Three-dimensional Organization of Layered Apical Cytoskeletal Networks Associated with Mouse Airway Tissue Development

NASA Astrophysics Data System (ADS)

Tateishi, Kazuhiro; Nishida, Tomoki; Inoue, Kanako; Tsukita, Sachiko

2017-03-01

The cytoskeleton is an essential cellular component that enables various sophisticated functions of epithelial cells by forming specialized subcellular compartments. However, the functional and structural roles of cytoskeletons in subcellular compartmentalization are still not fully understood. Here we identified a novel network structure consisting of actin filaments, intermediate filaments, and microtubules directly beneath the apical membrane in mouse airway multiciliated cells and in cultured epithelial cells. Three-dimensional imaging by ultra-high voltage electron microscopy and immunofluorescence revealed that the morphological features of each network depended on the cell type and were spatiotemporally integrated in association with tissue development. Detailed analyses using Odf2 mutant mice, which lack ciliary basal feet and apical microtubules, suggested a novel contribution of the intermediate filaments to coordinated ciliary beating. These findings provide a new perspective for viewing epithelial cell differentiation and tissue morphogenesis through the structure and function of apical cytoskeletal networks.

Topological data analysis of contagion maps for examining spreading processes on networks.

PubMed

Taylor, Dane; Klimm, Florian; Harrington, Heather A; Kramár, Miroslav; Mischaikow, Konstantin; Porter, Mason A; Mucha, Peter J

2015-07-21

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth's surface; however, in modern contagions long-range edges-for example, due to airline transportation or communication media-allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct 'contagion maps' that use multiple contagions on a network to map the nodes as a point cloud. By analysing the topology, geometry and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modelling, forecast and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.

Topological data analysis of contagion maps for examining spreading processes on networks

NASA Astrophysics Data System (ADS)

Taylor, Dane; Klimm, Florian; Harrington, Heather A.; Kramár, Miroslav; Mischaikow, Konstantin; Porter, Mason A.; Mucha, Peter J.

2015-07-01

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth's surface; however, in modern contagions long-range edges--for example, due to airline transportation or communication media--allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct `contagion maps' that use multiple contagions on a network to map the nodes as a point cloud. By analysing the topology, geometry and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modelling, forecast and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.

Network geometry with flavor: From complexity to quantum geometry

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph

2016-03-01

Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravity. In network geometry, simplicial complexes describing the interaction between two or more nodes play a special role. In fact these structures can be used to discretize a geometrical d -dimensional space, and for this reason they have already been widely used in quantum gravity. Here we introduce the network geometry with flavor s =-1 ,0 ,1 (NGF) describing simplicial complexes defined in arbitrary dimension d and evolving by a nonequilibrium dynamics. The NGF can generate discrete geometries of different natures, ranging from chains and higher-dimensional manifolds to scale-free networks with small-world properties, scale-free degree distribution, and nontrivial community structure. The NGF admits as limiting cases both the Bianconi-Barabási models for complex networks, the stochastic Apollonian network, and the recently introduced model for complex quantum network manifolds. The thermodynamic properties of NGF reveal that NGF obeys a generalized area law opening a new scenario for formulating its coarse-grained limit. The structure of NGF is strongly dependent on the dimensionality d . In d =1 NGFs grow complex networks for which the preferential attachment mechanism is necessary in order to obtain a scale-free degree distribution. Instead, for NGF with dimension d >1 it is not necessary to have an explicit preferential attachment rule to generate scale-free topologies. We also show that NGF admits a quantum mechanical description in terms of associated quantum network states. Quantum network states evolve by a Markovian dynamics and a quantum network state at time t encodes all possible NGF evolutions up to time t . Interestingly the NGF remains fully classical but its statistical properties reveal the relation to its quantum mechanical description. In fact the δ -dimensional faces of the NGF have generalized degrees that follow either the Fermi-Dirac, Boltzmann, or Bose-Einstein statistics depending on the flavor s and the dimensions d and δ .

Network geometry with flavor: From complexity to quantum geometry.

PubMed

Bianconi, Ginestra; Rahmede, Christoph

2016-03-01

Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravity. In network geometry, simplicial complexes describing the interaction between two or more nodes play a special role. In fact these structures can be used to discretize a geometrical d-dimensional space, and for this reason they have already been widely used in quantum gravity. Here we introduce the network geometry with flavor s=-1,0,1 (NGF) describing simplicial complexes defined in arbitrary dimension d and evolving by a nonequilibrium dynamics. The NGF can generate discrete geometries of different natures, ranging from chains and higher-dimensional manifolds to scale-free networks with small-world properties, scale-free degree distribution, and nontrivial community structure. The NGF admits as limiting cases both the Bianconi-Barabási models for complex networks, the stochastic Apollonian network, and the recently introduced model for complex quantum network manifolds. The thermodynamic properties of NGF reveal that NGF obeys a generalized area law opening a new scenario for formulating its coarse-grained limit. The structure of NGF is strongly dependent on the dimensionality d. In d=1 NGFs grow complex networks for which the preferential attachment mechanism is necessary in order to obtain a scale-free degree distribution. Instead, for NGF with dimension d>1 it is not necessary to have an explicit preferential attachment rule to generate scale-free topologies. We also show that NGF admits a quantum mechanical description in terms of associated quantum network states. Quantum network states evolve by a Markovian dynamics and a quantum network state at time t encodes all possible NGF evolutions up to time t. Interestingly the NGF remains fully classical but its statistical properties reveal the relation to its quantum mechanical description. In fact the δ-dimensional faces of the NGF have generalized degrees that follow either the Fermi-Dirac, Boltzmann, or Bose-Einstein statistics depending on the flavor s and the dimensions d and δ.

Through the looking glass: Unraveling the network structure of coal

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

Gregory, D. M.; Stec, D. F.; Botto, R. E.

1999-12-23

Since the original idea by Sanada and Honda of treating coal as a three-dimensional cross-linked network, coal structure has been probed by monitoring ingress of solvents using traditional volumetric or gravimetric methods. However, using these techniques has allowed only an indirect observation of the swelling process. More recently, the authors have developed magnetic resonance microscopy (MRM) approaches for studying solvent ingress in polymeric systems, about which fundamental aspects of the swelling process can be deduced directly and quantitatively. The aim of their work is to utilize solvent transport and network response parameters obtained from these methods to assess fundamental propertiesmore » of the system under investigation. Polymer and coal samples have been studied to date. Numerous swelling parameters measured by magnetic resonance microscopy are found to correlate with cross-link density of the polymer network under investigation. Use of these parameters to assess the three-dimensional network structure of coal is discussed.« less

Three-dimensional imaging of redundant Chiari's network prolapsing into right ventricle.

PubMed

Betrián Blasco, Pedro; Sarrat Torres, Rebeca; Pijuan Domènech, Maria Antonia; Marimón Blanch, Cristina; Pérez Herrera, Verónica; Girona Comas, Josep

2008-02-01

An asymptomatic 1-month-old girl was studied in another institution because of the presence of a cardiac murmur, and referred to our center for further evaluation as a result of tricuspid valve abnormalities detected in the echocardiographic study. Echocardiography revealed a very redundant, thin, freely mobile structure in the right atrium, moving rapidly in (systole) and out (diastole) of the right ventricle through the tricuspid orifice. It arose from near the border of the inferior vena cava and attached to the atrial wall close to the coronary sinus ostium, suggesting an unusually prominent Chiari's network. Three-dimensional imaging allowed definition of the structure in all the planes and dimensions, and the relationship of the structure with right atrium and ventricle. Chiari's network is an embryonic remnant present in 2% to 3% of the population. The identification of a Chiari's network is important because the widely mobile structure within the right atrium can be confused with other entities, such as right heart vegetation, flail tricuspid leaflet, ruptured chordae tendinae, or a thrombus.

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

Tuning zinc coordination architectures by benzenedicarboxylate position isomers and bis(triazole)

NASA Astrophysics Data System (ADS)

Peng, Yan-fen; Li, Ke; Zhao, Shan; Han, Shan-shan; Li, Bao-long; Li, Hai-Yan

2015-08-01

Three position isomers 1,2-, 1,3-, 1,4-benzenedicarboxylate and 1,4-bis(1,2,4-triazol-4-yl)benzene were used to assembly zinc(II) coordination polymers {[Zn2(btx)0.5(1,2-bdc)2(H2O)]·H2O}n (1), {[Zn(btx)(1,3-bdc)]·2H2O·(DMF)}n (2) and {[Zn(btx)(1,4-bdc)]·3H2O}n (3). 1 is a (3,4,4,4)-connected two-dimensional network with point symbol (42·6)(44·62)(43·62·8)(42·6·103). 2 shows a two-dimensional (4,4) network. 3 exhibits a 5-fold interpenetrated three-dimensional diamondoid network. The structural versatility shows that the structures of coordination polymers can be tuned by the position isomers ligands. The luminescence and thermal stability were investigated.

Mechanics of the Cell

NASA Astrophysics Data System (ADS)

Boal, David

2012-01-01

Preface; List of symbols; 1. Introduction to the cell; 2. Soft materials and fluids; Part I. Rods and Ropes: 3. Polymers; 4. Complex filaments; 5. Two-dimensional networks; 6. Three-dimensional networks; Part II. Membranes: 7. Biomembranes; 8. Membrane undulations; 9. Intermembrane and electrostatic forces; Part III. The Whole Cell: 10. Structure of the simplest cells; 11. Dynamic filaments; 12. Growth and division; 13. Signals and switches; Appendixes; Glossary; References; Index.

A clustering algorithm for determining community structure in complex networks

NASA Astrophysics Data System (ADS)

Jin, Hong; Yu, Wei; Li, ShiJun

2018-02-01

Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

Three-dimensionally networked graphene hydroxide with giant pores and its application in supercapacitors

PubMed Central

Lee, Dongwook; Seo, Jiwon

2014-01-01

The three-dimensionally networked and layered structure of graphene hydroxide (GH) was investigated. After lengthy immersion in a NaOH solution, most of the epoxy groups in the graphene oxide were destroyed, and more hydroxyl groups were generated, transforming the graphene oxide into graphene hydroxide. Additionally, benzoic acid groups were formed, and the ether groups link the neighboring layers, creating a near-3D structure in the GH. To utilize these unique structural features, electrodes with large pores for use in supercapacitors were fabricated using thermal reduction in vacuum. The reduced GH maintained its layered structure and developed a lot of large of pores between/inside the layers. The GH electrodes exhibited high gravimetric as well as high volumetric capacitance. PMID:25492227

Three-dimensionally networked graphene hydroxide with giant pores and its application in supercapacitors

NASA Astrophysics Data System (ADS)

Lee, Dongwook; Seo, Jiwon

2014-12-01

The three-dimensionally networked and layered structure of graphene hydroxide (GH) was investigated. After lengthy immersion in a NaOH solution, most of the epoxy groups in the graphene oxide were destroyed, and more hydroxyl groups were generated, transforming the graphene oxide into graphene hydroxide. Additionally, benzoic acid groups were formed, and the ether groups link the neighboring layers, creating a near-3D structure in the GH. To utilize these unique structural features, electrodes with large pores for use in supercapacitors were fabricated using thermal reduction in vacuum. The reduced GH maintained its layered structure and developed a lot of large of pores between/inside the layers. The GH electrodes exhibited high gravimetric as well as high volumetric capacitance.

Three-dimensionally networked graphene hydroxide with giant pores and its application in supercapacitors.

