. This was previously not thought to be possible at textural equilibrium and reconciles the theory with experimental observations. Wetting of the grain boundary faces leads to a dramatic redistribution of the melt from the edges to the faces that introduces strong anisotropy in the petro-physical properties such as permeability, effective electrical conductivity and mechanical properties. Figure, on left, shows that smaller grain boundaries become wetted at relatively low melt fractions of 3% in stretched polyhedral grains with elongation factor 1.5. Right plot represents the ratio of melt electrical conductivity to effective conductivity of medium (known as formation factor) as an example of anisotropy in physical properties. The plot shows that even slight anisotropy in grains induces considerable anisotropy in electrical properties.

Feedback Controller Design for the Synchronization of Boolean Control Networks.

PubMed

Liu, Yang; Sun, Liangjie; Lu, Jianquan; Liang, Jinling

2016-09-01

This brief investigates the partial and complete synchronization of two Boolean control networks (BCNs). Necessary and sufficient conditions for partial and complete synchronization are established by the algebraic representations of logical dynamics. An algorithm is obtained to construct the feedback controller that guarantees the synchronization of master and slave BCNs. Two biological examples are provided to illustrate the effectiveness of the obtained results.

Deriving an Abstraction Network to Support Quality Assurance in OCRe

PubMed Central

Ochs, Christopher; Agrawal, Ankur; Perl, Yehoshua; Halper, Michael; Tu, Samson W.; Carini, Simona; Sim, Ida; Noy, Natasha; Musen, Mark; Geller, James

2012-01-01

An abstraction network is an auxiliary network of nodes and links that provides a compact, high-level view of an ontology. Such a view lends support to ontology orientation, comprehension, and quality-assurance efforts. A methodology is presented for deriving a kind of abstraction network, called a partial-area taxonomy, for the Ontology of Clinical Research (OCRe). OCRe was selected as a representative of ontologies implemented using the Web Ontology Language (OWL) based on shared domains. The derivation of the partial-area taxonomy for the Entity hierarchy of OCRe is described. Utilizing the visualization of the content and structure of the hierarchy provided by the taxonomy, the Entity hierarchy is audited, and several errors and inconsistencies in OCRe’s modeling of its domain are exposed. After appropriate corrections are made to OCRe, a new partial-area taxonomy is derived. The generalizability of the paradigm of the derivation methodology to various families of biomedical ontologies is discussed. PMID:23304341

The importance of social play network for infant or juvenile wild chimpanzees at Mahale Mountains National Park, Tanzania.

PubMed

Shimada, Masaki; Sueur, Cédric

2014-11-01

Along with social grooming and food sharing, social play is considered to be an affiliative interaction among wild chimpanzees. However, infant, juvenile, and adolescent animals engage in social play more frequently than adult animals, while other affiliative interactions occur more commonly between adults. We studied the social play of well-habituated and individually identified wild chimpanzees of the M group in Mahale Mountains National Park, Tanzania over two research periods in 2010 and 2011 (21 and 17 observation days, respectively). In both periods, most members of the M group, including adolescents and adults, took part in social play at least once. The degree centralities of the play network in infants, juveniles, and adolescents were significantly higher than those seen in adults. There was a significant and positive correlation between the total number of participations in social play and the degree centrality of play networks. Partial play networks and partial association networks consisting of individuals in same-age categories were significantly and positively correlated in infants and juveniles, although they were not correlated in adolescents or adults. These results suggest that infants, juveniles and adolescents who played frequently were more central in the group, whilst the adults who played infrequently were more peripheral. In addition, the overall structure of the social play network was stable over time. The frequency of participation in social play positively contributed to the development of affiliative social relationships within the chimpanzee group during the infant or juvenile period, but did not have the same effect during the adolescent and adult period. The social play network may allow individuals to develop the social techniques necessary to acquire a central position in a society and enable them to develop affiliative relationships during the infant or juvenile period. © 2014 Wiley Periodicals, Inc.

Conducting single-molecule magnet materials.

PubMed

Cosquer, Goulven; Shen, Yongbing; Almeida, Manuel; Yamashita, Masahiro

2018-05-11

Multifunctional molecular materials exhibiting electrical conductivity and single-molecule magnet (SMM) behaviour are particularly attractive for electronic devices and related applications owing to the interaction between electronic conduction and magnetization of unimolecular units. The preparation of such materials remains a challenge that has been pursued by a bi-component approach of combination of SMM cationic (or anionic) units with conducting networks made of partially oxidized (or reduced) donor (or acceptor) molecules. The present status of the research concerning the preparation of molecular materials exhibiting SMM behaviour and electrical conductivity is reviewed, describing the few molecular compounds where both SMM properties and electrical conductivity have been observed. The evolution of this research field through the years is discussed. The first reported compounds are semiconductors in spite being able to present relatively high electrical conductivity, and the SMM behaviour is observed at low temperatures where the electrical conductivity of the materials is similar to that of an insulator. During the recent years, a breakthrough has been achieved with the coexistence of high electrical conductivity and SMM behaviour in a molecular compound at the same temperature range, but so far without evidence of a synergy between these properties. The combination of high electrical conductivity with SMM behaviour requires not only SMM units but also the regular and as far as possible uniform packing of partially oxidized molecules, which are able to provide a conducting network.

A comparative study of covariance selection models for the inference of gene regulatory networks.

PubMed

Stifanelli, Patrizia F; Creanza, Teresa M; Anglani, Roberto; Liuzzi, Vania C; Mukherjee, Sayan; Schena, Francesco P; Ancona, Nicola

2013-10-01

The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ℓ(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Study on Improving Partial Load by Connecting Geo-thermal Heat Pump System to Fuel Cell Network

NASA Astrophysics Data System (ADS)

Obara, Shinya; Kudo, Kazuhiko

Hydrogen piping, the electric power line, and exhaust heat recovery piping of the distributed fuel cells are connected with network, and operational planning is carried out. Reduction of the efficiency in partial load is improved by operation of the geo-thermal heat pump linked to the fuel cell network. The energy demand pattern of the individual houses in Sapporo was introduced. And the analysis method aiming at minimization of the fuel rate by the genetic algorithm was described. The fuel cell network system of an analysis example assumed connecting the fuel cell co-generation of five houses. When geo-thermal heat pump was introduced into fuel cell network system stated in this paper, fuel consumption was reduced 6% rather than the conventional method

Coherent periodic activity in excitatory Erdös-Renyi neural networks: the role of network connectivity.

PubMed

Tattini, Lorenzo; Olmi, Simona; Torcini, Alessandro

2012-06-01

In this article, we investigate the role of connectivity in promoting coherent activity in excitatory neural networks. In particular, we would like to understand if the onset of collective oscillations can be related to a minimal average connectivity and how this critical connectivity depends on the number of neurons in the networks. For these purposes, we consider an excitatory random network of leaky integrate-and-fire pulse coupled neurons. The neurons are connected as in a directed Erdös-Renyi graph with average connectivity scaling as a power law with the number of neurons in the network. The scaling is controlled by a parameter γ, which allows to pass from massively connected to sparse networks and therefore to modify the topology of the system. At a macroscopic level, we observe two distinct dynamical phases: an asynchronous state corresponding to a desynchronized dynamics of the neurons and a regime of partial synchronization (PS) associated with a coherent periodic activity of the network. At low connectivity, the system is in an asynchronous state, while PS emerges above a certain critical average connectivity (c). For sufficiently large networks, (c) saturates to a constant value suggesting that a minimal average connectivity is sufficient to observe coherent activity in systems of any size irrespectively of the kind of considered network: sparse or massively connected. However, this value depends on the nature of the synapses: reliable or unreliable. For unreliable synapses, the critical value required to observe the onset of macroscopic behaviors is noticeably smaller than for reliable synaptic transmission. Due to the disorder present in the system, for finite number of neurons we have inhomogeneities in the neuronal behaviors, inducing a weak form of chaos, which vanishes in the thermodynamic limit. In such a limit, the disordered systems exhibit regular (non chaotic) dynamics and their properties correspond to that of a homogeneous fully connected network for any γ-value. Apart for the peculiar exception of sparse networks, which remain intrinsically inhomogeneous at any system size.

A new methodology for determination of macroscopic transport parameters in drying porous media

NASA Astrophysics Data System (ADS)

Attari Moghaddam, A.; Kharaghani, A.; Tsotsas, E.; Prat, M.

2015-12-01

Two main approaches have been used to model the drying process: The first approach considers the partially saturated porous medium as a continuum and partial differential equations are used to describe the mass, momentum and energy balances of the fluid phases. The continuum-scale models (CM) obtained by this approach involve constitutive laws which require effective material properties, such as the diffusivity, permeability, and thermal conductivity which are often determined by experiments. The second approach considers the material at the pore scale, where the void space is represented by a network of pores (PN). Micro- or nanofluidics models used in each pore give rise to a large system of ordinary differential equations with degrees of freedom at each node of the pore network. In this work, the moisture transport coefficient (D), the pseudo desorption isotherm inside the network and at the evaporative surface are estimated from the post-processing of the three-dimensional pore network drying simulations for fifteen realizations of the pore space geometry from a given probability distribution. A slice sampling method is used in order to extract these parameters from PN simulations. The moisture transport coefficient obtained in this way is shown in Fig. 1a. The minimum of average D values demonstrates the transition between liquid dominated moisture transport region and vapor dominated moisture transport region; a similar behavior has been observed in previous experimental findings. A function is fitted to the average D values and then is fed into the non-linear moisture diffusion equation. The saturation profiles obtained from PN and CM simulations are shown in Fig. 1b. Figure 1: (a) extracted moisture transport coefficient during drying for fifteen realizations of the pore network, (b) average moisture profiles during drying obtained from PN and CM simulations.

A new routing enhancement scheme based on node blocking state advertisement in wavelength-routed WDM networks

NASA Astrophysics Data System (ADS)

Hu, Peigang; Jin, Yaohui; Zhang, Chunlei; He, Hao; Hu, WeiSheng

2005-02-01

The increasing switching capacity brings the optical node with considerable complexity. Due to the limitation in cost and technology, an optical node is often designed with partial switching capability and partial resource sharing. It means that the node is of blocking to some extent, for example multi-granularity switching node, which in fact is a structure using pass wavelength to reduce the dimension of OXC, and partial sharing wavelength converter (WC) OXC. It is conceivable that these blocking nodes will have great effects on the problem of routing and wavelength assignment. Some previous works studied the blocking case, partial WC OXC, using complicated wavelength assignment algorithm. But the complexities of these schemes decide them to be not in practice in real networks. In this paper, we propose a new scheme based on the node blocking state advertisement to reduce the retry or rerouting probability and improve the efficiency of routing in the networks with blocking nodes. In the scheme, node blocking state are advertised to the other nodes in networks, which will be used for subsequent route calculation to find a path with lowest blocking probability. The performance of the scheme is evaluated using discrete event model in 14-node NSFNET, all the nodes of which employ a kind of partial sharing WC OXC structure. In the simulation, a simple First-Fit wavelength assignment algorithm is used. The simulation results demonstrate that the new scheme considerably reduces the retry or rerouting probability in routing process.

Time Synchronization in Wireless Sensor Networks

DTIC Science & Technology

2003-01-01

University of California Los Angeles Time Synchronization in Wireless Sensor Networks A dissertation submitted in partial satisfaction of the...4. TITLE AND SUBTITLE Time Synchronization in Wireless Sensor Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...1 1.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Time Synchronization in Sensor Networks

Quantitative geometric description of fracture systems in an andesite lava flow using terrestrial laser scanner data

NASA Astrophysics Data System (ADS)

Massiot, Cécile; Nicol, Andrew; Townend, John; McNamara, David D.; Garcia-Sellés, David; Conway, Chris E.; Archibald, Garth

2017-07-01

Permeability hosted in andesitic lava flows is dominantly controlled by fracture systems, with geometries that are often poorly constrained. This paper explores the fracture system geometry of an andesitic lava flow formed during its emplacement and cooling over gentle paleo-topography, on the active Ruapehu volcano, New Zealand. The fracture system comprises column-forming and platy fractures within the blocky interior of the lava flow, bounded by autobreccias partially observed at the base and top of the outcrop. We use a terrestrial laser scanner (TLS) dataset to extract column-forming fractures directly from the point-cloud shape over an outcrop area of ∼3090 m2. Fracture processing is validated using manual scanlines and high-resolution panoramic photographs. Column-forming fractures are either steeply or gently dipping with no preferred strike orientation. Geometric analysis of fractures derived from the TLS, in combination with virtual scanlines and trace maps, reveals that: (1) steeply dipping column-forming fracture lengths follow a scale-dependent exponential or log-normal distribution rather than a scale-independent power-law; (2) fracture intensities (combining density and size) vary throughout the blocky zone but have similar mean values up and along the lava flow; and (3) the areal fracture intensity is higher in the autobreccia than in the blocky zone. The inter-connected fracture network has a connected porosity of ∼0.5 % that promote fluid flow vertically and laterally within the blocky zone, and is partially connected to the autobreccias. Autobreccias may act either as lateral permeability connections or barriers in reservoirs, depending on burial and alteration history. A discrete fracture network model generated from these geometrical parameters yields a highly connected fracture network, consistent with outcrop observations.

Analytical theory of polymer-network-mediated interaction between colloidal particles

PubMed Central

Di Michele, Lorenzo; Zaccone, Alessio; Eiser, Erika

2012-01-01

Nanostructured materials based on colloidal particles embedded in a polymer network are used in a variety of applications ranging from nanocomposite rubbers to organic-inorganic hybrid solar cells. Further, polymer-network-mediated colloidal interactions are highly relevant to biological studies whereby polymer hydrogels are commonly employed to probe the mechanical response of living cells, which can determine their biological function in physiological environments. The performance of nanomaterials crucially relies upon the spatial organization of the colloidal particles within the polymer network that depends, in turn, on the effective interactions between the particles in the medium. Existing models based on nonlocal equilibrium thermodynamics fail to clarify the nature of these interactions, precluding the way toward the rational design of polymer-composite materials. In this article, we present a predictive analytical theory of these interactions based on a coarse-grained model for polymer networks. We apply the theory to the case of colloids partially embedded in cross-linked polymer substrates and clarify the origin of attractive interactions recently observed experimentally. Monte Carlo simulation results that quantitatively confirm the theoretical predictions are also presented. PMID:22679289

Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

Self-organization in multilayer network with adaptation mechanisms based on competition

NASA Astrophysics Data System (ADS)

Pitsik, Elena N.; Makarov, Vladimir V.; Nedaivozov, Vladimir O.; Kirsanov, Daniil V.; Goremyko, Mikhail V.

2018-04-01

The paper considers the phenomena of competition in multiplex network whose structure evolves corresponding to dynamics of it's elements, forming closed loop of self-learning with the aim to reach the optimal topology. Numerical analysis of proposed model shows that it is possible to obtain scale-invariant structures for corresponding parameters as well as the structures with homogeneous distribution of connections in the layers. Revealed phenomena emerges as the consequence of the self-organization processes related to structure-dynamical selflearning based on homeostasis and homophily, as well as the result of the competition between the network's layers for optimal topology. It was shown that in the mode of partial and cluster synchronization the network reaches scale-free topology of complex nature that is different from layer to layer. However, in the mode of global synchronization the homogeneous topologies on all layer of the network are observed. This phenomenon is tightly connected with the competitive processes that represent themselves as the natural mechanism of reaching the optimal topology of the links in variety of real-world systems.

An observational and thermodynamic investigation of carbonate partial melting

NASA Astrophysics Data System (ADS)

Floess, David; Baumgartner, Lukas P.; Vonlanthen, Pierre

2015-01-01

Melting experiments available in the literature show that carbonates and pelites melt at similar conditions in the crust. While partial melting of pelitic rocks is common and well-documented, reports of partial melting in carbonates are rare and ambiguous, mainly because of intensive recrystallization and the resulting lack of criteria for unequivocal identification of melting. Here we present microstructural, textural, and geochemical evidence for partial melting of calcareous dolomite marbles in the contact aureole of the Tertiary Adamello Batholith. Petrographic observations and X-ray micro-computed tomography (X-ray μCT) show that calcite crystallized either in cm- to dm-scale melt pockets, or as an interstitial phase forming an interconnected network between dolomite grains. Calcite-dolomite thermometry yields a temperature of at least 670 °C, which is well above the minimum melting temperature of ∼600 °C reported for the CaO-MgO-CO2-H2O system. Rare-earth element (REE) partition coefficients (KDcc/do) range between 9-35 for adjacent calcite-dolomite pairs. These KD values are 3-10 times higher than equilibrium values between dolomite and calcite reported in the literature. They suggest partitioning of incompatible elements into a melt phase. The δ18O and δ13C isotopic values of calcite and dolomite support this interpretation. Crystallographic orientations measured by electron backscattered diffraction (EBSD) show a clustering of c-axes for dolomite and interstitial calcite normal to the foliation plane, a typical feature for compressional deformation, whereas calcite crystallized in pockets shows a strong clustering of c-axes parallel to the pocket walls, suggesting that it crystallized after deformation had stopped. All this together suggests the formation of partial melts in these carbonates. A Schreinemaker analysis of the experimental data for a CO2-H2O fluid-saturated system indeed predicts formation of calcite-rich melt between 650-880 °C, in agreement with our observations of partial melting. The presence of partial melts in crustal carbonates has important physical and chemical implications, including a drastic drop in rock viscosity and significant change in the dynamics and distribution of fluids within both the contact aureole and the intrusive body.

Set selection dynamical system neural networks with partial memories, with applications to Sudoku and KenKen puzzles.

PubMed

Boreland, B; Clement, G; Kunze, H

2015-08-01

After reviewing set selection and memory model dynamical system neural networks, we introduce a neural network model that combines set selection with partial memories (stored memories on subsets of states in the network). We establish that feasible equilibria with all states equal to ± 1 correspond to answers to a particular set theoretic problem. We show that KenKen puzzles can be formulated as a particular case of this set theoretic problem and use the neural network model to solve them; in addition, we use a similar approach to solve Sudoku. We illustrate the approach in examples. As a heuristic experiment, we use online or print resources to identify the difficulty of the puzzles and compare these difficulties to the number of iterations used by the appropriate neural network solver, finding a strong relationship. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed

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

2016-01-01

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

Spreading dynamics on complex networks: a general stochastic approach.

PubMed

Noël, Pierre-André; Allard, Antoine; Hébert-Dufresne, Laurent; Marceau, Vincent; Dubé, Louis J

2014-12-01

Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible and susceptible-infectious-removed dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.

Spectral analysis of stellar light curves by means of neural networks

NASA Astrophysics Data System (ADS)

Tagliaferri, R.; Ciaramella, A.; Milano, L.; Barone, F.; Longo, G.

1999-06-01

Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound. This work was been partially supported by IIASS, by MURST 40\\% and by the Italian Space Agency.

Network reconstructions with partially available data

NASA Astrophysics Data System (ADS)

Zhang, Chaoyang; Chen, Yang; Hu, Gang

2017-06-01

Many practical systems in natural and social sciences can be described by dynamical networks. Day by day we have measured and accumulated huge amounts of data from these networks, which can be used by us to further our understanding of the world. The structures of the networks producing these data are often unknown. Consequently, understanding the structures of these networks from available data turns to be one of the central issues in interdisciplinary fields, which is called the network reconstruction problem. In this paper, we considered problems of network reconstructions using partially available data and some situations where data availabilities are not sufficient for conventional network reconstructions. Furthermore, we proposed to infer subnetwork with data of the subnetwork available only and other nodes of the entire network hidden; to depict group-group interactions in networks with averages of groups of node variables available; and to perform network reconstructions with known data of node variables only when networks are driven by both unknown internal fast-varying noises and unknown external slowly-varying signals. All these situations are expected to be common in practical systems and the methods and results may be useful for real world applications.

Selective impairment of attention networks during propofol anesthesia after gynecological surgery in middle-aged women.

PubMed

Chen, Chen; Xu, Guang-hong; Li, Yuan-hai; Tang, Wei-xiang; Wang, Kai

2016-04-15

Postoperative cognitive dysfunction is a common complication of anesthesia and surgery. Attention networks are essential components of cognitive function and are subject to impairment after anesthesia and surgery. It is not known whether such impairment represents a global attention deficit or relates to a specific attention network. We used an Attention Network Task (ANT) to examine the efficiency of the alerting, orienting, and executive control attention networks in middle-aged women (40-60 years) undergoing gynecologic surgery. A matched group of medical inpatients were recruited as a control. Fifty female patients undergoing gynecologic surgery (observation group) and 50 female medical inpatients (control group) participated in this study. Preoperatively patients were administered a mini-mental state examination as a screening method. The preoperative efficiencies of three attention networks in an attention network test were compared to the 1st and 5th post-operative days. The control group did not have any significant attention network impairments. On the 1st postoperative day, significant impairment was shown in the alerting (p=0.003 vs. control group, p=0.015 vs. baseline), orienting (p<0.001 vs. both baseline level and control group), and executive control networks (p=0.007 vs. control group, p=0.002 vs. baseline) of the observation group. By the 5th postoperative day, the alerting network efficiency had recovered to preoperative levels (p=0.464 vs. baseline) and the orienting network efficiency had recovered partially (p=0.031 vs. 1st post-operative day), but not to preoperative levels (p=0.01 vs. baseline). The executive control network did not recover by the 5th postoperative day (p=0.001 vs. baseline, p=0.680 vs. 1st post-operative day). Attention networks of middle-aged women show a varying degree of significant impairment and differing levels of recovery after surgery and propofol anesthetic. Copyright © 2016 Elsevier B.V. All rights reserved.

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State

PubMed Central

2016-01-01

Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods. PMID:27891146

A multivariate analysis of age-related differences in functional networks supporting conflict resolution.

PubMed

Salami, Alireza; Rieckmann, Anna; Fischer, Håkan; Bäckman, Lars

2014-02-01

Functional neuroimaging studies demonstrate age-related differences in recruitment of a large-scale attentional network during interference resolution, especially within dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). These alterations in functional responses have been frequently observed despite equivalent task performance, suggesting age-related reallocation of neural resources, although direct evidence for a facilitating effect in aging is sparse. We used the multi-source interference task and multivariate partial-least-squares to investigate age-related differences in the neuronal signature of conflict resolution, and their behavioral implications in younger and older adults. There were interference-related increases in activity, involving fronto-parietal and basal ganglia networks that generalized across age. In addition an age-by-task interaction was observed within a distributed network, including DLPFC and ACC, with greater activity during interference in the old. Next, we combined brain-behavior and functional connectivity analyses to investigate whether compensatory brain changes were present in older adults, using DLPFC and ACC as regions of interest (i.e. seed regions). This analysis revealed two networks differentially related to performance across age groups. A structural analysis revealed age-related gray-matter losses in regions facilitating performance in the young, suggesting that functional reorganization may partly reflect structural alterations in aging. Collectively, these findings suggest that age-related structural changes contribute to reductions in the efficient recruitment of a youth-like interference network, which cascades into instantiation of a different network facilitating conflict resolution in elderly people. © 2013. Published by Elsevier Inc. All rights reserved.

Functional connectivity alterations in brain networks relevant to self-awareness in chronic cannabis users.

PubMed

Pujol, Jesus; Blanco-Hinojo, Laura; Batalla, Albert; López-Solà, Marina; Harrison, Ben J; Soriano-Mas, Carles; Crippa, Jose A; Fagundo, Ana B; Deus, Joan; de la Torre, Rafael; Nogué, Santiago; Farré, Magí; Torrens, Marta; Martín-Santos, Rocío

2014-04-01

Recreational drugs are generally used to intentionally alter conscious experience. Long-lasting cannabis users frequently seek this effect as a means to relieve negative affect states. As with conventional anxiolytic drugs, however, changes in subjective feelings may be associated with memory impairment. We have tested whether the use of cannabis, as a psychoactive compound, is associated with alterations in spontaneous activity in brain networks relevant to self-awareness, and whether such potential changes are related to perceived anxiety and memory performance. Functional connectivity was assessed in the Default and Insula networks during resting state using fMRI in 28 heavy cannabis users and 29 control subjects. Imaging assessments were conducted during cannabis use in the unintoxicated state and repeated after one month of controlled abstinence. Cannabis users showed increased functional connectivity in the core of the Default and Insula networks and selective enhancement of functional anticorrelation between both. Reduced functional connectivity was observed in areas overlapping with other brain networks. Observed alterations were associated with behavioral measurements in a direction suggesting anxiety score reduction and interference with memory performance. Alterations were also related to the amount of cannabis used and partially persisted after one month of abstinence. Chronic cannabis use was associated with significant effects on the tuning and coupling of brain networks relevant to self-awareness, which in turn are integrated into brain systems supporting the storage of personal experience and motivated behavior. The results suggest potential mechanisms for recreational drugs to interfere with higher-order network interactions generating conscious experience. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

PubMed

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

Estimating topological properties of weighted networks from limited information

NASA Astrophysics Data System (ADS)

Gabrielli, Andrea; Cimini, Giulio; Garlaschelli, Diego; Squartini, Angelo

A typical problem met when studying complex systems is the limited information available on their topology, which hinders our understanding of their structural and dynamical properties. A paramount example is provided by financial networks, whose data are privacy protected. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here we develop a reconstruction method, based on statistical mechanics concepts, that exploits the empirical link density in a highly non-trivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. Acknoweledgement to ``Growthcom'' ICT - EC project (Grant No: 611272) and ``Crisislab'' Italian Project.

Joint object and action recognition via fusion of partially observable surveillance imagery data

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex L.

2017-05-01

Partially observable group activities (POGA) occurring in confined spaces are epitomized by their limited observability of the objects and actions involved. In many POGA scenarios, different objects are being used by human operators for the conduct of various operations. In this paper, we describe the ontology of such as POGA in the context of In-Vehicle Group Activity (IVGA) recognition. Initially, we describe the virtue of ontology modeling in the context of IVGA and show how such an ontology and a priori knowledge about the classes of in-vehicle activities can be fused for inference of human actions that consequentially leads to understanding of human activity inside the confined space of a vehicle. In this paper, we treat the problem of "action-object" as a duality problem. We postulate a correlation between observed human actions and the object that is being utilized within those actions, and conversely, if an object being handled is recognized, we may be able to expect a number of actions that are likely to be performed on that object. In this study, we use partially observable human postural sequences to recognition actions. Inspired by convolutional neural networks (CNNs) learning capability, we present an architecture design using a new CNN model to learn "action-object" perception from surveillance videos. In this study, we apply a sequential Deep Hidden Markov Model (DHMM) as a post-processor to CNN to decode realized observations into recognized actions and activities. To generate the needed imagery data set for the training and testing of these new methods, we use the IRIS virtual simulation software to generate high-fidelity and dynamic animated scenarios that depict in-vehicle group activities under different operational contexts. The results of our comparative investigation are discussed and presented in detail.

A symmetric multivariate leakage correction for MEG connectomes

PubMed Central

Colclough, G.L.; Brookes, M.J.; Smith, S.M.; Woolrich, M.W.

2015-01-01

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections. PMID:25862259

Connecting the Kuramoto Model and the Chimera State

NASA Astrophysics Data System (ADS)

Kotwal, Tejas; Jiang, Xin; Abrams, Daniel M.

2017-12-01

Since its discovery in 2002, the chimera state has frequently been described as a counterintuitive, puzzling phenomenon. The Kuramoto model, in contrast, has become a celebrated paradigm useful for understanding a range of phenomena related to phase transitions, synchronization, and network effects. Here we show that the chimera state can be understood as emerging naturally through a symmetry-breaking bifurcation from the Kuramoto model's partially synchronized state. Our analysis sheds light on recent observations of chimera states in laser arrays, chemical oscillators, and mechanical pendula.

Partial regularity of weak solutions to a PDE system with cubic nonlinearity

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Xu, Xiangsheng

2018-04-01

In this paper we investigate regularity properties of weak solutions to a PDE system that arises in the study of biological transport networks. The system consists of a possibly singular elliptic equation for the scalar pressure of the underlying biological network coupled to a diffusion equation for the conductance vector of the network. There are several different types of nonlinearities in the system. Of particular mathematical interest is a term that is a polynomial function of solutions and their partial derivatives and this polynomial function has degree three. That is, the system contains a cubic nonlinearity. Only weak solutions to the system have been shown to exist. The regularity theory for the system remains fundamentally incomplete. In particular, it is not known whether or not weak solutions develop singularities. In this paper we obtain a partial regularity theorem, which gives an estimate for the parabolic Hausdorff dimension of the set of possible singular points.

Self-similarity and quasi-idempotence in neural networks and related dynamical systems.

PubMed

Minati, Ludovico; Winkel, Julia; Bifone, Angelo; Oświęcimka, Paweł; Jovicich, Jorge

2017-04-01

Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι(1) and ι(∞), which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.

Self-similarity and quasi-idempotence in neural networks and related dynamical systems

NASA Astrophysics Data System (ADS)

Minati, Ludovico; Winkel, Julia; Bifone, Angelo; Oświecimka, Paweł; Jovicich, Jorge

2017-04-01

Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι ( 1 ) and ι ( ∞ ) , which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.

Emotion regulation, attention to emotion, and the ventral attentional network

PubMed Central

Viviani, Roberto

2013-01-01

Accounts of the effect of emotional information on behavioral response and current models of emotion regulation are based on two opposed but interacting processes: automatic bottom-up processes (triggered by emotionally arousing stimuli) and top-down control processes (mapped to prefrontal cortical areas). Data on the existence of a third attentional network operating without recourse to limited-capacity processes but influencing response raise the issue of how it is integrated in emotion regulation. We summarize here data from attention to emotion, voluntary emotion regulation, and on the origin of biases against negative content suggesting that the ventral network is modulated by exposure to emotional stimuli when the task does not constrain the handling of emotional content. In the parietal lobes, preferential activation of ventral areas associated with “bottom-up” attention by ventral network theorists is strongest in studies of cognitive reappraisal. In conditions when no explicit instruction is given to change one's response to emotional stimuli, control of emotionally arousing stimuli is observed without concomitant activation of the dorsal attentional network, replaced by a shift of activation toward ventral areas. In contrast, in studies where emotional stimuli are placed in the role of distracter, the observed deactivation of these ventral semantic association areas is consistent with the existence of proactive control on the role emotional representations are allowed to take in generating response. It is here argued that attentional orienting mechanisms located in the ventral network constitute an intermediate kind of process, with features only partially in common with effortful and automatic processes, which plays an important role in handling emotion by conveying the influence of semantic networks, with which the ventral network is co-localized. Current neuroimaging work in emotion regulation has neglected this system by focusing on a bottom-up/top-down dichotomy of attentional control. PMID:24223546

Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex.

PubMed

Yeo, B T Thomas; Krienen, Fenna M; Chee, Michael W L; Buckner, Randy L

2014-03-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. © 2013.

Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex

PubMed Central

Yeo, BT Thomas; Krienen, Fenna M; Chee, Michael WL; Buckner, Randy L

2014-01-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1,000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. PMID:24185018

Chinese Writing of Deaf or Hard-of-Hearing Students and Normal-Hearing Peers from Complex Network Approach.

PubMed

Jin, Huiyuan; Liu, Haitao

2016-01-01

Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks are built in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students' exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences.

Speech reconstruction using a deep partially supervised neural network.

PubMed

McLoughlin, Ian; Li, Jingjie; Song, Yan; Sharifzadeh, Hamid R

2017-08-01

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.

Report on Partial Findings of an Ongoing Research: Social Networking Sites (SNS) as a Platform to Support Teaching and Learning in Secondary Schools

ERIC Educational Resources Information Center

Bt. Ubaidullah, Nor Hasbiah; Samsuddin, Khairulanuar; Bt. Fabil, Norsikin; Bt. Mahadi, Norhayati

2011-01-01

This paper reports the partial findings of a survey that was carried out in the analysis phase of an ongoing research for the development of a prototype of a Social Networking Site (SNS) to support teaching and learning in secondary schools. For the initial phase of the study, a quantitative research method was used based on a survey involving 383…

Development of programmable artificial neural networks

NASA Technical Reports Server (NTRS)

Meade, Andrew J.

1993-01-01

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Moderating effects of music on resting state networks.

PubMed

Kay, Benjamin P; Meng, Xiangxiang; Difrancesco, Mark W; Holland, Scott K; Szaflarski, Jerzy P

2012-04-04

Resting state networks (RSNs) are spontaneous, synchronous, low-frequency oscillations observed in the brains of subjects who are awake but at rest. A particular RSN called the default mode network (DMN) has been shown to exhibit changes associated with neurological disorders such as temporal lobe epilepsy or Alzheimer's disease. Previous studies have also found that differing experimental conditions such as eyes-open versus eyes-closed can produce measurable changes in the DMN. These condition-associated changes have the potential of confounding the measurements of changes in RSNs related to or caused by disease state(s). In this study, we use fMRI measurements of resting-state connectivity paired with EEG measurements of alpha rhythm and employ independent component analysis, undirected graphs of partial spectral coherence, and spatiotemporal regression to investigate the effect of music-listening on RSNs and the DMN in particular. We observed similar patterns of DMN connectivity in subjects who were listening to music compared with those who were not, with a trend toward a more introspective pattern of resting-state connectivity during music-listening. We conclude that music-listening is a valid condition under which the DMN can be studied. Copyright © 2012 Elsevier B.V. All rights reserved.

A passport to neurotransmitter identity.

PubMed

Smidt, Marten P; Burbach, J Peter H

2009-01-01

Comparison of a regulatory network that specifies dopaminergic neurons in Caenorhabditis elegans to the development of vertebrate dopamine systems in the mouse reveals a possible partial conservation of such a network.

Ground states of partially connected binary neural networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1990-01-01

Neural networks defined by outer products of vectors over (-1, 0, 1) are considered. Patterns over (-1, 0, 1) define by their outer products partially connected neural networks consisting of internally strongly connected, externally weakly connected subnetworks. Subpatterns over (-1, 1) define subnetworks, and their combinations that agree in the common bits define permissible words. It is shown that the permissible words are locally stable states of the network, provided that each of the subnetworks stores mutually orthogonal subwords, or, at most, two subwords. It is also shown that when each of the subnetworks stores two mutually orthogonal binary subwords at most, the permissible words, defined as the combinations of the subwords (one corresponding to each subnetwork), that agree in their common bits are the unique ground states of the associated energy function.

Phase transition of Boolean networks with partially nested canalizing functions

NASA Astrophysics Data System (ADS)

Jansen, Kayse; Matache, Mihaela Teodora

2013-07-01

We generate the critical condition for the phase transition of a Boolean network governed by partially nested canalizing functions for which a fraction of the inputs are canalizing, while the remaining non-canalizing inputs obey a complementary threshold Boolean function. Past studies have considered the stability of fully or partially nested canalizing functions paired with random choices of the complementary function. In some of those studies conflicting results were found with regard to the presence of chaotic behavior. Moreover, those studies focus mostly on ergodic networks in which initial states are assumed equally likely. We relax that assumption and find the critical condition for the sensitivity of the network under a non-ergodic scenario. We use the proposed mathematical model to determine parameter values for which phase transitions from order to chaos occur. We generate Derrida plots to show that the mathematical model matches the actual network dynamics. The phase transition diagrams indicate that both order and chaos can occur, and that certain parameters induce a larger range of values leading to order versus chaos. The edge-of-chaos curves are identified analytically and numerically. It is shown that the depth of canalization does not cause major dynamical changes once certain thresholds are reached; these thresholds are fairly small in comparison to the connectivity of the nodes.

Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

PubMed Central

Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

2015-01-01

In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645

Collective signaling behavior in a networked-oscillator model

NASA Astrophysics Data System (ADS)

Liu, Z.-H.; Hui, P. M.

2007-09-01

We propose and study the collective behavior of a model of networked signaling objects that incorporates several ingredients of real-life systems. These ingredients include spatial inhomogeneity with grouping of signaling objects, signal attenuation with distance, and delayed and impulsive coupling between non-identical signaling objects. Depending on the coupling strength and/or time-delay effect, the model exhibits completely, partially, and locally collective signaling behavior. In particular, a correlated signaling (CS) behavior is observed in which there exist time durations when nearly a constant fraction of oscillators in the system are in the signaling state. These time durations are much longer than the duration of a spike when a single oscillator signals, and they are separated by regular intervals in which nearly all oscillators are silent. Such CS behavior is similar to that observed in biological systems such as fireflies, cicadas, crickets, and frogs. The robustness of the CS behavior against noise is also studied. It is found that properly adjusting the coupling strength and noise level could enhance the correlated behavior.

Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model

PubMed Central

Farinella, Matteo; Ruedt, Daniel T.; Gleeson, Padraig; Lanore, Frederic; Silver, R. Angus

2014-01-01

In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such ‘background’ synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5) pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a ‘balanced’ background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales. PMID:24763087

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

PubMed

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

2012-01-01

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

Partial Correlation-Based Retinotopically Organized Resting-State Functional Connectivity Within and Between Areas of the Visual Cortex Reflects More Than Cortical Distance

PubMed Central

Dawson, Debra Ann; Lam, Jack; Lewis, Lindsay B.; Carbonell, Felix; Mendola, Janine D.

2016-01-01

Abstract Numerous studies have demonstrated functional magnetic resonance imaging (fMRI)-based resting-state functional connectivity (RSFC) between cortical areas. Recent evidence suggests that synchronous fluctuations in blood oxygenation level-dependent fMRI reflect functional organization at a scale finer than that of visual areas. In this study, we investigated whether RSFCs within and between lower visual areas are retinotopically organized and whether retinotopically organized RSFC merely reflects cortical distance. Subjects underwent retinotopic mapping and separately resting-state fMRI. Visual areas V1, V2, and V3, were subdivided into regions of interest (ROIs) according to quadrants and visual field eccentricity. Functional connectivity (FC) was computed based on Pearson's linear correlation (correlation), and Pearson's linear partial correlation (correlation between two time courses after the time courses from all other regions in the network are regressed out). Within a quadrant, within visual areas, all correlation and nearly all partial correlation FC measures showed statistical significance. Consistently in V1, V2, and to a lesser extent in V3, correlation decreased with increasing eccentricity separation. Consistent with previously reported monkey anatomical connectivity, correlation/partial correlation values between regions from adjacent areas (V1-V2 and V2-V3) were higher than those between nonadjacent areas (V1-V3). Within a quadrant, partial correlation showed consistent significance between regions from two different areas with the same or adjacent eccentricities. Pairs of ROIs with similar eccentricity showed higher correlation/partial correlation than pairs distant in eccentricity. Between dorsal and ventral quadrants, partial correlation between common and adjacent eccentricity regions within a visual area showed statistical significance; this extended to more distant eccentricity regions in V1. Within and between quadrants, correlation decreased approximately linearly with increasing distances separating the tested ROIs. Partial correlation showed a more complex dependence on cortical distance: it decreased exponentially with increasing distance within a quadrant, but was best fit by a quadratic function between quadrants. We conclude that RSFCs within and between lower visual areas are retinotopically organized. Correlation-based FC is nonselectively high across lower visual areas, even between regions that do not share direct anatomical connections. The mechanisms likely involve network effects caused by the dense anatomical connectivity within this network and projections from higher visual areas. FC based on partial correlation, which minimizes network effects, follows expectations based on direct anatomical connections in the monkey visual cortex better than correlation. Last, partial correlation-based retinotopically organized RSFC reflects more than cortical distance effects. PMID:26415043

Partial Correlation-Based Retinotopically Organized Resting-State Functional Connectivity Within and Between Areas of the Visual Cortex Reflects More Than Cortical Distance.

PubMed

Dawson, Debra Ann; Lam, Jack; Lewis, Lindsay B; Carbonell, Felix; Mendola, Janine D; Shmuel, Amir

2016-02-01

Numerous studies have demonstrated functional magnetic resonance imaging (fMRI)-based resting-state functional connectivity (RSFC) between cortical areas. Recent evidence suggests that synchronous fluctuations in blood oxygenation level-dependent fMRI reflect functional organization at a scale finer than that of visual areas. In this study, we investigated whether RSFCs within and between lower visual areas are retinotopically organized and whether retinotopically organized RSFC merely reflects cortical distance. Subjects underwent retinotopic mapping and separately resting-state fMRI. Visual areas V1, V2, and V3, were subdivided into regions of interest (ROIs) according to quadrants and visual field eccentricity. Functional connectivity (FC) was computed based on Pearson's linear correlation (correlation), and Pearson's linear partial correlation (correlation between two time courses after the time courses from all other regions in the network are regressed out). Within a quadrant, within visual areas, all correlation and nearly all partial correlation FC measures showed statistical significance. Consistently in V1, V2, and to a lesser extent in V3, correlation decreased with increasing eccentricity separation. Consistent with previously reported monkey anatomical connectivity, correlation/partial correlation values between regions from adjacent areas (V1-V2 and V2-V3) were higher than those between nonadjacent areas (V1-V3). Within a quadrant, partial correlation showed consistent significance between regions from two different areas with the same or adjacent eccentricities. Pairs of ROIs with similar eccentricity showed higher correlation/partial correlation than pairs distant in eccentricity. Between dorsal and ventral quadrants, partial correlation between common and adjacent eccentricity regions within a visual area showed statistical significance; this extended to more distant eccentricity regions in V1. Within and between quadrants, correlation decreased approximately linearly with increasing distances separating the tested ROIs. Partial correlation showed a more complex dependence on cortical distance: it decreased exponentially with increasing distance within a quadrant, but was best fit by a quadratic function between quadrants. We conclude that RSFCs within and between lower visual areas are retinotopically organized. Correlation-based FC is nonselectively high across lower visual areas, even between regions that do not share direct anatomical connections. The mechanisms likely involve network effects caused by the dense anatomical connectivity within this network and projections from higher visual areas. FC based on partial correlation, which minimizes network effects, follows expectations based on direct anatomical connections in the monkey visual cortex better than correlation. Last, partial correlation-based retinotopically organized RSFC reflects more than cortical distance effects.