PubMed

Lee, Dongwook; Seo, Jiwon

2014-12-10

The three-dimensionally networked and layered structure of graphene hydroxide (GH) was investigated. After lengthy immersion in a NaOH solution, most of the epoxy groups in the graphene oxide were destroyed, and more hydroxyl groups were generated, transforming the graphene oxide into graphene hydroxide. Additionally, benzoic acid groups were formed, and the ether groups link the neighboring layers, creating a near-3D structure in the GH. To utilize these unique structural features, electrodes with large pores for use in supercapacitors were fabricated using thermal reduction in vacuum. The reduced GH maintained its layered structure and developed a lot of large of pores between/inside the layers. The GH electrodes exhibited high gravimetric as well as high volumetric capacitance.

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.

PubMed

Zhang, J W; Rangan, A V

2015-04-01

In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Hydrothermal synthesis and structural characterization of a novel three-dimensional supramolecular framework constructed by zinc salt and pyridine-2,5-dicarboxylate

NASA Astrophysics Data System (ADS)

Wang, Xinlong; Qin, Chao; Wang, Enbo; Hu, Changwen; Xu, Lin

2004-07-01

A novel metal-organic coordination polymer, [Zn(PDB)(H 2O) 2] 4 n (H 2PDB=pyridine-2,5-dicarboxylic acid), has been hydrothermally synthesized and characterized by elemental analysis, IR, TG and single crystal X-ray diffraction. Colorless crystals crystallized in the triclinic system, space group P-1, a=7.0562(14) Å, b=7.38526(15) Å, c=18.4611(4) Å, α=90.01(3)°, β=96.98(3)°, γ=115.67(3)°, V=859.1(3) Å 3, Z=1 and R=0.0334. The structure of the compound exhibits a novel three-dimensional supramolecular network, mainly based on multipoint hydrogen bonds originated from within and outside of a large 24-membered ring. Interestingly, the three-dimensional network consists of one-dimensional parallelogrammic channels in which coordinated water molecules point into the channel wall.

Two-Dimensional Nanoporous Networks Formed by Liquid-to-Solid Transfer of Hydrogen-Bonded Macrocycles Built from DNA Bases.

PubMed

Bilbao, Nerea; Destoop, Iris; De Feyter, Steven; González-Rodríguez, David

2016-01-11

We present an approach that makes use of DNA base pairing to produce hydrogen-bonded macrocycles whose supramolecular structure can be transferred from solution to a solid substrate. A hierarchical assembly process ultimately leads to two-dimensional nanostructured porous networks that are able to host size-complementary guests. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Exploration and exploitation of homologous series of bis(acrylamido)alkanes containing pyridyl and phenyl groups: β-sheet versus two-dimensional layers in solid-state photochemical [2 + 2] reactions.

PubMed

Garai, Mousumi; Biradha, Kumar

2015-09-01

The homologous series of phenyl and pyridyl substituted bis(acrylamido)alkanes have been synthesized with the aim of systematic analysis of their crystal structures and their solid-state [2 + 2] reactivities. The changes in the crystal structures with respect to a small change in the molecular structure, that is by varying alkyl spacers between acrylamides and/or by varying the end groups (phenyl, 2-pyridyl, 3-pyridyl, 4-pyridyl) on the C-terminal of the amide, were analyzed in terms of hydrogen-bonding interference (N-H⋯Npy versus N-H⋯O=C) and network geometries. In this series, a greater tendency towards the formation of N-H⋯O hydrogen bonds (β-sheets and two-dimensional networks) over N-H⋯N hydrogen bonds was observed. Among all the structures seven structures were found to have the required alignments of double bonds for the [2 + 2] reaction such that the formations of single dimer, double dimer and polymer are facilitated. However, only four structures were found to exhibit such a solid-state [2 + 2] reaction to form a single dimer and polymers. The two-dimensional hydrogen-bonding layer via N-H⋯O hydrogen bonds was found to promote solid-state [2 + 2] photo-polymerization in a single-crystal-to-single-crystal manner. Such two-dimensional layers were encountered only when the spacer between acryl amide moieties is butyl. Only four out of the 16 derivatives were found to form hydrates, two each from 2-pyridyl and 4-pyridyl derivatives. The water molecules in these structures govern the hydrogen-bonding networks by the formation of an octameric water cluster and one-dimensional zigzag water chains. The trends in the melting points and densities were also analyzed.

Three-Dimensional Gene Map of Cancer Cell Types: Structural Entropy Minimisation Principle for Defining Tumour Subtypes

PubMed Central

Li, Angsheng; Yin, Xianchen; Pan, Yicheng

2016-01-01

In this study, we propose a method for constructing cell sample networks from gene expression profiles, and a structural entropy minimisation principle for detecting natural structure of networks and for identifying cancer cell subtypes. Our method establishes a three-dimensional gene map of cancer cell types and subtypes. The identified subtypes are defined by a unique gene expression pattern, and a three-dimensional gene map is established by defining the unique gene expression pattern for each identified subtype for cancers, including acute leukaemia, lymphoma, multi-tissue, lung cancer and healthy tissue. Our three-dimensional gene map demonstrates that a true tumour type may be divided into subtypes, each defined by a unique gene expression pattern. Clinical data analyses demonstrate that most cell samples of an identified subtype share similar survival times, survival indicators and International Prognostic Index (IPI) scores and indicate that distinct subtypes identified by our algorithms exhibit different overall survival times, survival ratios and IPI scores. Our three-dimensional gene map establishes a high-definition, one-to-one map between the biologically and medically meaningful tumour subtypes and the gene expression patterns, and identifies remarkable cells that form singleton submodules. PMID:26842724

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

PubMed

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model.

PubMed

Suemitsu, Yoshikazu; Nara, Shigetoshi

2004-09-01

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.

Fast Two-Dimensional Bubble Analysis of Biopolymer Filamentous Networks Pore Size from Confocal Microscopy Thin Data Stacks

PubMed Central

Molteni, Matteo; Magatti, Davide; Cardinali, Barbara; Rocco, Mattia; Ferri, Fabio

2013-01-01

The average pore size ξ0 of filamentous networks assembled from biological macromolecules is one of the most important physical parameters affecting their biological functions. Modern optical methods, such as confocal microscopy, can noninvasively image such networks, but extracting a quantitative estimate of ξ0 is a nontrivial task. We present here a fast and simple method based on a two-dimensional bubble approach, which works by analyzing one by one the (thresholded) images of a series of three-dimensional thin data stacks. No skeletonization or reconstruction of the full geometry of the entire network is required. The method was validated by using many isotropic in silico generated networks of different structures, morphologies, and concentrations. For each type of network, the method provides accurate estimates (a few percent) of the average and the standard deviation of the three-dimensional distribution of the pore sizes, defined as the diameters of the largest spheres that can be fit into the pore zones of the entire gel volume. When applied to the analysis of real confocal microscopy images taken on fibrin gels, the method provides an estimate of ξ0 consistent with results from elastic light scattering data. PMID:23473499

Two novel copper(II) complexes constructed from dicarboxylate ligands with different spacer lengths and 2-phenylimidazo[4,5- f]1,10-phenanthroline (PIP): Synthesis, structures and properties

NASA Astrophysics Data System (ADS)

Wang, X.-L.; Chen, Yongqiang; Liu, Guocheng; Lin, Hongyan; Zhang, Jinxia

2009-09-01

Two novel metal-organic coordination polymers [Cu(PIP)(bpea)(H 2O)]·H 2O ( 1) and [Cu(PIP)(1,4-bdc)] ( 2) have been obtained from hydrothermal reaction of copper(II) with the mixed ligands [biphenylethene-4,4'-dicarboxylic acid (bpea) for 1, benzene-1,4-dicarboxylic acid (1,4-H 2bdc) for 2, and 2-phenylimidazo[4,5- f]1,10-phenanthroline (PIP)]. Both complexes have been structurally characterized by elemental analyses, IR and single-crystal X-ray diffraction analyses. Structural analyses reveal that complex 1 possesses infinite one-dimensional zigzag chain, 2 exhibits a two-dimensional (4,4) network, both of which are extended into three-dimensional supramolecular network by weak interactions. The different structures of the title complexes illustrate the influence of the flexibility (the spacer length of carboxyl groups and the structural rigidity of the spacer) of organic dicarboxylate ligands on the formation of such coordination architectures. Moreover, the thermal properties and the voltammetric behavior of complexes 1 and 2 have been reported.

Porous polymer networks and ion-exchange media and metal-polymer composites made therefrom

DOEpatents

Kanatzidis, Mercouri G; Katsoulidis, Alexandros

2015-03-10

Porous polymeric networks and composite materials comprising metal nanoparticles distributed in the polymeric networks are provided. Also provided are methods for using the polymeric networks and the composite materials in liquid- and vapor-phase waste remediation applications. The porous polymeric networks, are highly porous, three-dimensional structures characterized by high surface areas. The polymeric networks comprise polymers polymerized from aldehydes and phenolic molecules.

Porous polymer networks and ion-exchange media and metal-polymer composites made therefrom

DOEpatents

Kanatzidis, Mercouri G.; Katsoulidis, Alexandros

2016-10-18

Porous polymeric networks and composite materials comprising metal nanoparticles distributed in the polymeric networks are provided. Also provided are methods for using the polymeric networks and the composite materials in liquid- and vapor-phase waste remediation applications. The porous polymeric networks, are highly porous, three-dimensional structures characterized by high surface areas. The polymeric networks comprise polymers polymerized from aldehydes and phenolic molecules.

Two-dimensional quantum repeaters

NASA Astrophysics Data System (ADS)

Wallnöfer, J.; Zwerger, M.; Muschik, C.; Sangouard, N.; Dür, W.

2016-11-01

The endeavor to develop quantum networks gave rise to a rapidly developing field with far-reaching applications such as secure communication and the realization of distributed computing tasks. This ultimately calls for the creation of flexible multiuser structures that allow for quantum communication between arbitrary pairs of parties in the network and facilitate also multiuser applications. To address this challenge, we propose a two-dimensional quantum repeater architecture to establish long-distance entanglement shared between multiple communication partners in the presence of channel noise and imperfect local control operations. The scheme is based on the creation of self-similar multiqubit entanglement structures at growing scale, where variants of entanglement swapping and multiparty entanglement purification are combined to create high-fidelity entangled states. We show how such networks can be implemented using trapped ions in cavities.

High pressure structural behavior of YGa2: A combined experimental and theoretical study

NASA Astrophysics Data System (ADS)

Sekar, M.; Shekar, N. V. Chandra; Babu, R.; Sahu, P. Ch.; Sinha, A. K.; Upadhyay, Anuj; Singh, M. N.; Babu, K. Ramesh; Appalakondaiah, S.; Vaitheeswaran, G.; Kanchana, V.

2015-03-01

High pressure structural stability studies were carried out on YGa2 (AlB2 type structure at NTP, space group P6/mmm) up to a pressure of 35 GPa using both laboratory based rotating anode and synchrotron X-ray sources. An isostructural transition with reduced c/a ratio, was observed at 6 GPa and above 17.5 GPa, the compound transformed to orthorhombic structure. Bulk modulus B0 for the parent and high pressure phases were estimated using Birch-Murnaghan and modified Birch-Murnaghan equation of state. Electronic structure calculations based on projector augmented wave method confirms the experimentally observed two high pressure structural transitions. The calculations also reveal that the 'Ga' networks remains as two dimensional in the high pressure isostructural phase, whereas the orthorhombic phase involves three dimensional networks of 'Ga' atoms interconnected by strong covalent bonds.

Gestational Age is Dimensionally Associated with Structural Brain Network Abnormalities Across Development.