Partial Information Community Detection in a Multilayer Network

DTIC Science & Technology

2016-06-01

Network was taken from the CORE Lab at the Naval Postgraduate School [27]. Facebook dataset We will use a subgraph of the Facebook network to build a...larger synthetic multilayer network. We want to use this Facebook data as a way to introduce a real world example of a network into our synthetic network...This data is provided by the Standford Large Network Dataset Collection [28]. This is a large anonymous subgraph of Facebook . It contains over 4,000

The patient-zero problem with noisy observations

NASA Astrophysics Data System (ADS)

Altarelli, Fabrizio; Braunstein, Alfredo; Dall'Asta, Luca; Ingrosso, Alessandro; Zecchina, Riccardo

2014-10-01

A belief propagation approach has been recently proposed for the patient-zero problem in SIR epidemics. The patient-zero problem consists of finding the initial source of an epidemic outbreak given observations at a later time. In this work, we study a more difficult but related inference problem, in which observations are noisy and there is confusion between observed states. In addition to studying the patient-zero problem, we also tackle the problem of completing and correcting the observations to possibly find undiscovered infected individuals and false test results. Moreover, we devise a set of equations, based on the variational expression of the Bethe free energy, to find the patient-zero along with maximum-likelihood epidemic parameters. We show, by means of simulated epidemics, that this method is able to infer details on the past history of an epidemic outbreak based solely on the topology of the contact network and a single snapshot of partial and noisy observations.

Review of the hydrologic data-collection network in the St Joseph River basin, Indiana

USGS Publications Warehouse

Crompton, E.J.; Peters, J.G.; Miller, R.L.; Stewart, J.A.; Banaszak, K.J.; Shedlock, R.J.

1986-01-01

The St. Joseph River Basin data-collection network in the St. Joseph River for streamflow, lake, ground water, and climatic stations was reviewed. The network review included only the 1700 sq mi part of the basin in Indiana. The streamflow network includes 11 continuous-record gaging stations and one partial-record station. Based on areal distribution, lake effect , contributing drainage area, and flow-record ratio, six of these stations can be used to describe regional hydrology. Gaging stations on lakes are used to collect long-term lake-level data on which to base legal lake levels, and to monitor lake-level fluctuations after legal levels are established. More hydrogeologic data are needed for determining the degree to which grouhd water affects lake levels. The current groundwater network comprises 15 observation wells and has four purposes: (1) to determine the interaction between groundwater and lakes; (2) to measure changes in groundwater levels near irrigation wells; (3) to measure water levels in wells at special purpose sites; and (4) to measure long-term changes in water levels in areas not affected by pumping. Seven wells near three lakes have provided sufficient information for correlating water levels in wells and lakes but are not adequate to quantify the effect of groundwater on lake levels. Water levels in five observation wells located in the vicinity of intensive irrigation are not noticeably affected by seasonal withdrawals. The National Weather Sevice operates eight climatic stations in the basin primarily to characterize regional climatic conditions and to aid in flood forecasting. The network meets network-density guidelines established by the World Meterological Organization for collection of precipitation and evaporation data but not guidelines suggested by the National Weather Service for density of precipitation gages in areas of significant convective rainfalls. (Author 's abstract)

Active turnover regulates pattern formation and stress transmission in disordered acto-myosin networks

NASA Astrophysics Data System (ADS)

McCall, Patrick; Stam, Samantha; Kovar, David; Gardel, Margaret

The shape and mechanics of animal cells are controlled by a dynamic, thin network of semiflexible actin filaments and myosin-II motor proteins called the actomyosin cortex. Motor-generated stresses in the cortex drive changes in cell shape during cell division and morphogenesis, while dynamic turnover of actin filaments dissipates stress. The relative effects that force generation, force dissipation, and disassembly and reassembly of material have on motion in these networks are unknown. We find that cross-linked actin networks in vitro contract under myosin-generated stresses, resulting in partial filament disassembly, the formation of asters, and clustering of myosin motors. We observe a rapid restoration of uniform polymer density in the presence of the assembly factors which catalyze network turnover through elongation of severed actin filaments. When severing is accelerated further by the addition of a severing protein, network contraction and motor clustering are dramatically suppressed. We test the relative effects of material regeneration and force transmission using image analysis, and conclude that the dominant mechanism for this effect is relatively short-lived stresses that do not propagate over considerable distance or push network deformation into the nonlinear contractile regime we have previously characterized. Our results present a framework to understand cytoskeletal active matter that are influenced by a complex interplay between stress generation, network reorganization, and polymer turnover.

Projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks.

PubMed

Feng, Cun-Fang; Xu, Xin-Jian; Wang, Sheng-Jun; Wang, Ying-Hai

2008-06-01

We study projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks. We relax some limitations of previous work, where projective-anticipating and projective-lag synchronization can be achieved only on two coupled chaotic systems. In this paper, we realize projective-anticipating and projective-lag synchronization on complex dynamical networks composed of a large number of interconnected components. At the same time, although previous work studied projective synchronization on complex dynamical networks, the dynamics of the nodes are coupled partially linear chaotic systems. In this paper, the dynamics of the nodes of the complex networks are time-delayed chaotic systems without the limitation of the partial linearity. Based on the Lyapunov stability theory, we suggest a generic method to achieve the projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random dynamical networks, and we find both its existence and sufficient stability conditions. The validity of the proposed method is demonstrated and verified by examining specific examples using Ikeda and Mackey-Glass systems on Erdos-Renyi networks.

Modulation of network pacemaker neurons by oxygen at the anaerobic threshold.

PubMed

Hill, Andrew A V; Simmers, John; Meyrand, Pierre; Massabuau, Jean-Charles

2012-07-01

Previous in vitro and in vivo studies showed that the frequency of rhythmic pyloric network activity in the lobster is modulated directly by oxygen partial pressure (PO(2)). We have extended these results by (1) increasing the period of exposure to low PO(2) and by (2) testing the sensitivity of the pyloric network to changes in PO(2) that are within the narrow range normally experienced by the lobster (1 to 6 kPa). We found that the pyloric network rhythm was indeed altered by changes in PO(2) within the range typically observed in vivo. Furthermore, a previous study showed that the lateral pyloric constrictor motor neuron (LP) contributes to the O(2) sensitivity of the pyloric network. Here, we expanded on this idea by testing the hypothesis that pyloric pacemaker neurons also contribute to pyloric O(2) sensitivity. A 2-h exposure to 1 kPa PO(2), which was twice the period used previously, decreased the frequency of an isolated group of pacemaker neurons, suggesting that changes in the rhythmogenic properties of these cells contribute to pyloric O(2) sensitivity during long-term near-anaerobic (anaerobic threshold, 0.7-1.2 kPa) conditions.

Index of surface-water stations in Texas, January 1986

USGS Publications Warehouse

Carrillo, E.R.; Buckner, H.D.; Rawson, Jack

1986-01-01

As of January 1, 1986, the surface-water data-collection network in Texas operated by the U.S. Geological Survey included 386 streamflow, 87 reservoir-contents, 33 stage, 10 crest-stage partial-record, 8 periodic discharge through range, 38 flood-hydrograph partial-record, 11 flood-profile partial-record , 36 low-flow partial-record 2 tide-level, 45 daily chemical-quality, 23 continuous-recording water-quality, 97 periodic biological, 19 lake surveys, 174 periodic organic- and (or) nutrient, 4 periodic insecticide, 58 periodic pesticide, 22 automatic sampler, 157 periodic minor elements, 141 periodic chemical-quality, 108 periodic physical-organic, 14 continuous-recording three- or four-parameter water-quality, 3 sediment, 39 periodic sediment, 26 continuous-recording temperature, and 37 national stream-quality accounting network stations were in operation. Tables describing the station location, type of data collected, and place where data are available are included, as well as maps showing the location of most of the stations. (USGS)

Apathy and impaired emotional facial recognition networks overlap in Parkinson's disease: a PET study with conjunction analyses.

PubMed

Robert, Gabriel; Le Jeune, Florence; Dondaine, Thibault; Drapier, Sophie; Péron, Julie; Lozachmeur, Clément; Sauleau, Paul; Houvenaghel, Jean-François; Travers, David; Millet, Bruno; Vérin, Marc; Drapier, Dominique

2014-10-01

Apathy is a disabling non-motor symptom that is frequently observed in Parkinson's disease (PD). Its description and physiopathology suggest that it is partially mediated by emotional impairment, but this research issue has never been addressed at a clinical and metabolic level. We therefore conducted a metabolic study using (18)fluorodeoxyglucose positron emission tomography ((18)FDG PET) in 36 PD patients without depression and dementia. Apathy was assessed on the Apathy Evaluation Scale (AES), and emotional facial recognition (EFR) performances (ie, percentage of correct responses) were calculated for each patient. Confounding factors such as age, antiparkinsonian and antidepressant medication, global cognitive functions and depressive symptoms were controlled for. We found a significant negative correlation between AES scores and performances on the EFR task. The apathy network was characterised by increased metabolism within the left posterior cingulate (PC) cortex (Brodmann area (BA) 31). The impaired EFR network was characterised by decreased metabolism within the bilateral PC gyrus (BA 31), right superior frontal gyrus (BAs 10, 9 and 6) and left superior frontal gyrus (BA 10 and 11). By applying conjunction analyses to both networks, we identified the right premotor cortex (BA 6), right orbitofrontal cortex (BA 10), left middle frontal gyrus (BA 8) and left posterior cingulate gyrus (BA 31) as the structures supporting the association between apathy and impaired EFR. These results confirm that apathy in PD is partially mediated by impaired EFR, opening up new prospects for alleviating apathy in PD, such as emotional rehabilitation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Combination of Markov state models and kinetic networks for the analysis of molecular dynamics simulations of peptide folding.

PubMed

Radford, Isolde H; Fersht, Alan R; Settanni, Giovanni

2011-06-09

Atomistic molecular dynamics simulations of the TZ1 beta-hairpin peptide have been carried out using an implicit model for the solvent. The trajectories have been analyzed using a Markov state model defined on the projections along two significant observables and a kinetic network approach. The Markov state model allowed for an unbiased identification of the metastable states of the system, and provided the basis for commitment probability calculations performed on the kinetic network. The kinetic network analysis served to extract the main transition state for folding of the peptide and to validate the results from the Markov state analysis. The combination of the two techniques allowed for a consistent and concise characterization of the dynamics of the peptide. The slowest relaxation process identified is the exchange between variably folded and denatured species, and the second slowest process is the exchange between two different subsets of the denatured state which could not be otherwise identified by simple inspection of the projected trajectory. The third slowest process is the exchange between a fully native and a partially folded intermediate state characterized by a native turn with a proximal backbone H-bond, and frayed side-chain packing and termini. The transition state for the main folding reaction is similar to the intermediate state, although a more native like side-chain packing is observed.

Artificial intelligence: Deep neural reasoning

NASA Astrophysics Data System (ADS)

Jaeger, Herbert

2016-10-01

The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

Concerted Perturbation Observed in a Hub Network in Alzheimer’s Disease

PubMed Central

Liang, Dapeng; Han, Guangchun; Feng, Xuemei; Sun, Jiya; Duan, Yong; Lei, Hongxing

2012-01-01

Alzheimer’s disease (AD) is a progressive neurodegenerative disease involving the alteration of gene expression at the whole genome level. Genome-wide transcriptional profiling of AD has been conducted by many groups on several relevant brain regions. However, identifying the most critical dys-regulated genes has been challenging. In this work, we addressed this issue by deriving critical genes from perturbed subnetworks. Using a recent microarray dataset on six brain regions, we applied a heaviest induced subgraph algorithm with a modular scoring function to reveal the significantly perturbed subnetwork in each brain region. These perturbed subnetworks were found to be significantly overlapped with each other. Furthermore, the hub genes from these perturbed subnetworks formed a connected hub network consisting of 136 genes. Comparison between AD and several related diseases demonstrated that the hub network was robustly and specifically perturbed in AD. In addition, strong correlation between the expression level of these hub genes and indicators of AD severity suggested that this hub network can partially reflect AD progression. More importantly, this hub network reflected the adaptation of neurons to the AD-specific microenvironment through a variety of adjustments, including reduction of neuronal and synaptic activities and alteration of survival signaling. Therefore, it is potentially useful for the development of biomarkers and network medicine for AD. PMID:22815752

Estimating topological properties of weighted networks from limited information.

PubMed

Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

2015-10-01

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.

Estimating topological properties of weighted networks from limited information

NASA Astrophysics Data System (ADS)

Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

2015-10-01

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.

Network Structure and Biased Variance Estimation in Respondent Driven Sampling

PubMed Central

Verdery, Ashton M.; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J.

2015-01-01

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network. PMID:26679927

Routing protocol for wireless quantum multi-hop mesh backbone network based on partially entangled GHZ state

NASA Astrophysics Data System (ADS)

Xiong, Pei-Ying; Yu, Xu-Tao; Zhang, Zai-Chen; Zhan, Hai-Tao; Hua, Jing-Yu

2017-08-01

Quantum multi-hop teleportation is important in the field of quantum communication. In this study, we propose a quantum multi-hop communication model and a quantum routing protocol with multihop teleportation for wireless mesh backbone networks. Based on an analysis of quantum multi-hop protocols, a partially entangled Greenberger-Horne-Zeilinger (GHZ) state is selected as the quantum channel for the proposed protocol. Both quantum and classical wireless channels exist between two neighboring nodes along the route. With the proposed routing protocol, quantum information can be transmitted hop by hop from the source node to the destination node. Based on multi-hop teleportation based on the partially entangled GHZ state, a quantum route established with the minimum number of hops. The difference between our routing protocol and the classical one is that in the former, the processes used to find a quantum route and establish quantum channel entanglement occur simultaneously. The Bell state measurement results of each hop are piggybacked to quantum route finding information. This method reduces the total number of packets and the magnitude of air interface delay. The deduction of the establishment of a quantum channel between source and destination is also presented here. The final success probability of quantum multi-hop teleportation in wireless mesh backbone networks was simulated and analyzed. Our research shows that quantum multi-hop teleportation in wireless mesh backbone networks through a partially entangled GHZ state is feasible.

A Formative Evaluation of CU-SeeMe.

DTIC Science & Technology

1995-02-01

CU- SeeMe is a video conferencing software package that was designed and programmed at Cornell University. The program works with the TCP/IP network...protocol and allows two or more parties to conduct a real-time video conference with full audio support. In this paper we evaluate CU- SeeMe through...caused the problem and why This helps in the process of formulating solutions for observed usability problems. All the testing results are combined in the Appendix in an illustrated partial redesign of the CU- SeeMe Interface.

The U.S. Rosetta Project : eighteen months in flight

NASA Technical Reports Server (NTRS)

Alexander, Claudia J.; Gulkis, Samuel; Frerking, Margaret A.; Holmes, Dwight P.; Weissman, Paul A.; Burch, J.; Stern, A.; Goldstein, R.; Parker, J.; Cravens, T.;

Water resources data for Pennsylvania, water year 1996. Volume 2. Susquehanna and Potomac River basins. Water-data report (Annual), 1 October 1995-30 September 1996

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

Durlin, R.R.; Schaffstall, W.P.

1997-07-01

This report, Volume, 2, contains (1) discharge records for 81 continuous-record streamflow-gaging stations, 16 partial-record stations, and 20 special study and miscellaneous streamflow sites; (2) elevation and contents records for 12 lakes and reservoirs; (3) water-quality records for 7 gaging stations and 46 ungaged stream sites; and (4) water-level records for 30 ground-water network observation wells. Site locations are shown in figures throughout the report.

The Influence of Closeness Centrality on Lexical Processing

PubMed Central

Goldstein, Rutherford; Vitevitch, Michael S.

2017-01-01

The present study examined how the network science measure known as closeness centrality (which measures the average distance between a node and all other nodes in the network) influences lexical processing. In the mental lexicon, a word such as CAN has high closeness centrality, because it is close to many other words in the lexicon. Whereas, a word such as CURE has low closeness centrality because it is far from other words in the lexicon. In an auditory lexical decision task (Experiment 1) participants responded more quickly to words with high closeness centrality. In Experiment 2 an auditory lexical decision task was again used, but with a wider range of stimulus characteristics. Although, there was no main effect of closeness centrality in Experiment 2, an interaction between closeness centrality and frequency of occurrence was observed on reaction times. The results are explained in terms of partial activation gradually strengthening over time word-forms that are centrally located in the phonological network. PMID:29018396

Bootstrapping Least Squares Estimates in Biochemical Reaction Networks

PubMed Central

Linder, Daniel F.

2015-01-01

The paper proposes new computational methods of computing confidence bounds for the least squares estimates (LSEs) of rate constants in mass-action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large volume limit of a reaction network, to network’s partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. PMID:25898769

Substrateless Welding of Self-Assembled Silver Nanowires at Air/Water Interface.

PubMed

Hu, Hang; Wang, Zhongyong; Ye, Qinxian; He, Jiaqing; Nie, Xiao; He, Gufeng; Song, Chengyi; Shang, Wen; Wu, Jianbo; Tao, Peng; Deng, Tao

2016-08-10

Integrating connected silver nanowire networks with flexible polymers has appeared as a popular way to prepare flexible electronics. To reduce the contact resistance and enhance the connectivity between silver nanowires, various welding techniques have been developed. Herein, rather than welding on solid supporting substrates, which often requires complicated transferring operations and also may pose damage to heat-sensitive substrates, we report an alternative approach to prepare easily transferrable conductive networks through welding of self-assembled silver nanowires at the air/water interface using plasmonic heating. The intriguing welding behavior of partially aligned silver nanowires was analyzed with combined experimental observation and theoretical modeling. The underlying water not only physically supports the assembled silver nanowires but also buffers potential overheating during the welding process, thereby enabling effective welding within a broad range of illumination power density and illumination duration. The welded networks could be directly integrated with PDMS substrates to prepare high-performance stable flexible heaters that are stretchable, bendable, and can be easily patterned to explore selective heating applications.

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.

PubMed

Dutta, Sangya; Kumar, Vinay; Shukla, Aditya; Mohapatra, Nihar R; Ganguly, Udayan

2017-08-15

Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10 11 neuron based) large neural networks.

Chinese Writing of Deaf or Hard-of-Hearing Students and Normal-Hearing Peers from Complex Network Approach

PubMed Central

Jin, Huiyuan; Liu, Haitao

2016-01-01

Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks are built in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students' exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences. PMID:27920733

Automatic Detection of Nausea Using Bio-Signals During Immerging in A Virtual Reality Environment

DTIC Science & Technology

2001-10-25

reduce the redundancy in those parameters, and constructed an artificial neural network with those principal components. Using the network we constructed, we could partially detect nausea in real time.

Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism

NASA Astrophysics Data System (ADS)

Kurmyshev, Evguenii; Juárez, Héctor A.; González-Silva, Ricardo A.

2011-08-01

Bounded confidence models of opinion dynamics in social networks have been actively studied in recent years, in particular, opinion formation and extremism propagation along with other aspects of social dynamics. In this work, after an analysis of limitations of the Deffuant-Weisbuch (DW) bounded confidence, relative agreement model, we propose the mixed model that takes into account two psychological types of individuals. Concord agents (C-agents) are friendly people; they interact in a way that their opinions always get closer. Agents of the other psychological type show partial antagonism in their interaction (PA-agents). Opinion dynamics in heterogeneous social groups, consisting of agents of the two types, was studied on different social networks: Erdös-Rényi random graphs, small-world networks and complete graphs. Limit cases of the mixed model, pure C- and PA-societies, were also studied. We found that group opinion formation is, qualitatively, almost independent of the topology of networks used in this work. Opinion fragmentation, polarization and consensus are observed in the mixed model at different proportions of PA- and C-agents, depending on the value of initial opinion tolerance of agents. As for the opinion formation and arising of “dissidents”, the opinion dynamics of the C-agents society was found to be similar to that of the DW model, except for the rate of opinion convergence. Nevertheless, mixed societies showed dynamics and bifurcation patterns notably different to those of the DW model. The influence of biased initial conditions over opinion formation in heterogeneous social groups was also studied versus the initial value of opinion uncertainty, varying the proportion of the PA- to C-agents. Bifurcation diagrams showed an impressive evolution of collective opinion, in particular, radical changes of left to right consensus or vice versa at an opinion uncertainty value equal to 0.7 in the model with the PA/C mixture of population near 50/50.

Hierarchical auto-configuration addressing in mobile ad hoc networks (HAAM)

NASA Astrophysics Data System (ADS)

Ram Srikumar, P.; Sumathy, S.

2017-11-01

Addressing plays a vital role in networking to identify devices uniquely. A device must be assigned with a unique address in order to participate in the data communication in any network. Different protocols defining different types of addressing are proposed in literature. Address auto-configuration is a key requirement for self organizing networks. Existing auto-configuration based addressing protocols require broadcasting probes to all the nodes in the network before assigning a proper address to a new node. This needs further broadcasts to reflect the status of the acquired address in the network. Such methods incur high communication overheads due to repetitive flooding. To address this overhead, a new partially stateful address allocation scheme, namely Hierarchical Auto-configuration Addressing (HAAM) scheme is extended and proposed. Hierarchical addressing basically reduces latency and overhead caused during address configuration. Partially stateful addressing algorithm assigns addresses without the need for flooding and global state awareness, which in turn reduces the communication overhead and space complexity respectively. Nodes are assigned addresses hierarchically to maintain the graph of the network as a spanning tree which helps in effectively avoiding the broadcast storm problem. Proposed algorithm for HAAM handles network splits and merges efficiently in large scale mobile ad hoc networks incurring low communication overheads.

Numerical solution of differential equations by artificial neural networks

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1995-01-01

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Wireless Sensor Network for Radiometric Detection and Assessment of Partial Discharge in High-Voltage Equipment

NASA Astrophysics Data System (ADS)

Upton, D. W.; Saeed, B. I.; Mather, P. J.; Lazaridis, P. I.; Vieira, M. F. Q.; Atkinson, R. C.; Tachtatzis, C.; Garcia, M. S.; Judd, M. D.; Glover, I. A.

2018-03-01

Monitoring of partial discharge (PD) activity within high-voltage electrical environments is increasingly used for the assessment of insulation condition. Traditional measurement techniques employ technologies that either require off-line installation or have high power consumption and are hence costly. A wireless sensor network is proposed that utilizes only received signal strength to locate areas of PD activity within a high-voltage electricity substation. The network comprises low-power and low-cost radiometric sensor nodes which receive the radiation propagated from a source of PD. Results are reported from several empirical tests performed within a large indoor environment and a substation environment using a network of nine sensor nodes. A portable PD source emulator was placed at multiple locations within the network. Signal strength measured by the nodes is reported via WirelessHART to a data collection hub where it is processed using a location algorithm. The results obtained place the measured location within 2 m of the actual source location.

Community Structure of a Bank-Firm Credit Network in Japan

NASA Astrophysics Data System (ADS)

Iyetomi, Hiroshi; Matsuura, Yuki

2014-03-01

We study temporal change of community structure in a Japanese credit network formed by banks and listed firms through their financial relations over the last 30 years. The credit connectedness is regarded as a potenital source of systemic risk. Our network is a bipartite graph consisting of two species of nodes connected with bidirectional links. The direction of links is identified with that of risk flows and their weights are relative credit/loan with respect to the targets. In a partial credit network obtained only with the links pointing from firms toward banks, the city banks forms one major community in most of the time period to share risk when firms go wrong. On the other hand, a partial network only with the links from banks toward firms is decomposed into communities of similar size each of which has its own city bank, reflecting the main-bank system in Japan. Finally we take overlapping parts of the two community sets to find cores of the risk concentration in the credit network. This work was supported by JSPS KAKENHI Grant Number 22300080.

Mitochondrial DNA Diversity of Modern, Ancient and Wild Sheep (Ovis gmelinii anatolica) from Turkey: New Insights on the Evolutionary History of Sheep

PubMed Central

Pişkin, Evangelia; Engin, Atilla; Özer, Füsun; Yüncü, Eren; Doğan, Şükrü Anıl; Togan, İnci

2013-01-01

In the present study, to contribute to the understanding of the evolutionary history of sheep, the mitochondrial (mt) DNA polymorphisms occurring in modern Turkish native domestic (n = 628), modern wild (Ovis gmelinii anatolica) (n = 30) and ancient domestic sheep from Oylum Höyük in Kilis (n = 33) were examined comparatively with the accumulated data in the literature. The lengths (75 bp/76 bp) of the second and subsequent repeat units of the mtDNA control region (CR) sequences differentiated the five haplogroups (HPGs) observed in the domestic sheep into two genetic clusters as was already implied by other mtDNA markers: the first cluster being composed of HPGs A, B, D and the second cluster harboring HPGs C, E. To manifest genetic relatedness between wild Ovis gmelinii and domestic sheep haplogroups, their partial cytochrome B sequences were examined together on a median-joining network. The two parallel but wider aforementioned clusters were observed also on the network of Ovis gmelenii individuals, within which domestic haplogroups were embedded. The Ovis gmelinii wilds of the present day appeared to be distributed on two partially overlapping geographic areas parallel to the genetic clusters that they belong to (the first cluster being in the western part of the overall distribution). Thus, the analyses suggested that the domestic sheep may be the products of two maternally distinct ancestral Ovis gmelinii populations. Furthermore, Ovis gmelinii anatolica individuals exhibited a haplotype of HPG A (n = 22) and another haplotype (n = 8) from the second cluster which was not observed among the modern domestic sheep. HPG E, with the newly observed members (n = 11), showed signs of expansion. Studies of ancient and modern mtDNA suggest that HPG C frequency increased in the Southeast Anatolia from 6% to 22% some time after the beginning of the Hellenistic period, 500 years Before Common Era (BCE). PMID:24349158

Mitochondrial DNA diversity of modern, ancient and wild sheep(Ovis gmelinii anatolica) from Turkey: new insights on the evolutionary history of sheep.

PubMed

Demirci, Sevgin; Koban Baştanlar, Evren; Dağtaş, Nihan Dilşad; Pişkin, Evangelia; Engin, Atilla; Ozer, Füsun; Yüncü, Eren; Doğan, Sükrü Anıl; Togan, Inci

2013-01-01

In the present study, to contribute to the understanding of the evolutionary history of sheep, the mitochondrial (mt) DNA polymorphisms occurring in modern Turkish native domestic (n = 628), modern wild (Ovis gmelinii anatolica) (n = 30) and ancient domestic sheep from Oylum Höyük in Kilis (n = 33) were examined comparatively with the accumulated data in the literature. The lengths (75 bp/76 bp) of the second and subsequent repeat units of the mtDNA control region (CR) sequences differentiated the five haplogroups (HPGs) observed in the domestic sheep into two genetic clusters as was already implied by other mtDNA markers: the first cluster being composed of HPGs A, B, D and the second cluster harboring HPGs C, E. To manifest genetic relatedness between wild Ovis gmelinii and domestic sheep haplogroups, their partial cytochrome B sequences were examined together on a median-joining network. The two parallel but wider aforementioned clusters were observed also on the network of Ovis gmelenii individuals, within which domestic haplogroups were embedded. The Ovis gmelinii wilds of the present day appeared to be distributed on two partially overlapping geographic areas parallel to the genetic clusters that they belong to (the first cluster being in the western part of the overall distribution). Thus, the analyses suggested that the domestic sheep may be the products of two maternally distinct ancestral Ovis gmelinii populations. Furthermore, Ovis gmelinii anatolica individuals exhibited a haplotype of HPG A (n = 22) and another haplotype (n = 8) from the second cluster which was not observed among the modern domestic sheep. HPG E, with the newly observed members (n = 11), showed signs of expansion. Studies of ancient and modern mtDNA suggest that HPG C frequency increased in the Southeast Anatolia from 6% to 22% some time after the beginning of the Hellenistic period, 500 years Before Common Era (BCE).

Water resources data for Oregon, water year 2004

USGS Publications Warehouse

Herrett, Thomas A.; Hess, Glenn W.; House, Jon G.; Ruppert, Gregory P.; Courts, Mary-Lorraine

2005-01-01

The annual Oregon water data report is one of a series of annual reports that document hydrologic data gathered from the U.S. Geological Survey's surface- and ground-water data-collection networks in each State, Puerto Rico, and the Trust Territories. These records of streamflow, ground-water levels, and quality of water provide the hydrologic information needed by State, local, Tribal, and Federal agencies and the private sector for developing and managing our Nation's land and water resources. This report contains water year 2004 data for both surface and ground water, including discharge records for 209 streamflow-gaging stations, 42 partial-record or miscellaneous streamflow stations, and 9 crest-stage partial-record streamflow stations; stage-only records for 6 gaging stations; stage and content records for 15 lakes and reservoirs; water-level records from 12 long-term observation wells; and water-quality records collected at 133 streamflow-gaging stations and 1 atmospheric deposition station.

Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach

NASA Astrophysics Data System (ADS)

Yuniarto, Budi; Kurniawan, Robert

2017-03-01

PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.

PubMed

Alvarez-Ponce, David; Feyertag, Felix; Chakraborty, Sandip

2017-06-01

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Looking for robust properties in the growth of an academic network: the case of the Uruguayan biological research community.

PubMed

Cabana, Alvaro; Mizraji, Eduardo; Pomi, Andrés; Valle-Lisboa, Juan Carlos

2008-04-01

Graph-theoretical methods have recently been used to analyze certain properties of natural and social networks. In this work, we have investigated the early stages in the growth of a Uruguayan academic network, the Biology Area of the Programme for the Development of Basic Science (PEDECIBA). This transparent social network is a territory for the exploration of the reliability of clustering methods that can potentially be used when we are confronted with opaque natural systems that provide us with a limited spectrum of observables (happens in research on the relations between brain, thought and language). From our social net, we constructed two different graph representations based on the relationships among researchers revealed by their co-participation in Master's thesis committees. We studied these networks at different times and found that they achieve connectedness early in their evolution and exhibit the small-world property (i.e. high clustering with short path lengths). The data seem compatible with power law distributions of connectivity, clustering coefficients and betweenness centrality. Evidence of preferential attachment of new nodes and of new links between old nodes was also found in both representations. These results suggest that there are topological properties observed throughout the growth of the network that do not depend on the representations we have chosen but reflect intrinsic properties of the academic collective under study. Researchers in PEDECIBA are classified according to their specialties. We analysed the community structure detected by a standard algorithm in both representations. We found that much of the pre-specified structure is recovered and part of the mismatches can be attributed to convergent interests between scientists from different sub-disciplines. This result shows the potentiality of some clustering methods for the analysis of partially known natural systems.

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

PubMed Central

Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

2012-01-01

Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network stabilizes. PMID:22754524

Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus

NASA Astrophysics Data System (ADS)

Lobo, Daniel; Lobikin, Maria; Levin, Michael

2017-01-01

Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.

Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

PubMed

Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

2017-10-01

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Clustering Coefficients for Correlation Networks.

PubMed

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

Clustering Coefficients for Correlation Networks

PubMed Central

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714

Pristionchus pacificus daf-16 is essential for dauer formation but dispensable for mouth form dimorphism.

PubMed

Ogawa, Akira; Bento, Gilberto; Bartelmes, Gabi; Dieterich, Christoph; Sommer, Ralf J

2011-04-01

The nematode Pristionchus pacificus shows two forms of phenotypic plasticity: dauer formation and dimorphism of mouth form morphologies. It can therefore serve as a model for studying the evolutionary mechanisms that underlie phenotypic plasticity. Formation of dauer larvae is observed in many other species and constitutes one of the most crucial survival strategies in nematodes, whereas the mouth form dimorphism is an evolutionary novelty observed only in P. pacificus and related nematodes. We have previously shown that the same environmental cues and steroid signaling control both dauer formation and mouth form dimorphism. Here, we examine by mutational analysis and whole-genome sequencing the function of P. pacificus (Ppa) daf-16, which encodes a forkhead transcription factor; in C. elegans, daf-16 is the target of insulin signaling and plays important roles in dauer formation. We found that mutations in Ppa-daf-16 cause strong dauer formation-defective phenotypes, suggesting that Ppa-daf-16 represents one of the evolutionarily conserved regulators of dauer formation. Upon strong dauer induction with lophenol, Ppa-daf-16 individuals formed arrested larvae that partially resemble wild-type dauer larvae, indicating that Ppa-daf-16 is also required for dauer morphogenesis. By contrast, regulation of mouth form dimorphism was unaffected by Ppa-daf-16 mutations and mutant animals responded normally to environmental cues. Our results suggest that mechanisms for dauer formation and mouth form regulation overlap partially, but not completely, and one of two key transcriptional regulators of the dauer regulatory network was either independently co-opted for, or subsequently lost by, the mouth form regulatory network.

The structure of a gene co-expression network reveals biological functions underlying eQTLs.

PubMed

Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

2013-01-01

What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks.

PubMed

Yuan, Qi; Ma, Chunguang; Yu, Haitao; Bian, Xuefen

2018-05-12

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity.

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks

PubMed Central

Yuan, Qi; Ma, Chunguang; Yu, Haitao; Bian, Xuefen

2018-01-01

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity. PMID:29757244

Partial synchronization in networks of non-linearly coupled oscillators: The Deserter Hubs Model

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

Freitas, Celso, E-mail: cbnfreitas@gmail.com; Macau, Elbert, E-mail: elbert.macau@inpe.br; Pikovsky, Arkady, E-mail: pikovsky@uni-potsdam.de

2015-04-15

We study the Deserter Hubs Model: a Kuramoto-like model of coupled identical phase oscillators on a network, where attractive and repulsive couplings are balanced dynamically due to nonlinearity of interactions. Under weak force, an oscillator tends to follow the phase of its neighbors, but if an oscillator is compelled to follow its peers by a sufficient large number of cohesive neighbors, then it actually starts to act in the opposite manner, i.e., in anti-phase with the majority. Analytic results yield that if the repulsion parameter is small enough in comparison with the degree of the maximum hub, then the fullmore » synchronization state is locally stable. Numerical experiments are performed to explore the model beyond this threshold, where the overall cohesion is lost. We report in detail partially synchronous dynamical regimes, like stationary phase-locking, multistability, periodic and chaotic states. Via statistical analysis of different network organizations like tree, scale-free, and random ones, we found a measure allowing one to predict relative abundance of partially synchronous stationary states in comparison to time-dependent ones.« less

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

PubMed

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

2016-08-01

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

Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

PubMed Central

Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

2014-01-01

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

Design tools for complex dynamic security systems.

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

Byrne, Raymond Harry; Rigdon, James Brian; Rohrer, Brandon Robinson

2007-01-01

The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systemsmore » are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.« less

Robust sequential working memory recall in heterogeneous cognitive networks

PubMed Central

Rabinovich, Mikhail I.; Sokolov, Yury; Kozma, Robert

2014-01-01

Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon—clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain—the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions. PMID:25452717

The Fram Strait integrated ocean observatory

NASA Astrophysics Data System (ADS)

Fahrbach, E.; Beszczynska-Möller, A.; Rettig, S.; Rohardt, G.; Sagen, H.; Sandven, S.; Hansen, E.

2012-04-01

A long-term oceanographic moored array has been operated since 1997 to measure the ocean water column properties and oceanic advective fluxes through Fram Strait. While the mooring line along 78°50'N is devoted to monitoring variability of the physical environment, the AWI Hausgarten observatory, located north of it, focuses on ecosystem properties and benthic biology. Under the EU DAMOCLES and ACOBAR projects, the oceanographic observatory has been extended towards the innovative integrated observing system, combining the deep ocean moorings, multipurpose acoustic system and a network of gliders. The main aim of this system is long-term environmental monitoring in Fram Strait, combining satellite data, acoustic tomography, oceanographic measurements at moorings and glider sections with high-resolution ice-ocean circulation models through data assimilation. In future perspective, a cable connection between the Hausgarten observatory and a land base on Svalbard is planned as the implementation of the ESONET Arctic node. To take advantage of the planned cabled node, different technologies for the underwater data transmission were reviewed and partially tested under the ESONET DM AOEM. The main focus was to design and evaluate available technical solutions for collecting data from different components of the Fram Strait ocean observing system, and an integration of available data streams for the optimal delivery to the future cabled node. The main components of the Fram Strait integrated observing system will be presented and the current status of available technologies for underwater data transfer will be reviewed. On the long term, an initiative of Helmholtz observatories foresees the interdisciplinary Earth-Observing-System FRAM which combines observatories such as the long term deep-sea ecological observatory HAUSGARTEN, the oceanographic Fram Strait integrated observing system and the Svalbard coastal stations maintained by the Norwegian ARCTOS network. A vision of this modular underwater observatory network in Fram Strait will be presented.

Promoting positive psychology using social networking sites: a study of new college entrants on Facebook.

PubMed

Chang, Shu-Man; Lin, Yung-Hsiu; Lin, Chi-Wei; Chang, Her-Kun; Chong, Ping Pete

2014-04-29

This study explores the potential of promoting college students' positive psychological development using popular online social networks. Online social networks have dramatically changed the ways college students manage their social relationships. Social network activities, such as checking Facebook posts dominates students' Internet time and has the potential to assist students' positive development. Positive psychology is a scientific study of how ordinary individuals can apply their strength effectively when facing objective difficulties and how this capability can be cultivated with certain approaches. A positive message delivery approach was designed for a group of new college entrants. A series of positive messages was edited by university counselors and delivered by students to their Facebook social groups. Responses from each posted positive messages were collected and analyzed by researchers. The responses indicated that: (1) relationships of individual engagement and social influence in this study can partially explain the observed student behavior; (2) using class-based social groups can promote a positive atmosphere to enhance strong-tie relationships in both the physical and virtual environments, and (3) promoting student's positive attitudes can substantially impact adolescents' future developments, and many positive attitudes can be cultivated by emotional events and social influence.

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

Promoting Positive Psychology Using Social Networking Sites: A Study of New College Entrants on Facebook

PubMed Central

Chang, Shu-Man; Lin, Yung-Hsiu; Lin, Chi-Wei; Chang, Her-Kun; Chong, Ping Pete

2014-01-01

This study explores the potential of promoting college students’ positive psychological development using popular online social networks. Online social networks have dramatically changed the ways college students manage their social relationships. Social network activities, such as checking Facebook posts dominates students’ Internet time and has the potential to assist students’ positive development. Positive psychology is a scientific study of how ordinary individuals can apply their strength effectively when facing objective difficulties and how this capability can be cultivated with certain approaches. A positive message delivery approach was designed for a group of new college entrants. A series of positive messages was edited by university counselors and delivered by students to their Facebook social groups. Responses from each posted positive messages were collected and analyzed by researchers. The responses indicated that: (1) relationships of individual engagement and social influence in this study can partially explain the observed student behavior; (2) using class-based social groups can promote a positive atmosphere to enhance strong-tie relationships in both the physical and virtual environments, and (3) promoting student’s positive attitudes can substantially impact adolescents’ future developments, and many positive attitudes can be cultivated by emotional events and social influence. PMID:24785540

General Anesthetics to Treat Major Depressive Disorder: Clinical Relevance and Underlying Mechanisms.

PubMed

Vutskits, Laszlo

2018-01-01

Major depressive disorder is a frequent and devastating psychological condition with tremendous public health impact. The underlying pathophysiological mechanisms involve abnormal neurotransmission and a relatedly impaired synaptic plasticity. Since general anesthetics are potent modulators of neuronal activity and, thereby, can exert long-term context-dependent impact on neural networks, an intriguing hypothesis is that these drugs could enhance impaired neural plasticity associated with certain psychiatric diseases. Clinical observations over the past few decades appear to confirm this possibility. Indeed, equipotency of general anesthesia alone in comparison with electroconvulsive therapy under general anesthesia has been demonstrated in several clinical trials. Importantly, in the past 15 years, intravenous administration of subanesthetic doses of ketamine have also been demonstrated to have rapid antidepressant effects. The molecular, cellular, and network mechanisms underlying these therapeutic effects have been partially identified. Although several important questions remain to be addressed, the ensemble of these experimental and clinical observations opens new therapeutic possibilities in the treatment of depressive disorders. Importantly, they also suggest a new therapeutic role for anesthetics that goes beyond their principal use in the perioperative period to facilitate surgery.

Experimental synchronization of chaos in a large ring of mutually coupled single-transistor oscillators: phase, amplitude, and clustering effects.

PubMed

Minati, Ludovico

2014-12-01

In this paper, experimental evidence of multiple synchronization phenomena in a large (n = 30) ring of chaotic oscillators is presented. Each node consists of an elementary circuit, generating spikes of irregular amplitude and comprising one bipolar junction transistor, one capacitor, two inductors, and one biasing resistor. The nodes are mutually coupled to their neighbours via additional variable resistors. As coupling resistance is decreased, phase synchronization followed by complete synchronization is observed, and onset of synchronization is associated with partial synchronization, i.e., emergence of communities (clusters). While component tolerances affect community structure, the general synchronization properties are maintained across three prototypes and in numerical simulations. The clusters are destroyed by adding long distance connections with distant notes, but are otherwise relatively stable with respect to structural connectivity changes. The study provides evidence that several fundamental synchronization phenomena can be reliably observed in a network of elementary single-transistor oscillators, demonstrating their generative potential and opening way to potential applications of this undemanding setup in experimental modelling of the relationship between network structure, synchronization, and dynamical properties.

Partial least squares correspondence analysis: A framework to simultaneously analyze behavioral and genetic data.

PubMed

Beaton, Derek; Dunlop, Joseph; Abdi, Hervé

2016-12-01

For nearly a century, detecting the genetic contributions to cognitive and behavioral phenomena has been a core interest for psychological research. Recently, this interest has been reinvigorated by the availability of genotyping technologies (e.g., microarrays) that provide new genetic data, such as single nucleotide polymorphisms (SNPs). These SNPs-which represent pairs of nucleotide letters (e.g., AA, AG, or GG) found at specific positions on human chromosomes-are best considered as categorical variables, but this coding scheme can make difficult the multivariate analysis of their relationships with behavioral measurements, because most multivariate techniques developed for the analysis between sets of variables are designed for quantitative variables. To palliate this problem, we present a generalization of partial least squares-a technique used to extract the information common to 2 different data tables measured on the same observations-called partial least squares correspondence analysis-that is specifically tailored for the analysis of categorical and mixed ("heterogeneous") data types. Here, we formally define and illustrate-in a tutorial format-how partial least squares correspondence analysis extends to various types of data and design problems that are particularly relevant for psychological research that include genetic data. We illustrate partial least squares correspondence analysis with genetic, behavioral, and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative. R code is available on the Comprehensive R Archive Network and via the authors' websites. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

Estimating the Size of a Large Network and its Communities from a Random Sample

PubMed Central

Chen, Lin; Karbasi, Amin; Crawford, Forrest W.

2017-01-01

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. PMID:28867924

Estimating the Size of a Large Network and its Communities from a Random Sample.

PubMed

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.

Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility.

PubMed

Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L

2014-01-01

Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. © 2013.

Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility

PubMed Central

Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L.

2013-01-01

Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks were found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus were found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) were partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. PMID:24084068

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

PubMed Central

Aguilera, Teodoro; Lozano, Jesús; Paredes, José A.; Álvarez, Fernando J.; Suárez, José I.

2012-01-01

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. PMID:22969387

Amateur-Professional Collaborations in the AAVSO

NASA Astrophysics Data System (ADS)

Hawkins, G.; Mattei, J. A.; Waagen, E. O.

2000-05-01

The AAVSO coordinates, collects, evaluates, and archives variable star observations made largely by amateur astronomers around the world, and publishes and disseminates these observations to researchers and educators worldwide. Its electronic database of nearly 10 million visual variable star observations contributed by 6,000 amateur astronomers in over 40 countries since 1911 is the world's largest and longest-running. The AAVSO has a long history of collaborations between its amateur astronomer observers and professional astronomers. Many of the over 275 requests received yearly from astronomers for AAVSO data and services result in collaborative projects - particularly in multiwavelength observations of variable stars using ground-based telescopes and/or satellites - to help schedule observing runs; provide sumultaneous optical coverage of observing targets and immediate notification of their activity during particular satellite observations; correlate multiwavelength data; and analyze long-term variable star behavior. Among the more dramatic collaborations AAVSO observers have participated in are numerous multi-satellite observing runs on specific variable stars triggered in response to real-time alerts to stellar activity from AAVSO observers; and the variable star observations made during the Astro-2 mission, in which real-time observations by AAVSO observers directed shuttle astronauts to observing targets, and resulted in seminal new information about the cataclysmic variable Z Camelopardalis. The AAVSO is embarking on an exciting new collaboration with Gamma-Ray astronomers at NASA/Marshall Space Flight Center. The AAVSO and the MSFC Gamma-Ray Burst Team have established a Gamma-Ray Burst Network, in which participating AAVSO observers will be alerted immediately via pagers and email to the detection of gamma-ray bursts and will use their own CCD-equipped telescopes to search for the optical counterpart. We gratefully acknowledge partial funding of this network by NASA. Contact the AAVSO at aavso@aavso.org or http://www.aavso.org.

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

Index of stations: surface-water data-collection network of Texas, September 1998

USGS Publications Warehouse

Gandara, Susan C.; Barbie, Dana L.

1999-01-01

As of September 30, 1998, the surface-water data-collection network of Texas (table 1) included 313 continuous-recording streamflow stations (D), 22 gage-height record only stations (G), 23 crest-stage partial-record stations (C), 39 flood-hydrograph partial-record stations (H), 25 low-flow partial-record stations (L), 1 continuous-recording temperature station (M1), 25 continuous-recording temperature and conductivity stations (M2), 3 continuous-recording temperature, conductivity, and dissolved oxygen stations (M3), 13 continuous-recording temperature, conductivity, dissolved oxygen, and pH stations (M4), 5 daily chemical-quality stations (Qd), 133 periodic chemical-quality stations (Qp), 16 reservoir/lake surveys for water quality (Qs), and 70 continuous or daily reservoir-content stations (R). Plate 1 identifies the major river basins in Texas and shows the location of the stations listed in table 1.

Multisynchronization of Coupled Heterogeneous Genetic Oscillator Networks via Partial Impulsive Control.

PubMed

He, Ding-Xin; Ling, Guang; Guan, Zhi-Hong; Hu, Bin; Liao, Rui-Quan

2018-02-01

This paper focuses on the collective dynamics of multisynchronization among heterogeneous genetic oscillators under a partial impulsive control strategy. The coupled nonidentical genetic oscillators are modeled by differential equations with uncertainties. The definition of multisynchronization is proposed to describe some more general synchronization behaviors in the real. Considering that each genetic oscillator consists of a large number of biochemical molecules, we design a more manageable impulsive strategy for dynamic networks to achieve multisynchronization. Not all the molecules but only a small fraction of them in each genetic oscillator are controlled at each impulsive instant. Theoretical analysis of multisynchronization is carried out by the control theory approach, and a sufficient condition of partial impulsive controller for multisynchronization with given error bounds is established. At last, numerical simulations are exploited to demonstrate the effectiveness of our results.

Control of amplitude chimeras by time delay in oscillator networks

NASA Astrophysics Data System (ADS)

Gjurchinovski, Aleksandar; Schöll, Eckehard; Zakharova, Anna

2017-04-01

We investigate the influence of time-delayed coupling in a ring network of nonlocally coupled Stuart-Landau oscillators upon chimera states, i.e., space-time patterns with coexisting partially coherent and partially incoherent domains. We focus on amplitude chimeras, which exhibit incoherent behavior with respect to the amplitude rather than the phase and are transient patterns, and we show that their lifetime can be significantly enhanced by coupling delay. To characterize their transition to phase-lag synchronization (coherent traveling waves) and other coherent structures, we generalize the Kuramoto order parameter. Contrasting the results for instantaneous coupling with those for constant coupling delay, for time-varying delay, and for distributed-delay coupling, we demonstrate that the lifetime of amplitude chimera states and related partially incoherent states can be controlled, i.e., deliberately reduced or increased, depending upon the type of coupling delay.

A Brain Centred View of Psychiatric Comorbidity in Tinnitus: From Otology to Hodology

PubMed Central

Minichino, Amedeo; Panico, Roberta; Testugini, Valeria; Altissimi, Giancarlo; Cianfrone, Giancarlo

2014-01-01

Introduction. Comorbid psychiatric disorders are frequent among patients affected by tinnitus. There are mutual clinical influences between tinnitus and psychiatric disorders, as well as neurobiological relations based on partially overlapping hodological and neuroplastic phenomena. The aim of the present paper is to review the evidence of alterations in brain networks underlying tinnitus physiopathology and to discuss them in light of the current knowledge of the neurobiology of psychiatric disorders. Methods. Relevant literature was identified through a search on Medline and PubMed; search terms included tinnitus, brain, plasticity, cortex, network, and pathways. Results. Tinnitus phenomenon results from systemic-neurootological triggers followed by neuronal remapping within several auditory and nonauditory pathways. Plastic reorganization and white matter alterations within limbic system, arcuate fasciculus, insula, salience network, dorsolateral prefrontal cortex, auditory pathways, ffrontocortical, and thalamocortical networks are discussed. Discussion. Several overlapping brain network alterations do exist between tinnitus and psychiatric disorders. Tinnitus, initially related to a clinicoanatomical approach based on a cortical localizationism, could be better explained by an holistic or associationist approach considering psychic functions and tinnitus as emergent properties of partially overlapping large-scale neural networks. PMID:25018882

Networks of conforming or nonconforming individuals tend to reach satisfactory decisions.

PubMed

Ramazi, Pouria; Riehl, James; Cao, Ming

2016-11-15

Binary decisions of agents coupled in networks can often be classified into two types: "coordination," where an agent takes an action if enough neighbors are using that action, as in the spread of social norms, innovations, and viral epidemics, and "anticoordination," where too many neighbors taking a particular action causes an agent to take the opposite action, as in traffic congestion, crowd dispersion, and division of labor. Both of these cases can be modeled using linear-threshold-based dynamics, and a fundamental question is whether the individuals in such networks are likely to reach decisions with which they are satisfied. We show that, in the coordination case, and perhaps more surprisingly, also in the anticoordination case, the agents will indeed always tend to reach satisfactory decisions, that is, the network will almost surely reach an equilibrium state. This holds for every network topology and every distribution of thresholds, for both asynchronous and partially synchronous decision-making updates. These results reveal that irregular network topology, population heterogeneity, and partial synchrony are not sufficient to cause cycles or nonconvergence in linear-threshold dynamics; rather, other factors such as imitation or the coexistence of coordinating and anticoordinating agents must play a role.

[Spectral quantitative analysis by nonlinear partial least squares based on neural network internal model for flue gas of thermal power plant].

PubMed

Cao, Hui; Li, Yao-Jiang; Zhou, Yan; Wang, Yan-Xia

2014-11-01

To deal with nonlinear characteristics of spectra data for the thermal power plant flue, a nonlinear partial least square (PLS) analysis method with internal model based on neural network is adopted in the paper. The latent variables of the independent variables and the dependent variables are extracted by PLS regression firstly, and then they are used as the inputs and outputs of neural network respectively to build the nonlinear internal model by train process. For spectra data of flue gases of the thermal power plant, PLS, the nonlinear PLS with the internal model of back propagation neural network (BP-NPLS), the non-linear PLS with the internal model of radial basis function neural network (RBF-NPLS) and the nonlinear PLS with the internal model of adaptive fuzzy inference system (ANFIS-NPLS) are compared. The root mean square error of prediction (RMSEP) of sulfur dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 16.96%, 16.60% and 19.55% than that of PLS, respectively. The RMSEP of nitric oxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 8.60%, 8.47% and 10.09% than that of PLS, respectively. The RMSEP of nitrogen dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 2.11%, 3.91% and 3.97% than that of PLS, respectively. Experimental results show that the nonlinear PLS is more suitable for the quantitative analysis of glue gas than PLS. Moreover, by using neural network function which can realize high approximation of nonlinear characteristics, the nonlinear partial least squares method with internal model mentioned in this paper have well predictive capabilities and robustness, and could deal with the limitations of nonlinear partial least squares method with other internal model such as polynomial and spline functions themselves under a certain extent. ANFIS-NPLS has the best performance with the internal model of adaptive fuzzy inference system having ability to learn more and reduce the residuals effectively. Hence, ANFIS-NPLS is an accurate and useful quantitative thermal power plant flue gas analysis method.

In vivo label-free lymphangiography of cutaneous lymphatic vessels in human burn scars using optical coherence tomography

PubMed Central

Gong, Peijun; Es’haghian, Shaghayegh; Harms, Karl-Anton; Murray, Alexandra; Rea, Suzanne; Wood, Fiona M.; Sampson, David D.; McLaughlin, Robert A.

2016-01-01

We present an automated, label-free method for lymphangiography of cutaneous lymphatic vessels in humans in vivo using optical coherence tomography (OCT). This method corrects for the variation in OCT signal due to the confocal function and sensitivity fall-off of a spectral-domain OCT system and utilizes a single-scattering model to compensate for A-scan signal attenuation to enable reliable thresholding of lymphatic vessels. A segment-joining algorithm is then incorporated into the method to mitigate partial-volume effects with small vessels. The lymphatic vessel images are augmented with images of the blood vessel network, acquired from the speckle decorrelation with additional weighting to differentiate blood vessels from the observed high decorrelation in lymphatic vessels. We demonstrate the method with longitudinal scans of human burn scar patients undergoing ablative fractional laser treatment, showing the visualization of the cutaneous lymphatic and blood vessel networks. PMID:28018713

Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Cognitive and Neural Effects of Vision-Based Speed-of-Processing Training in Older Adults with Amnestic Mild Cognitive Impairment: A Pilot Study.

PubMed

Lin, Feng; Heffner, Kathi L; Ren, Ping; Tivarus, Madalina E; Brasch, Judith; Chen, Ding-Geng; Mapstone, Mark; Porsteinsson, Anton P; Tadin, Duje

2016-06-01

To examine the cognitive and neural effects of vision-based speed-of-processing (VSOP) training in older adults with amnestic mild cognitive impairment (aMCI) and contrast those effects with an active control (mental leisure activities (MLA)). Randomized single-blind controlled pilot trial. Academic medical center. Individuals with aMCI (N = 21). Six-week computerized VSOP training. Multiple cognitive processing measures, instrumental activities of daily living (IADLs), and two resting state neural networks regulating cognitive processing: central executive network (CEN) and default mode network (DMN). VSOP training led to significantly greater improvements in trained (processing speed and attention: F1,19  = 6.61, partial η(2)  = 0.26, P = .02) and untrained (working memory: F1,19  = 7.33, partial η(2)  = 0.28, P = .01; IADLs: F1,19  = 5.16, partial η(2)  = 0.21, P = .03) cognitive domains than MLA and protective maintenance in DMN (F1, 9  = 14.63, partial η(2)  = 0.62, P = .004). VSOP training, but not MLA, resulted in a significant improvement in CEN connectivity (Z = -2.37, P = .02). Target and transfer effects of VSOP training were identified, and links between VSOP training and two neural networks associated with aMCI were found. These findings highlight the potential of VSOP training to slow cognitive decline in individuals with aMCI. Further delineation of mechanisms underlying VSOP-induced plasticity is necessary to understand in which populations and under what conditions such training may be most effective. © 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.

Architectures and Design for Next-Generation Hybrid Circuit/Packet Networks

NASA Astrophysics Data System (ADS)

Vadrevu, Sree Krishna Chaitanya

Internet traffic is increasing rapidly at an annual growth rate of 35% with aggregate traffic exceeding several Exabyte's per month. The traffic is also becoming heterogeneous in bandwidth and quality-of-service (QoS) requirements with growing popularity of cloud computing, video-on-demand (VoD), e-science, etc. Hybrid circuit/packet networks which can jointly support circuit and packet services along with the adoption of high-bit-rate transmission systems form an attractive solution to address the traffic growth. 10 Gbps and 40 Gbps transmission systems are widely deployed in telecom backbone networks such as Comcast, AT&T, etc., and network operators are considering migration to 100 Gbps and beyond. This dissertation proposes robust architectures, capacity migration strategies, and novel service frameworks for next-generation hybrid circuit/packet architectures. In this dissertation, we study two types of hybrid circuit/packet networks: a) IP-over-WDM networks, in which the packet (IP) network is overlaid on top of the circuit (optical WDM) network and b) Hybrid networks in which the circuit and packet networks are deployed side by side such as US DoE's ESnet. We investigate techniques to dynamically migrate capacity between the circuit and packet sections by exploiting traffic variations over a day, and our methods show that significant bandwidth savings can be obtained with improved reliability of services. Specifically, we investigate how idle backup circuit capacity can be used to support packet services in IP-over-WDM networks, and similarly, excess capacity in packet network to support circuit services in ESnet. Control schemes that enable our mechanisms are also discussed. In IP-over-WDM networks, with upcoming 100 Gbps and beyond, dedicated protection will induce significant under-utilization of backup resources. We investigate design strategies to loan idle circuit backup capacity to support IP/packet services. However, failure of backup circuits will preempt IP services routed over them, and thus it is important to ensure IP topology survivability to successfully re-route preempted IP services. Integer-linear-program (ILP) and heuristic solutions have been developed and network cost reduction up to 60% has been observed. In ESnet, we study loaning packet links to support circuit services. Mixed-line-rate (MLR) networks supporting 10/40/100 Gbps on the same fiber are becoming increasingly popular. Services that accept degradation in bandwidth, latency, jitter, etc. under failure scenarios for lower cost are known as degraded services. We study degradation in bandwidth for lower cost under failure scenarios, a concept called partial protection, in the context of MLR networks. We notice partial protection enables significant cost savings compared to full protection. To cope with traffic growth, network operators need to deploy equipment at periodic time intervals, and this is known as the multi-period planning and upgrade problem. We study three important multi-period planning approaches, namely incremental planning, all-period planning, and two-period planning with mixed line rates. Our approaches predict the network equipment that needs to be deployed optimally at which nodes and at which time periods in the network to meet QoS requirements.

Physical and social activities mediate the associations between social network types and ventilatory function in Chinese older adults.

PubMed

Cheng, Sheung-Tak; Leung, Edward M F; Chan, Trista Wai Sze

2014-06-01

This study examined the associations between social network types and peak expiratory flow (PEF), and whether these associations were mediated by social and physical activities and mood. Nine hundred twenty-four community-dwelling Chinese older adults, who were classified into five network types (diverse, friend-focused, family-focused, distant family, and restricted), provided data on demographics, social and physical activities, mood, smoking, chronic diseases, and instrumental activities of daily living. PEF and biological covariates, including blood lipids and glucose, blood pressure, and height and weight, were assessed. Two measures of PEF were analyzed: the raw reading in L/min and the reading expressed as percentage of predicted normal value on the basis of age, sex, and height. Diverse, friend-focused, and distant family networks were hypothesized to have better PEF values compared with restricted networks, through higher physical and/or social activities. No relative advantage was predicted for family-focused networks because such networks tend to be associated with lower physical activity. Older adults with diverse, friend-focused, and distant family networks had significantly better PEF measures than those with restricted networks. The associations between diverse network and PEF measures were partially mediated by physical exercise and socializing activity. The associations between friend-focused network and PEF measures were partially mediated by socializing activity. No significant PEF differences between family-focused and restricted networks were found. Findings suggest that social network types are associated with PEF in older adults, and that network-type differences in physical and socializing activity is partly responsible for this relationship. PsycINFO Database Record (c) 2014 APA, all rights reserved.

International Suspect Screening: NORMAN Suspect Exchange meets the US EPA CompTox Chemistry Dashboard (ICCE 2017 Oslo)

EPA Science Inventory

Members of the European NORMAN Network of Environmental Laboratories (www.norman-network.com) have many substance lists, including targets, suspects, surfactants, perfluorinated substances and regulated, partially confidential data sets of complex mixtures. The NORMAN Suspect Lis...

Determination of network origin-destination matrices using partial link traffic counts and virtual sensor information in an integrated corridor management framework.

DOT National Transportation Integrated Search

2014-04-01

Trip origin-destination (O-D) demand matrices are critical components in transportation network : modeling, and provide essential information on trip distributions and corresponding spatiotemporal : traffic patterns in traffic zones in vehicular netw...

Quantum demultiplexer of quantum parameter-estimation information in quantum networks

NASA Astrophysics Data System (ADS)

Xie, Yanqing; Huang, Yumeng; Wu, Yinzhong; Hao, Xiang

2018-05-01

The quantum demultiplexer is constructed by a series of unitary operators and multipartite entangled states. It is used to realize information broadcasting from an input node to multiple output nodes in quantum networks. The scheme of quantum network communication with respect to phase estimation is put forward through the demultiplexer subjected to amplitude damping noises. The generalized partial measurements can be applied to protect the transferring efficiency from environmental noises in the protocol. It is found out that there are some optimal coherent states which can be prepared to enhance the transmission of phase estimation. The dynamics of state fidelity and quantum Fisher information are investigated to evaluate the feasibility of the network communication. While the state fidelity deteriorates rapidly, the quantum Fisher information can be enhanced to a maximum value and then decreases slowly. The memory effect of the environment induces the oscillations of fidelity and quantum Fisher information. The adjustment of the strength of partial measurements is helpful to increase quantum Fisher information.

The National Eclipse Weather Experiment: an assessment of citizen scientist weather observations

PubMed Central

2016-01-01

The National Eclipse Weather Experiment (NEWEx) was a citizen science project designed to assess the effects of the 20 March 2015 partial solar eclipse on the weather over the United Kingdom (UK). NEWEx had two principal objectives: to provide a spatial network of meteorological observations across the UK to aid the investigation of eclipse-induced weather changes, and to develop a nationwide public engagement activity-based participation of citizen scientists. In total, NEWEx collected 15 606 observations of air temperature, cloudiness and wind speed and direction from 309 locations across the UK, over a 3 h window spanning the eclipse period. The headline results were processed in near real time, immediately published online, and featured in UK national press articles on the day of the eclipse. Here, we describe the technical development of NEWEx and how the observations provided by the citizen scientists were analysed. By comparing the results of the NEWEx analyses with results from other investigations of the same eclipse using different observational networks, including measurements from the University of Reading’s Atmospheric Observatory, we demonstrate that NEWEx provided a fair representation of the change in the UK meteorological conditions throughout the eclipse. Despite the simplicity of the approach adopted, robust reductions in both temperature and wind speed during the eclipse were observed. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’. PMID:27550767

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model

PubMed Central

Gigante, Guido; Deco, Gustavo; Marom, Shimon; Del Giudice, Paolo

2015-01-01

Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed ‘quasi-orbits’, which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network’s firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms. PMID:26558616

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

PubMed

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

2016-03-01

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

Scale-freeness or partial synchronization in neural mass phase oscillator networks: Pick one of two?

PubMed

Daffertshofer, Andreas; Ton, Robert; Pietras, Bastian; Kringelbach, Morten L; Deco, Gustavo

2018-04-04

Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Neonatal Morbidities among Moderately Preterm Infants with and without Exposure to Antenatal Corticosteroids.

PubMed

Chawla, Sanjay; Natarajan, Girija; Chowdhury, Dhuly; Das, Abhik; Walsh, Michele; Bell, Edward F; Laptook, Abbot R; Van Meurs, Krisa; D'Angio, Carl T; Stoll, Barbara J; DeMauro, Sara B; Shankaran, Seetha

2018-04-27

 We aimed to compare the rates of "surfactant treated respiratory disease" and other neonatal morbidities among moderately preterm (MPT) infants exposed to no, partial, or a complete course of antenatal corticosteroids (ANS).  This observational cohort study evaluated MPT infants (29 0/7 -33 6/7 weeks' gestational age), born between January 2012 and November 2013 and enrolled in the "MPT Registry" of the National Institute of Child Health and Human Development Neonatal Research Network.  Data were available for 5,886 infants, including 676 with no exposure, 1225 with partial, and 3,985 with a complete course of ANS. Among no, partial, and complete ANS groups, respectively, there were significant differences in rates of delivery room resuscitation (4.1, 1.4, and 1.2%), surfactant-treated respiratory disease (26.5, 26.3, and 20%), and severe intracranial hemorrhage (3, 2, and 0.8%). Complete ANS course was associated with lower surfactant-treated respiratory disease, compared with partial ANS (odds ratio [OR] 0.62; 95% confidence interval [CI] 0.52-0.74), and no ANS groups (OR 0.52; 95% CI 0.41-0.66) on adjusted analysis.  In MPT infants, ANS exposure is associated with lower delivery room resuscitation, surfactant-treated respiratory disease, and severe intracranial hemorrhage; with the lowest frequency of morbidities associated with a complete course. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein–Protein Interaction Network

PubMed Central

Feyertag, Felix; Chakraborty, Sandip

2017-01-01

Abstract The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. PMID:28854629

Evaluation of a method of estimating low-flow frequencies from base-flow measurements at Indiana streams

USGS Publications Warehouse

Wilson, John Thomas

2000-01-01

A mathematical technique of estimating low-flow frequencies from base-flow measurements was evaluated by using data for streams in Indiana. Low-flow frequencies at low- flow partial-record stations were estimated by relating base-flow measurements to concurrent daily flows at nearby streamflow-gaging stations (index stations) for which low-flowfrequency curves had been developed. A network of long-term streamflow-gaging stations in Indiana provided a sample of sites with observed low-flow frequencies. Observed values of 7-day, 10-year low flow and 7-day, 2-year low flow were compared to predicted values to evaluate the accuracy of the method. Five test cases were used to evaluate the method under a variety of conditions in which the location of the index station and its drainage area varied relative to the partial-record station. A total of 141 pairs of streamflow-gaging stations were used in the five test cases. Four of the test cases used one index station, the fifth test case used two index stations. The number of base-flow measurements was varied for each test case to see if the accuracy of the method was affected by the number of measurements used. The most accurate and least variable results were produced when two index stations on the same stream or tributaries of the partial-record station were used. All but one value of the predicted 7-day, 10-year low flow were within 15 percent of the values observed for the long-term continuous record, and all of the predicted values of the 7-day, 2-year lowflow were within 15 percent of the observed values. This apparent accuracy, to some extent, may be a result of the small sample set of 15. Of the four test cases that used one index station, the most accurate and least variable results were produced in the test case where the index station and partial-record station were on the same stream or on streams tributary to each other and where the index station had a larger drainage area than the partial-record station. In that test case, the method tended to over predict, based on the median relative error. In 23 of 28 test pairs, the predicted 7-day, 10-year low flow was within 15 percent of the observed value; in 26 of 28 test pairs, the predicted 7-day, 2-year low flow was within 15 percent of the observed value. When the index station and partial-record station were on the same stream or streams tributary to each other and the index station had a smaller drainage area than the partial-record station, the method tended to under predict the low-flow frequencies. Nineteen of 28 predicted values of the 7-day, 10-year low flow were within 15 percent of the observed values. Twenty-five of 28 predicted values of the 7-day, 2-year low flow were within 15 percent of the observed values. When the index station and the partial-record station were on different streams, the method tended to under predict regardless of whether the index station had a larger or smaller drainage area than that of the partial-record station. Also, the variability of the relative error of estimate was greatest for the test cases that used index stations and partial-record stations from different streams. This variability, in part, may be caused by using more streamflow-gaging stations with small low-flow frequencies in these test cases. A small difference in the predicted and observed values can equate to a large relative error when dealing with stations that have small low-flow frequencies. In the test cases that used one index station, the method tended to predict smaller low-flow frequencies as the number of base-flow measurements was reduced from 20 to 5. Overall, the average relative error of estimate and the variability of the predicted values increased as the number of base-flow measurements was reduced.

Inferring Boolean network states from partial information

PubMed Central

2013-01-01

Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpretation of the measurements is not straightforward and since the data contain noise. In order to facilitate a more reliable mapping between datasets and Boolean networks, we develop an algorithm that infers network trajectories from a dataset distorted by noise. We analyze our algorithm theoretically and demonstrate its accuracy using simulation and microarray expression data. PMID:24006954

Hydrogeologic data for the Big River-Mishnock River stream-aquifer system, central Rhode Island

USGS Publications Warehouse

Craft, P.A.

2001-01-01

Hydrogeology, ground-water development alternatives, and water quality in the BigMishnock stream-aquifer system in central Rhode Island are being investigated as part of a long-term cooperative program between the Rhode Island Water Resources Board and the U.S. Geological Survey to evaluate the ground-water resources throughout Rhode Island. The study area includes the Big River drainage basin and that portion of the Mishnock River drainage basin upstream from the Mishnock River at State Route 3. This report presents geologic data and hydrologic and water-quality data for ground and surface water. Ground-water data were collected from July 1996 through September 1998 from a network of observation wells consisting of existing wells and wells installed for this study, which provided a broad distribution of data-collection sites throughout the study area. Streambed piezometers were used to obtain differences in head data between surface-water levels and ground-water levels to help evaluate stream-aquifer interactions throughout the study area. The types of data presented include monthly ground-water levels, average daily ground-water withdrawals, drawdown data from aquifer tests, and water-quality data. Historical water-level data from other wells within the study area also are presented in this report. Surface-water data were obtained from a network consisting of surface-water impoundments, such as ponds and reservoirs, existing and newly established partial-record stream-discharge sites, and synoptic surface-water-quality sites. Water levels were collected monthly from the surface-water impoundments. Stream-discharge measurements were made at partial-record sites to provide measurements of inflow, outflow, and internal flow throughout the study area. Specific conductance was measured monthly at partial-record sites during the study, and also during the fall and spring of 1997 and 1998 at 41 synoptic sites throughout the study area. General geologic data, such as estimates of depth to bedrock and depth to water table, as well as indications of underlying geologic structure, were obtained from geophysical surveys. Site-specific geologic data were collected during the drilling of observation wells and test holes. These data include depth to bedrock or refusal, depth to water table, and lithologic information.

Enhanced reconstruction of weighted networks from strengths and degrees

NASA Astrophysics Data System (ADS)

Mastrandrea, Rossana; Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

2014-04-01

Network topology plays a key role in many phenomena, from the spreading of diseases to that of financial crises. Whenever the whole structure of a network is unknown, one must resort to reconstruction methods that identify the least biased ensemble of networks consistent with the partial information available. A challenging case, frequently encountered due to privacy issues in the analysis of interbank flows and Big Data, is when there is only local (node-specific) aggregate information available. For binary networks, the relevant ensemble is one where the degree (number of links) of each node is constrained to its observed value. However, for weighted networks the problem is much more complicated. While the naïve approach prescribes to constrain the strengths (total link weights) of all nodes, recent counter-intuitive results suggest that in weighted networks the degrees are often more informative than the strengths. This implies that the reconstruction of weighted networks would be significantly enhanced by the specification of both strengths and degrees, a computationally hard and bias-prone procedure. Here we solve this problem by introducing an analytical and unbiased maximum-entropy method that works in the shortest possible time and does not require the explicit generation of reconstructed samples. We consider several real-world examples and show that, while the strengths alone give poor results, the additional knowledge of the degrees yields accurately reconstructed networks. Information-theoretic criteria rigorously confirm that the degree sequence, as soon as it is non-trivial, is irreducible to the strength sequence. Our results have strong implications for the analysis of motifs and communities and whenever the reconstructed ensemble is required as a null model to detect higher-order patterns.

A group evolving-based framework with perturbations for link prediction

NASA Astrophysics Data System (ADS)

Si, Cuiqi; Jiao, Licheng; Wu, Jianshe; Zhao, Jin

2017-06-01

Link prediction is a ubiquitous application in many fields which uses partially observed information to predict absence or presence of links between node pairs. The group evolving study provides reasonable explanations on the behaviors of nodes, relations between nodes and community formation in a network. Possible events in group evolution include continuing, growing, splitting, forming and so on. The changes discovered in networks are to some extent the result of these events. In this work, we present a group evolving-based characterization of node's behavioral patterns, and via which we can estimate the probability they tend to interact. In general, the primary aim of this paper is to offer a minimal toy model to detect missing links based on evolution of groups and give a simpler explanation on the rationality of the model. We first introduce perturbations into networks to obtain stable cluster structures, and the stable clusters determine the stability of each node. Then fluctuations, another node behavior, are assumed by the participation of each node to its own belonging group. Finally, we demonstrate that such characteristics allow us to predict link existence and propose a model for link prediction which outperforms many classical methods with a decreasing computational time in large scales. Encouraging experimental results obtained on real networks show that our approach can effectively predict missing links in network, and even when nearly 40% of the edges are missing, it also retains stationary performance.

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Load capacity improvements in nucleic acid based systems using partially open feedback control.

PubMed

Kulkarni, Vishwesh; Kharisov, Evgeny; Hovakimyan, Naira; Kim, Jongmin

2014-08-15

Synthetic biology is facilitating novel methods and components to build in vivo and in vitro circuits to better understand and re-engineer biological networks. Recently, Kim and Winfree have synthesized a remarkably elegant network of transcriptional oscillators in vitro using a modular architecture of synthetic gene analogues and a few enzymes that, in turn, could be used to drive a variety of downstream circuits and nanodevices. However, these oscillators are sensitive to initial conditions and downstream load processes. Furthermore, the oscillations are not sustained since the inherently closed design suffers from enzyme deactivation, NTP fuel exhaustion, and waste product build up. In this paper, we show that a partially open architecture in which an [Symbol: see text]1 adaptive controller, implemented inside an in silico computer that resides outside the wet-lab apparatus, can ensure sustained tunable oscillations in two specific designs of the Kim-Winfree oscillator networks. We consider two broad cases of operation: (1) the oscillator network operating in isolation and (2) the oscillator network driving a DNA tweezer subject to a variable load. In both scenarios, our simulation results show a significant improvement in the tunability and robustness of these oscillator networks. Our approach can be easily adopted to improve the loading capacity of a wide range of synthetic biological devices.

Classification of conductance traces with recurrent neural networks

NASA Astrophysics Data System (ADS)

Lauritzen, Kasper P.; Magyarkuti, András; Balogh, Zoltán; Halbritter, András; Solomon, Gemma C.

2018-02-01

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry.

PubMed

Tang, Chen; Lu, Wenjing; Chen, Song; Zhang, Zhen; Li, Botao; Wang, Wenping; Han, Lin

2007-10-20

We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.

Application of ANNs approach for wave-like and heat-like equations

NASA Astrophysics Data System (ADS)

Jafarian, Ahmad; Baleanu, Dumitru

2017-12-01

Artificial neural networks are data processing systems which originate from human brain tissue studies. The remarkable abilities of these networks help us to derive desired results from complicated raw data. In this study, we intend to duplicate an efficient iterative method to the numerical solution of two famous partial differential equations, namely the wave-like and heat-like problems. It should be noted that many physical phenomena such as coupling currents in a flat multi-strand two-layer super conducting cable, non-homogeneous elastic waves in soils and earthquake stresses, are described by initial-boundary value wave and heat partial differential equations with variable coefficients. To the numerical solution of these equations, a combination of the power series method and artificial neural networks approach, is used to seek an appropriate bivariate polynomial solution of the mentioned initial-boundary value problem. Finally, several computer simulations confirmed the theoretical results and demonstrating applicability of the method.

Efficient Power Network Analysis with Modeling of Inductive Effects

NASA Astrophysics Data System (ADS)

Zeng, Shan; Yu, Wenjian; Hong, Xianlong; Cheng, Chung-Kuan

In this paper, an efficient method is proposed to accurately analyze large-scale power/ground (P/G) networks, where inductive parasitics are modeled with the partial reluctance. The method is based on frequency-domain circuit analysis and the technique of vector fitting [14], and obtains the time-domain voltage response at given P/G nodes. The frequency-domain circuit equation including partial reluctances is derived, and then solved with the GMRES algorithm with rescaling, preconditioning and recycling techniques. With the merit of sparsified reluctance matrix and iterative solving techniques for the frequency-domain circuit equations, the proposed method is able to handle large-scale P/G networks with complete inductive modeling. Numerical results show that the proposed method is orders of magnitude faster than HSPICE, several times faster than INDUCTWISE [4], and capable of handling the inductive P/G structures with more than 100, 000 wire segments.

Network Model-Assisted Inference from Respondent-Driven Sampling Data

PubMed Central

Gile, Krista J.; Handcock, Mark S.

2015-01-01

Summary Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population. PMID:26640328

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

PubMed

Gile, Krista J; Handcock, Mark S

2015-06-01

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

Study on the capability of four-level partial response equalization in RSOA-based WDM-PON

NASA Astrophysics Data System (ADS)

Guo, Qi; Tran, An Vu

2010-12-01

The expected development of advanced video services with HDTV quality demands the delivery of more than Gb/s link to end users across the last mile connection. Future access networks are also required to have long reach for reduction in the number of central offices (CO). Fueled by those requirements, we propose a novel equalization scheme that increases the capacity and reach of the wavelength division multiplexing passive optical network (WDM-PON) based on a low bandwidth reflective semiconductor optical amplifier (RSOA). We investigate the characteristics of 10 Gb/s upstream transmission in WDM-PON using RSOA with only 1.2 GHz electrical bandwidth and various lengths of fiber. It is proven that the proposed four-level partial response equalizer (PRE) is capable of mitigating the impact of ISI in the received signals from optical network units (ONU) located 0 km to 75 km away from the optical line terminal (OLT).

Entanglement distillation for quantum communication network with atomic-ensemble memories.

PubMed

Li, Tao; Yang, Guo-Jian; Deng, Fu-Guo

2014-10-06

Atomic ensembles are effective memory nodes for quantum communication network due to the long coherence time and the collective enhancement effect for the nonlinear interaction between an ensemble and a photon. Here we investigate the possibility of achieving the entanglement distillation for nonlocal atomic ensembles by the input-output process of a single photon as a result of cavity quantum electrodynamics. We give an optimal entanglement concentration protocol (ECP) for two-atomic-ensemble systems in a partially entangled pure state with known parameters and an efficient ECP for the systems in an unknown partially entangled pure state with a nondestructive parity-check detector (PCD). For the systems in a mixed entangled state, we introduce an entanglement purification protocol with PCDs. These entanglement distillation protocols have high fidelity and efficiency with current experimental techniques, and they are useful for quantum communication network with atomic-ensemble memories.

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

Effects of Natural Disaster Trends: A Case Study for Expanding the Pre-Positioning Network of CARE International

PubMed Central

Bozkurt, Melda; Duran, Serhan

2012-01-01

The increasing number of natural disasters in the last decade necessitates the increase in capacity and agility while delivering humanitarian relief. A common logistics strategy used by humanitarian organizations to respond this need is the establishment of pre-positioning warehouse networks. In the pre-positioning strategy, critical relief inventories are located near the regions at which they will be needed in advance of the onset of the disaster. Therefore, pre-positioning reduces the response time by totally or partially eliminating the procurement phase and increasing the availability of relief items just after the disaster strikes. Once the pre-positioning warehouse locations are decided and warehouses on those locations become operational, they will be in use for a long time. Therefore, the chosen locations should be robust enough to enable extensions, and to cope with changing trends in disaster types, locations and magnitudes. In this study, we analyze the effects of natural disaster trends on the expansion plan of pre-positioning warehouse network implemented by CARE International. We utilize a facility location model to identify the additional warehouse location(s) for relief items to be stored as an extension of the current warehouse network operated by CARE International, considering changing natural disaster trends observed over the past three decades. PMID:23066402

Effects of natural disaster trends: a case study for expanding the pre-positioning network of CARE International.

PubMed

Bozkurt, Melda; Duran, Serhan

2012-08-01

The increasing number of natural disasters in the last decade necessitates the increase in capacity and agility while delivering humanitarian relief. A common logistics strategy used by humanitarian organizations to respond this need is the establishment of pre-positioning warehouse networks. In the pre-positioning strategy, critical relief inventories are located near the regions at which they will be needed in advance of the onset of the disaster. Therefore, pre-positioning reduces the response time by totally or partially eliminating the procurement phase and increasing the availability of relief items just after the disaster strikes. Once the pre-positioning warehouse locations are decided and warehouses on those locations become operational, they will be in use for a long time. Therefore, the chosen locations should be robust enough to enable extensions, and to cope with changing trends in disaster types, locations and magnitudes. In this study, we analyze the effects of natural disaster trends on the expansion plan of pre-positioning warehouse network implemented by CARE International. We utilize a facility location model to identify the additional warehouse location(s) for relief items to be stored as an extension of the current warehouse network operated by CARE International, considering changing natural disaster trends observed over the past three decades.

Cingulo-opercular network efficiency mediates the association between psychotic-like experiences and cognitive ability in the general population.

PubMed

Sheffield, Julia M; Kandala, Sridhar; Burgess, Gregory C; Harms, Michael P; Barch, Deanna M

2016-11-01

Psychosis is hypothesized to occur on a spectrum between psychotic disorders and healthy individuals. In the middle of the spectrum are individuals who endorse psychotic-like experiences (PLEs) that may not impact daily functioning or cause distress. Individuals with PLEs show alterations in both cognitive ability and functional connectivity of several brain networks, but the relationship between PLEs, cognition, and functional networks remains poorly understood. We analyzed resting-state fMRI data, a range of neuropsychological tasks, and questions from the Achenbach Adult Self Report (ASR) in 468 individuals from the Human Connectome Project. We aimed to determine whether global efficiency of specific functional brain networks supporting higher-order cognition (the fronto-parietal network (FPN), cingulo-opercular network (CON), and default mode network (DMN)) was associated with PLEs and cognitive ability in a non-psychiatric sample. 21.6% of individuals in our sample endorsed at least one PLE. PLEs were significantly negatively associated with higher-order cognitive ability, CON global efficiency, and DMN global efficiency, but not crystallized knowledge. Higher-order cognition was significantly positively associated with CON and DMN global efficiency. Interestingly, the association between PLEs and cognitive ability was partially mediated by CON global efficiency and, in a subset of individuals who tested negative for drugs (N=405), the participation coefficient of the right anterior insula (a hub within the CON). These findings suggest that CON integrity may represent a shared mechanism that confers risk for psychotic experiences and the cognitive deficits observed across the psychosis spectrum.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

PubMed Central

Hawkins, Jeff; Ahmad, Subutai; Cui, Yuwei

2017-01-01

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed. PMID:29118696

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.

PubMed

Klimovskaia, Anna; Ganscha, Stefan; Claassen, Manfred

2016-12-01

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

A model of metastable dynamics during ongoing and evoked cortical activity

NASA Astrophysics Data System (ADS)

La Camera, Giancarlo

The dynamics of simultaneously recorded spike trains in alert animals often evolve through temporal sequences of metastable states. Little is known about the network mechanisms responsible for the genesis of such sequences, or their potential role in neural coding. In the gustatory cortex of alert rates, state sequences can be observed also in the absence of overt sensory stimulation, and thus form the basis of the so-called `ongoing activity'. This activity is characterized by a partial degree of coordination among neurons, sharp transitions among states, and multi-stability of single neurons' firing rates. A recurrent spiking network model with clustered topology can account for both the spontaneous generation of state sequences and the (network-generated) multi-stability. In the model, each network state results from the activation of specific neural clusters with potentiated intra-cluster connections. A mean field solution of the model shows a large number of stable states, each characterized by a subset of simultaneously active clusters. The firing rate in each cluster during ongoing activity depends on the number of active clusters, so that the same neuron can have different firing rates depending on the state of the network. Because of dense intra-cluster connectivity and recurrent inhibition, in finite networks the stable states lose stability due to finite size effects. Simulations of the dynamics show that the model ensemble activity continuously hops among the different states, reproducing the ongoing dynamics observed in the data. Moreover, when probed with external stimuli, the model correctly predicts the quenching of single neuron multi-stability into bi-stability, the reduction of dimensionality of the population activity, the reduction of trial-to-trial variability, and a potential role for metastable states in the anticipation of expected events. Altogether, these results provide a unified mechanistic model of ongoing and evoked cortical dynamics. NSF IIS-1161852, NIDCD K25-DC013557, NIDCD R01-DC010389.

Global Mapping of the Yeast Genetic Interaction Network

NASA Astrophysics Data System (ADS)

Tong, Amy Hin Yan; Lesage, Guillaume; Bader, Gary D.; Ding, Huiming; Xu, Hong; Xin, Xiaofeng; Young, James; Berriz, Gabriel F.; Brost, Renee L.; Chang, Michael; Chen, YiQun; Cheng, Xin; Chua, Gordon; Friesen, Helena; Goldberg, Debra S.; Haynes, Jennifer; Humphries, Christine; He, Grace; Hussein, Shamiza; Ke, Lizhu; Krogan, Nevan; Li, Zhijian; Levinson, Joshua N.; Lu, Hong; Ménard, Patrice; Munyana, Christella; Parsons, Ainslie B.; Ryan, Owen; Tonikian, Raffi; Roberts, Tania; Sdicu, Anne-Marie; Shapiro, Jesse; Sheikh, Bilal; Suter, Bernhard; Wong, Sharyl L.; Zhang, Lan V.; Zhu, Hongwei; Burd, Christopher G.; Munro, Sean; Sander, Chris; Rine, Jasper; Greenblatt, Jack; Peter, Matthias; Bretscher, Anthony; Bell, Graham; Roth, Frederick P.; Brown, Grant W.; Andrews, Brenda; Bussey, Howard; Boone, Charles

2004-02-01

A genetic interaction network containing ~1000 genes and ~4000 interactions was mapped by crossing mutations in 132 different query genes into a set of ~4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Fracture Reactivation in Chemically Reactive Rock Systems

NASA Astrophysics Data System (ADS)

Eichhubl, P.; Hooker, J. N.