PubMed

Nassar, Rula; Kaczkurkin, Antonia N; Xia, Cedric Huchuan; Sotiras, Aristeidis; Pehlivanova, Marieta; Moore, Tyler M; Garcia de La Garza, Angel; Roalf, David R; Rosen, Adon F G; Lorch, Scott A; Ruparel, Kosha; Shinohara, Russell T; Davatzikos, Christos; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D

2018-04-21

Prematurity is associated with diverse developmental abnormalities, yet few studies relate cognitive and neurostructural deficits to a dimensional measure of prematurity. Leveraging a large sample of children, adolescents, and young adults (age 8-22 years) studied as part of the Philadelphia Neurodevelopmental Cohort, we examined how variation in gestational age impacted cognition and brain structure later in development. Participants included 72 preterm youth born before 37 weeks' gestation and 206 youth who were born at term (37 weeks or later). Using a previously-validated factor analysis, cognitive performance was assessed in three domains: (1) executive function and complex reasoning, (2) social cognition, and (3) episodic memory. All participants completed T1-weighted neuroimaging at 3 T to measure brain volume. Structural covariance networks were delineated using non-negative matrix factorization, an advanced multivariate analysis technique. Lower gestational age was associated with both deficits in executive function and reduced volume within 11 of 26 structural covariance networks, which included orbitofrontal, temporal, and parietal cortices as well as subcortical regions including the hippocampus. Notably, the relationship between lower gestational age and executive dysfunction was accounted for in part by structural network deficits. Together, these findings emphasize the durable impact of prematurity on cognition and brain structure, which persists across development.

Defects in a nonlinear pseudo one-dimensional solid

NASA Astrophysics Data System (ADS)

Blanchet, Graciela B.; Fincher, C. R., Jr.

1985-03-01

These infrared studies of acetanilide together with the existence of two equivalent structures for the hydrogen-bonded chain suggest the possibility of a topological defect state rather than a Davydov soliton as suggested previously. Acetanilide is an example of a class of one-dimensional materials where solitons are a consequence of a twofold degenerate structure and the nonlinear dynamics of the hydrogen-bonded network.

Population activity structure of excitatory and inhibitory neurons

PubMed Central

Doiron, Brent

2017-01-01

Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure. PMID:28817581

C60-pentacene network formation by 2-D co-crystallization.

PubMed

Jin, Wei; Dougherty, Daniel B; Cullen, William G; Robey, Steven; Reutt-Robey, Janice E

2009-09-01

We report experiments highlighting the mechanistic role of mobile pentacene precursors in the formation of a network C(60)-pentacene co-crystalline structure on Ag(111). This co-crystalline arrangement was first observed by low temperature scanning tunneling microscopy (STM) by Zhang et al. (Zhang, H. L.; Chen, W.; Huang, H.; Chen, L.; Wee, A. T. S. J. Am. Chem. Soc. 2008, 130, 2720-2721). We now show that this structure forms readily at room temperature from a two-dimensional (2-D) mixture. Pentacene, evaporated onto Ag(111) to coverages of 0.4-1.0 ML, produces a two-dimensional (2-D) gas. Subsequently deposited C(60) molecules combine with the pentacene 2-D gas to generate a network structure, consisting of chains of close-packed C(60) molecules, spaced by individual C(60) linkers and 1 nm x 2.5 nm pores containing individual pentacene molecules. Spontaneous formation of this stoichiometric (C(60))(4)-pentacene network from a range of excess pentacene surface coverage (0.4 to 1.0 ML) indicates a self-limiting assembly process. We refine the structure model for this phase and discuss the generality of this co-crystallization mechanism.

Atomic structure of a metal-supported two-dimensional germania film

NASA Astrophysics Data System (ADS)

Lewandowski, Adrián Leandro; Schlexer, Philomena; Büchner, Christin; Davis, Earl M.; Burrall, Hannah; Burson, Kristen M.; Schneider, Wolf-Dieter; Heyde, Markus; Pacchioni, Gianfranco; Freund, Hans-Joachim

2018-03-01

The growth and microscopic characterization of two-dimensional germania films is presented. Germanium oxide monolayer films were grown on Ru(0001) by physical vapor deposition and subsequent annealing in oxygen. We obtain a comprehensive image of the germania film structure by combining intensity-voltage low-energy electron diffraction (I/V-LEED) and ab initio density functional theory (DFT) analysis with atomic-resolution scanning tunneling microscopy (STM) imaging. For benchmarking purposes, the bare Ru(0001) substrate and the (2 ×2 )3 O covered Ru(0001) were analyzed with I/V-LEED with respect to previous reports. STM topographic images of the germania film reveal a hexagonal network where the oxygen and germanium atom positions appear in different imaging contrasts. For quantitative LEED, the best agreement has been achieved with DFT structures where the germanium atoms are located preferentially on the top and fcc hollow sites of the Ru(0001) substrate. Moreover, in these atomically flat germania films, local site geometries, i.e., tetrahedral building blocks, ring structures, and domain boundaries, have been identified, indicating possible pathways towards two-dimensional amorphous networks.

Investigations of photosynthetic light harvesting by two-dimensional electronic spectroscopy

NASA Astrophysics Data System (ADS)

Read, Elizabeth Louise

Photosynthesis begins with the harvesting of sunlight by antenna pigments, organized in a network of pigment-protein complexes that rapidly funnel energy to photochemical reaction centers. The intricate design of these systems---the widely varying structural motifs of pigment organization within proteins and protein organization within a larger, cooperative network---underlies the remarkable speed and efficiency of light harvesting. Advances in femtosecond laser spectroscopy have enabled researchers to follow light energy on its course through the energetic levels of photosynthetic systems. Now, newly-developed femtosecond two-dimensional electronic spectroscopy reveals deeper insight into the fundamental molecular interactions and dynamics that emerge in these structures. The following chapters present investigations of a number of natural light-harvesting complexes using two-dimensional electronic spectroscopy. These studies demonstrate the various types of information contained in experimental two-dimensional spectra, and they show that the technique makes it possible to probe pigment-protein complexes on the length- and time-scales relevant to their functioning. New methods are described that further extend the capabilities of two-dimensional electronic spectroscopy, for example, by independently controlling the excitation laser pulse polarizations. The experiments, coupled with theoretical simulation, elucidate spatial pathways of energy flow, unravel molecular and electronic structures, and point to potential new quantum mechanical mechanisms of light harvesting.

Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

PubMed

Tian, Xinyu; Wang, Xuefeng; Chen, Jun

2014-01-01

Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.

[Network structures in biological systems].

PubMed

Oleskin, A V

2013-01-01

Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

Merging Bottom-Up with Top-Down: Continuous Lamellar Networks and Block Copolymer Lithography

NASA Astrophysics Data System (ADS)

Campbell, Ian Patrick

Block copolymer lithography is an emerging nanopatterning technology with capabilities that may complement and eventually replace those provided by existing optical lithography techniques. This bottom-up process relies on the parallel self-assembly of macromolecules composed of covalently linked, chemically distinct blocks to generate periodic nanostructures. Among the myriad potential morphologies, lamellar structures formed by diblock copolymers with symmetric volume fractions have attracted the most interest as a patterning tool. When confined to thin films and directed to assemble with interfaces perpendicular to the substrate, two-dimensional domains are formed between the free surface and the substrate, and selective removal of a single block creates a nanostructured polymeric template. The substrate exposed between the polymeric features can subsequently be modified through standard top-down microfabrication processes to generate novel nanostructured materials. Despite tremendous progress in our understanding of block copolymer self-assembly, continuous two-dimensional materials have not yet been fabricated via this robust technique, which may enable nanostructured material combinations that cannot be fabricated through bottom-up methods. This thesis aims to study the effects of block copolymer composition and processing on the lamellar network morphology of polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) and utilize this knowledge to fabricate continuous two-dimensional materials through top-down methods. First, block copolymer composition was varied through homopolymer blending to explore the physical phenomena surrounding lamellar network continuity. After establishing a framework for tuning the continuity, the effects of various processing parameters were explored to engineer the network connectivity via defect annihilation processes. Precisely controlling the connectivity and continuity of lamellar networks through defect engineering and optimizing the block copolymer lithography process thus enabled the top-down fabrication of continuous two-dimensional gold networks with nanoscale properties. The lamellar structure of these networks was found to confer unique mechanical properties on the nanowire networks and suggests that materials templated via this method may be excellent candidates for integration into stretchable and flexible devices.

A three-dimensional neural spheroid model for capillary-like network formation.

PubMed

Boutin, Molly E; Kramer, Liana L; Livi, Liane L; Brown, Tyler; Moore, Christopher; Hoffman-Kim, Diane

2018-04-01

In vitro three-dimensional neural spheroid models have an in vivo-like cell density, and have the potential to reduce animal usage and increase experimental throughput. The aim of this study was to establish a spheroid model to study the formation of capillary-like networks in a three-dimensional environment that incorporates both neuronal and glial cell types, and does not require exogenous vasculogenic growth factors. We created self-assembled, scaffold-free cellular spheroids using primary-derived postnatal rodent cortex as a cell source. The interactions between relevant neural cell types, basement membrane proteins, and endothelial cells were characterized by immunohistochemistry. Transmission electron microscopy was used to determine if endothelial network structures had lumens. Endothelial cells within cortical spheroids assembled into capillary-like networks with lumens. Networks were surrounded by basement membrane proteins, including laminin, fibronectin and collagen IV, as well as key neurovascular cell types. Existing in vitro models of the cortical neurovascular environment study monolayers of endothelial cells, either on transwell inserts or coating cellular spheroids. These models are not well suited to study vasculogenesis, a process hallmarked by endothelial cell cord formation and subsequent lumenization. The neural spheroid is a new model to study the formation of endothelial cell capillary-like structures in vitro within a high cell density three-dimensional environment that contains both neuronal and glial populations. This model can be applied to investigate vascular assembly in healthy or disease states, such as stroke, traumatic brain injury, or neurodegenerative disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Constructing financial network based on PMFG and threshold method

NASA Astrophysics Data System (ADS)

Nie, Chun-Xiao; Song, Fu-Tie

2018-04-01

Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

Three Dimensional Object Recognition Using an Unsupervised Neural Network: Understanding the Distinguishing Features

DTIC Science & Technology

1992-12-23

predominance of structural models of recognition, of which a recent example is the Recognition By Components (RBC) theory ( Biederman , 1987 ). Structural...related to recent statistical theory (Huber, 1985; Friedman, 1987 ) and is derived from a biologically motivated computational theory (Bienenstock et...dimensional object recognition (Intrator and Gold, 1991). The method is related to recent statistical theory (Huber, 1985; Friedman, 1987 ) and is derived

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

PubMed Central

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

2017-01-01

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

Surface Patterning of Benzene Carboxylic Acids on Graphite: Influence of structure, solvent, and concentration on molecular self-assembly

NASA Astrophysics Data System (ADS)

Florio, Gina; Stiso, Kimberly; Campanelli, Joseph; Dessources, Kimberly; Folkes, Trudi

2012-02-01

Scanning tunneling microscopy (STM) was used to investigate the molecular self-assembly of four different benzene carboxylic acid derivatives at the liquid/graphite interface: pyromellitic acid (1,2,4,5-benzenetetracarboxylic acid), trimellitic acid (1,2,4-benzenetricarboxylic acid), trimesic acid (1,3,5-benzenetricarboxylic acid), and 1,3,5-benzenetriacetic acid. A range of two dimensional networks are observed that depend sensitively on the number of carboxylic acids present, the nature of the solvent, and the solution concentration. We will describe our recent efforts to determine (a) the preferential two-dimensional structure(s) for each benzene carboxylic acid at the liquid/graphite interface, (b) the thermodynamic and kinetic factors influencing self-assembly (or lack thereof), (c) the role solvent plays in the assembly, (e) the effect of in situ versus ex situ dilution on surface packing density, and (f) the temporal evolution of the self-assembled monolayer. Results of computational analysis of analog molecules and model monolayer films will also be presented to aid assignment of network structures and to provide a qualitative picture of surface adsorption and network formation.