2013-12-01

Reactivation of existing fractures is a fundamental process of brittle failure that controls the nucleation of earthquake ruptures, propagation and linkage of hydraulic fractures in oil and gas production, and the evolution of fault and fracture networks and thus of fluid and heat transport in the upper crust. At depths below 2-3 km, and frequently shallower, brittle processes of fracture growth, linkage, and reactivation compete with chemical processes of fracture sealing by mineral precipitation, with precipitation rates similar to fracture opening rates. We recently found rates of fracture opening in tectonically quiescent settings of 10-20 μm/m.y., rates similar to euhedral quartz precipitation under these conditions. The tendency of existing partially or completely cemented fractures to reactivate will vary depending on strain rate, mineral precipitation kinetics, strength contrast between host rock and fracture cement, stress conditions, degree of fracture infill, and fracture network geometry. Natural fractures in quartzite of the Cambrian Eriboll Formation, NW Scotland, exhibit a complex history of fracture formation and reactivation, with reactivation involving both repeated crack-seal opening-mode failure and shear failure of fractures that formed in opening mode. Fractures are partially to completely sealed with crack-seal or euhedral quartz cement or quartz cement fragmented by shear reactivation. Degree of cementation controls the tendency of fractures for later shear reactivation, to interact elastically with adjacent open fractures, and their intersection behavior. Using kinematic, dynamic, and diagenetic criteria, we determine the sequence of opening-mode fracture formation and later shear reactivation. We find that sheared fracture systems of similar orientation display spatially varying sense of slip We attribute these inconsistent directions of shear reactivation to 1) a heterogeneous stress field in this highly fractured rock unit and 2) variations in the degree of fracture cement infill in fractures of same orientation, allowing fractures to reactivate at times when adjacent, more cemented fractures remain dormant. The observed interaction of chemical and mechanical fracture growth and sealing processes in this chemically reactive and heavily deformed rock unit results in a complex fracture network geometry not generally observed in less chemically reactive, shallower crustal environments.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Estimating Topology of Discrete Dynamical Networks

NASA Astrophysics Data System (ADS)

Guo, Shu-Juan; Fu, Xin-Chu

2010-07-01

In this paper, by applying Lasalle's invariance principle and some results about the trace of a matrix, we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamical evolution of the network. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, two examples, including a Hénon map and a central network, are illustrated to verify the theoretical results.

Alterations in metabolic pathways and networks in Alzheimer's disease

PubMed Central

Kaddurah-Daouk, R; Zhu, H; Sharma, S; Bogdanov, M; Rozen, S G; Matson, W; Oki, N O; Motsinger-Reif, A A; Churchill, E; Lei, Z; Appleby, D; Kling, M A; Trojanowski, J Q; Doraiswamy, P M; Arnold, S E

2013-01-01

The pathogenic mechanisms of Alzheimer's disease (AD) remain largely unknown and clinical trials have not demonstrated significant benefit. Biochemical characterization of AD and its prodromal phase may provide new diagnostic and therapeutic insights. We used targeted metabolomics platform to profile cerebrospinal fluid (CSF) from AD (n=40), mild cognitive impairment (MCI, n=36) and control (n=38) subjects; univariate and multivariate analyses to define between-group differences; and partial least square-discriminant analysis models to classify diagnostic groups using CSF metabolomic profiles. A partial correlation network was built to link metabolic markers, protein markers and disease severity. AD subjects had elevated methionine (MET), 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid, xanthosine and glutathione versus controls. MCI subjects had elevated 5-HIAA, MET, hypoxanthine and other metabolites versus controls. Metabolite ratios revealed changes within tryptophan, MET and purine pathways. Initial pathway analyses identified steps in several pathways that appear altered in AD and MCI. A partial correlation network showed total tau most directly related to norepinephrine and purine pathways; amyloid-β (Ab42) was related directly to an unidentified metabolite and indirectly to 5-HIAA and MET. These findings indicate that MCI and AD are associated with an overlapping pattern of perturbations in tryptophan, tyrosine, MET and purine pathways, and suggest that profound biochemical alterations are linked to abnormal Ab42 and tau metabolism. Metabolomics provides powerful tools to map interlinked biochemical pathway perturbations and study AD as a disease of network failure. PMID:23571809

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

Alterations in metabolic pathways and networks in Alzheimer's disease.

PubMed

Kaddurah-Daouk, R; Zhu, H; Sharma, S; Bogdanov, M; Rozen, S G; Matson, W; Oki, N O; Motsinger-Reif, A A; Churchill, E; Lei, Z; Appleby, D; Kling, M A; Trojanowski, J Q; Doraiswamy, P M; Arnold, S E

2013-04-09

The pathogenic mechanisms of Alzheimer's disease (AD) remain largely unknown and clinical trials have not demonstrated significant benefit. Biochemical characterization of AD and its prodromal phase may provide new diagnostic and therapeutic insights. We used targeted metabolomics platform to profile cerebrospinal fluid (CSF) from AD (n=40), mild cognitive impairment (MCI, n=36) and control (n=38) subjects; univariate and multivariate analyses to define between-group differences; and partial least square-discriminant analysis models to classify diagnostic groups using CSF metabolomic profiles. A partial correlation network was built to link metabolic markers, protein markers and disease severity. AD subjects had elevated methionine (MET), 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid, xanthosine and glutathione versus controls. MCI subjects had elevated 5-HIAA, MET, hypoxanthine and other metabolites versus controls. Metabolite ratios revealed changes within tryptophan, MET and purine pathways. Initial pathway analyses identified steps in several pathways that appear altered in AD and MCI. A partial correlation network showed total tau most directly related to norepinephrine and purine pathways; amyloid-β (Ab42) was related directly to an unidentified metabolite and indirectly to 5-HIAA and MET. These findings indicate that MCI and AD are associated with an overlapping pattern of perturbations in tryptophan, tyrosine, MET and purine pathways, and suggest that profound biochemical alterations are linked to abnormal Ab42 and tau metabolism. Metabolomics provides powerful tools to map interlinked biochemical pathway perturbations and study AD as a disease of network failure.

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Experimental synchronization of chaos in a large ring of mutually coupled single-transistor oscillators: Phase, amplitude, and clustering effects

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

Minati, Ludovico, E-mail: lminati@ieee.org, E-mail: ludovico.minati@unitn.it

In this paper, experimental evidence of multiple synchronization phenomena in a large (n = 30) ring of chaotic oscillators is presented. Each node consists of an elementary circuit, generating spikes of irregular amplitude and comprising one bipolar junction transistor, one capacitor, two inductors, and one biasing resistor. The nodes are mutually coupled to their neighbours via additional variable resistors. As coupling resistance is decreased, phase synchronization followed by complete synchronization is observed, and onset of synchronization is associated with partial synchronization, i.e., emergence of communities (clusters). While component tolerances affect community structure, the general synchronization properties are maintained across three prototypes andmore » in numerical simulations. The clusters are destroyed by adding long distance connections with distant notes, but are otherwise relatively stable with respect to structural connectivity changes. The study provides evidence that several fundamental synchronization phenomena can be reliably observed in a network of elementary single-transistor oscillators, demonstrating their generative potential and opening way to potential applications of this undemanding setup in experimental modelling of the relationship between network structure, synchronization, and dynamical properties.« less

Selectively active markers for solving of the partial occlusion problem in matchmoving and chromakeying workflow

NASA Astrophysics Data System (ADS)

Mazurek, Przemysław

2013-09-01

Matchmoving (Match Moving) is the process used for the estimation of camera movements for further integration of acquired video image with computer graphics. The estimation of movements is possible using pattern recognition, 2D and 3D tracking algorithms. The main problem for the workflow is the partial occlusion of markers by the actor, because manual rotoscoping is necessary for fixing of the chroma-keyed footage. In the paper, the partial occlusion problem is solved using the invented, selectively active electronic markers. The sensor network with multiple infrared links detects occlusion state (no-occlusion, partial, full) and switch LED's based markers.

Oxygen Exposure Resulting in Arterial Oxygen Tensions Above the Protocol Goal Was Associated With Worse Clinical Outcomes in Acute Respiratory Distress Syndrome.

PubMed

Aggarwal, Neil R; Brower, Roy G; Hager, David N; Thompson, B Taylor; Netzer, Giora; Shanholtz, Carl; Lagakos, Adrian; Checkley, William

2018-04-01

High fractions of inspired oxygen may augment lung damage to exacerbate lung injury in patients with acute respiratory distress syndrome. Participants enrolled in Acute Respiratory Distress Syndrome Network trials had a goal partial pressure of oxygen in arterial blood range of 55-80 mm Hg, yet the effect of oxygen exposure above this arterial oxygen tension range on clinical outcomes is unknown. We sought to determine if oxygen exposure that resulted in a partial pressure of oxygen in arterial blood above goal (> 80 mm Hg) was associated with worse outcomes in patients with acute respiratory distress syndrome. Longitudinal analysis of data collected in these trials. Ten clinical trials conducted at Acute Respiratory Distress Syndrome Network hospitals between 1996 and 2013. Critically ill patients with acute respiratory distress syndrome. None. We defined above goal oxygen exposure as the difference between the fraction of inspired oxygen and 0.5 whenever the fraction of inspired oxygen was above 0.5 and when the partial pressure of oxygen in arterial blood was above 80 mm Hg. We then summed above goal oxygen exposures in the first five days to calculate a cumulative above goal oxygen exposure. We determined the effect of a cumulative 5-day above goal oxygen exposure on mortality prior to discharge home at 90 days. Among 2,994 participants (mean age, 51.3 yr; 54% male) with a study-entry partial pressure of oxygen in arterial blood/fraction of inspired oxygen that met acute respiratory distress syndrome criteria, average cumulative above goal oxygen exposure was 0.24 fraction of inspired oxygen-days (interquartile range, 0-0.38). Participants with above goal oxygen exposure were more likely to die (adjusted interquartile range odds ratio, 1.20; 95% CI, 1.11-1.31) and have lower ventilator-free days (adjusted interquartile range mean difference of -0.83; 95% CI, -1.18 to -0.48) and lower hospital-free days (adjusted interquartile range mean difference of -1.38; 95% CI, -2.09 to -0.68). We observed a dose-response relationship between the cumulative above goal oxygen exposure and worsened clinical outcomes for participants with mild, moderate, or severe acute respiratory distress syndrome, suggesting that the observed relationship is not primarily influenced by severity of illness. Oxygen exposure resulting in arterial oxygen tensions above the protocol goal occurred frequently and was associated with worse clinical outcomes at all levels of acute respiratory distress syndrome severity.

Comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults: a network meta-analysis.

PubMed

Thorlund, Kristian; Druyts, Eric; Wu, Ping; Balijepalli, Chakrapani; Keohane, Denis; Mills, Edward

2015-05-01

To establish the comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults using the network meta-analysis approach. Systematic review and network meta-analysis. Individuals aged 60 and older. Data on partial response (defined as at least 50% reduction in depression score from baseline) and safety (dizziness, vertigo, syncope, falls, loss of consciousness) were extracted. A Bayesian network meta-analysis was performed on the efficacy and safety outcomes, and relative risks (RRs) with 95% credible intervals (CrIs) were produced. Fifteen randomized controlled trials were eligible for inclusion in the analysis. Citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, fluoxetine, and sertraline were represented. Reporting on partial response and dizziness was sufficient to conduct a network meta-analysis. Reporting on other outcomes was sparse. For partial response, sertraline (RR=1.28), paroxetine (RR=1.48), and duloxetine (RR=1.62) were significantly better than placebo. The remaining interventions yielded RRs lower than 1.20. For dizziness, duloxetine (RR=3.18) and venlafaxine (RR=2.94) were statistically significantly worse than placebo. Compared with placebo, sertraline had the lowest RR for dizziness (1.14) and fluoxetine the second lowest (1.31). Citalopram, escitalopram, and paroxetine all had RRs between 1.4 and 1.7. There was clear evidence of the effectiveness of sertraline, paroxetine, and duloxetine. There also appears to be a hierarchy of safety associated with the different antidepressants, although there appears to be a dearth of reporting of safety outcomes. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

The amygdala as a hub in brain networks that support social life

PubMed Central

Bickart, Kevin C.; Dickerson, Bradford C.; Barrett, Lisa Feldman

2016-01-01

A growing body of evidence suggests that the amygdala is central to handling the demands of complex social life in primates. In this paper, we synthesize extant anatomical and functional data from rodents, monkeys, and humans to describe the topography of three partially distinct large-scale brain networks anchored in the amygdala that each support unique functions for effectively managing social interactions and maintaining social relationships. These findings provide a powerful componential framework for parsing social behavior into partially distinct neural underpinnings that differ among healthy people and disintegrate or fail to develop in neuropsychiatric populations marked by social impairment, such as autism, antisocial personality disorder, and frontotemporal dementia. PMID:25152530

Partial Least Squares Regression Models for the Analysis of Kinase Signaling.

PubMed

Bourgeois, Danielle L; Kreeger, Pamela K

2017-01-01

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Fast sparsely synchronized brain rhythms in a scale-free neural network

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D

When Educational Resources Are Open

ERIC Educational Resources Information Center

Breck, Judy

2007-01-01

This article is a partial look at what the future of education might be if educational resources become open online. Intertwingularity is discussed as a general term for what OER will do online. Predictions about an open education future are based on nine quotations from books by popular writers about our networked age. When the network mechanisms…

78 FR 44164 - Notice of Intent To Grant Partially Exclusive License

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-23

... ``Chemical Sensors Using Coated Or Doped Carbon Nanotube Networks''; U.S. Patent No. 7,623,972 entitled... and Transmission of Gas Data; U.S. Patent No. 8,000,903 entitled ``Coated or Doped Carbon Nanotube Network Sensors as Affected by Environmental Parameters; ARC-16902-1, entitled ``Nanosensor Array for...

Structure solution of network materials by solid-state NMR without knowledge of the crystallographic space group.

PubMed

Brouwer, Darren H

2013-01-01

An algorithm is presented for solving the structures of silicate network materials such as zeolites or layered silicates from solid-state (29)Si double-quantum NMR data for situations in which the crystallographic space group is not known. The algorithm is explained and illustrated in detail using a hypothetical two-dimensional network structure as a working example. The algorithm involves an atom-by-atom structure building process in which candidate partial structures are evaluated according to their agreement with Si-O-Si connectivity information, symmetry restraints, and fits to (29)Si double quantum NMR curves followed by minimization of a cost function that incorporates connectivity, symmetry, and quality of fit to the double quantum curves. The two-dimensional network material is successfully reconstructed from hypothetical NMR data that can be reasonably expected to be obtained for real samples. This advance in "NMR crystallography" is expected to be important for structure determination of partially ordered silicate materials for which diffraction provides very limited structural information. Copyright © 2013 Elsevier Inc. All rights reserved.

Concurrency and HIV transmission network characteristics among MSM with recent HIV infection.

PubMed

Pines, Heather A; Wertheim, Joel O; Liu, Lin; Garfein, Richard S; Little, Susan J; Karris, Maile Y

2016-11-28

Sexual partner concurrency is common among MSM and may increase the probability of HIV transmission during recent (acute or early) infection. We examined the relationship between concurrency and HIV transmission network characteristics (proxies for HIV transmission) among MSM with recent HIV infection. Observational study integrating behavioral, clinical, and molecular epidemiology. We inferred a partial HIV transmission network using 986 HIV-1 pol sequences obtained from HIV-infected individuals in San Diego, California (1996-2015). We further analyzed data from 285 recently HIV-infected MSM in the network who provided information on up to three sexual partners in the past 3 months, including the timing of intercourse with each partner. Concurrency was defined as sexual partners overlapping in time. Logistic and negative binomial regressions were used to investigate the link between concurrency and HIV transmission network characteristics (i.e. clustering and degree or number of connections to others in the network) among these MSM. Of recently HIV-infected MSM (n = 285), 54% reported concurrent partnerships and 54% were connected by at least one putative transmission link to others (i.e. clustered) in the network (median degree = 1.0; interquartile range: 0.0-3.0). Concurrency was positively associated with HIV transmission network clustering (adjusted odds ratio = 1.83, 95% confidence interval: 1.08, 3.10) and degree (adjusted incidence rate ratio = 1.48, 95% confidence interval: 1.02, 2.15). Our findings provide empirical evidence consistent with the hypothesis that concurrency facilitates HIV transmission during recent infection. Interventions to mitigate the impact of concurrency on HIV transmission may help curb the HIV epidemic among MSM.

Tricriticality in the q-neighbor Ising model on a partially duplex clique.

PubMed

Chmiel, Anna; Sienkiewicz, Julian; Sznajd-Weron, Katarzyna

2017-12-01

We analyze a modified kinetic Ising model, a so-called q-neighbor Ising model, with Metropolis dynamics [Phys. Rev. E 92, 052105 (2015)PLEEE81539-375510.1103/PhysRevE.92.052105] on a duplex clique and a partially duplex clique. In the q-neighbor Ising model each spin interacts only with q spins randomly chosen from its whole neighborhood. In the case of a duplex clique the change of a spin is allowed only if both levels simultaneously induce this change. Due to the mean-field-like nature of the model we are able to derive the analytic form of transition probabilities and solve the corresponding master equation. The existence of the second level changes dramatically the character of the phase transition. In the case of the monoplex clique, the q-neighbor Ising model exhibits a continuous phase transition for q=3, discontinuous phase transition for q≥4, and for q=1 and q=2 the phase transition is not observed. On the other hand, in the case of the duplex clique continuous phase transitions are observed for all values of q, even for q=1 and q=2. Subsequently we introduce a partially duplex clique, parametrized by r∈[0,1], which allows us to tune the network from monoplex (r=0) to duplex (r=1). Such a generalized topology, in which a fraction r of all nodes appear on both levels, allows us to obtain the critical value of r=r^{*}(q) at which a tricriticality (switch from continuous to discontinuous phase transition) appears.

Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

NASA Astrophysics Data System (ADS)

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.

Robustness, Death of Spiral Wave in the Network of Neurons under Partial Ion Channel Block

NASA Astrophysics Data System (ADS)

Ma, Jun; Huang, Long; Wang, Chun-Ni; Pu, Zhong-Sheng

2013-02-01

The development of spiral wave in a two-dimensional square array due to partial ion channel block (Potassium, Sodium) is investigated, the dynamics of the node is described by Hodgkin—Huxley neuron and these neurons are coupled with nearest neighbor connection. The parameter ratio xNa (and xK), which defines the ratio of working ion channel number of sodium (potassium) to the total ion channel number of sodium (and potassium), is used to measure the shift conductance induced by channel block. The distribution of statistical variable R in the two-parameter phase space (parameter ratio vs. poisoning area) is extensively calculated to mark the parameter region for transition of spiral wave induced by partial ion channel block, the area with smaller factors of synchronization R is associated the parameter region that spiral wave keeps alive and robust to the channel poisoning. Spiral wave keeps alive when the poisoned area (potassium or sodium) and degree of intoxication are small, distinct transition (death, several spiral waves coexist or multi-arm spiral wave emergence) occurs under moderate ratio xNa (and xK) when the size of blocked area exceeds certain thresholds. Breakup of spiral wave occurs and multi-arm of spiral waves are observed when the channel noise is considered.

Network topology of olivine-basalt partial melts

NASA Astrophysics Data System (ADS)

Skemer, Philip; Chaney, Molly M.; Emmerich, Adrienne L.; Miller, Kevin J.; Zhu, Wen-lu

2017-07-01

The microstructural relationship between melt and solid grains in partially molten rocks influences many physical properties, including permeability, rheology, electrical conductivity and seismic wave speeds. In this study, the connectivity of melt networks in the olivine-basalt system is explored using a systematic survey of 3-D X-ray microtomographic data. Experimentally synthesized samples with 2 and 5 vol.% melt are analysed as a series of melt tubules intersecting at nodes. Each node is characterized by a coordination number (CN), which is the number of melt tubules that intersect at that location. Statistically representative volumes are described by coordination number distributions (CND). Polyhedral grains can be packed in many configurations yielding different CNDs, however widely accepted theory predicts that systems with small dihedral angles, such as olivine-basalt, should exhibit a predominant CN of four. In this study, melt objects are identified with CN = 2-8, however more than 50 per cent are CN = 4, providing experimental verification of this theoretical prediction. A conceptual model that considers the role of heterogeneity in local grain size and melt fraction is proposed to explain the formation of nodes with CN ≠ 4. Correctly identifying the melt network topology is essential to understanding the relationship between permeability and porosity, and hence the transport properties of partial molten mantle rocks.

Recognition of partially occluded threat objects using the annealed Hopefield network

NASA Technical Reports Server (NTRS)

Kim, Jung H.; Yoon, Sung H.; Park, Eui H.; Ntuen, Celestine A.

1992-01-01

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

High-Frequency Peaks in the Power Spectrum of Solar Velocity Observations from the GOLF Experiment

NASA Astrophysics Data System (ADS)

García, R. A.; Pallé, P. L.; Turck-Chièze, S.; Osaki, Y.; Shibahashi, H.; Jefferies, S. M.; Boumier, P.; Gabriel, A. H.; Grec, G.; Robillot, J. M.; Cortés, T. Roca; Ulrich, R. K.

1998-09-01

The power spectrum of more than 630 days of full-disk solar velocity data, provided by the GOLF spectrophotometer aboard the Solar and Heliospheric Observatory, has revealed the presence of modelike structure well beyond the acoustic cutoff frequency for the solar atmosphere (νac~5.4 mHz). Similar data produced by full-disk instruments deployed in Earth-based networks (BiSON and IRIS) had not shown any peak structure above νac: this is probably due to the higher levels of noise that are inherent in Earth-based experiments. We show that the observed peak structure (νac<=ν<=7.5 mHz) can be explained by a simple two-wave interference model if the high-frequency waves are partially reflected at the back side of the Sun.

Identifying the Oscillatory Mechanism of the Glucose Oxidase-Catalase Coupled Enzyme System.

PubMed

Muzika, František; Jurašek, Radovan; Schreiberová, Lenka; Radojković, Vuk; Schreiber, Igor

2017-10-12

We provide experimental evidence of periodic and aperiodic oscillations in an enzymatic system of glucose oxidase-catalase in a continuous-flow stirred reactor coupled by a membrane with a continuous-flow reservoir supplied with hydrogen peroxide. To describe such dynamics, we formulate a detailed mechanism based on partial results in the literature. Finally, we introduce a novel method for estimation of unknown kinetic parameters. The method is based on matching experimental data at an oscillatory instability with stoichiometric constraints of the mechanism formulated by applying the stability theory of reaction networks. This approach has been used to estimate rate coefficients in the catalase part of the mechanism. Remarkably, model simulations show good agreement with the observed oscillatory dynamics, including apparently chaotic intermittent behavior. Our method can be applied to any reaction system with an experimentally observable dynamical instability.

Human activities recognition by head movement using partial recurrent neural network

NASA Astrophysics Data System (ADS)

Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

2003-06-01

Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

Neural basis for brain responses to TV commercials: a high-resolution EEG study.

PubMed

Astolfi, Laura; De Vico Fallani, F; Cincotti, F; Mattia, D; Bianchi, L; Marciani, M G; Salinari, S; Colosimo, A; Tocci, A; Soranzo, R; Babiloni, F

2008-12-01

We investigated brain activity during the observation of TV commercials by tracking the cortical activity and the functional connectivity changes in normal subjects. The aim was to elucidate if the TV commercials that were remembered by the subjects several days after their first observation elicited particular brain activity and connectivity compared with those generated during the observation of TV commercials that were quickly forgotten. High-resolution electroencephalogram (EEG) recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). The patterns of cortical connectivity were obtained in the four principal frequency bands, Theta (3-7 Hz), Alpha (8-12 Hz), Beta (13-30 Hz), Gamma (30-40 Hz) and the directed influences between any given pair of the estimated cortical signals were evaluated by use of a multivariate spectral technique known as partial directed coherence. The topology of the cortical networks has been identified with tools derived from graph theory. Results suggest that the cortical activity and connectivity elicited by the viewing of the TV commercials that were remembered by the experimental subjects are markedly different from the brain activity elicited during the observation of the TV commercials that were forgotten. In particular, during the observation of the TV commercials that were remembered, the amount of cortical spectral activity from the frontal areas (BA 8 and 9) and from the parietal areas (BA 5, 7, and 40) is higher compared with the activity elicited by the observation of TV commercials that were forgotten. In addition, network analysis suggests a clear role of the parietal areas as a target of the incoming flow of information from all the other parts of the cortex during the observation of TV commercials that have been remembered. The techniques presented here shed new light on all the cortical networks and their behavior during the memorization of TV commercials. Such techniques could also be relevant in neuroeconomics and neuromarketing for the investigation of the neural substrates subserving other decision-making and recognition tasks.

Investigation of Ionospheric Anomalies related to moderate Romanian earthquakes occurred during last decade using VLF/LF INFREP and GNSS Global Networks

NASA Astrophysics Data System (ADS)

Moldovan, Iren-Adelina; Oikonomou, Christina; Haralambous, Haris; Nastase, Eduard; Emilian Toader, Victorin; Biagi, Pier Francesco; Colella, Roberto; Toma-Danila, Dragos

2017-04-01

Ionospheric TEC (Total Electron Content) variations and Low Frequency (LF) signal amplitude data prior to five moderate earthquakes (Mw≥5) occurred in Romania, in Vrancea crustal and subcrustal seismic zones, during the last decade were analyzed using observations from the Global Navigation Satellite System (GNSS) and the European INFREP (International Network for Frontier Research on Earthquake Precursors) networks respectively, aiming to detect potential ionospheric anomalies related to these events and describe their characteristics. For this, spectral analysis on TEC data and terminator time method on VLF/LF data were applied. It was found that TEC perturbations appeared few days (1-7) up to few hours before the events lasting around 2-3 hours, with periods 20 and 3-5 minutes which could be associated with the impending earthquakes. In addition, in all three events the sunrise terminator times were delayed approximately 20-40 min few days prior and during the earthquake day. Acknowledgments This work was partially supported by the Partnership in Priority Areas Program - PNII, under MEN-UEFISCDI, DARING Project no. 69/2014 and the Nucleu Program - PN 16-35, Project no. 03 01

Emerging Roles of Combat Communication Squadrons in Cyber Warfare as Related to Computer Network Attack, Defense and Exploitation

DTIC Science & Technology

2011-06-01

EMERGING ROLES OF COMBAT COMMUNICATION SQUADRONS IN CYBER WARFARE AS RELATED TO COMPUTER NETWORK ATTACK, DEFENSE AND EXPLOITATION GRADUATE RESEARCH...Communication Squadrons in Cyber Warfare as Related to Computer Network Attack, Defense and Exploitation GRADUATE RESEARCH PROJECT Presented to the Faculty...Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Cyber Warfare Michael J. Myers Major, USAF June 2011

A highly stretchable double-network composite.

PubMed

Feng, Xiangchao; Ma, Zhuo; MacArthur, Jonathan V; Giuffre, Christopher J; Bastawros, Ashraf F; Hong, Wei

2016-11-09

Inspired by the toughening mechanism of double-network (DN) hydrogels, a soft composite consisting of a fabric mesh and VHB tape layers was fabricated. The composite was as stiff as the fabric mesh, and as stretchable as the VHB tape. At certain compositions, the composite was significantly stronger and tougher than the base materials. The extensibility and toughness of the composite can be attributed to a damage delocalization mechanism similar to that of the DN gels. In the partially damaged regions, the fabric mesh fragmented into small islands, surrounded by the highly stretched VHB tapes. Accommodated by the finite sliding at the interface, the large deformation of the composite is highly non-affine. Just as the DN gels, the coexistence of the partially damaged and intact regions resulted in a stable necking in the composite when subjected to uniaxial tension. The propagation of the necking zone corresponded to a plateau on the stress-stretch curve. During cyclic loading, the composite also exhibited stress hysteresis with almost recoverable strain, similar to that in a DN gel. To rationalize these observations and to better understand the underlying physical mechanism, a simple 1D model has been developed for the damage evolution process in the composite. The predictions of the model have achieved good agreement with the measured properties of the composite of various compositions. Furthermore, the composite itself may also be regarded as a macroscopic model when studying the properties and toughening mechanism of the DN gels.

Aggregation of gluten proteins in model dough after fibre polysaccharide addition.

PubMed

Nawrocka, Agnieszka; Szymańska-Chargot, Monika; Miś, Antoni; Wilczewska, Agnieszka Z; Markiewicz, Karolina H

2017-09-15

FT-Raman spectroscopy, thermogravimetry and differential scanning calorimetry were used to study changes in structure of gluten proteins and their thermal properties influenced by four dietary fibre polysaccharides (microcrystalline cellulose, inulin, apple pectin and citrus pectin) during development of a model dough. The flour reconstituted from wheat starch and wheat gluten was mixed with the polysaccharides in five concentrations: 3%, 6%, 9%, 12% and 18%. The obtained results showed that all polysaccharides induced similar changes in secondary structure of gluten proteins concerning formation of aggregates (1604cm -1 ), H-bonded parallel- and antiparallel-β-sheets (1690cm -1 ) and H-bonded β-turns (1664cm -1 ). These changes concerned mainly glutenins since β-structures are characteristic for them. The observed structural changes confirmed hypothesis about partial dehydration of gluten network after polysaccharides addition. The gluten aggregation and dehydration processes were also reflected in the DSC results, while the TGA ones showed that gluten network remained thermally stable after polysaccharides addition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Retrieval Search and Strength Evoke Dissociable Brain Activity during Episodic Memory Recall

PubMed Central

Reas, Emilie T.; Brewer, James B.

2014-01-01

Neuroimaging studies of episodic memory retrieval have revealed activations in the human frontal, parietal, and medial-temporal lobes that are associated with memory strength. However, it remains unclear whether these brain responses are veritable signals of memory strength or are instead regulated by concomitant subcomponents of retrieval such as retrieval effort or mental search. This study used event-related fMRI during cued recall of previously memorized word-pair associates to dissociate brain responses modulated by memory search from those modulated by the strength of a recalled memory. Search-related deactivations, dissociated from activity due to memory strength, were observed in regions of the default network, whereas distinctly strength-dependent activations were present in superior and inferior parietal and dorsolateral PFC. Both search and strength regulated activity in dorsal anterior cingulate and anterior insula. These findings suggest that, although highly correlated and partially subserved by overlapping cognitive control mechanisms, search and memory strength engage dissociable regions of frontoparietal attention and default networks. PMID:23190328

On Similarity Transformation and Geodetic Network Distortions Based on Doppler Satellite Observations

NASA Technical Reports Server (NTRS)

Leick, Alfred; Vangelder, Boudewijn H. W.

1975-01-01

Models used in geodesy to transform two sets of coordinates are studied and distortions in geodetic networks are investigated. Commonly used transformation models are first reviewed and most of them are interpreted. Differences between various models are discussed. Pitfalls in partial solutions are then considered. It is shown that only as many chords and/or directional elements can be used in the computation as are needed to completely determine the size or shape of the polyhedron implied in the set of Cartesian coordinates. Each additional element causes the normal matrix to be singular provided that all correlations between the chords are used. A number of tables and maps indicating distortions in the NAD 27, Precise Traverse M-R '72, AUS, and SAD 69 geodetic datums are also included. The residuals of the coordinates are scanned for systematic patterns after transforming each geodetic system to the NWL9D Doppler system. Also, an attempt is made to show scale distortions in the NAD 27.

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations.

PubMed

Cámara, María S; Ferroni, Félix M; De Zan, Mercedes; Goicoechea, Héctor C

2003-07-01

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.

GABA Neurons and the Mechanisms of Network Oscillations: Implications for Understanding Cortical Dysfunction in Schizophrenia

PubMed Central

Gonzalez-Burgos, Guillermo; Lewis, David A.

2008-01-01

Synchronization of neuronal activity in the neocortex may underlie the coordination of neural representations and thus is critical for optimal cognitive function. Because cognitive deficits are the major determinant of functional outcome in schizophrenia, identifying their neural basis is important for the development of new therapeutic interventions. Here we review the data suggesting that phasic synaptic inhibition mediated by specific subtypes of cortical γ-aminobutyric acid (GABA) neurons is essential for the production of synchronized network oscillations. We also discuss evidence indicating that GABA neurotransmission is altered in schizophrenia and propose mechanisms by which such alterations can decrease the strength of inhibitory connections in a cell-type–specific manner. We suggest that some alterations observed in the neocortex of schizophrenia subjects may be compensatory responses that partially restore inhibitory synaptic efficacy. The findings of altered neural synchrony and impaired cognitive function in schizophrenia suggest that such compensatory responses are insufficient and that interventions aimed at augmenting the efficacy of GABA neurotransmission might be of therapeutic value. PMID:18586694

GABA neurons and the mechanisms of network oscillations: implications for understanding cortical dysfunction in schizophrenia.

PubMed

Gonzalez-Burgos, Guillermo; Lewis, David A

2008-09-01

Synchronization of neuronal activity in the neocortex may underlie the coordination of neural representations and thus is critical for optimal cognitive function. Because cognitive deficits are the major determinant of functional outcome in schizophrenia, identifying their neural basis is important for the development of new therapeutic interventions. Here we review the data suggesting that phasic synaptic inhibition mediated by specific subtypes of cortical gamma-aminobutyric acid (GABA) neurons is essential for the production of synchronized network oscillations. We also discuss evidence indicating that GABA neurotransmission is altered in schizophrenia and propose mechanisms by which such alterations can decrease the strength of inhibitory connections in a cell-type-specific manner. We suggest that some alterations observed in the neocortex of schizophrenia subjects may be compensatory responses that partially restore inhibitory synaptic efficacy. The findings of altered neural synchrony and impaired cognitive function in schizophrenia suggest that such compensatory responses are insufficient and that interventions aimed at augmenting the efficacy of GABA neurotransmission might be of therapeutic value.

Message propagation in the network based on node credibility

NASA Astrophysics Data System (ADS)

Nian, Fuzhong; Dang, Zhongkai

2018-04-01

In the propagation efficiency point of view, the node credibility is introduced in this paper. For the message receiver, the node would partially believe the message according to the credibility of the propagator. For a node, the credibility is variable. The more the true message spread, the higher the credibility, and vice versa, the credibility becomes smaller. Based on the idea, a new network was established with the node credibility. Finally, a comparing experiment between the fully trusted network and the network with the node credibility was implemented. The results indicate that the spread effect of messages is better in the network with the node credibility.

A network approach to assessing cognition in disorders of consciousness(e–Pub ahead of print)(CME)

PubMed Central

Rodriguez Moreno, D.; Schiff, N.D.; Giacino, J.; Kalmar, K.; Hirsch, J.

2010-01-01

Objective: Conventional assessments of consciousness rely on motor responses to indicate awareness. However, overt behaviors may be absent or ambiguous in patients with disorders of consciousness (DOC) resulting in underrating capacity for cognition. fMRI during a silent picture-naming task was evaluated as an indicator of command following when conventional methods are not sufficient. Methods: A total of 10 patients with and without conventional evidence of awareness, who met diagnostic criteria for the minimally conscious state (MCS) (n = 5), vegetative state (VS) (n = 3), emerged from MCS (EMCS) (n = 1), and locked-in syndrome (LIS) (n = 1), participated in this observational fMRI study. Results: The LIS and EMCS patients engaged a complete network of essential language-related regions during the object-naming task. The MCS and 2 of the VS patients demonstrated both complete and partial preservation of the object-naming system. Patients who engaged a complete network scored highest on the Coma Recovery Scale-Revised. Conclusions: This study supports the view that fMRI during object naming can elicit brain activations in patients with DOC similar to those observed in healthy subjects during command following, and patients can be stratified by completeness of the engaged neural system. These results suggest that activity of the language network may serve as an indicator of high-level cognition and possibly volitional processes that cannot be discerned through conventional behavioral assessment alone. GLOSSARY BA = Brodmann area; BOLD = blood oxygenation level–dependent; CRS-R = Coma Recovery Scale-Revised; DOC = disorders of consciousness; EMCS = emerged from minimally conscious state; GFi(d) = dorsal inferior frontal gyrus; GFi(v) = ventral inferior frontal gyrus; hrf = hemodynamic response function; LIS = locked-in syndrome; MCS = minimally conscious state; preSMA = pre-supplementary motor area; STG = superior temporal gyrus; VS = vegetative state. PMID:20980667

Estimability of geodetic parameters from space VLBI observables

NASA Technical Reports Server (NTRS)

Adam, Jozsef

1990-01-01

The feasibility of space very long base interferometry (VLBI) observables for geodesy and geodynamics is investigated. A brief review of space VLBI systems from the point of view of potential geodetic application is given. A selected notational convention is used to jointly treat the VLBI observables of different types of baselines within a combined ground/space VLBI network. The basic equations of the space VLBI observables appropriate for convariance analysis are derived and included. The corresponding equations for the ground-to-ground baseline VLBI observables are also given for a comparison. The simplified expression of the mathematical models for both space VLBI observables (time delay and delay rate) include the ground station coordinates, the satellite orbital elements, the earth rotation parameters, the radio source coordinates, and clock parameters. The observation equations with these parameters were examined in order to determine which of them are separable or nonseparable. Singularity problems arising from coordinate system definition and critical configuration are studied. Linear dependencies between partials are analytically derived. The mathematical models for ground-space baseline VLBI observables were tested with simulation data in the frame of some numerical experiments. Singularity due to datum defect is confirmed.

Altered Distant Synchronization of Background Network in Mild Cognitive Impairment during an Executive Function Task.

PubMed

Wang, Pengyun; Li, Rui; Yu, Jing; Huang, Zirui; Yan, Zhixiong; Zhao, Ke; Li, Juan

2017-01-01

Few studies to date have investigated the background network in the cognitive state relying on executive function in mild cognitive impairment (MCI) patients. Using the index of degree of centrality (DC), we explored distant synchronization of background network in MCI during a hybrid delayed-match-to-sample task (DMST), which mainly relies on the working memory component of executive function. We observed significant interactions between group and cognitive state in the bilateral posterior cingulate cortex (PCC) and the ventral subregion of precuneus. For normal control (NC) group, the long distance functional connectivity (FC) of the PCC/precuneus with the other regions of the brain was higher in rest state than that working memory state. For MCI patients, however, this pattern altered. There was no significant difference between rest and working memory state. The similar pattern was observed in the other cluster located in the right angular gyrus. To examine whether abnormal DC in PCC/precuneus and angular gyrus partially resulted from the deficit of FC between these regions and the other parts in the whole brain, we conducted a seed-based correlation analysis with these regions as seeds. The results indicated that the FC between bilateral PCC/precuneus and the right inferior parietal lobule (IPL) increased from rest to working memory state for NC participants. For MCI patients, however, there was no significant change between rest and working memory state. The similar pattern was observed for the FC between right angular gyrus and right anterior insula. However, there was no difference between MCI and NC groups in global efficiency and modularity. It may indicate a lack of efficient reorganization from rest state to a working memory state in the brain network of MCI patients. The present study demonstrates the altered distant synchronization of background network in MCI during a task relying on executive function. The results provide a new perspective regarding the neural mechanisms of executive function deficits in MCI patients, and extend our understanding of brain patterns in task-evoked cognitive states.

Atrophy in distinct corticolimbic networks in frontotemporal dementia relates to social impairments measured using the Social Impairment Rating Scale

PubMed Central

Bickart, Kevin C; Brickhouse, Michael; Negreira, Alyson; Sapolsky, Daisy

2015-01-01

Patients with frontotemporal dementia (FTD) often exhibit prominent, early and progressive impairments in social behaviour. We developed the Social Impairment Rating Scale (SIRS), rated by a clinician after a structured interview, which grades the types and severity of social behavioural symptoms in seven domains. In 20 FTD patients, we used the SIRS to study the anatomic basis of social impairments. In support of hypotheses generated from a prior study of healthy adults, we found that the relative magnitude of brain atrophy in three partially dissociable corticolimbic networks anchored in the amygdala predicted the severity of distinct social impairments measured using the SIRS. Patients with the greatest atrophy in a mesolimbic, reward-related (affiliation) network exhibited the most severe socioemotional detachment, whereas patients with the greatest atrophy in an interoceptive, pain-related (aversion) network exhibited the most severe lack of social apprehension. Patients with the greatest atrophy in a perceptual network exhibited the most severe lack of awareness or understanding of others’ social and emotional behaviour. Our findings underscore observations that FTD is associated with heterogeneous social symptoms that can be understood in a refined manner by measuring impairments in component processes subserved by dissociable neural networks. Furthermore, these findings support the validity of the SIRS as an instrument to measure the social symptoms of patients with FTD. Ultimately, we hope it will be useful as a longitudinal outcome measure in natural history studies and in clinical trials of putative interventions to improve social functioning. PMID:24133285

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

PubMed

López Chavira, Magali Alexander; Marcelín-Jiménez, Ricardo

2017-01-01

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

Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data

PubMed Central

Kadarmideen, Haja N; Watson-haigh, Nathan S

2012-01-01

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended. PMID:23144540

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

PubMed

Murabito, Ettore; Smallbone, Kieran; Swinton, Jonathan; Westerhoff, Hans V; Steuer, Ralf

2011-06-06

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity. © 2010 The Royal Society

Information Assurance for Network-Centric Naval Forces

DTIC Science & Technology

2010-01-01

of engineers are designing , implementing, and vigorously testing malicious codes prior to releasing them, not unlike well-funded commercial software...the likelihood that threats would partially succeed and partially degrade the system. Individual components of Aegis are designed and tested with a...of operations (CONOPS) set that is designed to work well in a low-bandwidth environment must be extensively tested and exercised within that low

Robustness of network of networks under targeted attack.

PubMed

Dong, Gaogao; Gao, Jianxi; Du, Ruijin; Tian, Lixin; Stanley, H Eugene; Havlin, Shlomo

2013-05-01

The robustness of a network of networks (NON) under random attack has been studied recently [Gao et al., Phys. Rev. Lett. 107, 195701 (2011)]. Understanding how robust a NON is to targeted attacks is a major challenge when designing resilient infrastructures. We address here the question how the robustness of a NON is affected by targeted attack on high- or low-degree nodes. We introduce a targeted attack probability function that is dependent upon node degree and study the robustness of two types of NON under targeted attack: (i) a tree of n fully interdependent Erdős-Rényi or scale-free networks and (ii) a starlike network of n partially interdependent Erdős-Rényi networks. For any tree of n fully interdependent Erdős-Rényi networks and scale-free networks under targeted attack, we find that the network becomes significantly more vulnerable when nodes of higher degree have higher probability to fail. When the probability that a node will fail is proportional to its degree, for a NON composed of Erdős-Rényi networks we find analytical solutions for the mutual giant component P(∞) as a function of p, where 1-p is the initial fraction of failed nodes in each network. We also find analytical solutions for the critical fraction p(c), which causes the fragmentation of the n interdependent networks, and for the minimum average degree k[over ¯](min) below which the NON will collapse even if only a single node fails. For a starlike NON of n partially interdependent Erdős-Rényi networks under targeted attack, we find the critical coupling strength q(c) for different n. When q>q(c), the attacked system undergoes an abrupt first order type transition. When q≤q(c), the system displays a smooth second order percolation transition. We also evaluate how the central network becomes more vulnerable as the number of networks with the same coupling strength q increases. The limit of q=0 represents no dependency, and the results are consistent with the classical percolation theory of a single network under targeted attack.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

Characterization of network structure in stereoEEG data using consensus-based partial coherence.

PubMed

Ter Wal, Marije; Cardellicchio, Pasquale; LoRusso, Giorgio; Pelliccia, Veronica; Avanzini, Pietro; Orban, Guy A; Tiesinga, Paul He

2018-06-06

Coherence is a widely used measure to determine the frequency-resolved functional connectivity between pairs of recording sites, but this measure is confounded by shared inputs to the pair. To remove shared inputs, the 'partial coherence' can be computed by conditioning the spectral matrices of the pair on all other recorded channels, which involves the calculation of a matrix (pseudo-) inverse. It has so far remained a challenge to use the time-resolved partial coherence to analyze intracranial recordings with a large number of recording sites. For instance, calculating the partial coherence using a pseudoinverse method produces a high number of false positives when it is applied to a large number of channels. To address this challenge, we developed a new method that randomly aggregated channels into a smaller number of effective channels on which the calculation of partial coherence was based. We obtained a 'consensus' partial coherence (cPCOH) by repeating this approach for several random aggregations of channels (permutations) and only accepting those activations in time and frequency with a high enough consensus. Using model data we show that the cPCOH method effectively filters out the effect of shared inputs and performs substantially better than the pseudo-inverse. We successfully applied the cPCOH procedure to human stereotactic EEG data and demonstrated three key advantages of this method relative to alternative procedures. First, it reduces the number of false positives relative to the pseudo-inverse method. Second, it allows for titration of the amount of false positives relative to the false negatives by adjusting the consensus threshold, thus allowing the data-analyst to prioritize one over the other to meet specific analysis demands. Third, it substantially reduced the number of identified interactions compared to coherence, providing a sparser network of connections from which clear spatial patterns emerged. These patterns can serve as a starting point of further analyses that provide insight into network dynamics during cognitive processes. These advantages likely generalize to other modalities in which shared inputs introduce confounds, such as electroencephalography (EEG) and magneto-encephalography (MEG). Copyright © 2018. Published by Elsevier Inc.

Visual attention in preterm born adults: specifically impaired attentional sub-mechanisms that link with altered intrinsic brain networks in a compensation-like mode.

PubMed

Finke, Kathrin; Neitzel, Julia; Bäuml, Josef G; Redel, Petra; Müller, Hermann J; Meng, Chun; Jaekel, Julia; Daamen, Marcel; Scheef, Lukas; Busch, Barbara; Baumann, Nicole; Boecker, Henning; Bartmann, Peter; Habekost, Thomas; Wolke, Dieter; Wohlschläger, Afra; Sorg, Christian

2015-02-15

Although pronounced and lasting deficits in selective attention have been observed for preterm born individuals it is unknown which specific attentional sub-mechanisms are affected and how they relate to brain networks. We used the computationally specified 'Theory of Visual Attention' together with whole- and partial-report paradigms to compare attentional sub-mechanisms of pre- (n=33) and full-term (n=32) born adults. Resting-state fMRI was used to evaluate both between-group differences and inter-individual variance in changed functional connectivity of intrinsic brain networks relevant for visual attention. In preterm born adults, we found specific impairments of visual short-term memory (vSTM) storage capacity while other sub-mechanisms such as processing speed or attentional weighting were unchanged. Furthermore, changed functional connectivity was found in unimodal visual and supramodal attention-related intrinsic networks. Among preterm born adults, the individual pattern of changed connectivity in occipital and parietal cortices was systematically associated with vSTM in such a way that the more distinct the connectivity differences, the better the preterm adults' storage capacity. These findings provide first evidence for selectively changed attentional sub-mechanisms in preterm born adults and their relation to altered intrinsic brain networks. In particular, data suggest that cortical changes in intrinsic functional connectivity may compensate adverse developmental consequences of prematurity on visual short-term storage capacity. Copyright © 2014 Elsevier Inc. All rights reserved.

Environmental factors shaping cultured free-living amoebae and their associated bacterial community within drinking water network.

PubMed

Delafont, Vincent; Bouchon, Didier; Héchard, Yann; Moulin, Laurent

2016-09-01

Free-living amoebae (FLA) constitute an important part of eukaryotic populations colonising drinking water networks. However, little is known about the factors influencing their ecology in such environments. Because of their status as reservoir of potentially pathogenic bacteria, understanding environmental factors impacting FLA populations and their associated bacterial community is crucial. Through sampling of a large drinking water network, the diversity of cultivable FLA and their bacterial community were investigated by an amplicon sequencing approach, and their correlation with physicochemical parameters was studied. While FLA ubiquitously colonised the water network all year long, significant changes in population composition were observed. These changes were partially explained by several environmental parameters, namely water origin, temperature, pH and chlorine concentration. The characterisation of FLA associated bacterial community reflected a diverse but rather stable consortium composed of nearly 1400 OTUs. The definition of a core community highlighted the predominance of only few genera, majorly dominated by Pseudomonas and Stenotrophomonas. Co-occurrence analysis also showed significant patterns of FLA-bacteria association, and allowed uncovering potentially new FLA - bacteria interactions. From our knowledge, this study is the first that combines a large sampling scheme with high-throughput identification of FLA together with associated bacteria, along with their influencing environmental parameters. Our results demonstrate the importance of physicochemical parameters in the ecology of FLA and their bacterial community in water networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Involvement of mesolimbic dopaminergic network in neuropathic pain relief by treadmill exercise

PubMed Central

Wakaizumi, Kenta; Kondo, Takashige; Hamada, Yusuke; Narita, Michiko; Kawabe, Rui; Narita, Hiroki; Watanabe, Moe; Kato, Shigeki; Senba, Emiko; Kobayashi, Kazuto; Yamanaka, Akihiro

2016-01-01

Background Exercise alleviates pain and it is a central component of treatment strategy for chronic pain in clinical setting. However, little is known about mechanism of this exercise-induced hypoalgesia. The mesolimbic dopaminergic network plays a role in positive emotions to rewards including motivation and pleasure. Pain negatively modulates these emotions, but appropriate exercise is considered to activate the dopaminergic network. We investigated possible involvement of this network as a mechanism of exercise-induced hypoalgesia. Methods In the present study, we developed a protocol of treadmill exercise, which was able to recover pain threshold under partial sciatic nerve ligation in mice, and investigated involvement of the dopaminergic reward network in exercise-induced hypoalgesia. To temporally suppress a neural activation during exercise, a genetically modified inhibitory G-protein-coupled receptor, hM4Di, was specifically expressed on dopaminergic pathway from the ventral tegmental area to the nucleus accumbens. Results The chemogenetic-specific neural suppression by Gi-DREADD system dramatically offset the effect of exercise-induced hypoalgesia in transgenic mice with hM4Di expressed on the ventral tegmental area dopamine neurons. Additionally, anti-exercise-induced hypoalgesia effect was significantly observed under the suppression of neurons projecting out of the ventral tegmental area to the nucleus accumbens as well. Conclusion Our findings suggest that the dopaminergic pathway from the ventral tegmental area to the nucleus accumbens is involved in the anti-nociception under low-intensity exercise under a neuropathic pain-like state. PMID:27909152

Linear-Optics-Based Entanglement Concentration of Four-Photon χ-type States for Quantum Communication Network

NASA Astrophysics Data System (ADS)

Li, Tao; Deng, Fu-Guo

2014-09-01

We present an efficient entanglement concentration protocol (ECP) for partially entangled four-photon χ-type states in the first time with only linear optical elements and single-photon detectors. Without any ancillary particles, the parties in quantum communication network can obtain a subset of four-photon systems in the standard | χ 00> state from a set of four-photon systems in a partially entangled χ-type state with the parameter-splitting method developed by Ren et al. (Phys. Rev. A 88:012302, 2013). The present ECP has the optimal success probability which is determined by the component with the minimal probability amplitude in the initial state. Moreover, it is easy to implement this ECP in experiment.

The amygdala as a hub in brain networks that support social life.

PubMed

Bickart, Kevin C; Dickerson, Bradford C; Barrett, Lisa Feldman

2014-10-01

A growing body of evidence suggests that the amygdala is central to handling the demands of complex social life in primates. In this paper, we synthesize extant anatomical and functional data from rodents, monkeys, and humans to describe the topography of three partially distinct large-scale brain networks anchored in the amygdala that each support unique functions for effectively managing social interactions and maintaining social relationships. These findings provide a powerful componential framework for parsing social behavior into partially distinct neural underpinnings that differ among healthy people and disintegrate or fail to develop in neuropsychiatric populations marked by social impairment, such as autism, antisocial personality disorder, and frontotemporal dementia. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling the glass transition of amorphous networks for shape-memory behavior

NASA Astrophysics Data System (ADS)

Xiao, Rui; Choi, Jinwoo; Lakhera, Nishant; Yakacki, Christopher M.; Frick, Carl P.; Nguyen, Thao D.

2013-07-01

In this paper, a thermomechanical constitutive model was developed for the time-dependent behaviors of the glass transition of amorphous networks. The model used multiple discrete relaxation processes to describe the distribution of relaxation times for stress relaxation, structural relaxation, and stress-activated viscous flow. A non-equilibrium thermodynamic framework based on the fictive temperature was introduced to demonstrate the thermodynamic consistency of the constitutive theory. Experimental and theoretical methods were developed to determine the parameters describing the distribution of stress and structural relaxation times and the dependence of the relaxation times on temperature, structure, and driving stress. The model was applied to study the effects of deformation temperatures and physical aging on the shape-memory behavior of amorphous networks. The model was able to reproduce important features of the partially constrained recovery response observed in experiments. Specifically, the model demonstrated a strain-recovery overshoot for cases programmed below Tg and subjected to a constant mechanical load. This phenomenon was not observed for materials programmed above Tg. Physical aging, in which the material was annealed for an extended period of time below Tg, shifted the activation of strain recovery to higher temperatures and increased significantly the initial recovery rate. For fixed-strain recovery, the model showed a larger overshoot in the stress response for cases programmed below Tg, which was consistent with previous experimental observations. Altogether, this work demonstrates how an understanding of the time-dependent behaviors of the glass transition can be used to tailor the temperature and deformation history of the shape-memory programming process to achieve more complex shape recovery pathways, faster recovery responses, and larger activation stresses.

From Ecology to Finance (and Back?): A Review on Entropy-Based Null Models for the Analysis of Bipartite Networks

NASA Astrophysics Data System (ADS)

Straka, Mika J.; Caldarelli, Guido; Squartini, Tiziano; Saracco, Fabio

2018-04-01

Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analyses of their topological structures have revealed the ubiquitous presence of properties which seem to characterize many—apparently different—systems. Nestedness, for example, has been observed in biological plant-pollinator as well as in country-product exportation networks. Due to the interdisciplinary character of complex networks, tools developed in one field, for example ecology, can greatly enrich other areas of research, such as economy and finance, and vice versa. With this in mind, we briefly review several entropy-based bipartite null models that have been recently proposed and discuss their application to real-world systems. The focus on these models is motivated by the fact that they show three very desirable features: analytical character, general applicability, and versatility. In this respect, entropy-based methods have been proven to perform satisfactorily both in providing benchmarks for testing evidence-based null hypotheses and in reconstructing unknown network configurations from partial information. Furthermore, entropy-based models have been successfully employed to analyze ecological as well as economic systems. As an example, the application of entropy-based null models has detected early-warning signals, both in economic and financial systems, of the 2007-2008 world crisis. Moreover, they have revealed a statistically-significant export specialization phenomenon of country export baskets in international trade, a result that seems to reconcile Ricardo's hypothesis in classical economics with recent findings on the (empirical) diversification industrial production at the national level. Finally, these null models have shown that the information contained in the nestedness is already accounted for by the degree sequence of the corresponding graphs.

Advancing Nucleosynthesis in Core-Collapse Supernovae Models Using 2D CHIMERA Simulations

NASA Astrophysics Data System (ADS)

Harris, J. A.; Hix, W. R.; Chertkow, M. A.; Bruenn, S. W.; Lentz, E. J.; Messer, O. B.; Mezzacappa, A.; Blondin, J. M.; Marronetti, P.; Yakunin, K.

2014-01-01

The deaths of massive stars as core-collapse supernovae (CCSN) serve as a crucial link in understanding galactic chemical evolution since the birth of the universe via the Big Bang. We investigate CCSN in polar axisymmetric simulations using the multidimensional radiation hydrodynamics code CHIMERA. Computational costs have traditionally constrained the evolution of the nuclear composition in CCSN models to, at best, a 14-species α-network. However, the limited capacity of the α-network to accurately evolve detailed composition, the neutronization and the nuclear energy generation rate has fettered the ability of prior CCSN simulations to accurately reproduce the chemical abundances and energy distributions as known from observations. These deficits can be partially ameliorated by "post-processing" with a more realistic network. Lagrangian tracer particles placed throughout the star record the temporal evolution of the initial simulation and enable the extension of the nuclear network evolution by incorporating larger systems in post-processing nucleosynthesis calculations. We present post-processing results of the four ab initio axisymmetric CCSN 2D models of Bruenn et al. (2013) evolved with the smaller α-network, and initiated from stellar metallicity, non-rotating progenitors of mass 12, 15, 20, and 25 M⊙ from Woosley & Heger (2007). As a test of the limitations of post-processing, we provide preliminary results from an ongoing simulation of the 15 M⊙ model evolved with a realistic 150 species nuclear reaction network in situ. With more accurate energy generation rates and an improved determination of the thermodynamic trajectories of the tracer particles, we can better unravel the complicated multidimensional "mass-cut" in CCSN simulations and probe for less energetically significant nuclear processes like the νp-process and the r-process, which require still larger networks.

Social network analysis for assessment of avian influenza spread and trading patterns of backyard chickens in Nakhon Pathom, Suphan Buri and Ratchaburi, Thailand.

PubMed

Poolkhet, C; Chairatanayuth, P; Thongratsakul, S; Yatbantoong, N; Kasemsuwan, S; Damchoey, D; Rukkwamsuk, T

2013-09-01

The aim of this study is to explain the social networks of the backyard chicken in Ratchaburi, Suphan Buri and Nakhon Pathom Provinces. In this study, we designed the nodes as groups of persons or places involved in activities relating to backyard chickens. The ties are all activities related to the nodes. The study applied a partial network approach to assess the spreading pattern of avian influenza. From 557 questionnaires collected from the nodes, the researchers found that the degree (the numbers of ties that a node has) and closeness (the distance from one node to the others) centralities of Nakhon Pathom were significantly higher than those of the others (P<0.001). The results show that compared with the remaining areas, this area is more quickly connected to many links. If the avian influenza virus subtype H5N1 was released into the network, the disease would spread throughout this province more rapidly than in Ratchaburi and Suphan Buri. The betweenness centrality in each of these provinces showed no differences (P>0.05). In this study, the nodes that play an important role in all networks are farmers who raise consumable chicken, farmers who raise both consumable chicken and fighting cocks, farmers' households that connect with dominant nodes, and the owners and observers of fighting cocks at arenas and training fields. In this study, we did not find cut points or blocks in the network. Moreover, we detected a random network in all provinces. Thus, connectivity between the nodes covers long or short distances, with less predictable behaviour. Finally, this study suggests that activities between the important nodes must receive special attention for disease control during future disease outbreaks. © 2012 Blackwell Verlag GmbH.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

PubMed

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-12-12

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

PubMed Central

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-01-01

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

PubMed

Sun, Delin; Haswell, Courtney C; Morey, Rajendra A; De Bellis, Michael D

2018-04-10

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

Three-stage nucleation and growth of Ge self-assembled quantum dots grown on partially relaxed SiGe buffer layers

NASA Astrophysics Data System (ADS)

Kim, H. J.; Zhao, Z. M.; Xie, Y. H.

2003-11-01

Three-stage nucleation and growth of Ge self-assembled quantum dots (SAQDs) on a relaxed SiGe buffer layer has been studied. Plastic relaxation of the SiGe buffer layer is associated with a network of buried 60° dislocations leading to an undulating strain field. As a result, the surface possesses three different types of sites for the nucleation and growth of Ge SAQDs: over the intersection of two perpendicular buried dislocations, over a single dislocation line, and in the region beyond one diffusion length away from any dislocation. Ge SAQDs are observed to nucleate exclusively over the dislocation intersections first, followed by over single dislocation lines, and finally in the region far away from dislocations. By increasing the Ge coverage at a slow rate, the prenucleation stage at the various sites is observed. It appears that the varying strain field has a significant effect on both the diffusion of Ge adatoms before SAQD nucleation, as well as the shape evolution of the SAQDs after they form. Moreover, two distinctly different self-assembly mechanisms are observed at different sites. There exist denuded zones free of Ge SAQDs adjacent to dislocation lines. The width of the denuded zone can be used to make direct determination of the Ge adatom diffusion lengths. The partially relaxed substrate provides a useful experimental vehicle for the in-depth understanding of the formation mechanism of SAQDs grown epitaxially in the Stranski-Krastanov growth mode.

Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

DTIC Science & Technology

2008-03-01

of biochemical networks. Trends Biochemical Sci 31: 284–291. 56. Blinov ML, Faeder JR, Goldstein B , Hlavacek WS (2006) A network model of early events...activation is dependent on the nature of connectivity of the two receptors to B -Raf and C-Raf, which form a partially incoherent bifan. The incoherent bifan...Wooster, R., Stratton, M. R., and Futreal, P. A. (2002) Mutations of the BRaf gene in human cancer. Nature 417, 949–954 11. Goydos, J. S., Mann, B

The Design and Implementation of a Relational to Network Query Translator for a Distributed Database Management System.

DTIC Science & Technology

1985-12-01

RELATIONAL TO NETWORK QUERY TRANSLATOR FOR A DISTRIBUTED DATABASE MANAGEMENT SYSTEM TH ESI S .L Kevin H. Mahoney -- Captain, USAF AFIT/GCS/ENG/85D-7...NETWORK QUERY TRANSLATOR FOR A DISTRIBUTED DATABASE MANAGEMENT SYSTEM - THESIS Presented to the Faculty of the School of Engineering of the Air Force...Institute of Technology Air University In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Systems - Kevin H. Mahoney

Emplacement of Pahoehoe Toe Networks: Observations of May, 2010 Tube-fed Flows at Kilauea Volcano, Hawai'i

NASA Astrophysics Data System (ADS)

Crown, D. A.; Ramsey, M.; Hon, K.

2010-12-01

Pahoehoe lava flows are compound features that consist of multiple overlapping and interfingering lobes and exhibit morphologically diverse surfaces characterized by channelized zones, smooth-surfaced sheets, and numerous, small toe networks. Previous work compiled detailed planform maps of solidified pahoehoe toe networks to document their morphology, morphometry and connective relationships in order to provide constraints on lava transport models. In order to expand this research to active flow emplacement, we observed slow-moving, tube-fed pahoehoe flows on the coastal plain near Kalapana, Hawaii in May, 2010. The evolution of pahoehoe toe and toe network characteristics over their emplacement history was examined and the role of small-scale flow inflation on the advance of pahoehoe lobes evaluated. We collected both visible video footage and high-speed, high-precision thermal infrared (TIR) data using a FLIR S-40 camera. The TIR data provide surface temperature maps that can be easily used to identify formation of new toes and track their growth and surface cooling. From these maps, lobe development, connective relationships, and frontal and lateral spreading rates can be analyzed. Preliminary results suggest that regular cycles of activity characterize the development of these pahoehoe lobes: 1) emplacement of new toes in local topographic lows at the front, margin, and within the interior of an active lobe forming small interconnected networks, 2) decline and sometimes temporary cessation in the production of new pahoehoe toes, 3) inflation of the recently emplaced flow surface, either partially or en masse depending on the rate of influx of new lava, the degree of irregularity of the pre-flow surface, and/or the slope across the recently emplaced lava surface, and 4) fracturing of the recently emplaced surface crust that feeds emplacement of new toes. Inflation fractures typically cut across several previously emplaced toes and can occur at the front, along the margins, or within the active lobe, even at significant distances behind the flow front.

An application of artificial neural networks to experimental data approximation

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1993-01-01

As an initial step in the evaluation of networks, a feedforward architecture is trained to approximate experimental data by the backpropagation algorithm. Several drawbacks were detected and an alternative learning algorithm was then developed to partially address the drawbacks. This noniterative algorithm has a number of advantages over the backpropagation method and is easily implemented on existing hardware.

Quantification of network structural dissimilarities.

PubMed

Schieber, Tiago A; Carpi, Laura; Díaz-Guilera, Albert; Pardalos, Panos M; Masoller, Cristina; Ravetti, Martín G

2017-01-09

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

Measurement of spin-lattice relaxation times and concentrations in systems with chemical exchange using the one-pulse sequence: breakdown of the Ernst model for partial saturation in nuclear magnetic resonance spectroscopy.

PubMed

Spencer, R G; Fishbein, K W

2000-01-01

A fundamental problem in Fourier transform NMR spectroscopy is the calculation of observed resonance amplitudes for a repetitively pulsed sample, as first analyzed by Ernst and Anderson in 1966. Applications include determination of spin-lattice relaxation times (T(1)'s) by progressive saturation and correction for partial saturation in order to determine the concentrations of the chemical constituents of a spectrum. Accordingly, the Ernst and Anderson formalism has been used in innumerable studies of chemical and, more recently, physiological systems. However, that formalism implicitly assumes that no chemical exchange occurs. Here, we present an analysis of N sites in an arbitrary chemical exchange network, explicitly focusing on the intermediate exchange rate regime in which the spin-lattice relaxation rates and the chemical exchange rates are comparable in magnitude. As a special case of particular importance, detailed results are provided for a system with three sites undergoing mutual exchange. Specific properties of the N-site network are then detailed. We find that (i) the Ernst and Anderson analysis describing the response of a system to repetitive pulsing is inapplicable to systems with chemical exchange and can result in large errors in T(1) and concentration measurements; (ii) T(1)'s for systems with arbitrary exchange networks may still be correctly determined from a one-pulse experiment using the Ernst formula, provided that a short interpulse delay time and a large flip angle are used; (iii) chemical concentrations for exchanging systems may be correctly determined from a one-pulse experiment either by using a short interpulse delay time with a large flip angle, as for measuring T(1)'s, and correcting for partial saturation by use of the Ernst formula, or directly by using a long interpulse delay time to avoid saturation; (iv) there is a significant signal-to-noise penalty for performing one-pulse experiments under conditions which permit accurate measurements of T(1)'s and chemical concentrations. The present results are analogous to but are much more general than those that we have previously derived for systems with two exchanging sites. These considerations have implications for the design and interpretation of one-pulse experiments for all systems exhibiting chemical exchange in the intermediate exchange regime, including virtually all physiologic samples.

Flexible Redistribution in Cognitive Networks.

PubMed

Hartwigsen, Gesa

2018-06-15

Previous work has emphasized that cognitive functions in the human brain are organized into large-scale networks. However, the mechanisms that allow these networks to compensate for focal disruptions remain elusive. I suggest a new perspective on the compensatory flexibility of cognitive networks. First, I demonstrate that cognitive networks can rapidly change the functional weight of the relative contribution of different regions. Second, I argue that there is an asymmetry in the compensatory potential of different kinds of networks. Specifically, recruitment of domain-general functions can partially compensate for focal disruptions of specialized cognitive functions, but not vice versa. Considering the compensatory potential within and across networks will increase our understanding of functional adaptation and reorganization after brain lesions and offers a new perspective on large-scale neural network (re-)organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Left Frontal Meningioangiomatosis Associated with Type IIIc Focal Cortical Dysplasia Causing Refractory Epilepsy and Literature Review.

PubMed

Roux, Alexandre; Mellerio, Charles; Lechapt-Zalcman, Emmanuelle; Still, Megan; Zerah, Michel; Bourgeois, Marie; Pallud, Johan

2018-06-01

We report the surgical management of a lesional drug-resistant epilepsy caused by a meningioangiomatosis associated with a type IIIc focal cortical dysplasia located in the left supplementary motor area in a young male patient. A first anatomically based partial surgical resection was performed on an 11-year-old under general anesthesia without intraoperative mapping, which allowed for postoperative seizure control (Engel IA) for 6 years. The patient then exhibited intractable right sensatory and aphasic focal onset seizures despite 2 appropriate antiepileptic drugs. A second functional-based surgical resection was performed using intraoperative corticosubcortical functional mapping with direct electrical stimulation under awake conditions. A complete surgical resection was performed, and a left partial supplementary motor area syndrome was observed. At 6 months postoperatively, the patient is seizure free (Engel IA) with an ongoing decrease in antiepileptic drug therapy. Intraoperative functional brain mapping can be applied to preserve the brain function and networks around a meningioangiomatosis to facilitate the resection of potentially epileptogenic perilesional dysplastic cortex and to tailor the extent of resection to functional boundaries. Copyright © 2018 Elsevier Inc. All rights reserved.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Tradeoff on Phenotype Robustness in Biological Networks Part II: Ecological Networks

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

In ecological networks, network robustness should be large enough to confer intrinsic robustness for tolerating intrinsic parameter fluctuations, as well as environmental robustness for resisting environmental disturbances, so that the phenotype stability of ecological networks can be maintained, thus guaranteeing phenotype robustness. However, it is difficult to analyze the network robustness of ecological systems because they are complex nonlinear partial differential stochastic systems. This paper develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance sensitivity in ecological networks. We found that the phenotype robustness criterion for ecological networks is that if intrinsic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations and environmental disturbances. These results in robust ecological networks are similar to that in robust gene regulatory networks and evolutionary networks even they have different spatial-time scales. PMID:23515112

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

PubMed

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Fast sparsely synchronized brain rhythms in a scale-free neural network.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D

Impacts of subsidy policies on vaccination decisions in contact networks

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Wu, Zhi-Xi; Xu, Xiao-Ke; Small, Michael; Wang, Lin; Wang, Bing-Hong

2013-07-01

To motivate more people to participate in vaccination campaigns, various subsidy policies are often supplied by government and the health sectors. However, these external incentives may also alter the vaccination decisions of the broader public, and hence the choice of incentive needs to be carefully considered. Since human behavior and the networking-constrained interactions among individuals significantly impact the evolution of an epidemic, here we consider the voluntary vaccination on human contact networks. To this end, two categories of typical subsidy policies are considered: (1) under the free subsidy policy, the total amount of subsidy is distributed to a certain fraction of individual and who are vaccinated without personal cost, and (2) under the partial-offset subsidy policy, each vaccinated person is offset by a certain amount of subsidy. A vaccination decision model based on evolutionary game theory is established to study the effects of these different subsidy policies on disease control. Simulations suggest that, because the partial-offset subsidy policy encourages more people to take vaccination, its performance is significantly better than that of the free subsidy policy. However, an interesting phenomenon emerges in the partial-offset scenario: with limited amount of total subsidy, a moderate subsidy rate for each vaccinated individual can guarantee the group-optimal vaccination, leading to the maximal social benefits, while such an optimal phenomenon is not evident for the free subsidy scenario.

EEG functional connectivity is partially predicted by underlying white matter connectivity

PubMed Central

Chu, CJ; Tanaka, N; Diaz, J; Edlow, BL; Wu, O; Hämäläinen, M; Stufflebeam, S; Cash, SS; Kramer, MA.

2015-01-01

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales. PMID:25534110

Synchronization in a non-uniform network of excitatory spiking neurons

NASA Astrophysics Data System (ADS)

Echeveste, Rodrigo; Gros, Claudius

Spontaneous synchronization of pulse coupled elements is ubiquitous in nature and seems to be of vital importance for life. Networks of pacemaker cells in the heart, extended populations of southeast asian fireflies, and neuronal oscillations in cortical networks, are examples of this. In the present work, a rich repertoire of dynamical states with different degrees of synchronization are found in a network of excitatory-only spiking neurons connected in a non-uniform fashion. In particular, uncorrelated and partially correlated states are found without the need for inhibitory neurons or external currents. The phase transitions between these states, as well the robustness, stability, and response of the network to external stimulus are studied.

Performance analysis of SS7 congestion controls under sustained overload

NASA Astrophysics Data System (ADS)

Manfield, David R.; Millsteed, Gregory K.; Zukerman, Moshe

1994-04-01

Congestion controls are a key factor in achieving the robust performance required of common channel signaling (CCS) networks in the face of partial network failures and extreme traffic loads, especially as networks become large and carry high traffic volume. The CCITT recommendations define a number of types of congestion control, and the parameters of the controls must be well set in order to ensure their efficacy under transient and sustained signalling network overload. The objective of this paper is to present a modeling approach to the determination of the network parameters that govern the performance of the SS7 congestion controls under sustained overload. Results of the investigation by simulation are presented and discussed.

Subjective well-being associated with size of social network and social support of elderly.

PubMed

Wang, Xingmin

2016-06-01

The current study examined the impact of size of social network on subjective well-being of elderly, mainly focused on confirmation of the mediator role of perceived social support. The results revealed that both size of social network and perceived social support were significantly correlated with subjective well-being. Structural equation modeling indicated that perceived social support partially mediated size of social network to subjective well-being. The final model also revealed significant both paths from size of social network to subjective well-being through perceived social support. The findings extended prior researches and provided valuable evidence on how to promote mental health of the elderly. © The Author(s) 2014.

Applications of a formal approach to decipher discrete genetic networks.

PubMed

Corblin, Fabien; Fanchon, Eric; Trilling, Laurent

2010-07-20

A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language. In this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria. The formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.

Variations of the Hydrogen Bonding and of the Hydrogen Bonded Network in Ethanol-Water Mixtures on Cooling.

PubMed

Pothoczki, Szilvia; Pusztai, Laszlo; Bako, Imre

2018-06-12

Molecular dynamics computer simulations have been conducted for ethanol-water liquid mixtures in the water-rich side of the composition range, with 10, 20 and 30 mol % of the alcohol, at temperatures between room temperature and the experimental freezing point of the given mixture. All-atom type (OPLS) interatomic potentials have been assumed for ethanol, in combination with two kinds of rigid water models (SPC/E and TIP4P/2005). Both combinations have provided excellent reproductions of the experimental X-ray total structure factors at each temperature; this yielded a strong basis for further structural analyses. Beyond partial radial distribution functions, various descriptors of hydrogen bonded assemblies, as well as of the hydrogen bonded network have been determined. A clear tendency was observed towards that an increasing proportion of water molecules participate in hydrogen bonding with exactly 2 donor- and 2 acceptor sites as temperature decreases. Concerning larger assemblies held together by hydrogen bonding, the main focus was put on the properties of cyclic entities: it was found that, similarly to methanol-water mixtures, the number of hydrogen bonded rings has increased with lowering temperature. However, for ethanol-water mixtures the dominance of not the six-, but of the five-fold rings could be observed.

Modification of wave propagation and wave travel-time by the presence of magnetic fields in the solar network atmosphere

NASA Astrophysics Data System (ADS)

Nutto, C.; Steiner, O.; Schaffenberger, W.; Roth, M.

2012-02-01

Context. Observations of waves at frequencies above the acoustic cut-off frequency have revealed vanishing wave travel-times in the vicinity of strong magnetic fields. This detection of apparently evanescent waves, instead of the expected propagating waves, has remained a riddle. Aims: We investigate the influence of a strong magnetic field on the propagation of magneto-acoustic waves in the atmosphere of the solar network. We test whether mode conversion effects can account for the shortening in wave travel-times between different heights in the solar atmosphere. Methods: We carry out numerical simulations of the complex magneto-atmosphere representing the solar magnetic network. In the simulation domain, we artificially excite high frequency waves whose wave travel-times between different height levels we then analyze. Results: The simulations demonstrate that the wave travel-time in the solar magneto-atmosphere is strongly influenced by mode conversion. In a layer enclosing the surface sheet defined by the set of points where the Alfvén speed and the sound speed are equal, called the equipartition level, energy is partially transferred from the fast acoustic mode to the fast magnetic mode. Above the equipartition level, the fast magnetic mode is refracted due to the large gradient of the Alfvén speed. The refractive wave path and the increasing phase speed of the fast mode inside the magnetic canopy significantly reduce the wave travel-time, provided that both observing levels are above the equipartition level. Conclusions: Mode conversion and the resulting excitation and propagation of fast magneto-acoustic waves is responsible for the observation of vanishing wave travel-times in the vicinity of strong magnetic fields. In particular, the wave propagation behavior of the fast mode above the equipartition level may mimic evanescent behavior. The present wave propagation experiments provide an explanation of vanishing wave travel-times as observed with multi-line high-cadence instruments. Movies are available in electronic form at http://www.aanda.org

High temperature strain gages

NASA Technical Reports Server (NTRS)

Gregory, Otto J. (Inventor); You, Tao (Inventor)

2011-01-01

A ceramic strain gage based on reactively sputtered indium-tin-oxide (ITO) thin films is used to monitor the structural integrity of components employed in aerospace propulsion systems operating at temperatures in excess of 1500.degree. C. A scanning electron microscopy (SEM) of the thick ITO sensors reveals a partially sintered microstructure comprising a contiguous network of submicron ITO particles with well defined necks and isolated nanoporosity. Densification of the ITO particles was retarded during high temperature exposure with nitrogen thus stabilizing the nanoporosity. ITO strain sensors were prepared by reactive sputtering in various nitrogen/oxygen/argon partial pressures to incorporate more nitrogen into the films. Under these conditions, sintering and densification of the ITO particles containing these nitrogen rich grain boundaries was retarded and a contiguous network of nano-sized ITO particles was established.

An algorithm for direct causal learning of influences on patient outcomes.

PubMed

Rathnam, Chandramouli; Lee, Sanghoon; Jiang, Xia

2017-01-01

This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association study (GWAS) datasets and compared its performance to classic causal learning algorithms. The DCL algorithm learns the causes of a single target from passive data using Bayesian-scoring, instead of using independence checks, and a novel deletion algorithm. We generate 14,400 simulated datasets and measure the number of datasets for which DCL correctly and partially predicts the direct causes. We then compare its performance with the constraint-based path consistency (PC) and conservative PC (CPC) algorithms, the Bayesian-score based fast greedy search (FGS) algorithm, and the partial ancestral graphs algorithm fast causal inference (FCI). In addition, we extend our comparison of all five algorithms to both a real GWAS dataset and real breast cancer datasets over various time-points in order to observe how effective they are at predicting the causal influences of Alzheimer's disease and breast cancer survival. DCL consistently outperforms FGS, PC, CPC, and FCI in discovering the parents of the target for the datasets simulated using a simple network. Overall, DCL predicts significantly more datasets correctly (McNemar's test significance: p<0.0001) than any of the other algorithms for these network types. For example, when assessing overall performance (simple and complex network results combined), DCL correctly predicts approximately 1400 more datasets than the top FGS method, 1600 more datasets than the top CPC method, 4500 more datasets than the top PC method, and 5600 more datasets than the top FCI method. Although FGS did correctly predict more datasets than DCL for the complex networks, and DCL correctly predicted only a few more datasets than CPC for these networks, there is no significant difference in performance between these three algorithms for this network type. However, when we use a more continuous measure of accuracy, we find that all the DCL methods are able to better partially predict more direct causes than FGS and CPC for the complex networks. In addition, DCL consistently had faster runtimes than the other algorithms. In the application to the real datasets, DCL identified rs6784615, located on the NISCH gene, and rs10824310, located on the PRKG1 gene, as direct causes of late onset Alzheimer's disease (LOAD) development. In addition, DCL identified ER category as a direct predictor of breast cancer mortality within 5 years, and HER2 status as a direct predictor of 10-year breast cancer mortality. These predictors have been identified in previous studies to have a direct causal relationship with their respective phenotypes, supporting the predictive power of DCL. When the other algorithms discovered predictors from the real datasets, these predictors were either also found by DCL or could not be supported by previous studies. Our results show that DCL outperforms FGS, PC, CPC, and FCI in almost every case, demonstrating its potential to advance causal learning. Furthermore, our DCL algorithm effectively identifies direct causes in the LOAD and Metabric GWAS datasets, which indicates its potential for clinical applications. Copyright © 2016 Elsevier B.V. All rights reserved.

Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations

NASA Astrophysics Data System (ADS)

Puskarczyk, Edyta

2018-03-01

The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly, classification and thirdly, the accurate characterization of divisional intervals. Data set comprised of standard well logs from the selected well. Shale formations are represented mainly by claystones, siltstones, and mudstones. The formations are also partially rich in organic matter. During the calculations, information about lithology of stratigraphy weren't taken into account. In the analysis, selforganizing neural network - Kohonen Algorithm (ANN) was used for sweet spots identification. Different networks and different software were tested and the best network was used for application and interpretation. As a results of Kohonen networks, groups corresponding to the gas-bearing intervals were found. The analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in sweet spots is present. Kohonen algorithm was also used for geological interpretation of well log data and electrofacies prediction. Reliable characteristic into groups shows that Ja Mb and Sa Fm which are usually treated as potential sweet spots only partially have good reservoir conditions. It is concluded that ANN appears to be useful and quick tool for preliminary classification of members and sweet spots identification.

Continuum Model for River Networks

NASA Astrophysics Data System (ADS)

Giacometti, Achille; Maritan, Amos; Banavar, Jayanth R.

1995-07-01

The effects of erosion, avalanching, and random precipitation are captured in a simple stochastic partial differential equation for modeling the evolution of river networks. Our model leads to a self-organized structured landscape and to abstraction and piracy of the smaller tributaries as the evolution proceeds. An algebraic distribution of the average basin areas and a power law relationship between the drainage basin area and the river length are found.

Online sequential Monte Carlo smoother for partially observed diffusion processes

NASA Astrophysics Data System (ADS)

Gloaguen, Pierre; Étienne, Marie-Pierre; Le Corff, Sylvain

2018-12-01

This paper introduces a new algorithm to approximate smoothed additive functionals of partially observed diffusion processes. This method relies on a new sequential Monte Carlo method which allows to compute such approximations online, i.e., as the observations are received, and with a computational complexity growing linearly with the number of Monte Carlo samples. The original algorithm cannot be used in the case of partially observed stochastic differential equations since the transition density of the latent data is usually unknown. We prove that it may be extended to partially observed continuous processes by replacing this unknown quantity by an unbiased estimator obtained for instance using general Poisson estimators. This estimator is proved to be consistent and its performance are illustrated using data from two models.

Distributed multiple path routing in complex networks

NASA Astrophysics Data System (ADS)

Chen, Guang; Wang, San-Xiu; Wu, Ling-Wei; Mei, Pan; Yang, Xu-Hua; Wen, Guang-Hui

2016-12-01

Routing in complex transmission networks is an important problem that has garnered extensive research interest in the recent years. In this paper, we propose a novel routing strategy called the distributed multiple path (DMP) routing strategy. For each of the O-D node pairs in a given network, the DMP routing strategy computes and stores multiple short-length paths that overlap less with each other in advance. And during the transmission stage, it rapidly selects an actual routing path which provides low transmission cost from the pre-computed paths for each transmission task, according to the real-time network transmission status information. Computer simulation results obtained for the lattice, ER random, and scale-free networks indicate that the strategy can significantly improve the anti-congestion ability of transmission networks, as well as provide favorable routing robustness against partial network failures.

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

PubMed

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

Application of artificial neural network for heat transfer in porous cone

NASA Astrophysics Data System (ADS)

Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum

2018-05-01

Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.

Mesoscopic model for filament orientation in growing actin networks: the role of obstacle geometry

NASA Astrophysics Data System (ADS)

Weichsel, Julian; Schwarz, Ulrich S.

2013-03-01

Propulsion by growing actin networks is a universal mechanism used in many different biological systems, ranging from the sheet-like lamellipodium of crawling animal cells to the actin comet tails induced by certain bacteria and viruses in order to move within their host cells. Although the core molecular machinery for actin network growth is well preserved in all of these cases, the geometry of the propelled obstacle varies considerably. During recent years, filament orientation distribution has emerged as an important observable characterizing the structure and dynamical state of the growing network. Here we derive several continuum equations for the orientation distribution of filaments growing behind stiff obstacles of various shapes and validate the predicted steady state orientation patterns by stochastic computer simulations based on discrete filaments. We use an ordinary differential equation approach to demonstrate that for flat obstacles of finite size, two fundamentally different orientation patterns peaked at either ±35° or +70°/0°/ - 70° exhibit mutually exclusive stability, in agreement with earlier results for flat obstacles of very large lateral extension. We calculate and validate phase diagrams as a function of model parameters and show how this approach can be extended to obstacles with piecewise straight contours. For curved obstacles, we arrive at a partial differential equation in the continuum limit, which again is in good agreement with the computer simulations. In all cases, we can identify the same two fundamentally different orientation patterns, but only within an appropriate reference frame, which is adjusted to the local orientation of the obstacle contour. Our results suggest that two fundamentally different network architectures compete with each other in growing actin networks, irrespective of obstacle geometry, and clarify how simulated and electron tomography data have to be analyzed for non-flat obstacle geometries.

Isles within islets: The lattice origin of small-world networks in pancreatic tissues

NASA Astrophysics Data System (ADS)

Barua, Amlan K.; Goel, Pranay

2016-02-01

The traditional computational model of the pancreatic islets of Langerhans is a lattice of β-cells connected with gap junctions. Numerous studies have investigated the behavior of networks of coupled β-cells and have shown that gap junctions synchronize bursting strongly. This simplistic architecture of islets, however, seems increasingly untenable at the face of recent experimental advances. In a microfluidics experiment on isolated islets, Rocheleau et al. (2004) showed a failure of penetration of excitation when one end received high glucose and other end was not excited sufficiently; this suggested that gap junctions may not be efficient at inducing synchrony throughout the islet. Recently, Stozer et al. (2013) have argued that the functional networks of β-cells in an islet are small world. Their results implicate the existence of a few long-range connections among cells in the network. The physiological reason underlying this claim is not well understood. These studies cast doubt on the original lattice model that largely predict an all-or-none synchrony among the cells. Here we have attempted to reconcile these observations in a unified framework. We assume that cells in the islet are coupled randomly to their nearest neighbors with some probability, p. We simulated detailed β-cell bursting in such islets. By varying p systematically we were led to network parameters similar to those obtained by Stozer et al. (2013). We find that the networks within islets break up into components giving rise to smaller isles within the super structure-isles-within-islets, as it were. This structure can also account for the partial excitation seen by Rocheleau et al. (2004). Our updated view of islet architecture thus explains the paradox how islets can have strongly synchronizing gap junctions, and be weakly coordinated at the same time.