2-Dimensional micro-network of boron-doped diamond film: fabrication and electrochemical sensing application.

PubMed

Luo, Daibing; Wu, Liangzhuan; Zhi, Jinfang

2010-09-21

By means of delicate and conventional methods based on photolithography and hot filament chemical vapor deposition (HFCVD) technology, a novel boron-doped diamond micro-network (BDDMN) film was fabricated, and this micro-structure showed excellent electrochemical sensing properties.

Assembly of one-dimensional supramolecular objects: From monomers to networks

NASA Astrophysics Data System (ADS)

Sayar, Mehmet; Stupp, Samuel I.

2005-07-01

One-dimensional supramolecular aggregates can form networks at exceedingly low concentrations. Recent experiments in several laboratories, including our own, have demonstrated the formation of gels by these systems at concentrations well under 1% by weight. The systems of interest in our laboratory form either cylindrical nanofibers or ribbons as a result of strong noncovalent interactions among monomers. The stiffness and interaction energies among these thread-like objects can vary significantly depending on the chemical structure of the monomers used. We have used Monte Carlo simulations to study the structure of the threads and their ability to form networks through bundle formation. The persistence length of the threads was found to be strongly affected not only by stiffness, but also by the strength of attractive two-body interactions among thread segments. The relative values of stiffness and attractive two-body interaction strength determine if threads collapse or create bundles. Only in the presence of sufficiently long threads and bundle formation can these systems assemble into networks of high connectivity.

Anatomy of triply-periodic network assemblies: characterizing skeletal and inter-domain surface geometry of block copolymer gyroids.

PubMed

Prasad, Ishan; Jinnai, Hiroshi; Ho, Rong-Ming; Thomas, Edwin L; Grason, Gregory M

2018-05-09

Triply-periodic networks (TPNs), like the well-known gyroid and diamond network phases, abound in soft matter assemblies, from block copolymers (BCPs), lyotropic liquid crystals and surfactants to functional architectures in biology. While TPNs are, in reality, volume-filling patterns of spatially-varying molecular composition, physical and structural models most often reduce their structure to lower-dimensional geometric objects: the 2D interfaces between chemical domains; and the 1D skeletons that thread through inter-connected, tubular domains. These lower-dimensional structures provide a useful basis of comparison to idealized geometries based on triply-periodic minimal, or constant-mean curvature surfaces, and shed important light on the spatially heterogeneous packing of molecular constituents that form the networks. Here, we propose a simple, efficient and flexible method to extract a 1D skeleton from 3D volume composition data of self-assembled networks. We apply this method to both self-consistent field theory predictions as well as experimental electron microtomography reconstructions of the double-gyroid phase of an ABA triblock copolymer. We further demonstrate how the analysis of 1D skeleton, 2D inter-domain surfaces, and combinations therefore, provide physical and structural insight into TPNs, across multiple length scales. Specifically, we propose and compare simple measures of network chirality as well as domain thickness, and analyze their spatial and statistical distributions in both ideal (theoretical) and non-ideal (experimental) double gyroid assemblies.

Classification of 2-dimensional array patterns: assembling many small neural networks is better than using a large one.

PubMed

Chen, Liang; Xue, Wei; Tokuda, Naoyuki

2010-08-01

In many pattern classification/recognition applications of artificial neural networks, an object to be classified is represented by a fixed sized 2-dimensional array of uniform type, which corresponds to the cells of a 2-dimensional grid of the same size. A general neural network structure, called an undistricted neural network, which takes all the elements in the array as inputs could be used for problems such as these. However, a districted neural network can be used to reduce the training complexity. A districted neural network usually consists of two levels of sub-neural networks. Each of the lower level neural networks, called a regional sub-neural network, takes the elements in a region of the array as its inputs and is expected to output a temporary class label, called an individual opinion, based on the partial information of the entire array. The higher level neural network, called an assembling sub-neural network, uses the outputs (opinions) of regional sub-neural networks as inputs, and by consensus derives the label decision for the object. Each of the sub-neural networks can be trained separately and thus the training is less expensive. The regional sub-neural networks can be trained and performed in parallel and independently, therefore a high speed can be achieved. We prove theoretically in this paper, using a simple model, that a districted neural network is actually more stable than an undistricted neural network in noisy environments. We conjecture that the result is valid for all neural networks. This theory is verified by experiments involving gender classification and human face recognition. We conclude that a districted neural network is highly recommended for neural network applications in recognition or classification of 2-dimensional array patterns in highly noisy environments. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Crystal structures of the three closely related compounds: bis-[(1H-tetra-zol-5-yl)meth-yl]nitramide, tri-amino-guanidinium 5-({[(1H-tetra-zol-5-yl)meth-yl](nitro)-amino}-meth-yl)tetra-zol-1-ide, and di-ammonium bis-[(tetra-zol-1-id-5-yl)meth-yl]nitramide monohydrate.

PubMed

Mitchell, Lauren A; Imler, Gregory H; Parrish, Damon A; Deschamps, Jeffrey R; Leonard, Philip W; Chavez, David E

2017-07-01

In the mol-ecule of neutral bis-[(1 H -tetra-zol-5-yl)meth-yl]nitramide, (I), C 4 H 6 N 10 O 2 , there are two intra-molecular N-H⋯O hydrogen bonds. In the crystal, N-H⋯N hydrogen bonds link mol-ecules, forming a two-dimensional network parallel to (-201) and weak C-H⋯O, C-H⋯N hydrogen bonds, and inter-molecular π-π stacking completes the three-dimensional network. The anion in the molecular salt, tri-amino-guanidinium 5-({[(1 H -tetra-zol-5-yl)meth-yl](nitro)-amino}-meth-yl)tetra-zol-1-ide, (II), CH 9 N 6 + ·C 4 H 5 N 10 O 2 - , displays intra-molecular π-π stacking and in the crystal, N-H⋯N and N-H⋯O hydrogen bonds link the components of the structure, forming a three-dimensional network. In the crystal of di-ammonium bis-[(tetra-zol-1-id-5-yl)meth-yl]nitramide monohydrate, (III), 2NH 4 + ·C 4 H 4 N 10 O 2 2- ·H 2 O, O-H⋯N, N-H⋯N, and N-H⋯O hydrogen bonds link the components of the structure into a three-dimensional network. In addition, there is inter-molecular π-π stacking. In all three structures, the central N atom of the nitramide is mainly sp 2 -hybridized. Bond lengths indicate delocalization of charges on the tetra-zole rings for all three compounds. Compound (II) was found to be a non-merohedral twin and was solved and refined in the major component.

2D-HB-Network at the air-water interface: A structural and dynamical characterization by means of ab initio and classical molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Pezzotti, Simone; Serva, Alessandra; Gaigeot, Marie-Pierre

2018-05-01

Following our previous work where the existence of a special 2-Dimensional H-Bond (2D-HB)-Network was revealed at the air-water interface [S. Pezzotti et al., J. Phys. Chem. Lett. 8, 3133 (2017)], we provide here a full structural and dynamical characterization of this specific arrangement by means of both Density Functional Theory based and Force Field based molecular dynamics simulations. We show in particular that water at the interface with air reconstructs to maximize H-Bonds formed between interfacial molecules, which leads to the formation of an extended and non-interrupted 2-Dimensional H-Bond structure involving on average ˜90% of water molecules at the interface. We also show that the existence of such an extended structure, composed of H-Bonds all oriented parallel to the surface, constrains the reorientional dynamics of water that is hence slower at the interface than in the bulk. The structure and dynamics of the 2D-HB-Network provide new elements to possibly rationalize several specific properties of the air-water interface, such as water surface tension, anisotropic reorientation of interfacial water under an external field, and proton hopping.

Three-Dimensional Bi₂Te₃ Networks of Interconnected Nanowires: Synthesis and Optimization.

PubMed

Ruiz-Clavijo, Alejandra; Caballero-Calero, Olga; Martín-González, Marisol

2018-05-18

Self-standing Bi₂Te₃ networks of interconnected nanowires were fabricated in three-dimensional porous anodic alumina templates (3D⁻AAO) with a porous structure spreading in all three spatial dimensions. Pulsed electrodeposition parameters were optimized to grow highly oriented Bi₂Te₃ interconnected nanowires with stoichiometric composition inside those 3D⁻AAO templates. The nanowire networks were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), and Raman spectroscopy. The results are compared to those obtained in films and 1D nanowires grown under similar conditions. The crystalline structure and composition of the 3D Bi⁻Te nanowire network are finely tuned by controlling the applied voltage and the relaxation time off at zero current density during the deposition. With this fabrication method, and controlling the electrodeposition parameters, stoichiometric Bi₂Te₃ networks of interconnected nanowires have been obtained, with a preferential orientation along [1 1 0], which makes them optimal candidates for out-of-plane thermoelectric applications. Moreover, the templates in which they are grown can be dissolved and the network of interconnected nanowires is self-standing without affecting its composition and orientation properties.

Annual-ring-type quasi-phase-matching crystal for generation of narrowband high-dimensional entanglement

NASA Astrophysics Data System (ADS)

Hua, Yi-Lin; Zhou, Zong-Quan; Liu, Xiao; Yang, Tian-Shu; Li, Zong-Feng; Li, Pei-Yun; Chen, Geng; Xu, Xiao-Ye; Tang, Jian-Shun; Xu, Jin-Shi; Li, Chuan-Feng; Guo, Guang-Can

2018-01-01

A photon pair can be entangled in many degrees of freedom such as polarization, time bins, and orbital angular momentum (OAM). Among them, the OAM of photons can be entangled in an infinite-dimensional Hilbert space which enhances the channel capacity of sharing information in a network. Twisted photons generated by spontaneous parametric down-conversion offer an opportunity to create this high-dimensional entanglement, but a photon pair generated by this process is typically wideband, which makes it difficult to interface with the quantum memories in a network. Here we propose an annual-ring-type quasi-phase-matching (QPM) crystal for generation of the narrowband high-dimensional entanglement. The structure of the QPM crystal is designed by tracking the geometric divergences of the OAM modes that comprise the entangled state. The dimensionality and the quality of the entanglement can be greatly enhanced with the annual-ring-type QPM crystal.

Spatio-temporal organization of dynamics in a two-dimensional periodically driven vortex flow: A Lagrangian flow network perspective.

PubMed

Lindner, Michael; Donner, Reik V

2017-03-01

We study the Lagrangian dynamics of passive tracers in a simple model of a driven two-dimensional vortex resembling real-world geophysical flow patterns. Using a discrete approximation of the system's transfer operator, we construct a directed network that describes the exchange of mass between distinct regions of the flow domain. By studying different measures characterizing flow network connectivity at different time-scales, we are able to identify the location of dynamically invariant structures and regions of maximum dispersion. Specifically, our approach allows us to delimit co-existing flow regimes with different dynamics. To validate our findings, we compare several network characteristics to the well-established finite-time Lyapunov exponents and apply a receiver operating characteristic analysis to identify network measures that are particularly useful for unveiling the skeleton of Lagrangian chaos.

Fast Rotational Diffusion of Water Molecules in a 2D Hydrogen Bond Network at Cryogenic Temperatures

NASA Astrophysics Data System (ADS)

Prisk, T. R.; Hoffmann, C.; Kolesnikov, A. I.; Mamontov, E.; Podlesnyak, A. A.; Wang, X.; Kent, P. R. C.; Anovitz, L. M.