Social Network Resources and Management of Hypertension*

PubMed Central

Cornwell, Erin York; Waite, Linda J.

2013-01-01

Hypertension is one of the most prevalent chronic diseases among older adults, but rates of blood pressure control are low. In this paper, we explore the role of social network ties and network-based resources (e.g., information and support) in hypertension diagnosis and management. We use data from the National Social Life, Health, and Aging Project (NSHAP) to identify older adults with undiagnosed or uncontrolled hypertension. We find that network characteristics and emotional support are associated with hypertension diagnosis and control. Importantly, the risks of undiagnosed and uncontrolled hypertension are lower among those with larger social networks -- if they discuss health issues with their network members. When these lines of communication are closed, network size is associated with greater risk of undiagnosed and uncontrolled hypertension. Health care utilization partially mediates associations with diagnosis, but the benefits of network resources for hypertension control do not seem to stem from health-related behaviors. PMID:22660826

Artificial Neural Network with Regular Graph for Maximum Air Temperature Forecasting:. the Effect of Decrease in Nodes Degree on Learning

NASA Astrophysics Data System (ADS)

Ghaderi, A. H.; Darooneh, A. H.

The behavior of nonlinear systems can be analyzed by artificial neural networks. Air temperature change is one example of the nonlinear systems. In this work, a new neural network method is proposed for forecasting maximum air temperature in two cities. In this method, the regular graph concept is used to construct some partially connected neural networks that have regular structures. The learning results of fully connected ANN and networks with proposed method are compared. In some case, the proposed method has the better result than conventional ANN. After specifying the best network, the effect of input pattern numbers on the prediction is studied and the results show that the increase of input patterns has a direct effect on the prediction accuracy.

Origin and implications of zero degeneracy in networks spectra.

PubMed

Yadav, Alok; Jalan, Sarika

2015-04-01

The spectra of many real world networks exhibit properties which are different from those of random networks generated using various models. One such property is the existence of a very high degeneracy at the zero eigenvalue. In this work, we provide all the possible reasons behind the occurrence of the zero degeneracy in the network spectra, namely, the complete and partial duplications, as well as their implications. The power-law degree sequence and the preferential attachment are the properties which enhances the occurrence of such duplications and hence leading to the zero degeneracy. A comparison of the zero degeneracy in protein-protein interaction networks of six different species and in their corresponding model networks indicates importance of the degree sequences and the power-law exponent for the occurrence of zero degeneracy.

Artificial Neural Networks Equivalent to Fuzzy Algebra T-Norm Conjunction Operators

NASA Astrophysics Data System (ADS)

Iliadis, L. S.; Spartalis, S. I.

2007-12-01

This paper describes the construction of three Artificial Neural Networks with fuzzy input and output, imitating the performance of fuzzy algebra conjunction operators. More specifically, it is applied over the results of a previous research effort that used T-Norms in order to produce a characteristic torrential risk index that unified the partial risk indices for the area of Xanthi. Each one of the three networks substitutes a T-Norm and consequently they can be used as equivalent operators. This means that ANN performing Fuzzy Algebra operations can be designed and developed.

Involvement of mesolimbic dopaminergic network in neuropathic pain relief by treadmill exercise: A study for specific neural control with Gi-DREADD in mice.

PubMed

Wakaizumi, Kenta; Kondo, Takashige; Hamada, Yusuke; Narita, Michiko; Kawabe, Rui; Narita, Hiroki; Watanabe, Moe; Kato, Shigeki; Senba, Emiko; Kobayashi, Kazuto; Kuzumaki, Naoko; Yamanaka, Akihiro; Morisaki, Hiroshi; Narita, Minoru

2016-01-01

Exercise alleviates pain and it is a central component of treatment strategy for chronic pain in clinical setting. However, little is known about mechanism of this exercise-induced hypoalgesia. The mesolimbic dopaminergic network plays a role in positive emotions to rewards including motivation and pleasure. Pain negatively modulates these emotions, but appropriate exercise is considered to activate the dopaminergic network. We investigated possible involvement of this network as a mechanism of exercise-induced hypoalgesia. In the present study, we developed a protocol of treadmill exercise, which was able to recover pain threshold under partial sciatic nerve ligation in mice, and investigated involvement of the dopaminergic reward network in exercise-induced hypoalgesia. To temporally suppress a neural activation during exercise, a genetically modified inhibitory G-protein-coupled receptor, hM4Di, was specifically expressed on dopaminergic pathway from the ventral tegmental area to the nucleus accumbens. The chemogenetic-specific neural suppression by Gi-DREADD system dramatically offset the effect of exercise-induced hypoalgesia in transgenic mice with hM4Di expressed on the ventral tegmental area dopamine neurons. Additionally, anti-exercise-induced hypoalgesia effect was significantly observed under the suppression of neurons projecting out of the ventral tegmental area to the nucleus accumbens as well. Our findings suggest that the dopaminergic pathway from the ventral tegmental area to the nucleus accumbens is involved in the anti-nociception under low-intensity exercise under a neuropathic pain-like state. © The Author(s) 2016.

Frontoparietal cognitive control of verbal memory recall in Alzheimer's disease.

PubMed

Dhanjal, Novraj S; Wise, Richard J S

2014-08-01

Episodic memory retrieval is reliant upon cognitive control systems, of which 2 have been identified with functional neuroimaging: a cingulo-opercular salience network (SN) and a frontoparietal executive network (EN). In Alzheimer's disease (AD), pathology is distributed throughout higher-order cortices. The hypotheses were that this frontoparietal pathology would impair activity associated with verbal memory recall; and that central cholinesterase inhibition (ChI) would modulate this, improving memory recall. Functional magnetic resonance imaging was used to study normal participants and 2 patient groups: mild cognitive impairment (MCI) and AD. Activity within the EN and SN was observed during free recall of previously heard sentences, and related to measures of recall accuracy. In normal subjects, trials with reduced recall were associated with greater activity in both the SN and EN. Better recall was associated with greater activity in medial regions of the default mode network. By comparison, AD patients showed attenuated responses in both the SN and EN compared with either controls or MCI patients, even after recall performance was matched between groups. Following ChI, AD patients showed no modulation of activity within the SN, but increased activity within the EN. There was also enhanced activity within regions associated with episodic and semantic memory during less successful recall, requiring greater cognitive control. The results indicate that in AD, impaired responses of cognitive control networks during verbal memory recall are partly responsible for reduced recall performance. One action of symptom-modifying treatment is partially to reverse the abnormal function of frontoparietal cognitive control and temporal lobe memory networks. © 2014 American Neurological Association.

Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks.

PubMed

Özbalci, Beril; Boyaci, İsmail Hakkı; Topcu, Ali; Kadılar, Cem; Tamer, Uğur

2013-02-15

The aim of this study was to quantify glucose, fructose, sucrose and maltose contents of honey samples using Raman spectroscopy as a rapid method. By performing a single measurement, quantifications of sugar contents have been said to be unaffordable according to the molecular similarities between sugar molecules in honey matrix. This bottleneck was overcome by coupling Raman spectroscopy with chemometric methods (principal component analysis (PCA) and partial least squares (PLS)) and an artificial neural network (ANN). Model solutions of four sugars were processed with PCA and significant separation was observed. This operation, done with the spectral features by using PLS and ANN methods, led to the discriminant analysis of sugar contents. Models/trained networks were created using a calibration data set and evaluated using a validation data set. The correlation coefficient values between actual and predicted values of glucose, fructose, sucrose and maltose were determined as 0.964, 0.965, 0.968 and 0.949 for PLS and 0.965, 0.965, 0.978 and 0.956 for ANN, respectively. The requirement of rapid analysis of sugar contents of commercial honeys has been met by the data processed within this article. Copyright © 2012 Elsevier Ltd. All rights reserved.

Collective Intelligence: Aggregation of Information from Neighbors in a Guessing Game.

PubMed

Pérez, Toni; Zamora, Jordi; Eguíluz, Víctor M

2016-01-01

Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.

The study of RMB exchange rate complex networks based on fluctuation mode

NASA Astrophysics Data System (ADS)

Yao, Can-Zhong; Lin, Ji-Nan; Zheng, Xu-Zhou; Liu, Xiao-Feng

2015-10-01

In the paper, we research on the characteristics of RMB exchange rate time series fluctuation with methods of symbolization and coarse gaining. First, based on fluctuation features of RMB exchange rate, we define the first type of fluctuation mode as one specific foreign currency against RMB in four days' fluctuating situations, and the second type as four different foreign currencies against RMB in one day's fluctuating situation. With the transforming method, we construct the unique-currency and multi-currency complex networks. Further, through analyzing the topological features including out-degree, betweenness centrality and clustering coefficient of fluctuation-mode complex networks, we find that the out-degree distribution of both types of fluctuation mode basically follows power-law distributions with exponents between 1 and 2. The further analysis reveals that the out-degree and the clustering coefficient generally obey the approximated negative correlation. With this result, we confirm previous observations showing that the RMB exchange rate exhibits a characteristic of long-range memory. Finally, we analyze the most probable transmission route of fluctuation modes, and provide probability prediction matrix. The transmission route for RMB exchange rate fluctuation modes exhibits the characteristics of partially closed loop, repeat and reversibility, which lays a solid foundation for predicting RMB exchange rate fluctuation patterns with large volume of data.

Collective Intelligence: Aggregation of Information from Neighbors in a Guessing Game

PubMed Central

Pérez, Toni; Zamora, Jordi; Eguíluz, Víctor M.

2016-01-01

Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena. PMID:27093274

An Amorphous Network Model for Capillary Flow and Dispersion in a Partially Saturated Porous Medium

NASA Astrophysics Data System (ADS)

Simmons, C. S.; Rockhold, M. L.

2013-12-01

Network models of capillary flow are commonly used to represent conduction of fluids at pore scales. Typically, a flow system is described by a regular geometric lattice of interconnected tubes. Tubes constitute the pore throats, while connection junctions (nodes) are pore bodies. Such conceptualization of the geometry, however, is questionable for the pore scale, where irregularity clearly prevails, although prior published models using a regular lattice have demonstrated successful descriptions of the flow in the bulk medium. Here a network is allowed to be amorphous, and is not subject to any particular lattice structure. Few network flow models have treated partially saturated or even multiphase conditions. The research trend is toward using capillary tubes with triangular or square cross sections that have corners and always retain some fluid by capillarity when drained. In contrast, this model uses only circular capillaries, whose filled state is controlled by a capillary pressure rule for the junctions. The rule determines which capillary participate in the flow under an imposed matric potential gradient during steady flow conditions. Poiseuille's Law and Laplace equation are used to describe flow and water retention in the capillary units of the model. A modified conjugate gradient solution for steady flow that tracks which capillary in an amorphous network contribute to fluid conduction was devised for partially saturated conditions. The model thus retains the features of classical capillary models for determining hydraulic flow properties under unsaturated conditions based on distribution of non-interacting tubes, but now accounts for flow exchange at junctions. Continuity of the flow balance at every junction is solved simultaneously. The effective water retention relationship and unsaturated permeability are evaluated for an extensive enough network to represent a small bulk sample of porous medium. The model is applied for both a hypothetically randomly generate network and for a directly measured porous medium structure, by means of xray-CT scan. A randomly generated network has the benefit of providing ensemble averages for sample replicates of a medium's properties, whereas network structure measurements are expected to be more predictive. Dispersion of solute in a network flow is calculate by using particle tracking to determine the travel time breakthrough between inflow and outflow boundaries. The travel time distribution can exhibit substantial skewness that reflects both network velocity variability and mixing dilution at junctions. When local diffusion is not included, and transport is strictly advective, then the skew breakthrough is not due to mobile-immobile flow region behavior. The approach of dispersivity to its asymptotic value with sample size is examined, and may be only an indicator of particular stochastic flow variation. It is not proven that a simplified network flow model can accurately predict the hydraulic properties of a sufficiently large-size medium sample, but such a model can at least demonstrate macroscopic flow resulting from the interaction of physical processes at pore scales.

The new Wide-band Solar Neutrino Trigger for Super-Kamiokande

NASA Astrophysics Data System (ADS)

Carminati, Giada

Super-Kamiokande observes low energy electrons induced by the elastic scattering of 8B solar neutrinos. The transition region between vacuum and matter oscillations, with neutrino energy near 3 MeV, is still partially unexplored by any detector. Super-Kamiokande can study this intermediate regime adding a new software trigger. The Wide-band Intelligent Trigger (WIT) has been developed to simultaneously trigger and reconstruct very low energy electrons (above 2.49 kinetic MeV) with an e_ciency close to 100%. The WIT system, comprising 256-Hyperthreaded CPU cores and one 10-Gigabit Ethernet network switch, has been recently installed and integrated in the online DAQ system of SK and the complete system is currently in an advanced status of online data testing.

Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease.

PubMed

Nestor, Sean M; Mišić, Bratislav; Ramirez, Joel; Zhao, Jiali; Graham, Simon J; Verhoeff, Nicolaas P L G; Stuss, Donald T; Masellis, Mario; Black, Sandra E

2017-07-01

Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD. We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects. SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD. These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Distributed Coordination of Heterogeneous Agents Using a Semantic Overlay Network and a Goal-Directed Graphplan Planner

PubMed Central

Lopes, António Luís; Botelho, Luís Miguel

2013-01-01

In this paper, we describe a distributed coordination system that allows agents to seamlessly cooperate in problem solving by partially contributing to a problem solution and delegating the subproblems for which they do not have the required skills or knowledge to appropriate agents. The coordination mechanism relies on a dynamically built semantic overlay network that allows the agents to efficiently locate, even in very large unstructured networks, the necessary skills for a specific problem. Each agent performs partial contributions to the problem solution using a new distributed goal-directed version of the Graphplan algorithm. This new goal-directed version of the original Graphplan algorithm provides an efficient solution to the problem of "distraction", which most forward-chaining algorithms suffer from. We also discuss a set of heuristics to be used in the backward-search process of the planning algorithm in order to distribute this process amongst idle agents in an attempt to find a solution in less time. The evaluation results show that our approach is effective in building a scalable and efficient agent society capable of solving complex distributable problems. PMID:23704885

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Novel sulfated xylogalactoarabinans from green seaweed Cladophora falklandica: Chemical structure and action on the fibrin network.

PubMed

Arata, Paula X; Quintana, Irene; Raffo, María Paula; Ciancia, Marina

2016-12-10

The water-soluble sulfated xylogalactoarabinans from green seaweed Cladophora falklandica are constituted by a backbone of 4-linked β-l-arabinopyranose units partially sulfated mainly on C3 and also on C2. Besides, partial glycosylation mostly on C2 with single stubs of β-d-xylopyranose, or single stubs of β-d-galactofuranose or short chains comprising (1→5)- and/or (1→6)-linkages, was also found. These compounds showed anticoagulant activity, although much lower than that of heparin. The effect of a purified fraction (F1) on the fibrin network was studied in detail. It modifies the kinetics of fibrin formation, suggesting an impaired polymerization process. Scanning electron microscopy showed a laxer conformation, with larger interstitial pores than the control. Accordingly, this network was lysed more easily. These fibrin properties would reduce the time of permanence of the clot in the blood vessel, inducing a lesser thrombogenic state. One of the possible mechanisms of its anticoagulant effect is direct thrombin inhibition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

PubMed Central

Prettejohn, Brenton J.; Berryman, Matthew J.; McDonnell, Mark D.

2011-01-01

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks. PMID:21441986

VINE: A Variational Inference -Based Bayesian Neural Network Engine

DTIC Science & Technology

2018-01-01

networks are trained using the same dataset and hyper parameter settings as discussed. Table 1 Performance evaluation of the proposed transfer learning...multiplication/addition/subtraction. These operations can be implemented using nested loops in which various iterations of a loop are independent of...each other. This introduces an opportunity for optimization where a loop may be unrolled fully or partially to increase parallelism at the cost of

On-line Monitoring Device for High-voltage Switch Cabinet Partial Discharge Based on Pulse Current Method

NASA Astrophysics Data System (ADS)

Y Tao, S.; Zhang, X. Z.; Cai, H. W.; Li, P.; Feng, Y.; Zhang, T. C.; Li, J.; Wang, W. S.; Zhang, X. K.

2017-12-01

The pulse current method for partial discharge detection is generally applied in type testing and other off-line tests of electrical equipment at delivery. After intensive analysis of the present situation and existing problems of partial discharge detection in switch cabinets, this paper designed the circuit principle and signal extraction method for partial discharge on-line detection based on a high-voltage presence indicating systems (VPIS), established a high voltage switch cabinet partial discharge on-line detection circuit based on the pulse current method, developed background software integrated with real-time monitoring, judging and analyzing functions, carried out a real discharge simulation test on a real-type partial discharge defect simulation platform of a 10KV switch cabinet, and verified the sensitivity and validity of the high-voltage switch cabinet partial discharge on-line monitoring device based on the pulse current method. The study presented in this paper is of great significance for switch cabinet maintenance and theoretical study on pulse current method on-line detection, and has provided a good implementation method for partial discharge on-line monitoring devices for 10KV distribution network equipment.

Heteroassociative storage of hippocampal pattern sequences in the CA3 subregion

PubMed Central

Recio, Renan S.; Reyes, Marcelo B.

2018-01-01

Background Recent research suggests that the CA3 subregion of the hippocampus has properties of both autoassociative network, due to its ability to complete partial cues, tolerate noise, and store associations between memories, and heteroassociative one, due to its ability to store and retrieve sequences of patterns. Although there are several computational models of the CA3 as an autoassociative network, more detailed evaluations of its heteroassociative properties are missing. Methods We developed a model of the CA3 subregion containing 10,000 integrate-and-fire neurons with both recurrent excitatory and inhibitory connections, and which exhibits coupled oscillations in the gamma and theta ranges. We stored thousands of pattern sequences using a heteroassociative learning rule with competitive synaptic scaling. Results We showed that a purely heteroassociative network model can (i) retrieve pattern sequences from partial cues with external noise and incomplete connectivity, (ii) achieve homeostasis regarding the number of connections per neuron when many patterns are stored when using synaptic scaling, (iii) continuously update the set of retrievable patterns, guaranteeing that the last stored patterns can be retrieved and older ones can be forgotten. Discussion Heteroassociative networks with synaptic scaling rules seem sufficient to achieve many desirable features regarding connectivity homeostasis, pattern sequence retrieval, noise tolerance and updating of the set of retrievable patterns. PMID:29312826

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

PubMed

Basu, Sumanta; Duren, William; Evans, Charles R; Burant, Charles F; Michailidis, George; Karnovsky, Alla

2017-05-15

Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. http://metscape.med.umich.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Medium scale carbon nanotube thin film integrated circuits on flexible plastic substrates

DOEpatents

Rogers, John A; Cao, Qing; Alam, Muhammad; Pimparkar, Ninad

2015-02-03

The present invention provides device components geometries and fabrication strategies for enhancing the electronic performance of electronic devices based on thin films of randomly oriented or partially aligned semiconducting nanotubes. In certain aspects, devices and methods of the present invention incorporate a patterned layer of randomly oriented or partially aligned carbon nanotubes, such as one or more interconnected SWNT networks, providing a semiconductor channel exhibiting improved electronic properties relative to conventional nanotubes-based electronic systems.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Exercise alters resting state functional connectivity of motor circuits in Parkinsonian rats

PubMed Central

Wang, Zhuo; Guo, Yumei; Myers, Kalisa G.; Heintz, Ryan; Peng, Yu-Hao; Maarek, Jean-Michel I.; Holschneider, Daniel P.

2014-01-01

Few studies have examined changes in functional connectivity after long-term aerobic exercise. We examined the effects of 4 weeks of forced running wheel exercise on the resting-state functional connectivity (rsFC) of motor circuits of rats subjected to bilateral 6-hydroxydopamine lesion of the dorsal striatum. Our results showed substantial similarity between lesion-induced changes in rsFC in the rats and alterations in rsFC reported in Parkinson’s disease subjects, including disconnection of the dorsolateral striatum. Exercise in lesioned rats resulted in: (a) normalization of many of the lesion-induced alterations in rsFC, including reintegration of the dorsolateral striatum into the motor network; (b) emergence of the ventrolateral striatum as a new broadly connected network hub; (c) increased rsFC among the motor cortex, motor thalamus, basal ganglia, and cerebellum. Our results showed for the first time that long-term exercise training partially reversed lesion-induced alterations in rsFC of the motor circuits, and in addition enhanced functional connectivity in specific motor pathways in the Parkinsonian rats, which could underlie recovery in motor functions observed in these rats. PMID:25219465

A neural network simulating human reach-grasp coordination by continuous updating of vector positioning commands.

PubMed

Ulloa, Antonio; Bullock, Daniel

2003-10-01

We developed a neural network model to simulate temporal coordination of human reaching and grasping under variable initial grip apertures and perturbations of object size and object location/orientation. The proposed model computes reach-grasp trajectories by continuously updating vector positioning commands. The model hypotheses are (1) hand/wrist transport, grip aperture, and hand orientation control modules are coupled by a gating signal that fosters synchronous completion of the three sub-goals. (2) Coupling from transport and orientation velocities to aperture control causes maximum grip apertures that scale with these velocities and exceed object size. (3) Part of the aperture trajectory is attributable to an aperture-reducing passive biomechanical effect that is stronger for larger apertures. (4) Discrepancies between internal representations of targets partially inhibit the gating signal, leading to movement time increases that compensate for perturbations. Simulations of the model replicate key features of human reach-grasp kinematics observed under three experimental protocols. Our results indicate that no precomputation of component movement times is necessary for online temporal coordination of the components of reaching and grasping.

Oscillatory Protein Expression Dynamics Endows Stem Cells with Robust Differentiation Potential

PubMed Central

Kaneko, Kunihiko

2011-01-01

The lack of understanding of stem cell differentiation and proliferation is a fundamental problem in developmental biology. Although gene regulatory networks (GRNs) for stem cell differentiation have been partially identified, the nature of differentiation dynamics and their regulation leading to robust development remain unclear. Herein, using a dynamical system modeling cell approach, we performed simulations of the developmental process using all possible GRNs with a few genes, and screened GRNs that could generate cell type diversity through cell-cell interactions. We found that model stem cells that both proliferated and differentiated always exhibited oscillatory expression dynamics, and the differentiation frequency of such stem cells was regulated, resulting in a robust number distribution. Moreover, we uncovered the common regulatory motifs for stem cell differentiation, in which a combination of regulatory motifs that generated oscillatory expression dynamics and stabilized distinct cellular states played an essential role. These findings may explain the recently observed heterogeneity and dynamic equilibrium in cellular states of stem cells, and can be used to predict regulatory networks responsible for differentiation in stem cell systems. PMID:22073296

Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models

NASA Astrophysics Data System (ADS)

Philip, Sajeev; Martin, Randall V.; Snider, Graydon; Weagle, Crystal L.; van Donkelaar, Aaron; Brauer, Michael; Henze, Daven K.; Klimont, Zbigniew; Venkataraman, Chandra; Guttikunda, Sarath K.; Zhang, Qiang

2017-04-01

Global measurements of the elemental composition of fine particulate matter across several urban locations by the Surface Particulate Matter Network reveal an enhanced fraction of anthropogenic dust compared to natural dust sources, especially over Asia. We develop a global simulation of anthropogenic fugitive, combustion, and industrial dust which, to our knowledge, is partially missing or strongly underrepresented in global models. We estimate 2-16 μg m-3 increase in fine particulate mass concentration across East and South Asia by including anthropogenic fugitive, combustion, and industrial dust emissions. A simulation including anthropogenic fugitive, combustion, and industrial dust emissions increases the correlation from 0.06 to 0.66 of simulated fine dust in comparison with Surface Particulate Matter Network measurements at 13 globally dispersed locations, and reduces the low bias by 10% in total fine particulate mass in comparison with global in situ observations. Global population-weighted PM2.5 increases by 2.9 μg m-3 (10%). Our assessment ascertains the urgent need of including this underrepresented fine anthropogenic dust source into global bottom-up emission inventories and global models.

Distributed intelligent monitoring and reporting facilities

NASA Astrophysics Data System (ADS)

Pavlou, George; Mykoniatis, George; Sanchez-P, Jorge-A.

1996-06-01

Distributed intelligent monitoring and reporting facilities are of paramount importance in both service and network management as they provide the capability to monitor quality of service and utilization parameters and notify degradation so that corrective action can be taken. By intelligent, we refer to the capability of performing the monitoring tasks in a way that has the smallest possible impact on the managed network, facilitates the observation and summarization of information according to a number of criteria and in its most advanced form and permits the specification of these criteria dynamically to suit the particular policy in hand. In addition, intelligent monitoring facilities should minimize the design and implementation effort involved in such activities. The ISO/ITU Metric, Summarization and Performance management functions provide models that only partially satisfy the above requirements. This paper describes our extensions to the proposed models to support further capabilities, with the intention to eventually lead to fully dynamically defined monitoring policies. The concept of distributing intelligence is also discussed, including the consideration of security issues and the applicability of the model in ODP-based distributed processing environments.

Modelling the structural controls of primary kaolinite formation

NASA Astrophysics Data System (ADS)

Tierney, R. L.; Glass, H. J.

2016-09-01

An abundance of kaolinite was formed within the St. Austell outcrop of the Cornubian batholith in Cornwall, southwest England, by the hydrous dissolution of feldspar crystals. The permeability of Cornish granites is low and alteration acts pervasively from discontinuity features, with montmorillonite recognised as an intermediate assemblage in partially kaolinised material. Structural features allowed fluids to channel through the impermeable granite and pervade deep into the rock. Areas of high structural control are hypothesised to link well with areas of advanced alteration. As kaolinisation results in a loss of competence, we present a method of utilising discontinuity orientations from nearby unaltered granites alongside the local tectonic history to calculate strain rates and delineate a discrete fracture network. Simulation of the discrete fracture network is demonstrated through a case study at Higher Moor, where kaolinite is actively extracted from a pit. Reconciliation of fracture connectivity and permeability against measured subsurface data show that higher values of modelled properties match with advanced kaolinisation observed in the field. This suggests that the technique may be applicable across various industries and disciplines.

Reconstructing networks from dynamics with correlated noise

NASA Astrophysics Data System (ADS)

Tam, H. C.; Ching, Emily S. C.; Lai, Pik-Yin

2018-07-01

Reconstructing the structure of complex networks from measurements of the nodes is a challenge in many branches of science. External influences are always present and act as a noise to the networks of interest. In this paper, we present a method for reconstructing networks from measured dynamics of the nodes subjected to correlated noise that cannot be approximated by a white noise. This method can reconstruct the links of both bidirectional and directed networks, the correlation time and strength of the noise, and also the relative coupling strength of the links when the coupling functions have certain properties. Our method is built upon theoretical relations between network structure and measurable quantities from the dynamics that we have derived for systems that have fixed point dynamics in the noise-free limit. Using these theoretical results, we can further explain the shortcomings of two common practices of inferring links for bidirectional networks using the Pearson correlation coefficient and the partial correlation coefficient.

Intrinsic Amygdala-Cortical Functional Connectivity Predicts Social Network Size in Humans

PubMed Central

Bickart, Kevin C.; Hollenbeck, Mark C.; Barrett, Lisa Feldman; Dickerson, Bradford C.

2012-01-01

Using resting-state functional MRI data from two independent samples of healthy adults, we parsed the amygdala’s intrinsic connectivity into three partially-distinct large-scale networks that strongly resemble the known anatomical organization of amygdala connectivity in rodents and monkeys. Moreover, in a third independent sample, we discovered that people who fostered and maintained larger and more complex social networks not only had larger amygdala volumes, but also amygdalae with stronger intrinsic connectivity within two of these networks, one putatively subserving perceptual abilities and one subserving affiliative behaviors. Our findings were anatomically specific to amygdalar circuitry in that individual differences in social network size and complexity could not be explained by the strength of intrinsic connectivity between nodes within two networks that do not typically involve the amygdala (i.e., the mentalizing and mirror networks), and were behaviorally specific in that amygdala connectivity did not correlate with other self-report measures of sociality. PMID:23077058

Cellular and network properties of the subiculum in the pilocarpine model of temporal lobe epilepsy.

PubMed

Knopp, Andreas; Kivi, Anatol; Wozny, Christian; Heinemann, Uwe; Behr, Joachim

2005-03-21

The subiculum was recently shown to be crucially involved in the generation of interictal activity in human temporal lobe epilepsy. Using the pilocarpine model of epilepsy, this study examines the anatomical substrates for network hyperexcitability recorded in the subiculum. Regular- and burst-spiking subicular pyramidal cells were stained with fluorescence dyes and reconstructed to analyze seizure-induced alterations of the dendritic and axonal system. In control animals burst-spiking cells outnumbered regular-spiking cells by about two to one. Regular- and burst-spiking cells were characterized by extensive axonal branching and autapse-like contacts, suggesting a high intrinsic connectivity. In addition, subicular axons projecting to CA1 indicate a CA1-subiculum-CA1 circuit. In the subiculum of pilocarpine-treated rats we found an enhanced network excitability characterized by spontaneous rhythmic activity, polysynaptic responses, and all-or-none evoked bursts of action potentials. In pilocarpine-treated rats the subiculum showed cell loss of about 30%. The ratio of regular- and burst-spiking cells was practically inverse as compared to control preparations. A reduced arborization and spine density in the proximal part of the apical dendrites suggests a partial deafferentiation from CA1. In pilocarpine-treated rats no increased axonal outgrowth of pyramidal cells was observed. Hence, axonal sprouting of subicular pyramidal cells is not mandatory for the development of the pathological events. We suggest that pilocarpine-induced seizures cause an unmasking or strengthening of synaptic contacts within the recurrent subicular network. Copyright 2005 Wiley-Liss, Inc.

Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans

PubMed Central

Devauchelle, Anne-Dominique; Béranger, Benoît; Tallon-Baudry, Catherine

2018-01-01

Resting-state networks offer a unique window into the brain’s functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with partial overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics. PMID:29561263

Solving differential equations with unknown constitutive relations as recurrent neural networks

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

Hagge, Tobias J.; Stinis, Panagiotis; Yeung, Enoch H.

We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and use a recurrent neural network to “learn” the reaction rate from this data. This is achieved by including discretized ordinary differential equations as part of a recurrent neural network training problem. We extend TensorFlow’s recurrent neural network architecture to create a simple but scalable and effective solver for the unknown functions, and apply it to a fedbatch bioreactor simulation problem. Use of techniques from recent deep learningmore » literature enables training of functions with behavior manifesting over thousands of time steps. Our networks are structurally similar to recurrent neural networks, but differ in purpose, and require modified training strategies.« less

Exploring drug-target interaction networks of illicit drugs.

PubMed

Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming

2013-01-01

Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. This study presents the first systematic review of the network characteristics of illicit drugs, their targets, and other drugs that share the targets of these illicit drugs. The results, though preliminary, provide some novel insights into the molecular mechanisms of drug addiction. The observation of illicit-related drugs, with partial verification from previous studies, demonstrated that the network-assisted approach is promising for the identification of drug repositioning.

Exploring drug-target interaction networks of illicit drugs

PubMed Central

2013-01-01

Background Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. Results In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. Conclusions This study presents the first systematic review of the network characteristics of illicit drugs, their targets, and other drugs that share the targets of these illicit drugs. The results, though preliminary, provide some novel insights into the molecular mechanisms of drug addiction. The observation of illicit-related drugs, with partial verification from previous studies, demonstrated that the network-assisted approach is promising for the identification of drug repositioning. PMID:24268016

An analysis of the sectorial influence of CSI300 stocks within the directed network

NASA Astrophysics Data System (ADS)

Mai, Yong; Chen, Huan; Meng, Lei

2014-02-01

This paper uses the Partial Correlation Planar maximally filtered Graph (PCPG) method to construct a directed network for the constituent stocks underlying the China Securities Index 300 (CSI300). We also analyse the impact of individual stocks. We find that the CSI300 market is a scale-free network with a relatively small power law exponent. The volatility of the stock prices has significant impact on other stocks. In the sectorial network, the industrial sector is the most influential one over other sectors, the financial sector only has a modest influence, while the telecommunication services sector’s influence is marginal. In addition, such inter-sector influence displays quarterly stability.

Example of a Bayes network of relations among visual features

NASA Astrophysics Data System (ADS)

Agosta, John M.

1991-10-01

Bayes probability networks, also termed `influence diagrams,' promise to be a versatile, rigorous, and expressive uncertainty reasoning tool. This paper presents an example of how a Bayes network can express constraints among visual hypotheses. An example is presented of a model composed of cylindric primitives, inferred from a line drawing of a plumbing fixture. Conflict between interpretations of candidate cylinders is expressed by two parameters, one for the presence and one for the absence of visual evidence of their intersection. It is shown how `partial exclusion' relations are so generated and how they determine the degree of competition among the set of hypotheses. Solving this network obtains the assemblies of cylinders most likely to form an object.

Chemical Zoning of Feldspars in Lunar Granitoids: Implications for the Origins of Lunar Silicic Magmas

NASA Technical Reports Server (NTRS)

Mills, R. D; Simon, J. I.; Alexander, C.M. O'D.; Wang, J.; Christoffersen, R.; Rahman, Z..

2014-01-01

Fine-scale chemical and textural measurements of alkali and plagioclase feldspars in the Apollo granitoids (ex. Fig. 1) can be used to address their petrologic origin(s). Recent findings suggest that these granitoids may hold clues of global importance, rather than of only local significance for small-scale fractionation. Observations of morphological features that resemble silicic domes on the unsampled portion of the Moon suggest that local, sizable net-works of high-silica melt (>65 wt % SiO2) were present during crust-formation. Remote sensing data from these regions suggest high concentrations of Si and heat-producing elements (K, U, and Th). To help under-stand the role of high-silica melts in the chemical differentiation of the Moon, three questions must be answered: (1) when were these magmas generated?, (2) what was the source material?, and (3) were these magmas produced from internal differentiation. or impact melting and crystallization? Here we focus on #3. It is difficult to produce high-silica melts solely by fractional crystallization. Partial melting of preexisting crust may therefore also have been important and pos-sibly the primary mechanism that produced the silicic magmas on the Moon. Experimental studies demonstrate that partial melting of gabbroic rock under mildly hydrated conditions can produce high-silica compositions and it has been suggested by that partial melting by basaltic underplating is the mechanism by which high-silica melts were produced on the Moon. TEM and SIMS analyses, coordinated with isotopic dating and tracer studies, can help test whether the minerals in the Apollo granitoids formed in a plutonic setting or were the result of impact-induced partial melting. We analyzed granitoid clasts from 3 Apollo samples: polymict breccia 12013,141, crystalline-matrix breccia 14303,353, and breccia 15405,78

Symmetries of Chimera States

NASA Astrophysics Data System (ADS)

Kemeth, Felix P.; Haugland, Sindre W.; Krischer, Katharina

2018-05-01

Symmetry broken states arise naturally in oscillatory networks. In this Letter, we investigate chaotic attractors in an ensemble of four mean-coupled Stuart-Landau oscillators with two oscillators being synchronized. We report that these states with partially broken symmetry, so-called chimera states, have different setwise symmetries in the incoherent oscillators, and in particular, some are and some are not invariant under a permutation symmetry on average. This allows for a classification of different chimera states in small networks. We conclude our report with a discussion of related states in spatially extended systems, which seem to inherit the symmetry properties of their counterparts in small networks.

Insertion algorithms for network model database management systems

NASA Astrophysics Data System (ADS)

Mamadolimov, Abdurashid; Khikmat, Saburov

2017-12-01

The network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema, viewed as a graph in which object types are nodes and relationship types are arcs, forms partial order. When a database is large and a query comparison is expensive then the efficiency requirement of managing algorithms is minimizing the number of query comparisons. We consider updating operation for network model database management systems. We develop a new sequantial algorithm for updating operation. Also we suggest a distributed version of the algorithm.

Rainfall, streamflow, and peak stage data collected at the Murfreesboro, Tennessee, gaging network, March 1989 through July 1992

USGS Publications Warehouse

Outlaw, G.S.; Butner, D.E.; Kemp, R.L.; Oaks, A.T.; Adams, G.S.

1992-01-01

Rainfall, stage, and streamflow data in the Murfreesboro area, Middle Tennessee, were collected from March 1989 through July 1992 from a network of 68 gaging stations. The network consists of 10 tipping-bucket rain gages, 2 continuous-record streamflow gages, 4 partial-record flood hydrograph gages, and 72 crest-stage gages. Data collected by the gages includes 5minute time-step rainfall hyetographs, 15-minute time-step flood hydrographs, and peak-stage elevations. Data are stored in a computer data base and are available for many computer modeling and engineering applications.

Convolutional Neural Networks for 1-D Many-Channel Data

DTIC Science & Technology

Deep convolutional neural networks (CNNs) represent the state of the art in image recognition. The same properties that led to their success in that... crack detection ( 8,000 data points, 72 channels). Though the models predictive ability is limited to fitting the trend , its partial success suggests that...originally written to classify digits in the MNIST database (28 28 pixels, 1 channel), for use on 1-D acoustic data taken from experiments focused on

An Evaluation of the Network Efficiency Required in Order to Support Multicast and Synchronous Distributed Learning Network Traffic

DTIC Science & Technology

2003-09-01

This restriction limits the deployment to small and medium sized enterprises. The Internet cannot universally use DVMRP for this reason. In addition...20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE September 2003 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE... University , 1996 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN COMPUTER SCIENCE from

Actor-network Procedures: Modeling Multi-factor Authentication, Device Pairing, Social Interactions

DTIC Science & Technology

2011-08-29

unmodifiable properties of your body; or the capabilities that you cannot convey to others, such as your handwriting . An identity can thus be determined by...network, two principals with the same set of secrets but, say , different computational powers, can be distinguished by timing their responses. Or they... says that configurations are finite sets. Partially ordered multisets, or pomsets were introduced and extensively studied by Vaughan Pratt and his

Low temperature process for obtaining thin glass films

DOEpatents

Brinker, C. Jeffrey; Reed, Scott T.

1984-01-01

A method for coating a substrate with a glass-like film comprises, applying to the substrate an aqueous alcoholic solution containing a polymeric network of partially hydrolyzed metal alkoxide into which network there is incorporated finely powdered glass, whereby there is achieved on the substrate a coherent and adherent initial film; and heating said film to a temperature sufficient to melt said powdered glass component, thereby converting said initial film to a final densified film.

Low temperature process for obtaining thin glass films

DOEpatents

Brinker, C.J.; Reed, S.T.

A method for coating a substrate with a glass-like film comprises, applying to the substrate an aqueous alcoholic solution containing a polymeric network of partially hydrolyzed metal alkoxide into which network there is incorporated finely powdered glass, whereby there is achieved on the substrate a coherent and adherent initial film; and heating said film to a temperature sufficient to melt said powdered glass component, thereby converting said initial film to a final densified film.

Distribution of melt beneath Mount St Helens and Mount Adams inferred from magnetotelluric data

NASA Astrophysics Data System (ADS)

Hill, Graham J.; Caldwell, T. Grant; Heise, Wiebke; Chertkoff, Darren G.; Bibby, Hugh M.; Burgess, Matt K.; Cull, James P.; Cas, Ray A. F.

2009-11-01

Three prominent volcanoes that form part of the Cascade mountain range in Washington State (USA)-Mounts St Helens, Adams and Rainier-are located on the margins of a mid-crustal zone of high electrical conductivity. Interconnected melt can increase the bulk conductivity of the region containing the melt, which leads us to propose that the anomalous conductivity in this region is due to partial melt associated with the volcanism. Here we test this hypothesis by using magnetotelluric data recorded at a network of 85 locations in the area of the high-conductivity anomaly. Our data reveal that a localized zone of high conductivity beneath this volcano extends downwards to join the mid-crustal conductor. As our measurements were made during the recent period of lava extrusion at Mount St Helens, we infer that the conductivity anomaly associated with the localized zone, and by extension with the mid-crustal conductor, is caused by the presence of partial melt. Our interpretation is consistent with the crustal origin of silicic magmas erupting from Mount St Helens, and explains the distribution of seismicity observed at the time of the catastrophic eruption in 1980 (refs 9, 10).

Provider-based research networks and diffusion of surgical technologies among patients with early-stage kidney cancer.

PubMed

Tan, Hung-Jui; Meyer, Anne-Marie; Kuo, Tzy-Mey; Smith, Angela B; Wheeler, Stephanie B; Carpenter, William R; Nielsen, Matthew E

2015-03-15

Provider-based research networks such as the National Cancer Institute's Community Clinical Oncology Program (CCOP) have been shown to facilitate the translation of evidence-based cancer care into clinical practice. This study compared the utilization of laparoscopy and partial nephrectomy among patients with early-stage kidney cancer according to their exposure to CCOP-affiliated providers. With linked Surveillance, Epidemiology, and End Results-Medicare data, patients with T1aN0M0 kidney cancer who had been treated with nephrectomy from 2000 to 2007 were identified. For each patient, the receipt of care from a CCOP physician or hospital and treatment with laparoscopy or partial nephrectomy were determined. Adjusted for patient characteristics (eg, age, sex, and marital status) and other organizational features (eg, community hospital and National Cancer Institute-designated cancer center), multivariate logistic regression was used to estimate the association between each surgical innovation and CCOP affiliation. During the study interval, 1578 patients (26.8%) were treated by a provider with a CCOP affiliation. Trends in the utilization of laparoscopy and partial nephrectomy remained similar between affiliated and nonaffiliated providers (P ≥ .05). With adjustments for patient characteristics, organizational features, and clustering, no association was noted between CCOP affiliation and the use of laparoscopy (odds ratio [OR], 1.11; 95% confidence interval [CI], 0.81-1.53) or partial nephrectomy (OR, 1.04; 95% CI, 0.82-1.32) despite the more frequent receipt of these treatments in academic settings (P < .05). At a population level, patients treated by providers affiliated with CCOP were no more likely to receive at least 1 of 2 surgical innovations for treatment of their kidney cancer, indicating perhaps a more limited scope to provider-based research networks as they pertain to translational efforts in cancer care. © 2014 American Cancer Society.

Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

PubMed Central

Antal, Péter; Kiszel, Petra Sz.; Gézsi, András; Hadadi, Éva; Virág, Viktor; Hajós, Gergely; Millinghoffer, András; Nagy, Adrienne; Kiss, András; Semsei, Ágnes F.; Temesi, Gergely; Melegh, Béla; Kisfali, Péter; Széll, Márta; Bikov, András; Gálffy, Gabriella; Tamási, Lilla; Falus, András; Szalai, Csaba

2012-01-01

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance. PMID:22432035

Team Formation in Partially Observable Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2004-01-01

Sets of multi-agent teams often need to maximize a global utility rating the performance of the entire system where a team cannot fully observe other teams agents. Such limited observability hinders team-members trying to pursue their team utilities to take actions that also help maximize the global utility. In this article, we show how team utilities can be used in partially observable systems. Furthermore, we show how team sizes can be manipulated to provide the best compromise between having easy to learn team utilities and having them aligned with the global utility, The results show that optimally sized teams in a partially observable environments outperform one team in a fully observable environment, by up to 30%.

A neural network prototyping package within IRAF

NASA Technical Reports Server (NTRS)

Bazell, D.; Bankman, I.

1992-01-01

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

Factors which motivate the use of social networks by students.

PubMed

González Sanmamed, Mercedes; Muñoz Carril, Pablo C; Dans Álvarez de Sotomayor, Isabel

2017-05-01

The aim of this research was to identify those factors which motivate the use of social networks by 4th year students in Secondary Education between the ages of 15 and 18. 1,144 students from 29 public and private schools took part. The data were analysed using Partial Least Squares Structural Equation Modelling technique. Versatility was confirmed to be the variable which most influences the motivation of students in their use of social networks. The positive relationship between versatility in the use of social networks and educational uses was also significant. The characteristics of social networks are analysed according to their versatility and how this aspect makes them attractive to students. The positive effects of social networks are discussed in terms of educational uses and their contribution to school learning. There is also a warning about the risks associated with misuse of social networks, and finally, the characteristics and conditions for the development of good educational practice through social networks are identified.

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

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

Cen Haiyan; Bao Yidan; He Yong

2006-10-10

Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it ismore » concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy.« less

Generic strategies for chemical space exploration.

PubMed

Andersen, Jakob L; Flamm, Christoph; Merkle, Daniel; Stadler, Peter F

2014-01-01

The chemical universe of molecules reachable from a set of start compounds by iterative application of a finite number of reactions is usually so vast, that sophisticated and efficient exploration strategies are required to cope with the combinatorial complexity. A stringent analysis of (bio)chemical reaction networks, as approximations of these complex chemical spaces, forms the foundation for the understanding of functional relations in Chemistry and Biology. Graphs and graph rewriting are natural models for molecules and reactions. Borrowing the idea of partial evaluation from functional programming, we introduce partial applications of rewrite rules. A framework for the specification of exploration strategies in graph-rewriting systems is presented. Using key examples of complex reaction networks from carbohydrate chemistry we demonstrate the feasibility of this high-level strategy framework. While being designed for chemical applications, the framework can also be used to emulate higher-level transformation models such as illustrated in a small puzzle game.

Energy-Efficient Channel Handoff for Sensor Network-Assisted Cognitive Radio Network

PubMed Central

Usman, Muhammad; Sajjad Khan, Muhammad; Vu-Van, Hiep; Insoo, Koo

2015-01-01

The visiting and less-privileged status of the secondary users (SUs) in a cognitive radio network obligates them to release the occupied channel instantly when it is reclaimed by the primary user. The SU has a choice to make: either wait for the channel to become free, thus conserving energy at the expense of delayed transmission and delivery, or find and switch to a vacant channel, thereby avoiding delay in transmission at the expense of increased energy consumption. An energy-efficient decision that considers the tradeoff between energy consumption and continuous transmission needs to be taken as to whether to switch the channels. In this work, we consider a sensor network-assisted cognitive radio network and propose a backup channel, which is sensed by the SU in parallel with the operating channel that is being sensed by the sensor nodes. Imperfect channel sensing and residual energy of the SU are considered in order to develop an energy-efficient handoff strategy using the partially observable Markov decision process (POMDP), which considers beliefs about the operating and backup channels and the remaining energy of the SU in order to take an optimal channel handoff decision on the question “Should we switch the channel?” The objective is to dynamically decide in each time slot whether the SU should switch the channel or not in order to maximize throughput by utilizing energy efficiently. Extensive simulations were performed to show the effectiveness of the proposed channel handoff strategy, which was demonstrated in the form of throughput with respect to various parameters, i.e., detection probability, the channel idle probabilities of the operating and backup channels, and the maximum energy of the SU. PMID:26213936

Embedding dynamical networks into distributed models

NASA Astrophysics Data System (ADS)

Innocenti, Giacomo; Paoletti, Paolo

2015-07-01

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

Optimization of wireless sensor networks based on chicken swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Wang, Qingxi; Zhu, Lihua

2017-05-01

In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.

A revised limbic system model for memory, emotion and behaviour.

PubMed

Catani, Marco; Dell'acqua, Flavio; Thiebaut de Schotten, Michel

2013-09-01

Emotion, memories and behaviour emerge from the coordinated activities of regions connected by the limbic system. Here, we propose an update of the limbic model based on the seminal work of Papez, Yakovlev and MacLean. In the revised model we identify three distinct but partially overlapping networks: (i) the Hippocampal-diencephalic and parahippocampal-retrosplenial network dedicated to memory and spatial orientation; (ii) The temporo-amygdala-orbitofrontal network for the integration of visceral sensation and emotion with semantic memory and behaviour; (iii) the default-mode network involved in autobiographical memories and introspective self-directed thinking. The three networks share cortical nodes that are emerging as principal hubs in connectomic analysis. This revised network model of the limbic system reconciles recent functional imaging findings with anatomical accounts of clinical disorders commonly associated with limbic pathology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Quantifying immediate price impact of trades based on the k-shell decomposition of stock trading networks

NASA Astrophysics Data System (ADS)

Xie, Wen-Jie; Li, Ming-Xia; Xu, Hai-Chuan; Chen, Wei; Zhou, Wei-Xing; Stanley, H. Eugene

2016-10-01

Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition method. We investigate the influences of trading behaviors on the price impact by comparing a closed national market (A-shares) with an international market (B-shares), individuals and institutions, partially filled and filled trades, buyer-initiated and seller-initiated trades, and trades at different positions of a trading network. Institutional traders professionally use some trading strategies to reduce the price impact and individuals at the same positions in the trading network have a higher price impact than institutions. We also find that trades in the core have higher price impacts than those in the peripheral shell.

Recurrent Network models of sequence generation and memory

PubMed Central

Rajan, Kanaka; Harvey, Christopher D; Tank, David W

2016-01-01

SUMMARY Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here, we demonstrate that starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network training (PINning), to model and match cellular-resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced choice task [Harvey, Coen and Tank, 2012]. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. PMID:26971945

Information recall using relative spike timing in a spiking neural network.

PubMed

Sterne, Philip

2012-08-01

We present a neural network that is capable of completing and correcting a spiking pattern given only a partial, noisy version. It operates in continuous time and represents information using the relative timing of individual spikes. The network is capable of correcting and recalling multiple patterns simultaneously. We analyze the network's performance in terms of information recall. We explore two measures of the capacity of the network: one that values the accurate recall of individual spike times and another that values only the presence or absence of complete patterns. Both measures of information are found to scale linearly in both the number of neurons and the period of the patterns, suggesting these are natural measures of network information. We show a smooth transition from encodings that provide precise spike times to flexible encodings that can encode many scenes. This makes it plausible that many diverse tasks could be learned with such an encoding.

Understanding how social networking influences perceived satisfaction with conference experiences

USGS Publications Warehouse

van Riper, Carena J.; van Riper, Charles; Kyle, Gerard T.; Lee, Martha E.

2013-01-01

Social networking is a key benefit derived from participation in conferences that bind the ties of a professional community. Building social networks can lead to satisfactory experiences while furthering participants' long- and short-term career goals. Although investigations of social networking can lend insight into how to effectively engage individuals and groups within a professional cohort, this area has been largely overlooked in past research. The present study investigates the relationship between social networking and satisfaction with the 10th Biennial Conference of Research on the Colorado Plateau using structural equation modelling. Results partially support the hypothesis that three dimensions of social networking – interpersonal connections, social cohesion, and secondary associations – positively contribute to the performance of various conference attributes identified in two focus group sessions. The theoretical and applied contributions of this paper shed light on the social systems formed within professional communities and resource allocation among service providers.

Can We Control Contaminant Transport In Hydrologic Networks? Application Of Control Theory Concepts To Watershed Management

NASA Astrophysics Data System (ADS)

Yeghiazarian, L.; Riasi, M. S.

2016-12-01

Although controlling the level of contamination everywhere in the surface water network may not be feasible, it is vital to maintain safe water quality levels in specific areas, e.g. recreational waters. The question then is "what is the most efficient way to fully/partially control water quality in surface water networks?". This can be posed as a control problem where the goal is to efficiently drive the system to a desired state by manipulating few input variables. Such problems reduce to (1) finding the best control locations in the network to influence the state of the system; and (2) choosing the time-variant inputs at the control locations to achieve the desired state of the system with minimum effort. We demonstrate that the optimal solution to control the level of contamination in the network can be found through application of control theory concepts to transport in dendritic surface water networks.

Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma

PubMed Central

Sun, Jingchun; Gong, Xue; Purow, Benjamin; Zhao, Zhongming

2012-01-01

Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and, potentially, other cancers. PMID:22829753

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise.

PubMed

Chi, Baofang; Tao, Shiheng; Liu, Yanlin

2015-01-01

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.

Stability and performance of propulsion control systems with distributed control architectures and failures

NASA Astrophysics Data System (ADS)

Belapurkar, Rohit K.

Future aircraft engine control systems will be based on a distributed architecture, in which, the sensors and actuators will be connected to the Full Authority Digital Engine Control (FADEC) through an engine area network. Distributed engine control architecture will allow the implementation of advanced, active control techniques along with achieving weight reduction, improvement in performance and lower life cycle cost. The performance of a distributed engine control system is predominantly dependent on the performance of the communication network. Due to the serial data transmission policy, network-induced time delays and sampling jitter are introduced between the sensor/actuator nodes and the distributed FADEC. Communication network faults and transient node failures may result in data dropouts, which may not only degrade the control system performance but may even destabilize the engine control system. Three different architectures for a turbine engine control system based on a distributed framework are presented. A partially distributed control system for a turbo-shaft engine is designed based on ARINC 825 communication protocol. Stability conditions and control design methodology are developed for the proposed partially distributed turbo-shaft engine control system to guarantee the desired performance under the presence of network-induced time delay and random data loss due to transient sensor/actuator failures. A fault tolerant control design methodology is proposed to benefit from the availability of an additional system bandwidth and from the broadcast feature of the data network. It is shown that a reconfigurable fault tolerant control design can help to reduce the performance degradation in presence of node failures. A T-700 turbo-shaft engine model is used to validate the proposed control methodology based on both single input and multiple-input multiple-output control design techniques.

Explaining ecological clusters of maternal depression in South Western Sydney.

PubMed

Eastwood ED, John; Kemp, Lynn; Jalaludin, Bin

2014-01-24

The aim of the qualitative study reported here was to: 1) explain the observed clustering of postnatal depressive symptoms in South Western Sydney; and 2) identify group-level mechanisms that would add to our understanding of the social determinants of maternal depression. Critical realism provided the methodological underpinning for the study. The setting was four local government areas in South Western Sydney, Australia. Child and Family practitioners and mothers in naturally occurring mothers groups were interviewed. Using an open coding approach to maximise emergence of patterns and relationships we have identified seven theoretical concepts that might explain the observed spatial clustering of maternal depression. The theoretical concepts identified were: Community-level social networks; Social Capital and Social Cohesion; "Depressed community"; Access to services at the group level; Ethnic segregation and diversity; Supportive social policy; and Big business. We postulate that these regional structural, economic, social and cultural mechanisms partially explain the pattern of maternal depression observed in families and communities within South Western Sydney. We further observe that powerful global economic and political forces are having an impact on the local situation. The challenge for policy and practice is to support mothers and their families within this adverse regional and global-economic context.

Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.

PubMed

Wang, Yue; Halper, Michael; Wei, Duo; Gu, Huanying; Perl, Yehoshua; Xu, Junchuan; Elhanan, Gai; Chen, Yan; Spackman, Kent A; Case, James T; Hripcsak, George

2012-02-01

Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. "Complex" concepts, which by their very nature are more difficult to model, fall neatly into this category. A special kind of grouping, called a partial-area, is utilized in the characterization of complex concepts. In particular, the complex concepts that are the focus of this work are those appearing in intersections of multiple partial-areas and are thus referred to as overlapping concepts. In a companion paper, an automatic methodology for identifying and partitioning the entire collection of overlapping concepts into disjoint, singly-rooted groups, that are more manageable to work with and comprehend, has been presented. The partitioning methodology formed the foundation for the development of an abstraction network for the overlapping concepts called a disjoint partial-area taxonomy. This new disjoint partial-area taxonomy offers a collection of semantically uniform partial-areas and is exploited herein as the basis for a novel auditing methodology. The review of the overlapping concepts is done in a top-down order within semantically uniform groups. These groups are themselves reviewed in a top-down order, which proceeds from the less complex to the more complex overlapping concepts. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. Hypotheses regarding error ratios for overlapping concepts and between different kinds of overlapping concepts are formulated. Two phases of auditing the Specimen hierarchy for two releases of SNOMED are reported on. With the use of the double bootstrap and Fisher's exact test (two-tailed), the auditing of concepts and especially roots of overlapping partial-areas is shown to yield a statistically significant higher proportion of errors. Copyright © 2011 Elsevier Inc. All rights reserved.

Auditing Complex Concepts of SNOMED using a Refined Hierarchical Abstraction Network

PubMed Central

Wang, Yue; Halper, Michael; Wei, Duo; Gu, Huanying; Perl, Yehoshua; Xu, Junchuan; Elhanan, Gai; Chen, Yan; Spackman, Kent A.; Case, James T.; Hripcsak, George

2012-01-01

Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. “Complex” concepts, which by their very nature are more difficult to model, fall neatly into this category. A special kind of grouping, called a partial-area, is utilized in the characterization of complex concepts. In particular, the complex concepts that are the focus of this work are those appearing in intersections of multiple partial-areas and are thus referred to as overlapping concepts. In a companion paper, an automatic methodology for identifying and partitioning the entire collection of overlapping concepts into disjoint, singly-rooted groups, that are more manageable to work with and comprehend, has been presented. The partitioning methodology formed the foundation for the development of an abstraction network for the overlapping concepts called a disjoint partial-area taxonomy. This new disjoint partial-area taxonomy offers a collection of semantically uniform partial-areas and is exploited herein as the basis for a novel auditing methodology. The review of the overlapping concepts is done in a top-down order within semantically uniform groups. These groups are themselves reviewed in a top-down order, which proceeds from the less complex to the more complex overlapping concepts. The results of applying the methodology to SNOMED’s Specimen hierarchy are presented. Hypotheses regarding error ratios for overlapping concepts and between different kinds of overlapping concepts are formulated. Two phases of auditing the Specimen hierarchy for two releases of SNOMED are reported on. With the use of the double bootstrap and Fisher’s exact test (two-tailed), the auditing of concepts and especially roots of overlapping partial-areas is shown to yield a statistically significant higher proportion of errors. PMID:21907827

Direct Observations of Graphene Dispersed in Solution by Twilight Fluorescence Microscopy.

PubMed

Matsuno, Yutaka; Sato, Yu-Uya; Sato, Hikaru; Sano, Masahito

2017-06-01

Graphene and graphene oxide (GO) in solution were directly observed by a newly developed twilight fluorescence (TwiF) microscopy. A nanocarbon dispersion was mixed with a highly concentrated fluorescent dye solution and placed in a cell with a viewing glass at the bottom. TwiF microscopy images the nanocarbon material floating within a few hundred μm of the glass surface by utilizing two optical processes to provide a faintly illuminating backlight and visualizes GO as either a dark image by absorption and energy transfer processes or a bright image by alternation of fluorophore chemistry and autofluorescence. Individual graphene and GO sheets ranging from submicron to submillimeter widths were clearly imaged at different wavelengths, which were selectable based on the dye used. Graphene could be differentiated from GO coexisting in the same solution. Partial transparency revealed layering and network structures. Motions in tumbling flow were recognized in real time. An effect of changing the solvent and the process of adhesion on the glass surface were followed in situ.

Strain relaxation induced surface morphology of heterogeneous GaInNAs layers grown on GaAs substrate

NASA Astrophysics Data System (ADS)

Gelczuk, Ł.; Jóźwiak, G.; Moczała, M.; Dłużewski, P.; Dąbrowska-Szata, M.; Gotszalk, T. P.

2017-07-01

The partially-relaxed heterogeneous GaInNAs layers grown on GaAs substrate by atmospheric pressure vapor phase epitaxy (AP-MOVPE) were investigated by transmission electron microscopy (TEM) and atomic force microscopy (AFM). The planar-view TEM image shows a regular 2D network of misfit dislocations oriented in two orthogonal 〈1 1 0〉 crystallographic directions at the (0 0 1) layer interface. Moreover, the cross-sectional view TEM image reveals InAs-rich and V-shaped precipitates in the near surface region of the GaInNAs epitaxial layer. The resultant undulating surface morphology, known as a cross-hatch pattern, is formed as observed by AFM. The numerical analysis of the AFM image of the GaInNAs layer surface with the well-defined cross-hatch morphology enabled us to determine a lower bound of actual density of misfit dislocations. However, a close correspondence between the asymmetric distribution of interfacial misfit dislocations and undulating surface morphology is observed.

Shear rheological characterization of gel healing response and construction of rheo-PIV system

NASA Astrophysics Data System (ADS)

Bawiskar, Abhishek D.

Thermo-reversible gels are solvent-filled 3D networks of polymer chains interconnected by physical (transient) crosslinks. On applying a high shear stress, the crosslinks are broken and these gels show a typical stress-strain behavior due to cohesive fracture of the gel. When heated above a critical temperature and cooled back to room temperature, all the crosslinks are re-formed. Interestingly, partial to full recovery of broken crosslinks is also observed by simply letting the gel stand at room temperature. In this study, the fracture and healing behavior of a model acrylic triblock copolymer gel has been characterized by shear rheometry. A mathematical model has also been proposed to better understand the mechanics at the molecular level and predict the healing time of a system. A rheo-PIV system was built as part of the project, to observe and confirm the bulk healing process in situ. Spontaneous self-healing behavior has immense potential in controlled drug delivery systems, coatings, food and various other applications.

Conformational Heterogeneity of the HIV Envelope Glycan Shield.

PubMed

Yang, Mingjun; Huang, Jing; Simon, Raphael; Wang, Lai-Xi; MacKerell, Alexander D

2017-06-30

To better understand the conformational properties of the glycan shield covering the surface of the HIV gp120/gp41 envelope (Env) trimer, and how the glycan shield impacts the accessibility of the underlying protein surface, we performed enhanced sampling molecular dynamics (MD) simulations of a model glycosylated HIV Env protein and related systems. Our simulation studies revealed a conformationally heterogeneous glycan shield with a network of glycan-glycan interactions more extensive than those observed to date. We found that partial preorganization of the glycans potentially favors binding by established broadly neutralizing antibodies; omission of several specific glycans could increase the accessibility of other glycans or regions of the protein surface to antibody or CD4 receptor binding; the number of glycans that can potentially interact with known antibodies is larger than that observed in experimental studies; and specific glycan conformations can maximize or minimize interactions with individual antibodies. More broadly, the enhanced sampling MD simulations described here provide a valuable tool to guide the engineering of specific Env glycoforms for HIV vaccine design.

Interaction in planning vocalizations and grasping.

PubMed

Tiainen, Mikko; Tiippana, Kaisa; Vainio, Martti; Komeilipoor, Naeem; Vainio, Lari

2017-08-01

Previous studies have shown a congruency effect between manual grasping and syllable articulation. For instance, a power grip is associated with syllables whose articulation involves the tongue body and/or large mouth aperture ([kɑ]) whereas a precision grip is associated with articulations that involve the tongue tip and/or small mouth aperture ([ti]). Previously, this effect has been observed in manual reaction times. The primary aim of the current study was to investigate whether this congruency effect also takes place in vocal responses and to investigate involvement of action selection processes in the effect. The congruency effect was found in vocal and manual responses regardless of whether or not the syllable or grip was known a priori, suggesting that the effect operates with minimal or absent action selection processes. In addition, the effect was observed in vocal responses even when the grip was only prepared but not performed, suggesting that merely planning a grip response primes the corresponding articulatory response. These results support the view that articulation and grasping are processed in a partially overlapping network.

Phase Distribution and Selection of Partially Correlated Persistent Scatterers

NASA Astrophysics Data System (ADS)

Lien, J.; Zebker, H. A.

2012-12-01

Interferometric synthetic aperture radar (InSAR) time-series methods can effectively estimate temporal surface changes induced by geophysical phenomena. However, such methods are susceptible to decorrelation due to spatial and temporal baselines (radar pass separation), changes in orbital geometries, atmosphere, and noise. These effects limit the number of interferograms that can be used for differential analysis and obscure the deformation signal. InSAR decorrelation effects may be ameliorated by exploiting pixels that exhibit phase stability across the stack of interferograms. These so-called persistent scatterer (PS) pixels are dominated by a single point-like scatterer that remains phase-stable over the spatial and temporal baseline. By identifying a network of PS pixels for use in phase unwrapping, reliable deformation measurements may be obtained even in areas of low correlation, where traditional InSAR techniques fail to produce useful observations. Many additional pixels can be added to the PS list if we are able to identify those in which a dominant scatterer exhibits partial, rather than complete, correlation across all radar scenes. In this work, we quantify and exploit the phase stability of partially correlated PS pixels. We present a new system model for producing interferometric pixel values from a complex surface backscatter function characterized by signal-to-clutter ratio (SCR). From this model, we derive the joint probabilistic distribution for PS pixel phases in a stack of interferograms as a function of SCR and spatial baselines. This PS phase distribution generalizes previous results that assume the clutter phase contribution is uncorrelated between radar passes. We verify the analytic distribution through a series of radar scattering simulations. We use the derived joint PS phase distribution with maximum-likelihood SCR estimation to analyze an area of the Hayward Fault Zone in the San Francisco Bay Area. We obtain a series of 38 interferometric images of the area from C-band ERS radar satellite passes between May 1995 and December 2000. We compare the estimated SCRs to those calculated with previously derived PS phase distributions. Finally, we examine the PS network density resulting from varying selection thresholds of SCR and compare to other PS identification techniques.

Rule-based spatial modeling with diffusing, geometrically constrained molecules.

PubMed

Gruenert, Gerd; Ibrahim, Bashar; Lenser, Thorsten; Lohel, Maiko; Hinze, Thomas; Dittrich, Peter

2010-06-07

We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

Rule-based spatial modeling with diffusing, geometrically constrained molecules

PubMed Central

2010-01-01

Background We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Results Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. Conclusions We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly. PMID:20529264

Antiepileptic drug monotherapy for epilepsy: a network meta-analysis of individual participant data.

PubMed

Nevitt, Sarah J; Sudell, Maria; Weston, Jennifer; Tudur Smith, Catrin; Marson, Anthony G

2017-06-29

Epilepsy is a common neurological condition with a worldwide prevalence of around 1%. Approximately 60% to 70% of people with epilepsy will achieve a longer-term remission from seizures, and most achieve that remission shortly after starting antiepileptic drug treatment. Most people with epilepsy are treated with a single antiepileptic drug (monotherapy) and current guidelines from the National Institute for Health and Care Excellence (NICE) in the United Kingdom for adults and children recommend carbamazepine or lamotrigine as first-line treatment for partial onset seizures and sodium valproate for generalised onset seizures; however a range of other antiepileptic drug (AED) treatments are available, and evidence is needed regarding their comparative effectiveness in order to inform treatment choices. To compare the time to withdrawal of allocated treatment, remission and first seizure of 10 AEDs (carbamazepine, phenytoin, sodium valproate, phenobarbitone, oxcarbazepine, lamotrigine, gabapentin, topiramate, levetiracetam, zonisamide) currently used as monotherapy in children and adults with partial onset seizures (simple partial, complex partial or secondary generalised) or generalised tonic-clonic seizures with or without other generalised seizure types (absence, myoclonus). We searched the following databases: Cochrane Epilepsy's Specialised Register, CENTRAL, MEDLINE and SCOPUS, and two clinical trials registers. We handsearched relevant journals and contacted pharmaceutical companies, original trial investigators, and experts in the field. The date of the most recent search was 27 July 2016. We included randomised controlled trials of a monotherapy design in adults or children with partial onset seizures or generalised onset tonic-clonic seizures (with or without other generalised seizure types). This was an individual participant data (IPD) review and network meta-analysis. Our primary outcome was 'time to withdrawal of allocated treatment', and our secondary outcomes were 'time to achieve 12-month remission', 'time to achieve six-month remission', 'time to first seizure post-randomisation', and 'occurrence of adverse events'. We presented all time-to-event outcomes as Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs). We performed pairwise meta-analysis of head-to-head comparisons between drugs within trials to obtain 'direct' treatment effect estimates and we performed frequentist network meta-analysis to combine direct evidence with indirect evidence across the treatment network of 10 drugs. We investigated inconsistency between direct estimates and network meta-analysis via node splitting. Due to variability in methods and detail of reporting adverse events, we have not performed an analysis. We have provided a narrative summary of the most commonly reported adverse events. IPD was provided for at least one outcome of this review for 12,391 out of a total of 17,961 eligible participants (69% of total data) from 36 out of the 77 eligible trials (47% of total trials). We could not include IPD from the remaining 41 trials in analysis for a variety of reasons, such as being unable to contact an author or sponsor to request data, data being lost or no longer available, cost and resources required to prepare data being prohibitive, or local authority or country-specific restrictions.We were able to calculate direct treatment effect estimates for between half and two thirds of comparisons across the outcomes of the review, however for many of the comparisons, data were contributed by only a single trial or by a small number of participants, so confidence intervals of estimates were wide.Network meta-analysis showed that for the primary outcome 'Time to withdrawal of allocated treatment,' for individuals with partial seizures; levetiracetam performed (statistically) significantly better than both current first-line treatments carbamazepine and lamotrigine; lamotrigine performed better than all other treatments (aside from levetiracetam), and carbamazepine performed significantly better than gabapentin and phenobarbitone (high-quality evidence). For individuals with generalised onset seizures, first-line treatment sodium valproate performed significantly better than carbamazepine, topiramate and phenobarbitone (moderate- to high-quality evidence). Furthermore, for both partial and generalised onset seizures, the earliest licenced treatment, phenobarbitone seems to perform worse than all other treatments (moderate- to high-quality evidence).Network meta-analysis also showed that for secondary outcomes 'Time to 12-month remission of seizures' and 'Time to six-month remission of seizures,' few notable differences were shown for either partial or generalised seizure types (moderate- to high-quality evidence). For secondary outcome 'Time to first seizure,' for individuals with partial seizures; phenobarbitone performed significantly better than both current first-line treatments carbamazepine and lamotrigine; carbamazepine performed significantly better than sodium valproate, gabapentin and lamotrigine. Phenytoin also performed significantly better than lamotrigine (high-quality evidence). In general, the earliest licenced treatments (phenytoin and phenobarbitone) performed better than the other treatments for both seizure types (moderate- to high-quality evidence).Generally, direct evidence and network meta-analysis estimates (direct plus indirect evidence) were numerically similar and consistent with confidence intervals of effect sizes overlapping.The most commonly reported adverse events across all drugs were drowsiness/fatigue, headache or migraine, gastrointestinal disturbances, dizziness/faintness and rash or skin disorders. Overall, the high-quality evidence provided by this review supports current guidance (e.g. NICE) that carbamazepine and lamotrigine are suitable first-line treatments for individuals with partial onset seizures and also demonstrates that levetiracetam may be a suitable alternative. High-quality evidence from this review also supports the use of sodium valproate as the first-line treatment for individuals with generalised tonic-clonic seizures (with or without other generalised seizure types) and also demonstrates that lamotrigine and levetiracetam would be suitable alternatives to either of these first-line treatments, particularly for those of childbearing potential, for whom sodium valproate may not be an appropriate treatment option due to teratogenicity.

Antiepileptic drug monotherapy for epilepsy: a network meta-analysis of individual participant data.

PubMed

Nevitt, Sarah J; Sudell, Maria; Weston, Jennifer; Tudur Smith, Catrin; Marson, Anthony G

2017-12-15

Epilepsy is a common neurological condition with a worldwide prevalence of around 1%. Approximately 60% to 70% of people with epilepsy will achieve a longer-term remission from seizures, and most achieve that remission shortly after starting antiepileptic drug treatment. Most people with epilepsy are treated with a single antiepileptic drug (monotherapy) and current guidelines from the National Institute for Health and Care Excellence (NICE) in the United Kingdom for adults and children recommend carbamazepine or lamotrigine as first-line treatment for partial onset seizures and sodium valproate for generalised onset seizures; however a range of other antiepileptic drug (AED) treatments are available, and evidence is needed regarding their comparative effectiveness in order to inform treatment choices. To compare the time to withdrawal of allocated treatment, remission and first seizure of 10 AEDs (carbamazepine, phenytoin, sodium valproate, phenobarbitone, oxcarbazepine, lamotrigine, gabapentin, topiramate, levetiracetam, zonisamide) currently used as monotherapy in children and adults with partial onset seizures (simple partial, complex partial or secondary generalised) or generalised tonic-clonic seizures with or without other generalised seizure types (absence, myoclonus). We searched the following databases: Cochrane Epilepsy's Specialised Register, CENTRAL, MEDLINE and SCOPUS, and two clinical trials registers. We handsearched relevant journals and contacted pharmaceutical companies, original trial investigators, and experts in the field. The date of the most recent search was 27 July 2016. We included randomised controlled trials of a monotherapy design in adults or children with partial onset seizures or generalised onset tonic-clonic seizures (with or without other generalised seizure types). This was an individual participant data (IPD) review and network meta-analysis. Our primary outcome was 'time to withdrawal of allocated treatment', and our secondary outcomes were 'time to achieve 12-month remission', 'time to achieve six-month remission', 'time to first seizure post-randomisation', and 'occurrence of adverse events'. We presented all time-to-event outcomes as Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs). We performed pairwise meta-analysis of head-to-head comparisons between drugs within trials to obtain 'direct' treatment effect estimates and we performed frequentist network meta-analysis to combine direct evidence with indirect evidence across the treatment network of 10 drugs. We investigated inconsistency between direct estimates and network meta-analysis via node splitting. Due to variability in methods and detail of reporting adverse events, we have not performed an analysis. We have provided a narrative summary of the most commonly reported adverse events. IPD was provided for at least one outcome of this review for 12,391 out of a total of 17,961 eligible participants (69% of total data) from 36 out of the 77 eligible trials (47% of total trials). We could not include IPD from the remaining 41 trials in analysis for a variety of reasons, such as being unable to contact an author or sponsor to request data, data being lost or no longer available, cost and resources required to prepare data being prohibitive, or local authority or country-specific restrictions.We were able to calculate direct treatment effect estimates for between half and two thirds of comparisons across the outcomes of the review, however for many of the comparisons, data were contributed by only a single trial or by a small number of participants, so confidence intervals of estimates were wide.Network meta-analysis showed that for the primary outcome 'Time to withdrawal of allocated treatment,' for individuals with partial seizures; levetiracetam performed (statistically) significantly better than current first-line treatment carbamazepine and other current first-line treatment lamotrigine performed better than all other treatments (aside from levetiracetam); carbamazepine performed significantly better than gabapentin and phenobarbitone (high-quality evidence). For individuals with generalised onset seizures, first-line treatment sodium valproate performed significantly better than carbamazepine, topiramate and phenobarbitone (moderate- to high-quality evidence). Furthermore, for both partial and generalised onset seizures, the earliest licenced treatment, phenobarbitone seems to perform worse than all other treatments (moderate- to high-quality evidence).Network meta-analysis also showed that for secondary outcomes 'Time to 12-month remission of seizures' and 'Time to six-month remission of seizures,' few notable differences were shown for either partial or generalised seizure types (moderate- to high-quality evidence). For secondary outcome 'Time to first seizure,' for individuals with partial seizures; phenobarbitone performed significantly better than both current first-line treatments carbamazepine and lamotrigine; carbamazepine performed significantly better than sodium valproate, gabapentin and lamotrigine. Phenytoin also performed significantly better than lamotrigine (high-quality evidence). In general, the earliest licenced treatments (phenytoin and phenobarbitone) performed better than the other treatments for both seizure types (moderate- to high-quality evidence).Generally, direct evidence and network meta-analysis estimates (direct plus indirect evidence) were numerically similar and consistent with confidence intervals of effect sizes overlapping.The most commonly reported adverse events across all drugs were drowsiness/fatigue, headache or migraine, gastrointestinal disturbances, dizziness/faintness and rash or skin disorders. Overall, the high-quality evidence provided by this review supports current guidance (e.g. NICE) that carbamazepine and lamotrigine are suitable first-line treatments for individuals with partial onset seizures and also demonstrates that levetiracetam may be a suitable alternative. High-quality evidence from this review also supports the use of sodium valproate as the first-line treatment for individuals with generalised tonic-clonic seizures (with or without other generalised seizure types) and also demonstrates that lamotrigine and levetiracetam would be suitable alternatives to either of these first-line treatments, particularly for those of childbearing potential, for whom sodium valproate may not be an appropriate treatment option due to teratogenicity.

The Threshold Shortest Path Interdiction Problem for Critical Infrastructure Resilience Analysis

DTIC Science & Technology

2017-09-01

being pushed over the minimum designated threshold. 1.4 Motivation A simple setting to motivate this research is the “30 minutes or it’s free” guarantee...parallel network structure in Fig. 4.4 is simple in design , yet shows a relatively high resilience when compared to the other networks in general. The high...United States Naval Academy, 2002 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN OPERATIONS RESEARCH

Fabrication and Characterization of Surrogate Glasses Aimed to Validate Nuclear Forensic Techniques

DTIC Science & Technology

2017-12-01

sample is processed while submerged and produces fine sized particles the exposure levels and risk of contamination from the samples is also greatly...induced the partial collapses of the xerogel network strengthened the network while the sample sizes were reduced [22], [26]. As a result the wt...inhomogeneous, making it difficult to clearly determine which features were present in the sample before LDHP and which were caused by it. In this study

Network dynamics and systems biology

NASA Astrophysics Data System (ADS)

Norrell, Johannes A.

The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the transition point, called critical, exhibit many of the features of regulatory networks, and recent studies suggest that some specific regulatory networks are indeed near-critical. We ask whether certain statistical measures of the ensemble behavior of large continuous networks are reproduced by Boolean models. We find that, in spite of the lack of correspondence between attractors observed in smaller systems, the statistical characterization given by the continuous and Boolean models show close agreement, and the transition between order and disorder known in Boolean systems can occur in continuous systems as well. One effect that is not present in Boolean systems, the failure of information to propagate down chains of elements of arbitrary length, is present in a class of continuous networks. In these systems, a modified Boolean theory that takes into account the collective effect of propagation failure on chains throughout the network gives a good description of the observed behavior. We find that propagation failure pushes the system toward greater order, resulting in a partial or complete suppression of the disordered phase. Finally, we explore a dynamical process of direct biological relevance: asymmetric cell division in A. thaliana. The long term goal is to develop a model for the process that accurately accounts for both wild type and mutant behavior. To contribute to this endeavor, we use confocal microscopy to image roots in a SHORT-ROOT inducible mutant. We compute correlation functions between the locations of asymmetrically divided cells, and we construct stochastic models based on a few simple assumptions that accurately predict the non-zero correlations. Our result shows that intracellular processes alone cannot be responsible for the observed divisions, and that an intercell signaling mechanism could account for the measured correlations.

Self-organization of linear nanochannel networks

NASA Astrophysics Data System (ADS)

Annabattula, R. K.; Veenstra, J. M.; Mei, Y. F.; Schmidt, O. G.; Onck, P. R.

2010-06-01

A theoretical study has been conducted to explore the mechanics of self-organizing channel networks with dimensions in the submicron range and nanorange. The channels form by the partial release and bond back of prestressed thin films. In the release phase, the film spontaneously buckles into wrinkles of a certain wavelength, followed by a bond-back phase in which the final channel geometry is established through cohesive interface attractions. Results are presented in terms of the channel spacing, height, and width as a function of the film stiffness, thickness, eigenstrain, etch width, and interface energy. We have identified two dimensionless parameters that fully quantify the network assembly, showing excellent agreement with experiments. Our results provide valuable insight for the design of submicron and nanoscale channel networks with specific geometries.

The evolving cobweb of relations among partially rational investors

PubMed Central

DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents’ behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors. PMID:28196144

The evolving cobweb of relations among partially rational investors.

PubMed

DeLellis, Pietro; DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents' behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors.

Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

PubMed

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.

Bonding, moment formation, and magnetic interactions in Ca14MnBi11 and Ba14MnBi11

NASA Astrophysics Data System (ADS)

Sánchez-Portal, D.; Martin, Richard M.; Kauzlarich, S. M.; Pickett, W. E.

2002-04-01

``14-1-11'' phase compounds, based on magnetic Mn ions and typified by Ca14MnBi11 and Ba14MnBi11, show an unusual magnetic behavior, but the large number (104) of atoms in the primitive cell has precluded any previous full electronic structure study. Using an efficient, local-orbital-based method within the local-spin-density approximation to study the electronic structure, we find a gap between a bonding valence-band complex and an antibonding conduction-band continuum. The bonding bands lack one electron per formula unit of being filled, making them low carrier density p-type metals. The hole resides in the MnBi4 tetrahedral unit, and partially compensates for the high-spin d5 Mn moment, leaving a net spin near 4μB that is consistent with experiment. These manganites are composed of two disjoint but interpenetrating ``jungle gym'' networks of spin-4/2 MnBi9-4 units with ferromagnetic interactions within the same network, and weaker couplings between the networks whose sign and magnitude is sensitive to materials parameters. Ca14MnBi11 is calculated to be ferromagnetic as observed, while for Ba14MnBi11 (which is antiferromagnetic) the ferromagnetic and antiferromagnetic states are calculated to be essentially degenerate. The band structure of the ferromagnetic states is very close to half metallic.

HIV infection across aging: Synergistic effects on intrinsic functional connectivity of the brain.

PubMed

Egbert, Anna R; Biswal, Bharat; Karunakaran, Keerthana; Pluta, Agnieszka; Wolak, Tomasz; Rao, Stephen; Bornstein, Robert; Szymańska, Bogna; Horban, Andrzej; Firląg-Burkacka, Ewa; Sobańska, Marta; Gawron, Natalia; Bieńkowski, Przemysław; Sienkiewicz-Jarosz, Halina; Ścińska-Bieńkowska, Anna; Łojek, Emilia

2018-06-12

The objective of the study was to examine additive and synergistic effects of age and HIV infection on resting state (RS) intra- and inter-network functional connectivity (FC) of the brain. We also aimed to assess relationships with neurocognition and determine clinical-, treatment-, and health-related factors moderating intrinsic brain activity in aging HIV-positive (HIV+) individuals. The current report presents data on 54 HIV+ individuals (age M = 41, SD = 12 years) stabilized on cART and 54 socio-demographically matched healthy (HIV-) comparators (age M = 43, SD = 12 years), with cohort education mean of 16 years (SD = 12). Age at seroconversion ranged 20-55 years old. ANOVA assessed additive and synergistic effects of age and HIV in 133 ROIs. Bivariate statistics examined relationships of FC indices vulnerable to age-HIV interactions and neurocognitive domains T-scores (attention, executive, memory, psychomotor, semantic skills). Multivariate logistic models determined covariates of FC. This study found no statistically significant age-HIV effects on RS-FC after correcting for multiple comparisons except for synergistic effects on connectivity within cingulo-opercular network (CON) at the trending level. However, for uncorrected RS connectivity analyses, we observed HIV-related strengthening between regions of fronto-parietal network (FPN) and default mode network (DMN), and particular DMN regions and sensorimotor network (SMN). Simultaneously, FC weakening was observed within FPN and between other regions of DMN-SMN, in HIV+ vs. HIV- individuals. Ten ROI pairs revealed age-HIV interactions, with FC decreasing with age in HIV+, while increasing in controls. FC correlated with particular cognitive domains positively in HIV+ vs. negatively in HIV- group. Proportion of life prior-to-after HIV-seroconversion, post-infection years, and treatment determined within-FPN and SMN-DMN FC. In sum, highly functioning HIV+/cART+ patients do not reveal significantly altered RS-FC from healthy comparators. Nonetheless, the current findings uncorrected for multiple comparisons suggest that HIV infection may lead to simultaneous increases and decreases in FC in distinct brain regions even in patients successfully stabilized on cART. Moreover, RS-fMRI ROI-based analysis can be sensitive to age-HIV interactions, which are especially pronounced for inter-network FC in relation to neurocognition. Aging and treatment-related factors partially explain RS-FC in aging HIV+ patients. Copyright © 2017. Published by Elsevier Inc.

Amplitude reconstruction from complete photoproduction experiments and truncated partial-wave expansions

NASA Astrophysics Data System (ADS)

Workman, R. L.; Tiator, L.; Wunderlich, Y.; Döring, M.; Haberzettl, H.

2017-01-01

We compare the methods of amplitude reconstruction, for a complete experiment and a truncated partial-wave analysis, applied to the photoproduction of pseudoscalar mesons. The approach is pedagogical, showing in detail how the amplitude reconstruction (observables measured at a single energy and angle) is related to a truncated partial-wave analysis (observables measured at a single energy and a number of angles).

Amplitude reconstruction from complete photoproduction experiments and truncated partial-wave expansions

DOE PAGES

Workman, R. L.; Tiator, L.; Wunderlich, Y.; ...

2017-01-19

Here, we compare the methods of amplitude reconstruction, for a complete experiment and a truncated partial-wave analysis, applied to the photoproduction of pseudoscalar mesons. The approach is pedagogical, showing in detail how the amplitude reconstruction (observables measured at a single energy and angle) is related to a truncated partial-wave analysis (observables measured at a single energy and a number of angles).