2018-05-01

Individual water molecules or small clusters of water molecules contained within microporous minerals present an extreme case of confinement where the local structure of hydrogen bond networks are dramatically altered from bulk water. In the zinc silicate hemimorphite, the water molecules form a two-dimensional hydrogen bond network with hydroxyl groups in the crystal framework. Here, we present a combined experimental and theoretical study of the structure and dynamics of water molecules within this network. The water molecules undergo a continuous phase transition in their orientational configuration analogous to a two-dimensional Ising model. The incoherent dynamic structure factor reveals two thermally activated relaxation processes, one on a subpicosecond timescale and another on a 10-100 ps timescale, between 70 and 130 K. The slow process is an in-plane reorientation of the water molecule involving the breaking of hydrogen bonds with a framework that, despite the low temperatures involved, is analogous to rotational diffusion of water molecules in the bulk liquid. The fast process is a localized motion of the water molecule with no apparent analogs among known bulk or confined phases of water.

Fast Rotational Diffusion of Water Molecules in a 2D Hydrogen Bond Network at Cryogenic Temperatures

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

Prisk, Timothy; Hoffmann, Christina; Kolesnikov, Alexander I.

Individual water molecules or small clusters of water molecules contained within microporous minerals present an extreme case of confinement where the local structure of hydrogen bond networks are dramatically altered from bulk water. In the zinc silicate hemimorphite, the water molecules form a two-dimensional hydrogen bond network with hydroxyl groups in the crystal framework. Here in this paper, we present a combined experimental and theoretical study of the structure and dynamics of water molecules within this network. The water molecules undergo a continuous phase transition in their orientational configuration analogous to a two-dimensional Ising model. The incoherent dynamic structure factormore » reveals two thermally activated relaxation processes, one on a subpicosecond timescale and another on a 10–100 ps timescale, between 70 and 130 K. The slow process is an in-plane reorientation of the water molecule involving the breaking of hydrogen bonds with a framework that, despite the low temperatures involved, is analogous to rotational diffusion of water molecules in the bulk liquid. The fast process is a localized motion of the water molecule with no apparent analogs among known bulk or confined phases of water.« less

Fast Rotational Diffusion of Water Molecules in a 2D Hydrogen Bond Network at Cryogenic Temperatures

DOE PAGES

Prisk, Timothy; Hoffmann, Christina; Kolesnikov, Alexander I.; ...

2018-05-09

Individual water molecules or small clusters of water molecules contained within microporous minerals present an extreme case of confinement where the local structure of hydrogen bond networks are dramatically altered from bulk water. In the zinc silicate hemimorphite, the water molecules form a two-dimensional hydrogen bond network with hydroxyl groups in the crystal framework. Here in this paper, we present a combined experimental and theoretical study of the structure and dynamics of water molecules within this network. The water molecules undergo a continuous phase transition in their orientational configuration analogous to a two-dimensional Ising model. The incoherent dynamic structure factormore » reveals two thermally activated relaxation processes, one on a subpicosecond timescale and another on a 10–100 ps timescale, between 70 and 130 K. The slow process is an in-plane reorientation of the water molecule involving the breaking of hydrogen bonds with a framework that, despite the low temperatures involved, is analogous to rotational diffusion of water molecules in the bulk liquid. The fast process is a localized motion of the water molecule with no apparent analogs among known bulk or confined phases of water.« less

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

PubMed

Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

2015-08-01

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

PubMed Central

Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

2015-01-01

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure. PMID:26291697

Modeling and numerical simulations of growth and morphologies of three dimensional aggregated silver films

NASA Astrophysics Data System (ADS)

Davis, L. J.; Boggess, M.; Kodpuak, E.; Deutsch, M.

2012-11-01

We report on a model for the deposition of three dimensional, aggregated nanocrystalline silver films, and an efficient numerical simulation method developed for visualizing such structures. We compare our results to a model system comprising chemically deposited silver films with morphologies ranging from dilute, uniform distributions of nanoparticles to highly porous aggregated networks. Disordered silver films grown in solution on silica substrates are characterized using digital image analysis of high resolution scanning electron micrographs. While the latter technique provides little volume information, plane-projected (two dimensional) island structure and surface coverage may be reliably determined. Three parameters governing film growth are evaluated using these data and used as inputs for the deposition model, greatly reducing computing requirements while still providing direct access to the complete (bulk) structure of the films throughout the growth process. We also show how valuable three dimensional characteristics of the deposited materials can be extracted using the simulated structures.

A Three-Dimensional Computational Model of Collagen Network Mechanics

PubMed Central

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

2014-01-01

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

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Silicone elastomers capable of large isotropic dimensional change

DOEpatents

Lewicki, James; Worsley, Marcus A.

2017-07-18

Described herein is a highly effective route towards the controlled and isotropic reduction in size-scale, of complex 3D structures using silicone network polymer chemistry. In particular, a class of silicone structures were developed that once patterned and cured can `shrink` micron scale additive manufactured and lithographically patterned structures by as much as 1 order of magnitude while preserving the dimensions and integrity of these parts. This class of silicone materials is compatible with existing additive manufacture and soft lithographic fabrication processes and will allow access to a hitherto unobtainable dimensionality of fabrication.

Network Reconstruction From High-Dimensional Ordinary Differential Equations.

PubMed

Chen, Shizhe; Shojaie, Ali; Witten, Daniela M

2017-01-01

We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.

All-optical routing and switching for three-dimensional photonic circuitry

PubMed Central

Keil, Robert; Heinrich, Matthias; Dreisow, Felix; Pertsch, Thomas; Tünnermann, Andreas; Nolte, Stefan; Christodoulides, Demetrios N.; Szameit, Alexander

2011-01-01

The ability to efficiently transmit and rapidly process huge amounts of data has become almost indispensable to our daily lives. It turned out that all-optical networks provide a very promising platform to deal with this task. Within such networks opto-optical switches, where light is directed by light, are a crucial building block for an effective operation. In this article, we present an experimental analysis of the routing and switching behaviour of light in two-dimensional evanescently coupled waveguide arrays of Y- and T-junction geometries directly inscribed into fused silica using ultrashort laser pulses. These systems have the fundamental advantage of supporting three-dimensional network topologies, thereby breaking the limitations on complexity associated with planar structures while maintaining a high dirigibility of the light. Our results show how such arrays can be used to control the flow of optical signals within integrated photonic circuits. PMID:22355612

Evaluation of Deep Learning Representations of Spatial Storm Data

NASA Astrophysics Data System (ADS)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

Topological Patterns for Scalable Representation and Analysis of Dataflow Graphs

DTIC Science & Technology

2011-11-01

dimensional mesh structure. Such a structure is of particular use to model DSP architectures in which data flows across a network of processing elements...ACSSC.1998.751616 3. Andrews, J.G., Ghosh, A., Muhamed, R.: Fundamentals of WiMAX: understanding broad- band wireless networking . Prentice Hall (2007... SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 23 19a. NAME OF RESPONSIBLE PERSON a. REPORT

A bicontinuous tetrahedral structure in a liquid-crystalline lipid

NASA Astrophysics Data System (ADS)

Longley, William; McIntosh, Thomas J.

1983-06-01

The structure of most lipid-water phases can be visualized as an ordered distribution of two liquid media, water and hydrocarbons, separated by a continuous surface covered by the polar groups of the lipid molecules1. In the cubic phases in particular, rod-like elements are linked into three-dimensional networks1,2. Two of these phases (space groups Ia3d and Pn3m) contain two such three-dimensional networks mutually inter-woven and unconnected. Under the constraints of energy minimization3, the interface between the components in certain of these `porous fluids' may well resemble one of the periodic minimal surface structures of the type described mathematically by Schwarz4,5. A structure of this sort has been proposed for the viscous isotropic (cubic) form of glycerol monooleate (GMO) by Larsson et al.6 who suggested that the X-ray diagrams of Lindblom et al.7 indicated a body-centred crystal structure in which lipid bilayers might be arranged as in Schwarz's octahedral surface4. We have now found that at high water contents, a primitive cubic lattice better fits the X-ray evidence with the material in the crystal arranged in a tetrahedral way. The lipid appears to form a single bilayer, continuous in three dimensions, separating two continuous interlinked networks of water. Each of the water networks has the symmetry of the diamond crystal structure and the bilayer lies in the space between them following a surface resembling Schwarz's tetrahedral surface4.

Mechanisms of leading edge protrusion in interstitial migration

PubMed Central

Wilson, Kerry; Lewalle, Alexandre; Fritzsche, Marco; Thorogate, Richard; Duke, Tom; Charras, Guillaume

2013-01-01

While the molecular and biophysical mechanisms underlying cell protrusion on two-dimensional substrates are well understood, our knowledge of the actin structures driving protrusion in three-dimensional environments is poor, despite relevance to inflammation, development and cancer. Here we report that, during chemotactic migration through microchannels with 5 μm × 5 μm cross-sections, HL60 neutrophil-like cells assemble an actin-rich slab filling the whole channel cross-section at their front. This leading edge comprises two distinct F-actin networks: an adherent network that polymerizes perpendicular to cell-wall interfaces and a ‘free’ network that grows from the free membrane at the cell front. Each network is polymerized by a distinct nucleator and, due to their geometrical arrangement, the networks interact mechanically. On the basis of our experimental data, we propose that, during interstitial migration, medial growth of the adherent network compresses the free network preventing its retrograde movement and enabling new polymerization to be converted into forward protrusion. PMID:24305616

The evolutions of medical building network structure for emerging infectious disease protection and control.

PubMed

Liu, Nan; Zhang, Hongzhe; Zhang, Shanshan

2014-12-01

Emerging infectious disease is one of the most minatory threats in modern society. A perfect medical building network system need to be established to protect and control emerging infectious disease. Although in China a preliminary medical building network is already set up with disease control center, the infectious disease hospital, infectious diseases department in general hospital and basic medical institutions, there are still many defects in this system, such as simple structural model, weak interoperability among subsystems, and poor capability of the medical building to adapt to outbreaks of infectious disease. Based on the characteristics of infectious diseases, the whole process of its prevention and control and the comprehensive influence factors, three-dimensional medical architecture network system is proposed as an inevitable trend. In this conception of medical architecture network structure, the evolutions are mentioned, such as from simple network system to multilayer space network system, from static network to dynamic network, and from mechanical network to sustainable network. Ultimately, a more adaptable and corresponsive medical building network system will be established and argued in this paper.

Geometry of complex networks and topological centrality

NASA Astrophysics Data System (ADS)

Ranjan, Gyan; Zhang, Zhi-Li

2013-09-01

We explore the geometry of complex networks in terms of an n-dimensional Euclidean embedding represented by the Moore-Penrose pseudo-inverse of the graph Laplacian (L). The squared distance of a node i to the origin in this n-dimensional space (lii+), yields a topological centrality index, defined as C∗(i)=1/lii+. In turn, the sum of reciprocals of individual node centralities, ∑i1/C∗(i)=∑ilii+, or the trace of L, yields the well-known Kirchhoff index (K), an overall structural descriptor for the network. To put into context this geometric definition of centrality, we provide alternative interpretations of the proposed indices that connect them to meaningful topological characteristics - first, as forced detour overheads and frequency of recurrences in random walks that has an interesting analogy to voltage distributions in the equivalent electrical network; and then as the average connectedness of i in all the bi-partitions of the graph. These interpretations respectively help establish the topological centrality (C∗(i)) of node i as a measure of its overall position as well as its overall connectedness in the network; thus reflecting the robustness of i to random multiple edge failures. Through empirical evaluations using synthetic and real world networks, we demonstrate how the topological centrality is better able to distinguish nodes in terms of their structural roles in the network and, along with Kirchhoff index, is appropriately sensitive to perturbations/re-wirings in the network.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

PubMed

Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

2016-01-01

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Ambipolar behavior and thermoelectric properties of WS2 nanotubes

NASA Astrophysics Data System (ADS)

Yomogida, Yohei; Kawai, Hideki; Sugahara, Mitsunari; Okada, Ryotaro; Yanagi, Kazuhiro

WS2 nanotubes are rolled multi-walled nanotubes made by a layered material, tungsten disulfides Since the discovery by Tenne et al in 1992, various physical properties have been revealed. Theoretical studies have suggested their distinct electronic properties from those of two dimensional sheet, such as one-dimensional electronic strucutures with sharp van Hove singularities and chiralitiy depended electronic structures. Their fibril structures enable us to make their random network films, however, the films are not conducting, and thus have not been used for electronic applications. Here we demonstrate that carrier injections on the WS2 networks by an electrolyte gating approach could make the networks as a semiconducting channel. We clarified the Raman characteristics of WS2 nanotubes networks under electrolyte gating, and confirmed capability of electron and hole injections. We revealed ambipolar behaviors of the WS2 nanotube networks in field effect transistor setups with electrolyte gating. In additio, we demosntrate N-type and P-type control of thermoelectric properties of WS2 nanotubes by electrolyte gating.The power factor of the WS2 nanotubes almost approached to that of the single crystalline WS2 flakes, suggesting good potential for thermoelectric applications..

Guiding Neuronal Growth in Tissues with Light

DTIC Science & Technology

2010-02-27

and structural properties of their surroundings in addition to the biochemical properties. Furthermore, three-dimensional biopolymer matrices provide...Properties of Biopolymer Networks Biopolymer networks exhibit unique nonlinear rheological behavior that differs dramatically from most synthetic...and presumably other biopolymers , is not well defined in variable gap geometries. These findings have broad implications for the interpretation of

[Cu(I)(bpp)]BF4: the first extended coordination network prepared solvothermally in an ionic liquid solvent.

PubMed

Jin, Kun; Huang, Xiaoying; Pan, Long; Li, Jing; Appel, Aaron; Wherland, Scot; Pang, Long

2002-12-07

Use of an ionic liquid [bmim][BF4] (bmim = 1-butyl-3-methylimidazolium) as solvent has resulted in the first extended coordination structure, the two-dimensional network [Cu(bpp)]BF4 [bpp = 1,3-bis(4-pyridyl)propane], produced via a solvothermal route.

Social Support, Network Structure, and the Inventory of Socially Supportive Behaviors.

ERIC Educational Resources Information Center

Stokes, Joseph P.; Wilson, Diane Grimard

The Inventory of Socially Supportive Behaviors (ISSB) appears to be a satisfactory measure of social support with good reliability and some evidence of validity. To investigate the dimensionality of the ISSB through factor analytic procedures and to predict social support from social network variables, 179 college students (97 male, 82 female)…

3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

PubMed

Li, Hui; Liu, Chunmei

2014-06-14

3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Graphene--nanotube--iron hierarchical nanostructure as lithium ion battery anode.

PubMed

Lee, Si-Hwa; Sridhar, Vadahanambi; Jung, Jung-Hwan; Karthikeyan, Kaliyappan; Lee, Yun-Sung; Mukherjee, Rahul; Koratkar, Nikhil; Oh, Il-Kwon

2013-05-28

In this study, we report a novel route via microwave irradiation to synthesize a bio-inspired hierarchical graphene--nanotube--iron three-dimensional nanostructure as an anode material in lithium-ion batteries. The nanostructure comprises vertically aligned carbon nanotubes grown directly on graphene sheets along with shorter branches of carbon nanotubes stemming out from both the graphene sheets and the vertically aligned carbon nanotubes. This bio-inspired hierarchical structure provides a three-dimensional conductive network for efficient charge-transfer and prevents the agglomeration and restacking of the graphene sheets enabling Li-ions to have greater access to the electrode material. In addition, functional iron-oxide nanoparticles decorated within the three-dimensional hierarchical structure provides outstanding lithium storage characteristics, resulting in very high specific capacities. The anode material delivers a reversible capacity of ~1024 mA · h · g(-1) even after prolonged cycling along with a Coulombic efficiency in excess of 99%, which reflects the ability of the hierarchical network to prevent agglomeration of the iron-oxide nanoparticles.

Power-scaling performance of a three-dimensional tritium betavoltaic diode

NASA Astrophysics Data System (ADS)

Liu, Baojun; Chen, Kevin P.; Kherani, Nazir P.; Zukotynski, Stefan

2009-12-01

Three-dimensional diodes fabricated by electrochemical etching are exposed to tritium gas at pressures from 0.05 to 33 atm at room temperature to examine its power scaling performance. It is shown that the three-dimensional microporous structure overcomes the self-absorption limited saturation of beta flux at high tritium pressures. These results are contrasted against the three-dimensional device powered in one instance by tritium absorbed in the near surface region of the three-dimensional microporous network, and in another by a planar scandium tritide foil. These findings suggest that direct tritium occlusion in the near surface of three-dimensional diode can improve the specific power production.

Highly-ordered supportless three-dimensional nanowire networks with tunable complexity and interwire connectivity for device integration.

PubMed

Rauber, Markus; Alber, Ina; Müller, Sven; Neumann, Reinhard; Picht, Oliver; Roth, Christina; Schökel, Alexander; Toimil-Molares, Maria Eugenia; Ensinger, Wolfgang

2011-06-08

The fabrication of three-dimensional assemblies consisting of large quantities of nanowires is of great technological importance for various applications including (electro-)catalysis, sensitive sensing, and improvement of electronic devices. Because the spatial distribution of the nanostructured material can strongly influence the properties, architectural design is required in order to use assembled nanowires to their full potential. In addition, special effort has to be dedicated to the development of efficient methods that allow precise control over structural parameters of the nanoscale building blocks as a means of tuning their characteristics. This paper reports the direct synthesis of highly ordered large-area nanowire networks by a method based on hard templates using electrodeposition within nanochannels of ion track-etched polymer membranes. Control over the complexity of the networks and the dimensions of the integrated nanostructures are achieved by a modified template fabrication. The networks possess high surface area and excellent transport properties, turning them into a promising electrocatalyst material as demonstrated by cyclic voltammetry studies on platinum nanowire networks catalyzing methanol oxidation. Our method opens up a new general route for interconnecting nanowires to stable macroscopic network structures of very high integration level that allow easy handling of nanowires while maintaining their connectivity.

Hydrothermal synthesis and crystal structure of alkaline earth metal (Mg, Ca) based on 2,5-Dimethylbenzene-1,4-diylbis(methylene) diphosphonic acid

NASA Astrophysics Data System (ADS)

Xie, Y. C.; Cheng, Q. R.; Pan, Z. Q.

2018-02-01

New magnesium phosphonates Mg(H2L)31 (H4L = 2,5-dimethylbenzene-1,4 -diylbis(methylene)diphosphonic acid) and Ca(H2L)·2H2O 2 have been hydrothermally synthesized from H4L and the corresponding metal salts. Complex 1 and 2 have been characterized by IR, powder and single-crystal X-ray diffraction methods. Complex 1 crystallizes in trigonal space group R-3c and complex 2 belongs to the triclinic space group. The complexes both form two-dimensional (2D) network structure and show three-dimensional (3D) network through hydrogen bonds. Thermal stability of complex 1 and 2 have also been investigated. CCDC: 1534599 for 1; 1536423 for 2.

Centrality measures in temporal networks with time series analysis

NASA Astrophysics Data System (ADS)

Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun

2017-05-01

The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.

Describing spatial pattern in stream networks: A practical approach

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

A geostatistical approach for describing spatial pattern in stream networks

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity.

PubMed

Milanesi, P; Holderegger, R; Bollmann, K; Gugerli, F; Zellweger, F

2017-02-01

Estimating connectivity among fragmented habitat patches is crucial for evaluating the functionality of ecological networks. However, current estimates of landscape resistance to animal movement and dispersal lack landscape-level data on local habitat structure. Here, we used a landscape genetics approach to show that high-fidelity habitat structure maps derived from Light Detection and Ranging (LiDAR) data critically improve functional connectivity estimates compared to conventional land cover data. We related pairwise genetic distances of 128 Capercaillie (Tetrao urogallus) genotypes to least-cost path distances at multiple scales derived from land cover data. Resulting β values of linear mixed effects models ranged from 0.372 to 0.495, while those derived from LiDAR ranged from 0.558 to 0.758. The identification and conservation of functional ecological networks suffering from habitat fragmentation and homogenization will thus benefit from the growing availability of detailed and contiguous data on three-dimensional habitat structure and associated habitat quality. © 2016 by the Ecological Society of America.

Crystalline Coordination Networks of Zero-Valent Metal Centers: Formation of a 3-Dimensional Ni(0) Framework with m-Terphenyl Diisocyanides.

PubMed

Agnew, Douglas W; DiMucci, Ida M; Arroyave, Alejandra; Gembicky, Milan; Moore, Curtis E; MacMillan, Samantha N; Rheingold, Arnold L; Lancaster, Kyle M; Figueroa, Joshua S

2017-12-06

A permanently porous, three-dimensional metal-organic material formed from zero-valent metal nodes is presented. Combination of ditopic m-terphenyl diisocyanide, [CNAr Mes2 ] 2 , and the d 10 Ni(0) precursor Ni(COD) 2 , produces a porous metal-organic material featuring tetrahedral [Ni(CNAr Mes2 ) 4 ] n structural sites. X-ray absorption spectroscopy provides firm evidence for the presence of Ni(0) centers, whereas gas-sorption and thermogravimetric analysis reveal the characteristics of a robust network with a microdomain N 2 -adsorption profile.

Crystalline Coordination Networks of Zero-Valent Metal Centers: Formation of a 3-Dimensional Ni(0) Framework with m-Terphenyl Diisocyanides

DOE PAGES

Agnew, Douglas W.; DiMucci, Ida M.; Arroyave, Alejandra; ...

2017-11-13

A permanently porous, three-dimensional metal–organic material formed from zero-valent metal nodes is presented. Combination of ditopic m-terphenyl diisocyanide, [CNAr Mes2] 2, and the d 10 Ni(0) precursor Ni(COD) 2, produces a porous metal–organic material featuring tetrahedral [Ni(CNAr Mes2) 4] n structural sites. X-ray absorption spectroscopy provides firm evidence for the presence of Ni(0) centers, whereas gas-sorption and thermogravimetric analysis reveal the characteristics of a robust network with a microdomain N 2-adsorption profile.

Local self-uniformity in photonic networks.

PubMed

Sellers, Steven R; Man, Weining; Sahba, Shervin; Florescu, Marian

2017-02-17

The interaction of a material with light is intimately related to its wavelength-scale structure. Simple connections between structure and optical response empower us with essential intuition to engineer complex optical functionalities. Here we develop local self-uniformity (LSU) as a measure of a random network's internal structural similarity, ranking networks on a continuous scale from crystalline, through glassy intermediate states, to chaotic configurations. We demonstrate that complete photonic bandgap structures possess substantial LSU and validate LSU's importance in gap formation through design of amorphous gyroid structures. Amorphous gyroid samples are fabricated via three-dimensional ceramic printing and the bandgaps experimentally verified. We explore also the wing-scale structuring in the butterfly Pseudolycaena marsyas and show that it possesses substantial amorphous gyroid character, demonstrating the subtle order achieved by evolutionary optimization and the possibility of an amorphous gyroid's self-assembly.