Attenuation of coda waves in the Aswan Reservoir area, Egypt

NASA Astrophysics Data System (ADS)

Mohamed, H. H.; Mukhopadhyay, S.; Sharma, J.

2010-09-01

Coda attenuation characteristics of Aswan Reservoir area of Egypt were analyzed using data recorded by a local earthquake network operated around the reservoir. 330 waveforms obtained from 28 earthquakes recorded by a network of 13 stations were used for this analysis. Magnitude of these earthquakes varied between 1.4 and 2.5. The maximum epicentral distance and depth of focus of these earthquakes were 45 km and 16 km respectively. Single back-scattering method was used for estimation of coda Q ( Qc). The Q0 values ( Qc at 1 Hz) vary between 54 and 100 and frequency dependence parameter " n" values vary between 1 and 1.2 for lapse time varying between 15 s and 60 s. It is observed that coda Q ( Qc) and related parameters are similar at similar lapse times to those observed for those for Koyna, India, where reservoir induced seismicity is also observed. For both regions these parameters are also similar to those observed for tectonically active regions of the world, although Aswan is located in a moderately active region and Koyna is located in a tectonically stable region. However, Qc does not increase uniformly with increasing lapse time, as is observed for several parts of the world. Converting lapse time to depth/distance it is observed that Qc becomes lower or remains almost constant at around 70 to 90 km and 120 km depth/distance. This indicates presence of more attenuative material at those depth levels or distances compared to their immediate surroundings. It is proposed that this variation indicates presence of fluid filled fractures and/or partial melts at some depths/distance from the area of study. The Qc values are higher than those obtained for the Gulf of Suez and Al Dabbab region of Egypt at distances greater than 300 km from the study area by other workers. The turbidity decreases with depth in the study area.

Financial networks based on Granger causality: A case study

NASA Astrophysics Data System (ADS)

Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-09-01

Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to specify the direction of the interrelationships among the international stock indexes and portray the links of the resulting networks, by the presence of direct couplings between variables exploiting all available information. However, their differences are assessed due to the presence of nonlinearity. The weighted networks formed with respect to the causality measures are transformed to binary ones using a significance test. The financial networks are formed on sliding windows in order to examine the network characteristics and trace changes in the connectivity structure. Subsequently, two statistical network quantities are calculated; the average degree and the average shortest path length. The empirical findings reveal interesting time-varying properties of the constructed network, which are clearly dependent on the nature of the financial cycle.

Face recognition: a convolutional neural-network approach.

PubMed

Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

1997-01-01

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

Suppressing epidemics on networks by exploiting observer nodes.

PubMed

Takaguchi, Taro; Hasegawa, Takehisa; Yoshida, Yuichi

2014-07-01

To control infection spreading on networks, we investigate the effect of observer nodes that recognize infection in a neighboring node and make the rest of the neighbor nodes immune. We numerically show that random placement of observer nodes works better on networks with clustering than on locally treelike networks, implying that our model is promising for realistic social networks. The efficiency of several heuristic schemes for observer placement is also examined for synthetic and empirical networks. In parallel with numerical simulations of epidemic dynamics, we also show that the effect of observer placement can be assessed by the size of the largest connected component of networks remaining after removing observer nodes and links between their neighboring nodes.

Suppressing epidemics on networks by exploiting observer nodes

NASA Astrophysics Data System (ADS)

Takaguchi, Taro; Hasegawa, Takehisa; Yoshida, Yuichi

2014-07-01

To control infection spreading on networks, we investigate the effect of observer nodes that recognize infection in a neighboring node and make the rest of the neighbor nodes immune. We numerically show that random placement of observer nodes works better on networks with clustering than on locally treelike networks, implying that our model is promising for realistic social networks. The efficiency of several heuristic schemes for observer placement is also examined for synthetic and empirical networks. In parallel with numerical simulations of epidemic dynamics, we also show that the effect of observer placement can be assessed by the size of the largest connected component of networks remaining after removing observer nodes and links between their neighboring nodes.

Chemical networks with inflows and outflows: a positive linear differential inclusions approach.

PubMed

Angeli, David; De Leenheer, Patrick; Sontag, Eduardo D

2009-01-01

Certain mass-action kinetics models of biochemical reaction networks, although described by nonlinear differential equations, may be partially viewed as state-dependent linear time-varying systems, which in turn may be modeled by convex compact valued positive linear differential inclusions. A result is provided on asymptotic stability of such inclusions, and applied to a ubiquitous biochemical reaction network with inflows and outflows, known as the futile cycle. We also provide a characterization of exponential stability of general homogeneous switched systems which is not only of interest in itself, but also plays a role in the analysis of the futile cycle. 2009 American Institute of Chemical Engineers

Chimera states in complex networks: interplay of fractal topology and delay

NASA Astrophysics Data System (ADS)

Sawicki, Jakub; Omelchenko, Iryna; Zakharova, Anna; Schöll, Eckehard

2017-06-01

Chimera states are an example of intriguing partial synchronization patterns emerging in networks of identical oscillators. They consist of spatially coexisting domains of coherent (synchronized) and incoherent (desynchronized) dynamics. We analyze chimera states in networks of Van der Pol oscillators with hierarchical connectivities, and elaborate the role of time delay introduced in the coupling term. In the parameter plane of coupling strength and delay time we find tongue-like regions of existence of chimera states alternating with regions of existence of coherent travelling waves. We demonstrate that by varying the time delay one can deliberately stabilize desired spatio-temporal patterns in the system.

Analysis and synthesis of distributed-lumped-active networks by digital computer

NASA Technical Reports Server (NTRS)

1973-01-01

The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.

A cloud-based data network approach for translational cancer research.

PubMed

Xing, Wei; Tsoumakos, Dimitrios; Ghanem, Moustafa

2015-01-01

We develop a new model and associated technology for constructing and managing self-organizing data to support translational cancer research studies. We employ a semantic content network approach to address the challenges of managing cancer research data. Such data is heterogeneous, large, decentralized, growing and continually being updated. Moreover, the data originates from different information sources that may be partially overlapping, creating redundancies as well as contradictions and inconsistencies. Building on the advantages of elasticity of cloud computing, we deploy the cancer data networks on top of the CELAR Cloud platform to enable more effective processing and analysis of Big cancer data.

Study of Jovian synchrotron emission with the NASA's Deep Space Network for Juno mission

NASA Astrophysics Data System (ADS)

Garcia-Miro, Cristina; Horiuchi, Shinji; Levin, Steve; Orton, Glenn S.; Bolton, Scott; Jauncey, David; Kuiper, T. B. H.; Teitelbaum, Lawrence

2016-10-01

We are monitoring Jupiter's synchrotron emission with the purpose of connecting the measurements of the Juno mission's MicroWave Radiometer (MWR) experiment to the historical baseline of non-thermal emission, using NASA's Deep Space Network (DSN). The DSN has the most sensitive network of antennas dedicated to tracking spacecraft that are exploring deep space, whose state-of-the-art receivers are considered among the best radio telescopes in the world. Availability for radio astronomy studies is subject to demand from space projects using the DSN. These antennas have previously contributed to the study of the Jovian non-thermal synchroton emission [1].NASA's New Frontiers Juno mission was placed into a nominal orbit on the 4th of July, 2016, allowing it to begin a detailed exploration of Jupiter. Among its scientific objectives is the characterization and exploration of the 3D structure of Jupiter's polar magnetosphere and auroras. It is important to provide a means to connect these detailed MWR measurements with the historical record of synchrotron emission. Ideally, these measurements should be performed on a regular basis during the whole extent of the mission. The DSN has the advantage of being able to perform uninterrupted 24-hour observations using antennas from the different complexes located in USA, Australia and Spain.Additionally, this monitoring program links with and validates the Jupiter observations currently performed by the triplet of educational programs GAVRT, STARS and PARTNeR in USA, Australia and Spain, respectively. These educational programs are partially supported by the DSN and use some of its antennas for teaching purposes, involving students in professional research and exploration.We will describe the DSN single-dish continuum observations of Jupiter in detail: the antennas, receivers and the equipment used to collect the data, the observing procedure, and the data-reduction process. Preliminary results of the Jupiter beaming curve will also be presented.References[1] Bolton, S.J., Janssen, M., Thorne, R., et al.: Ultra-relativistic electrons in Jupiter's radiation belts, Nature, 415, 2002.

An automated method for the evaluation of the pointing accuracy of Sun-tracking devices

NASA Astrophysics Data System (ADS)

Baumgartner, Dietmar J.; Pötzi, Werner; Freislich, Heinrich; Strutzmann, Heinz; Veronig, Astrid M.; Rieder, Harald E.

2017-03-01

The accuracy of solar radiation measurements, for direct (DIR) and diffuse (DIF) radiation, depends significantly on the precision of the operational Sun-tracking device. Thus, rigid targets for instrument performance and operation have been specified for international monitoring networks, e.g., the Baseline Surface Radiation Network (BSRN) operating under the auspices of the World Climate Research Program (WCRP). Sun-tracking devices that fulfill these accuracy requirements are available from various instrument manufacturers; however, none of the commercially available systems comprise an automatic accuracy control system allowing platform operators to independently validate the pointing accuracy of Sun-tracking sensors during operation. Here we present KSO-STREAMS (KSO-SunTRackEr Accuracy Monitoring System), a fully automated, system-independent, and cost-effective system for evaluating the pointing accuracy of Sun-tracking devices. We detail the monitoring system setup, its design and specifications, and the results from its application to the Sun-tracking system operated at the Kanzelhöhe Observatory (KSO) Austrian radiation monitoring network (ARAD) site. The results from an evaluation campaign from March to June 2015 show that the tracking accuracy of the device operated at KSO lies within BSRN specifications (i.e., 0.1° tracking accuracy) for the vast majority of observations (99.8 %). The evaluation of manufacturer-specified active-tracking accuracies (0.02°), during periods with direct solar radiation exceeding 300 W m-2, shows that these are satisfied in 72.9 % of observations. Tracking accuracies are highest during clear-sky conditions and on days where prevailing clear-sky conditions are interrupted by frontal movement; in these cases, we obtain the complete fulfillment of BSRN requirements and 76.4 % of observations within manufacturer-specified active-tracking accuracies. Limitations to tracking surveillance arise during overcast conditions and periods of partial solar-limb coverage by clouds. On days with variable cloud cover, 78.1 % (99.9 %) of observations meet active-tracking (BSRN) accuracy requirements while for days with prevailing overcast conditions these numbers reduce to 64.3 % (99.5 %).

Perspectives on Social Network Analysis for Observational Scientific Data

NASA Astrophysics Data System (ADS)

Singh, Lisa; Bienenstock, Elisa Jayne; Mann, Janet

This chapter is a conceptual look at data quality issues that arise during scientific observations and their impact on social network analysis. We provide examples of the many types of incompleteness, bias and uncertainty that impact the quality of social network data. Our approach is to leverage the insights and experience of observational behavioral scientists familiar with the challenges of making inference when data are not complete, and suggest avenues for extending these to relational data questions. The focus of our discussion is on network data collection using observational methods because they contain high dimensionality, incomplete data, varying degrees of observational certainty, and potential observer bias. However, the problems and recommendations identified here exist in many other domains, including online social networks, cell phone networks, covert networks, and disease transmission networks.

A review of covariate selection for non-experimental comparative effectiveness research.

PubMed

Sauer, Brian C; Brookhart, M Alan; Roy, Jason; VanderWeele, Tyler

2013-11-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. Copyright © 2013 John Wiley & Sons, Ltd.

A Review of Covariate Selection for Nonexperimental Comparative Effectiveness Research

PubMed Central

Sauer, Brian C.; Brookhart, Alan; Roy, Jason; Vanderweele, Tyler

2014-01-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research (CER), and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for on a common cause pathway between treatment and outcome can remove confounding, while adjustment for other structural types may increase bias. For this reason variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely know. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses the high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher’s knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically-derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. PMID:24006330

Managing Documents in the Wider Area: Intelligent Document Management.

ERIC Educational Resources Information Center

Bittleston, Richard

1995-01-01

Discusses techniques for managing documents in wide area networks, reviews technique limitations, and offers recommendations to database designers. Presented techniques include: increasing bandwidth, reducing data traffic, synchronizing documentation, partial synchronization, audit trials, navigation, and distribution control and security. Two…

The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

PubMed

Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

2016-01-15

Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the derivation of these networks, we also observed some brain-behavioral correlations, notably for the fluid-reasoning network whose network score correlated with performance on the memory and fluid-reasoning tasks. While age did not influence the expression of this RANN, the slope of the association between network score and fluid-reasoning performance was negatively associated with higher ages. These results provide support for the hypothesis that a set of specific, age-invariant neural networks underlies these four RAs, and that these networks maintain their cognitive specificity and level of intensity across age. Activation common to all 12 tasks was identified as another activation pattern resulting from a mean-contrast Partial-Least-Squares technique. This common pattern did show associations with age and some subject demographics for some of the reference domains, lending support to the overall conclusion that aspects of neural processing that are specific to any cognitive reference ability stay constant across age, while aspects that are common to all reference abilities differ across age. Copyright © 2015 Elsevier Inc. All rights reserved.

[All-ceramic resin bonded fixed partial denture made of IPS hot-pressed casting porcelain restore anterior missing teeth: a three years clinical observation].

PubMed

Zhou, Tuan feng; Wang, Xin zhi; Zhang, Gui rong

2011-02-18

To clinic observation of IPS Empress2 and IPS e.max all ceramic resin bonded fixed partial dentures used in one anterior teeth lost in upper jaw or less than two anterior tooth lost in lower jaw. 22 patients, 26 restorations had been made, which included 16 single-retainer all ceramic resin bonded fixed partial dentures and 10 two-retainers all ceramic resin bonded fixed partial dentures. Secondary caries of the abutments, shade in the margin of the retainers and the integrity of the restorations had been observed at 3 months, 6 months, 1 year, 2 years and 3 years after all ceramic resin bonded fixed partial dentures having been bonded. In the 3 years of clinic observation of the anterior all ceramic resin bonded fixed partial dentures, 1 two-retainers restoration lost bond after it had been made for 3 months, a retainer of one two-retainers restoration was broken after 6 months, but they are still used after modified as one-retainer all ceramic resin bonded fixed partial dentures, 1 two-retainers restoration lost bond two year later, It was integrity and re-bonded again that was still stable. No secondary carries and no shade in margin of the retainers had been found. Their color matches with the nature teeth excellently. The success rate was 88.5%. IPS Empress 2 and IPS e.max all ceramic resin bonded fixed partial dentures should be a good selection in one or two teeth lose in anterior jaws.

Emergence of synchronization induced by the interplay between two prisoner's dilemma games with volunteering in small-world networks

NASA Astrophysics Data System (ADS)

Chen, Yong; Qin, Shao-Meng; Yu, Lianchun; Zhang, Shengli

2008-03-01

We studied synchronization between prisoner’s dilemma games with voluntary participation in two Newman-Watts small-world networks. It was found that there are three kinds of synchronization: partial phase synchronization, total phase synchronization, and complete synchronization, for varied coupling factors. Besides, two games can reach complete synchronization for the large enough coupling factor. We also discussed the effect of the coupling factor on the amplitude of oscillation of cooperator density.

Inferring Adolescent Social Networks Using Partial Ego-Network Substance Use Data

DTIC Science & Technology

2008-05-15

initiation among West Coast youth from age 5 to 23.” Preventive Medicine 44. Ellickson, Phyllis L. and Robert M. Bell. 1990. “Drug prevention in junior high...21:615–632. Jessor, Richard and Shirley L. Jessor. 1978. “Theory testing in longitudinal research on Marihuana use.” In Longitudinal Research on Drug...Science and Medicine 44:1861–1869. Mollison, Denis, Valerie Isham, and Bryan Grenfell. 1994. “Epidemics: models and data.” Journal of the Royal Statistics

Observation of partial relaxation mechanisms via anisotropic strain relief on epitaxial islands using semiconductor nanomembranes

NASA Astrophysics Data System (ADS)

Rosa, Barbara L. T.; Marçal, Lucas A. B.; Ribeiro Andrade, Rodrigo; Dornellas Pinto, Luciana; Rodrigues, Wagner N.; Lustoza Souza, Patrícia; Pamplona Pires, Mauricio; Wagner Nunes, Ricardo; Malachias, Angelo

2017-07-01

In this work we attempt to directly observe anisotropic partial relaxation of epitaxial InAs islands using transmission electron microscopy (TEM) and synchrotron x-ray diffraction on a 15 nm thick InAs:GaAs nanomembrane. We show that under such conditions TEM provides improved real-space statistics, allowing the observation of partial relaxation processes that were not previously detected by other techniques or by usual TEM cross section images. Besides the fully coherent and fully relaxed islands that are known to exist above previously established critical thickness, we prove the existence of partially relaxed islands, where incomplete 60° half-loop misfit dislocations lead to a lattice relaxation along one of the <110> directions, keeping a strained lattice in the perpendicular direction. Although individual defects cannot be directly observed, their implications to the resulting island registry are identified and discussed within the frame of half-loops propagations.

Observation of partial relaxation mechanisms via anisotropic strain relief on epitaxial islands using semiconductor nanomembranes.

PubMed

Rosa, Barbara L T; Marçal, Lucas A B; Andrade, Rodrigo Ribeiro; Pinto, Luciana Dornellas; Rodrigues, Wagner N; Souza, Patrícia Lustoza; Pires, Mauricio Pamplona; Nunes, Ricardo Wagner; Malachias, Angelo

2017-07-28

In this work we attempt to directly observe anisotropic partial relaxation of epitaxial InAs islands using transmission electron microscopy (TEM) and synchrotron x-ray diffraction on a 15 nm thick InAs:GaAs nanomembrane. We show that under such conditions TEM provides improved real-space statistics, allowing the observation of partial relaxation processes that were not previously detected by other techniques or by usual TEM cross section images. Besides the fully coherent and fully relaxed islands that are known to exist above previously established critical thickness, we prove the existence of partially relaxed islands, where incomplete 60° half-loop misfit dislocations lead to a lattice relaxation along one of the 〈110〉 directions, keeping a strained lattice in the perpendicular direction. Although individual defects cannot be directly observed, their implications to the resulting island registry are identified and discussed within the frame of half-loops propagations.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

PubMed

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k -NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k -NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Properties of networks with partially structured and partially random connectivity

NASA Astrophysics Data System (ADS)

Ahmadian, Yashar; Fumarola, Francesco; Miller, Kenneth D.

2015-01-01

Networks studied in many disciplines, including neuroscience and mathematical biology, have connectivity that may be stochastic about some underlying mean connectivity represented by a non-normal matrix. Furthermore, the stochasticity may not be independent and identically distributed (iid) across elements of the connectivity matrix. More generally, the problem of understanding the behavior of stochastic matrices with nontrivial mean structure and correlations arises in many settings. We address this by characterizing large random N ×N matrices of the form A =M +L J R , where M ,L , and R are arbitrary deterministic matrices and J is a random matrix of zero-mean iid elements. M can be non-normal, and L and R allow correlations that have separable dependence on row and column indices. We first provide a general formula for the eigenvalue density of A . For A non-normal, the eigenvalues do not suffice to specify the dynamics induced by A , so we also provide general formulas for the transient evolution of the magnitude of activity and frequency power spectrum in an N -dimensional linear dynamical system with a coupling matrix given by A . These quantities can also be thought of as characterizing the stability and the magnitude of the linear response of a nonlinear network to small perturbations about a fixed point. We derive these formulas and work them out analytically for some examples of M ,L , and R motivated by neurobiological models. We also argue that the persistence as N →∞ of a finite number of randomly distributed outlying eigenvalues outside the support of the eigenvalue density of A , as previously observed, arises in regions of the complex plane Ω where there are nonzero singular values of L-1(z 1 -M ) R-1 (for z ∈Ω ) that vanish as N →∞ . When such singular values do not exist and L and R are equal to the identity, there is a correspondence in the normalized Frobenius norm (but not in the operator norm) between the support of the spectrum of A for J of norm σ and the σ pseudospectrum of M .

System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance

PubMed Central

Montastier, Emilie; Villa-Vialaneix, Nathalie; Caspar-Bauguil, Sylvie; Hlavaty, Petr; Tvrzicka, Eva; Gonzalez, Ignacio; Saris, Wim H. M.; Langin, Dominique; Kunesova, Marie; Viguerie, Nathalie

2015-01-01

Nutrigenomics investigates relationships between nutrients and all genome-encoded molecular entities. This holistic approach requires systems biology to scrutinize the effects of diet on tissue biology. To decipher the adipose tissue (AT) response to diet induced weight changes we focused on key molecular (lipids and transcripts) AT species during a longitudinal dietary intervention. To obtain a systems model, a network approach was used to combine all sets of variables (bio-clinical, fatty acids and mRNA levels) and get an overview of their interactions. AT fatty acids and mRNA levels were quantified in 135 obese women at baseline, after an 8-week low calorie diet (LCD) and after 6 months of ad libitum weight maintenance diet (WMD). After LCD, individuals were stratified a posteriori according to weight change during WMD. A 3 steps approach was used to infer a global model involving the 3 sets of variables. It consisted in inferring intra-omic networks with sparse partial correlations and inter-omic networks with regularized canonical correlation analysis and finally combining the obtained omic-specific network in a single global model. The resulting networks were analyzed using node clustering, systematic important node extraction and cluster comparisons. Overall, AT showed both constant and phase-specific biological signatures in response to dietary intervention. AT from women regaining weight displayed growth factors, angiogenesis and proliferation signaling signatures, suggesting unfavorable tissue hyperplasia. By contrast, after LCD a strong positive relationship between AT myristoleic acid (a fatty acid with low AT level) content and de novo lipogenesis mRNAs was found. This relationship was also observed, after WMD, in the group of women that continued to lose weight. This original system biology approach provides novel insight in the AT response to weight control by highlighting the central role of myristoleic acid that may account for the beneficial effects of weight loss. PMID:25590576

Late Noachian fluvial erosion on Mars: Cumulative water volumes required to carve the valley networks and grain size of bed-sediment

NASA Astrophysics Data System (ADS)

Rosenberg, Eliott N.; Head, James W., III

2015-11-01

Our goal is to quantify the cumulative water volume that was required to carve the Late Noachian valley networks on Mars. We employ an improved methodology in which fluid/sediment flux ratios are based on empirical data, not assumed. We use a large quantity of data from terrestrial rivers to assess the variability of actual fluid/sediment flux sediment ratios. We find the flow depth by using an empirical relationship to estimate the fluid flux from the estimated channel width, and then using estimated grain sizes (theoretical sediment grain size predictions and comparison with observations by the Curiosity rover) to find the flow depth to which the resulting fluid flux corresponds. Assuming that the valley networks contained alluvial bed rivers, we find, from their current slopes and widths, that the onset of suspended transport occurs near the sand-gravel boundary. Thus, any bed sediment must have been fine gravel or coarser, whereas fine sediment would be carried downstream. Subsequent to the cessation of fluvial activity, aeolian processes have partially redistributed fine-grain particles in the valleys, often forming dunes. It seems likely that the dominant bed sediment size was near the threshold for suspension, and assuming that this was the case could make our final results underestimates, which is the same tendency that our other assumptions have. Making this assumption, we find a global equivalent layer (GEL) of 3-100 m of water to be the most probable cumulative volume that passed through the valley networks. This value is similar to the ∼34 m water GEL currently on the surface and in the near-surface in the form of ice. Note that the amount of water required to carve the valley networks could represent the same water recycled through a surface valley network hydrological system many times in separate or continuous precipitation/runoff/collection/evaporation/precipitation cycles.

Partially Observed Mixtures of IRT Models: An Extension of the Generalized Partial-Credit Model

ERIC Educational Resources Information Center

Von Davier, Matthias; Yamamoto, Kentaro

2004-01-01

The generalized partial-credit model (GPCM) is used frequently in educational testing and in large-scale assessments for analyzing polytomous data. Special cases of the generalized partial-credit model are the partial-credit model--or Rasch model for ordinal data--and the two parameter logistic (2PL) model. This article extends the GPCM to the…

Auto-Detection of Partial Discharges in Power Cables by Descrete Wavelet Transform

NASA Astrophysics Data System (ADS)

Yasuda, Yoh; Hara, Takehisa; Urano, Koji; Chen, Min

One of the serious problems that may happen in power XLPE cables is destruction of insulator. The best and conventional way to prevent such a crucial accident is generally supposed to ascertain partial corona discharges occurring at small void in organic insulator. However, there are some difficulties to detect those partial discharges because of existence of external noises in detected data, whose patterns are hardly identified at a glance. By the reason of the problem, there have been a number of researches on the way of development to accomplish detecting partial discharges by employing neural network (NN) system, which is widely known as the system for pattern recognition. We have been developing the NN system of the auto-detection for partial discharges, which we actually input numerical data of waveform itself into and obtained appropriate performance from. In this paper, we employed Descrete Wavelet Transform (DWT) to acquire more detailed transformed data in order to put them into the NN system. Employing DWT, we became able to express the waveform data in time-frequency space, and achieved effective detectiton of partial discharges by NN system. We present here the results using DWT analysis for partial discharges and noise signals which we obtained actually. Moreover, we present results out of the NN system which were dealt with those transformed data.

Decentralized Routing and Diameter Bounds in Entangled Quantum Networks

NASA Astrophysics Data System (ADS)

Gyongyosi, Laszlo; Imre, Sandor

2017-04-01

Entangled quantum networks are a necessity for any future quantum internet, long-distance quantum key distribution, and quantum repeater networks. The entangled quantum nodes can communicate through several different levels of entanglement, leading to a heterogeneous, multi-level entangled network structure. The level of entanglement between the quantum nodes determines the hop distance, the number of spanned nodes, and the probability of the existence of an entangled link in the network. In this work we define a decentralized routing for entangled quantum networks. We show that the probability distribution of the entangled links can be modeled by a specific distribution in a base-graph. The results allow us to perform efficient routing to find the shortest paths in entangled quantum networks by using only local knowledge of the quantum nodes. We give bounds on the maximum value of the total number of entangled links of a path. The proposed scheme can be directly applied in practical quantum communications and quantum networking scenarios. This work was partially supported by the Hungarian Scientific Research Fund - OTKA K-112125.

Quantum Graphical Models and Belief Propagation

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

Leifer, M.S.; Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo Ont., N2L 2Y5; Poulin, D.

Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markovmore » Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersley-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described.« less

I Keep my Problems to Myself: Negative Social Network Orientation, Social Resources, and Health-Related Quality of Life in Cancer Survivors

PubMed Central

Symes, Yael; Campo, Rebecca A.; Wu, Lisa M.; Austin, Jane

2016-01-01

Background Cancer survivors treated with hematopoietic stem cell transplant rely on their social network for successful recovery. However, some survivors have negative attitudes about using social resources (negative social network orientation) that are critical for their recovery. Purpose We examined the association between survivors’ social network orientation and health-related quality of life (HRQoL) and whether it was mediated by social resources (network size, perceived support, and negative and positive support-related social exchanges). Methods In a longitudinal study, 255 survivors completed validated measures of social network orientation, HRQoL, and social resources. Hypotheses were tested using path analysis. Results More negative social network orientation predicted worse HRQoL (p < .001). This association was partially mediated by lower perceived support and more negative social exchanges. Conclusions Survivors with negative social network orientation may have poorer HRQoL in part due to deficits in several key social resources. Findings highlight a subgroup at risk for poor transplant outcomes and can guide intervention development. PMID:26693932

Network-Level Structure-Function Relationships in Human Neocortex

PubMed Central

Mišić, Bratislav; Betzel, Richard F.; de Reus, Marcel A.; van den Heuvel, Martijn P.; Berman, Marc G.; McIntosh, Anthony R.; Sporns, Olaf

2016-01-01

The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate. PMID:27102654

Reducing a cortical network to a Potts model yields storage capacity estimates

NASA Astrophysics Data System (ADS)

Naim, Michelangelo; Boboeva, Vezha; Kang, Chol Jun; Treves, Alessandro

2018-04-01

An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e. in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback w, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss the implications for semantic memory in humans.

Facile solid-state synthesis of Ni@C nanosheet-assembled hierarchical network for high-performance lithium storage

NASA Astrophysics Data System (ADS)

Gu, Jinghe; Li, Qiyun; Zeng, Pan; Meng, Yulin; Zhang, Xiukui; Wu, Ping; Zhou, Yiming

2017-08-01

Micro/nano-architectured transition-metal@C hybrids possess unique structural and compositional features toward lithium storage, and are thus expected to manifest ideal anodic performances in advanced lithium-ion batteries (LIBs). Herein, we propose a facile and scalable solid-state coordination and subsequent pyrolysis route for the formation of a novel type of micro/nano-architectured transition-metal@C hybrid (i.e., Ni@C nanosheet-assembled hierarchical network, Ni@C network). Moreover, this coordination-pyrolysis route has also been applied for the construction of bare carbon network using zinc salts instead of nickel salts as precursors. When applied as potential anodic materials in LIBs, the Ni@C network exhibits Ni-content-dependent electrochemical performances, and the partially-etched Ni@C network manifests markedly enhanced Li-storage performances in terms of specific capacities, cycle life, and rate capability than the pristine Ni@C network and carbon network. The proposed solid-state coordination and pyrolysis strategy would open up new opportunities for constructing micro/nano-architectured transition-metal@C hybrids as advanced anode materials for LIBs.

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

PubMed Central

Park, Juyong; Yook, Soon-Hyung

2014-01-01

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest – essential in determining reward and penalty – is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the “Natural Ranking,” an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks. PMID:25163528

Application of artificial neural networks to thermal detection of disbonds

NASA Technical Reports Server (NTRS)

Prabhu, D. R.; Howell, P. A.; Syed, H. I.; Winfree, W. P.

1992-01-01

A novel technique for processing thermal data is presented and applied to simulation as well as experimental data. Using a neural network of thermal data classification, good classification accuracies are obtained, and the resulting images exhibit very good contrast between bonded and disbonded locations. In order to minimize the preprocessing required before using the network of classification, the temperature values were directly employed to train a network using data from an on-site testing run of a commercial aircraft. Training was extremely fast, and the resulting classification also agreed reasonably well with an ultrasonic characterization of the panel. The results obtained using one sample show the partially disbonded vertical doubler. The vertical lines along the doubler correspond to the original extent of the doubler obtained using blueprints of the aircraft.

Mitigation of short-term disturbance negative impacts in the agent-based model of a production companies network

NASA Astrophysics Data System (ADS)

Shevchuk, G. K.; Berg, D. B.; Zvereva, O. M.; Medvedeva, M. A.

2017-11-01

This article is devoted to the study of a supply chain disturbance impact on manufacturing volumes in a production system network. Each network agent's product can be used as a resource by other system agents (manufacturers). A supply chain disturbance can lead to operating cease of the entire network. Authors suggest using of short-term partial resources reservation to mitigate negative consequences of such disturbances. An agent-based model with a reservation algorithm compatible with strategies for resource procurement in terms of financial constraints was engineered. This model works in accordance with the static input-output Leontief 's model. The results can be used for choosing the ways of system's stability improving, and protecting it from various disturbances and imbalance.

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

NASA Astrophysics Data System (ADS)

Park, Juyong; Yook, Soon-Hyung

2014-08-01

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest - essential in determining reward and penalty - is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the ``Natural Ranking,'' an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks.

Caustics and Rogue Waves in an Optical Sea.

PubMed

Mathis, Amaury; Froehly, Luc; Toenger, Shanti; Dias, Frédéric; Genty, Goëry; Dudley, John M

2015-08-06

There are many examples in physics of systems showing rogue wave behaviour, the generation of high amplitude events at low probability. Although initially studied in oceanography, rogue waves have now been seen in many other domains, with particular recent interest in optics. Although most studies in optics have focussed on how nonlinearity can drive rogue wave emergence, purely linear effects have also been shown to induce extreme wave amplitudes. In this paper, we report a detailed experimental study of linear rogue waves in an optical system, using a spatial light modulator to impose random phase structure on a coherent optical field. After free space propagation, different random intensity patterns are generated, including partially-developed speckle, a broadband caustic network, and an intermediate pattern with characteristics of both speckle and caustic structures. Intensity peaks satisfying statistical criteria for rogue waves are seen especially in the case of the caustic network, and are associated with broader spatial spectra. In addition, the electric field statistics of the intermediate pattern shows properties of an "optical sea" with near-Gaussian statistics in elevation amplitude, and trough-to-crest statistics that are near-Rayleigh distributed but with an extended tail where a number of rogue wave events are observed.

Caustics and Rogue Waves in an Optical Sea

PubMed Central

Mathis, Amaury; Froehly, Luc; Toenger, Shanti; Dias, Frédéric; Genty, Goëry; Dudley, John M.

2015-01-01

There are many examples in physics of systems showing rogue wave behaviour, the generation of high amplitude events at low probability. Although initially studied in oceanography, rogue waves have now been seen in many other domains, with particular recent interest in optics. Although most studies in optics have focussed on how nonlinearity can drive rogue wave emergence, purely linear effects have also been shown to induce extreme wave amplitudes. In this paper, we report a detailed experimental study of linear rogue waves in an optical system, using a spatial light modulator to impose random phase structure on a coherent optical field. After free space propagation, different random intensity patterns are generated, including partially-developed speckle, a broadband caustic network, and an intermediate pattern with characteristics of both speckle and caustic structures. Intensity peaks satisfying statistical criteria for rogue waves are seen especially in the case of the caustic network, and are associated with broader spatial spectra. In addition, the electric field statistics of the intermediate pattern shows properties of an “optical sea” with near-Gaussian statistics in elevation amplitude, and trough-to-crest statistics that are near-Rayleigh distributed but with an extended tail where a number of rogue wave events are observed. PMID:26245864

Variational Principles for Buckling of Microtubules Modeled as Nonlocal Orthotropic Shells

PubMed Central

2014-01-01

A variational principle for microtubules subject to a buckling load is derived by semi-inverse method. The microtubule is modeled as an orthotropic shell with the constitutive equations based on nonlocal elastic theory and the effect of filament network taken into account as an elastic surrounding. Microtubules can carry large compressive forces by virtue of the mechanical coupling between the microtubules and the surrounding elastic filament network. The equations governing the buckling of the microtubule are given by a system of three partial differential equations. The problem studied in the present work involves the derivation of the variational formulation for microtubule buckling. The Rayleigh quotient for the buckling load as well as the natural and geometric boundary conditions of the problem is obtained from this variational formulation. It is observed that the boundary conditions are coupled as a result of nonlocal formulation. It is noted that the analytic solution of the buckling problem for microtubules is usually a difficult task. The variational formulation of the problem provides the basis for a number of approximate and numerical methods of solutions and furthermore variational principles can provide physical insight into the problem. PMID:25214886

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

Exercise alters resting-state functional connectivity of motor circuits in parkinsonian rats.

PubMed

Wang, Zhuo; Guo, Yumei; Myers, Kalisa G; Heintz, Ryan; Peng, Yu-Hao; Maarek, Jean-Michel I; Holschneider, Daniel P

2015-01-01

Few studies have examined changes in functional connectivity after long-term aerobic exercise. We examined the effects of 4 weeks of forced running wheel exercise on the resting-state functional connectivity (rsFC) of motor circuits of rats subjected to bilateral 6-hydroxydopamine lesion of the dorsal striatum. Our results showed substantial similarity between lesion-induced changes in rsFC in the rats and alterations in rsFC reported in Parkinson's disease subjects, including disconnection of the dorsolateral striatum. Exercise in lesioned rats resulted in: (1) normalization of many of the lesion-induced alterations in rsFC, including reintegration of the dorsolateral striatum into the motor network; (2) emergence of the ventrolateral striatum as a new broadly connected network hub; and (3) increased rsFC among the motor cortex, motor thalamus, basal ganglia, and cerebellum. Our results showed for the first time that long-term exercise training partially reversed lesion-induced alterations in rsFC of the motor circuits, and in addition enhanced functional connectivity in specific motor pathways in the parkinsonian rats, which could underlie recovery in motor functions observed in these animals. Copyright © 2015 Elsevier Inc. All rights reserved.

A neuropsychological perspective on the link between language and praxis in modern humans

PubMed Central

Roby-Brami, Agnes; Hermsdörfer, Joachim; Roy, Alice C.; Jacobs, Stéphane

2012-01-01

Hypotheses about the emergence of human cognitive abilities postulate strong evolutionary links between language and praxis, including the possibility that language was originally gestural. The present review considers functional and neuroanatomical links between language and praxis in brain-damaged patients with aphasia and/or apraxia. The neural systems supporting these functions are predominantly located in the left hemisphere. There are many parallels between action and language for recognition, imitation and gestural communication suggesting that they rely partially on large, common networks, differentially recruited depending on the nature of the task. However, this relationship is not unequivocal and the production and understanding of gestural communication are dependent on the context in apraxic patients and remains to be clarified in aphasic patients. The phonological, semantic and syntactic levels of language seem to share some common cognitive resources with the praxic system. In conclusion, neuropsychological observations do not allow support or rejection of the hypothesis that gestural communication may have constituted an evolutionary link between tool use and language. Rather they suggest that the complexity of human behaviour is based on large interconnected networks and on the evolution of specific properties within strategic areas of the left cerebral hemisphere. PMID:22106433

Efficient network disintegration under incomplete information: the comic effect of link prediction

NASA Astrophysics Data System (ADS)

Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

2016-03-01

The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized.

Efficient network disintegration under incomplete information: the comic effect of link prediction.

PubMed

Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

2016-03-10

The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the "comic effect" of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized.

Efficient network disintegration under incomplete information: the comic effect of link prediction

PubMed Central

Tan, Suo-Yi; Wu, Jun; Lü, Linyuan; Li, Meng-Jun; Lu, Xin

2016-01-01

The study of network disintegration has attracted much attention due to its wide applications, including suppressing the epidemic spreading, destabilizing terrorist network, preventing financial contagion, controlling the rumor diffusion and perturbing cancer networks. The crux of this matter is to find the critical nodes whose removal will lead to network collapse. This paper studies the disintegration of networks with incomplete link information. An effective method is proposed to find the critical nodes by the assistance of link prediction techniques. Extensive experiments in both synthetic and real networks suggest that, by using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance. Besides, to our surprise, we find that when the size of missing information is relatively small, our method even outperforms than the results based on complete information. We refer to this phenomenon as the “comic effect” of link prediction, which means that the network is reshaped through the addition of some links that identified by link prediction algorithms, and the reshaped network is like an exaggerated but characteristic comic of the original one, where the important parts are emphasized. PMID:26960247

Fault-Tolerant Algorithms for Connectivity Restoration in Wireless Sensor Networks.

PubMed

Zeng, Yali; Xu, Li; Chen, Zhide

2015-12-22

As wireless sensor network (WSN) is often deployed in a hostile environment, nodes in the networks are prone to large-scale failures, resulting in the network not working normally. In this case, an effective restoration scheme is needed to restore the faulty network timely. Most of existing restoration schemes consider more about the number of deployed nodes or fault tolerance alone, but fail to take into account the fact that network coverage and topology quality are also important to a network. To address this issue, we present two algorithms named Full 2-Connectivity Restoration Algorithm (F2CRA) and Partial 3-Connectivity Restoration Algorithm (P3CRA), which restore a faulty WSN in different aspects. F2CRA constructs the fan-shaped topology structure to reduce the number of deployed nodes, while P3CRA constructs the dual-ring topology structure to improve the fault tolerance of the network. F2CRA is suitable when the restoration cost is given the priority, and P3CRA is suitable when the network quality is considered first. Compared with other algorithms, these two algorithms ensure that the network has stronger fault-tolerant function, larger coverage area and better balanced load after the restoration.

Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins.

PubMed

Yalçın, Belgin; Zhao, Lu; Stofanko, Martin; O'Sullivan, Niamh C; Kang, Zi Han; Roost, Annika; Thomas, Matthew R; Zaessinger, Sophie; Blard, Olivier; Patto, Alex L; Sohail, Anood; Baena, Valentina; Terasaki, Mark; O'Kane, Cahir J

2017-07-25

Axons contain a smooth tubular endoplasmic reticulum (ER) network that is thought to be continuous with ER throughout the neuron; the mechanisms that form this axonal network are unknown. Mutations affecting reticulon or REEP proteins, with intramembrane hairpin domains that model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). We show that Drosophila axons have a dynamic axonal ER network, which these proteins help to model. Loss of HSP hairpin proteins causes ER sheet expansion, partial loss of ER from distal motor axons, and occasional discontinuities in axonal ER. Ultrastructural analysis reveals an extensive ER network in axons, which shows larger and fewer tubules in larvae that lack reticulon and REEP proteins, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of axonal ER, thus suggesting roles for ER modeling in axon maintenance and function.

On Adding Structure to Unstructured Overlay Networks

NASA Astrophysics Data System (ADS)

Leitão, João; Carvalho, Nuno A.; Pereira, José; Oliveira, Rui; Rodrigues, Luís

Unstructured peer-to-peer overlay networks are very resilient to churn and topology changes, while requiring little maintenance cost. Therefore, they are an infrastructure to build highly scalable large-scale services in dynamic networks. Typically, the overlay topology is defined by a peer sampling service that aims at maintaining, in each process, a random partial view of peers in the system. The resulting random unstructured topology is suboptimal when a specific performance metric is considered. On the other hand, structured approaches (for instance, a spanning tree) may optimize a given target performance metric but are highly fragile. In fact, the cost for maintaining structures with strong constraints may easily become prohibitive in highly dynamic networks. This chapter discusses different techniques that aim at combining the advantages of unstructured and structured networks. Namely we focus on two distinct approaches, one based on optimizing the overlay and another based on optimizing the gossip mechanism itself.

Social Network, Activity Participation, and Cognition: A Complex Relationship.

PubMed

Litwin, Howard; Stoeckel, Kimberly J

2016-01-01

This study examined how two domains of engagement-social network and activity participation-associate with objective and subjective cognitive function in later life. Specific consideration was given as to how these two spheres intersect in regard to recall and memory. The analytic sample included Europeans aged 60 and older drawn from the fourth wave of the Survey of Health Ageing and Retirement in Europe in which a new name-generated social network inventory was implemented. Multivariate analyses revealed that activity participation yielded stronger positive associations with word recall and self-rated memory than social network alone. However, the interactions indicate that this association lessened in strength for both the objective and subjective cognitive outcome measures as social network resources increased. The findings suggest that the social component of activity participation may be partially contributing to the positive role that such engagement has on cognitive well-being in later life. © The Author(s) 2015.