Local self-uniformity in photonic networks

NASA Astrophysics Data System (ADS)

Sellers, Steven R.; Man, Weining; Sahba, Shervin; Florescu, Marian

2017-02-01

The interaction of a material with light is intimately related to its wavelength-scale structure. Simple connections between structure and optical response empower us with essential intuition to engineer complex optical functionalities. Here we develop local self-uniformity (LSU) as a measure of a random network's internal structural similarity, ranking networks on a continuous scale from crystalline, through glassy intermediate states, to chaotic configurations. We demonstrate that complete photonic bandgap structures possess substantial LSU and validate LSU's importance in gap formation through design of amorphous gyroid structures. Amorphous gyroid samples are fabricated via three-dimensional ceramic printing and the bandgaps experimentally verified. We explore also the wing-scale structuring in the butterfly Pseudolycaena marsyas and show that it possesses substantial amorphous gyroid character, demonstrating the subtle order achieved by evolutionary optimization and the possibility of an amorphous gyroid's self-assembly.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

PubMed

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

High-speed all-optical DNA local sequence alignment based on a three-dimensional artificial neural network.

PubMed

Maleki, Ehsan; Babashah, Hossein; Koohi, Somayyeh; Kavehvash, Zahra

2017-07-01

This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DNA, whose output is fed to a novel three-dimensional artificial neural network for local DNA alignment. All-optical implementation of the proposed 3D artificial neural network is developed and its accuracy is verified in Zemax. Thanks to its parallel processing capability, the proposed structure performs local alignment of 4 million sequences of 150 base pairs in a few seconds, which is much faster than its electrical counterparts, such as the basic local alignment search tool.

Three-dimensional Aerographite-GaN hybrid networks: Single step fabrication of porous and mechanically flexible materials for multifunctional applications

PubMed Central

Schuchardt, Arnim; Braniste, Tudor; Mishra, Yogendra K.; Deng, Mao; Mecklenburg, Matthias; Stevens-Kalceff, Marion A.; Raevschi, Simion; Schulte, Karl; Kienle, Lorenz; Adelung, Rainer; Tiginyanu, Ion

2015-01-01

Three dimensional (3D) elastic hybrid networks built from interconnected nano- and microstructure building units, in the form of semiconducting-carbonaceous materials, are potential candidates for advanced technological applications. However, fabrication of these 3D hybrid networks by simple and versatile methods is a challenging task due to the involvement of complex and multiple synthesis processes. In this paper, we demonstrate the growth of Aerographite-GaN 3D hybrid networks using ultralight and extremely porous carbon based Aerographite material as templates by a single step hydride vapor phase epitaxy process. The GaN nano- and microstructures grow on the surface of Aerographite tubes and follow the network architecture of the Aerographite template without agglomeration. The synthesized 3D networks are integrated with the properties from both, i.e., nanoscale GaN structures and Aerographite in the form of flexible and semiconducting composites which could be exploited as next generation materials for electronic, photonic, and sensors applications. PMID:25744694

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

NASA Astrophysics Data System (ADS)

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

2008-09-01

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

Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

ERIC Educational Resources Information Center

Hinton, Geoffrey; Osindero, Simon; Welling, Max; Teh, Yee-Whye

2006-01-01

We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of…

Syntheses, crystal structures, and properties of four complexes based on polycarboxylate and imidazole ligands

NASA Astrophysics Data System (ADS)

Qiao, Rui; Chen, Shui-Sheng; Sheng, Liang-Quan; Yang, Song; Li, Wei-Dong

2015-08-01

Four metal-organic coordination polymers [Zn(HL)(H2O)]·4H2O (1), [Zn(HL)(L1)]·4H2O (2), [Cu(HL)(H2O)]·3H2O (3) and [Cu(HL)(L1)]·5H2O (4) were synthesized by reactions of the corresponding metal(II) salts with semirigid polycarboxylate ligand (5-((4-carboxypiperidin-1-yl)methyl)isophthalic acid hydrochloride, H3L·HCl) or auxiliary ligand (1,4-di(1H-imidazol-4-yl)benzene, L1). The structures of the compounds were characterized by elemental analysis, FT-IR spectroscopy and single-crystal X-ray diffraction. The use of auxiliary ligand L1 has great influence on the structures of two pairs of complexes 1, 2 and 3, 4. Complex 1 is a uninodal 3-connected rare 2-fold interpenetrating ZnSc net with a Point (Schlafli) symbol of (103) while 2 is a one-dimensional (1D) ladder structure. Compound 3 features a two-dimensional (2D) honeycomb network with typical 63-hcb topology, while 4 is 2D network with (4, 4) sql topology based on binuclear CuII subunits. The non-covalent bonding interactions such as hydrogen bonds, π···π stacking and C-H···π exist in complexes 1-4, which contributes to stabilize crystal structure and extend the low-dimensional entities into high-dimensional frameworks. And the photoluminescent property of 1 and 2 and gas sorption property of 4 have been investigated.

Low-Dimensional Network Formation in Molten Sodium Carbonate

PubMed Central

Wilding, Martin C.; Wilson, Mark; Alderman, Oliver L. G.; Benmore, Chris; Weber, J. K. R.; Parise, John B.; Tamalonis, Anthony; Skinner, Lawrie

2016-01-01

Molten carbonates are highly inviscid liquids characterized by low melting points and high solubility of rare earth elements and volatile molecules. An understanding of the structure and related properties of these intriguing liquids has been limited to date. We report the results of a study of molten sodium carbonate (Na2CO3) which combines high energy X-ray diffraction, containerless techniques and computer simulation to provide insight into the liquid structure. Total structure factors (Fx(Q)) are collected on the laser-heated carbonate spheres suspended in flowing gases of varying composition in an aerodynamic levitation furnace. The respective partial structure factor contributions to Fx(Q) are obtained by performing molecular dynamics simulations treating the carbonate anions as flexible entities. The carbonate liquid structure is found to be heavily temperature-dependent. At low temperatures a low-dimensional carbonate chain network forms, at T = 1100 K for example ~55% of the C atoms form part of a chain. The mean chain lengths decrease as temperature is increased and as the chains become shorter the rotation of the carbonate anions becomes more rapid enhancing the diffusion of Na+ ions. PMID:27080401

Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.

PubMed

Rosenthal, Gideon; Váša, František; Griffa, Alessandra; Hagmann, Patric; Amico, Enrico; Goñi, Joaquín; Avidan, Galia; Sporns, Olaf

2018-06-05

Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.

Black Holes as Brains: Neural Networks with Area Law Entropy

NASA Astrophysics Data System (ADS)

Dvali, Gia

2018-04-01

Motivated by the potential similarities between the underlying mechanisms of the enhanced memory storage capacity in black holes and in brain networks, we construct an artificial quantum neural network based on gravity-like synaptic connections and a symmetry structure that allows to describe the network in terms of geometry of a d-dimensional space. We show that the network possesses a critical state in which the gapless neurons emerge that appear to inhabit a (d-1)-dimensional surface, with their number given by the surface area. In the excitations of these neurons, the network can store and retrieve an exponentially large number of patterns within an arbitrarily narrow energy gap. The corresponding micro-state entropy of the brain network exhibits an area law. The neural network can be described in terms of a quantum field, via identifying the different neurons with the different momentum modes of the field, while identifying the synaptic connections among the neurons with the interactions among the corresponding momentum modes. Such a mapping allows to attribute a well-defined sense of geometry to an intrinsically non-local system, such as the neural network, and vice versa, it allows to represent the quantum field model as a neural network.

Dimensional control of supramolecular assemblies of diacetylene-derived peptide gemini amphiphile: from spherical micelles to foamlike networks.

PubMed

Jiang, Hao; Ehlers, Martin; Hu, Xiao-Yu; Zellermann, Elio; Schmuck, Carsten

2018-05-22

Peptide amphiphiles capable of assembling into multidimensional nanostructures have attracted much attention over the past decade due to their potential applications in materials science. Herein, a novel diacetylene-derived peptide gemini amphiphile with a fluorenylmethyloxycarbonyl (Fmoc) group at the N-terminus is reported to hierarchically assemble into spherical micelles, one-dimensional nanorods, two-dimensional foamlike networks and lamellae. Solvent polarity shows a remarkable effect on the self-assembled structures by changing the balance of four weak noncovalent interactions (hydrogen-bonding, π-π stacking, hydrophobic interaction, and electrostatic repulsion). We also show the time-evolution not only from spherical micelles to helical nanofibers in aqueous solution, but also from branched wormlike micelles to foamlike networks in methanol solution. In this work, the presence of the Fmoc group plays a key role in the self-assembly process. This work provides an efficient strategy for precise morphological control, aiding the future development in materials science.

Single-photon-level quantum image memory based on cold atomic ensembles

PubMed Central

Ding, Dong-Sheng; Zhou, Zhi-Yuan; Shi, Bao-Sen; Guo, Guang-Can

2013-01-01

A quantum memory is a key component for quantum networks, which will enable the distribution of quantum information. Its successful development requires storage of single-photon light. Encoding photons with spatial shape through higher-dimensional states significantly increases their information-carrying capability and network capacity. However, constructing such quantum memories is challenging. Here we report the first experimental realization of a true single-photon-carrying orbital angular momentum stored via electromagnetically induced transparency in a cold atomic ensemble. Our experiments show that the non-classical pair correlation between trigger photon and retrieved photon is retained, and the spatial structure of input and retrieved photons exhibits strong similarity. More importantly, we demonstrate that single-photon coherence is preserved during storage. The ability to store spatial structure at the single-photon level opens the possibility for high-dimensional quantum memories. PMID:24084711

Unusual (mu-aqua)bis(mu-carboxylate) bridge in homometallic M(II) (M=Mn, Co and Ni) two-dimensional compounds based on the 1,2,3,4-butanetetracarboxylic acid: synthesis, structure, and magnetic properties.

PubMed

Cañadillas-Delgado, Laura; Fabelo, Oscar; Pásan, Jorge; Delgado, Fernando S; Lloret, Francesc; Julve, Miguel; Ruiz-Pérez, Catalina

2007-09-03

The first coordination compounds of 1,2,3,4-butanetetracarboxylate anion (butca4-) of the formula [M2(butca)(H2O)5]n.2nH2O [M=Mn(II) (1), Co(II) (2), and Ni(II) (3)] were prepared and their X-ray crystal structures and magnetic properties investigated. The three complexes have a very similar two-dimensional structure which consists of (4,4) networks, 1 and 2 being isostructural. The tetracarboxylate ligand acts as a 4-fold connector leading to two-dimensional (4,4) networks of metal atoms, this topology being possible because of its planar conformation. The nodes of these networks are formed by dinuclear motifs which exhibit the unusual (mu-aqua)bis(mu-carboxylate) bridging unit which is analogous to that observed in some molecules of biological interest. The variable-temperature magnetic susceptibility measurements of 1-3 show that 1 and 2 are antiferromagnetically coupled systems whereas 3 exhibits a ferromagnetic behavior. The analysis of the magnetic data of 1-3 through a simple dinuclear model allowed the determination of the values of the magnetic coupling (J) -3.6 (1), -1.2 (2), and +1.47 cm(-1) (3) with the Hamiltonian being defined as H=-JSA.SB. The countercomplementarity between the two bridges (aqua and syn-syn carboxylate) accounts for the trend exhibited by the values of the magnetic coupling in this family.

Characterizing the correlations between local phase fractions of gas-liquid two-phase flow with wire-mesh sensor.

PubMed

Tan, C; Liu, W L; Dong, F

2016-06-28

Understanding of flow patterns and their transitions is significant to uncover the flow mechanics of two-phase flow. The local phase distribution and its fluctuations contain rich information regarding the flow structures. A wire-mesh sensor (WMS) was used to study the local phase fluctuations of horizontal gas-liquid two-phase flow, which was verified through comparing the reconstructed three-dimensional flow structure with photographs taken during the experiments. Each crossing point of the WMS is treated as a node, so the measurement on each node is the phase fraction in this local area. An undirected and unweighted flow pattern network was established based on connections that are formed by cross-correlating the time series of each node under different flow patterns. The structure of the flow pattern network reveals the relationship of the phase fluctuations at each node during flow pattern transition, which is then quantified by introducing the topological index of the complex network. The proposed analysis method using the WMS not only provides three-dimensional visualizations of the gas-liquid two-phase flow, but is also a thorough analysis for the structure of flow patterns and the characteristics of flow pattern transition. This article is part of the themed issue 'Supersensing through industrial process tomography'. © 2016 The Author(s).

Isomer-Specific Spectroscopy of Benzene-(H2O)n, n = 6,7: Benzene's Role in Reshaping Water's Three-Dimensional Networks.

PubMed

Tabor, Daniel P; Kusaka, Ryoji; Walsh, Patrick S; Sibert, Edwin L; Zwier, Timothy S

2015-05-21

The water hexamer and heptamer are the smallest sized water clusters that support three-dimensional hydrogen-bonded networks, with several competing structures that could be altered by interactions with a solute. Using infrared-ultraviolet double resonance spectroscopy, we record isomer-specific OH stretch infrared spectra of gas-phase benzene-(H2O)(6,7) clusters that demonstrate benzene's surprising role in reshaping (H2O)(6,7). The single observed isomer of benzene-(H2O)6 incorporates an inverted book structure rather than the cage or prism. The main conformer of benzene-(H2O)7 is an inserted-cubic structure in which benzene replaces one water molecule in the S4-symmetry cube of the water octamer, inserting itself into the water cluster by engaging as a π H-bond acceptor with one water and via C-H···O donor interactions with two others. The corresponding D(2d)-symmetry inserted-cube structure is not observed, consistent with the calculated energetic preference for the S4 over the D(2d) inserted cube. A reduced-dimension model that incorporates stretch-bend Fermi resonance accounts for the spectra in detail and sheds light on the hydrogen-bonding networks themselves and on the perturbations imposed on them by benzene.

Characterizing the correlations between local phase fractions of gas–liquid two-phase flow with wire-mesh sensor

PubMed Central

Liu, W. L.; Dong, F.

2016-01-01

Understanding of flow patterns and their transitions is significant to uncover the flow mechanics of two-phase flow. The local phase distribution and its fluctuations contain rich information regarding the flow structures. A wire-mesh sensor (WMS) was used to study the local phase fluctuations of horizontal gas–liquid two-phase flow, which was verified through comparing the reconstructed three-dimensional flow structure with photographs taken during the experiments. Each crossing point of the WMS is treated as a node, so the measurement on each node is the phase fraction in this local area. An undirected and unweighted flow pattern network was established based on connections that are formed by cross-correlating the time series of each node under different flow patterns. The structure of the flow pattern network reveals the relationship of the phase fluctuations at each node during flow pattern transition, which is then quantified by introducing the topological index of the complex network. The proposed analysis method using the WMS not only provides three-dimensional visualizations of the gas–liquid two-phase flow, but is also a thorough analysis for the structure of flow patterns and the characteristics of flow pattern transition. This article is part of the themed issue ‘Supersensing through industrial process tomography’. PMID:27185959

Moving beyond Stylized Economic Network Models: The Hybrid World of the Indian Firm Ownership Network1

PubMed Central

Mani, Dalhia; Moody, James

2014-01-01

A central theme of economic sociology has been to highlight the complexity and diversity of real world markets, but many network models of economic social structure ignore this feature and rely instead on stylized one-dimensional characterizations. Here, the authors return to the basic insight of structural diversity in economic sociology. Using the Indian interorganizational ownership network as their case, they discover a composite—or “hybrid”—model of economic networks that combines elements of prior stylized models. The network contains a disconnected periphery conforming closely to a “transactional” model; a semiperiphery characterized by small, dense clusters with sporadic links, as predicted in “small-world” models; and finally a nested core composed of clusters connected via multiple independent paths. The authors then show how a firm’s position within the mesolevel structure is associated with demographic features such as age and industry and differences in the extent to which firms engage in multiplex and high-value exchanges. PMID:25418990

Assessment of geometry in 2D immune systems using high accuracy laser-based bioprinting techniques (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lauzurica, Sara; Márquez, Andrés.; Molpeceres, Carlos; Notario, Laura; Gómez-Fontela, Miguel; Lauzurica, Pilar

2017-02-01

The immune system is a very complex system that comprises a network of genetic and signaling pathways subtending a network of interacting cells. The location of the cells in a network, along with the gene products they interact with, rules the behavior of the immune system. Therefore, there is a great interest in understanding properly the role of a cell in such networks to increase our knowledge of the immune system response. In order to acquire a better understanding of these processes, cell printing with high spatial resolution emerges as one of the promising approaches to organize cells in two and three-dimensional patterns to enable the study the geometry influence in these interactions. In particular, laser assisted bio-printing techniques using sub-nanosecond laser sources have better characteristics for application in this field, mainly due to its higher spatial resolution, cell viability percentage and process automation. This work presents laser assisted bio-printing of antigen-presenting cells (APCs) in two-dimensional geometries, placing cellular components on a matrix previously generated on demand, permitting to test the molecular interactions between APCs and lymphocytes; as well as the generation of two-dimensional structures designed ad hoc in order to study the mechanisms of mobilization of immune system cells. The use of laser assisted bio-printing, along with APCs and lymphocytes emulate the structure of different niches of the immune system so that we can analyse functional requirement of these interaction.

[Advances in the research of application of hydrogels in three-dimensional bioprinting].

PubMed

Yang, J; Zhao, Y; Li, H H; Zhu, S H

2016-08-20

Hydrogels are three-dimensional networks made of hydrophilic polymer crosslinked through covalent bonds or physical intermolecular attractions, which can contain growth media and growth factors to support cell growth. In bioprinting, hydrogels are used to provide accurate control over cellular microenvironment and to dramatically reduce experimental repetition times, meanwhile we can obtain three-dimensional cell images of high quality. Hydrogels in three-dimensional bioprinting have received a considerable interest due to their structural similarities to the natural extracellular matrix and polyporous frameworks which can support the cellular proliferation and survival. Meanwhile, they are accompanied by many challenges.

A Case Study of the Introduction of RISC-based Computing and a Telecommunications Link to a Suburban High School.

ERIC Educational Resources Information Center

Hakerem, Gita; And Others

This study reports the efforts of the Water and Molecular Networks Project (WAMNet), a program in which high school chemistry students use computer simulations developed at Boston University (Massachusetts) to model the three-dimensional structure of molecules and the hydrogen bond network that holds water molecules together. This case study…

Construction and manipulation of functional three-dimensional droplet networks.

PubMed

Wauer, Tobias; Gerlach, Holger; Mantri, Shiksha; Hill, Jamie; Bayley, Hagan; Sapra, K Tanuj

2014-01-28

Previously, we reported the manual assembly of lipid-coated aqueous droplets in oil to form two-dimensional (2D) networks in which the droplets are connected through single lipid bilayers. Here we assemble lipid-coated droplets in robust, freestanding 3D geometries: for example, a 14-droplet pyramidal assembly. The networks are designed, and each droplet is placed in a designated position. When protein pores are inserted in the bilayers between specific constituent droplets, electrical and chemical communication pathways are generated. We further describe an improved means to construct 3D droplet networks with defined organizations by the manipulation of aqueous droplets containing encapsulated magnetic beads. The droplets are maneuvered in a magnetic field to form simple construction modules, which are then used to form larger 2D and 3D structures including a 10-droplet pyramid. A methodology to construct freestanding, functional 3D droplet networks is an important step toward the programmed and automated manufacture of synthetic minimal tissues.

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model.

PubMed

Tedesco, Mariateresa; Frega, Monica; Martinoia, Sergio; Pesce, Mattia; Massobrio, Paolo

2015-10-18

Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the gold-standard experimental model to study basic neurophysiological mechanisms involved in the formation and maintenance of neuronal cell assemblies. However, in vitro studies have been limited to the recording of the electrophysiological activity generated by bi-dimensional (2D) neural networks. Nonetheless, given the intricate relationship between structure and dynamics, a significant improvement is necessary to investigate the formation and the developing dynamics of three-dimensional (3D) networks. In this work, a novel experimental platform in which 3D hippocampal or cortical networks are coupled to planar Micro-Electrode Arrays (MEAs) is presented. 3D networks are realized by seeding neurons in a scaffold constituted of glass microbeads (30-40 µm in diameter) on which neurons are able to grow and form complex interconnected 3D assemblies. In this way, it is possible to design engineered 3D networks made up of 5-8 layers with an expected final cell density. The increasing complexity in the morphological organization of the 3D assembly induces an enhancement of the electrophysiological patterns displayed by this type of networks. Compared with the standard 2D networks, where highly stereotyped bursting activity emerges, the 3D structure alters the bursting activity in terms of duration and frequency, as well as it allows observation of more random spiking activity. In this sense, the developed 3D model more closely resembles in vivo neural networks.

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

PubMed Central

Tedesco, Mariateresa; Frega, Monica; Martinoia, Sergio; Pesce, Mattia; Massobrio, Paolo

2015-01-01

Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the gold-standard experimental model to study basic neurophysiological mechanisms involved in the formation and maintenance of neuronal cell assemblies. However, in vitro studies have been limited to the recording of the electrophysiological activity generated by bi-dimensional (2D) neural networks. Nonetheless, given the intricate relationship between structure and dynamics, a significant improvement is necessary to investigate the formation and the developing dynamics of three-dimensional (3D) networks. In this work, a novel experimental platform in which 3D hippocampal or cortical networks are coupled to planar Micro-Electrode Arrays (MEAs) is presented. 3D networks are realized by seeding neurons in a scaffold constituted of glass microbeads (30-40 µm in diameter) on which neurons are able to grow and form complex interconnected 3D assemblies. In this way, it is possible to design engineered 3D networks made up of 5-8 layers with an expected final cell density. The increasing complexity in the morphological organization of the 3D assembly induces an enhancement of the electrophysiological patterns displayed by this type of networks. Compared with the standard 2D networks, where highly stereotyped bursting activity emerges, the 3D structure alters the bursting activity in terms of duration and frequency, as well as it allows observation of more random spiking activity. In this sense, the developed 3D model more closely resembles in vivo neural networks. PMID:26554533

VRML metabolic network visualizer.

PubMed

Rojdestvenski, Igor

2003-03-01

A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.

Three-dimensional carbon allotropes comprising phenyl rings and acetylenic chains in sp+ sp 2 hybrid networks

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

Wang, Jian -Tao; Chen, Changfeng; Li, Han -Dong

Here, we here identify by ab initio calculations a new type of three-dimensional (3D) carbon allotropes that consist of phenyl rings connected by linear acetylenic chains in sp+ sp 2 bonding networks. These structures are constructed by inserting acetylenic or diacetylenic bonds into an all sp 2-hybridized rhombohedral polybenzene lattice, and the resulting 3D phenylacetylene and phenyldiacetylene nets comprise a 12-atom and 18-atom rhombohedral primitive unit cells R - 3m symmetry, which are characterized as the 3D chiral crystalline modification of 2D graphyne and graphdiyne, respectively. Simulated phonon spectra reveal that these structures are dynamically stable. Electronic band calculations indicatemore » that phenylacetylene is metallic, while phenyldiacetylene is a semiconductor with an indirect band gap of 0.58 eV. The present results establish a new type of carbon phases and offer insights into their outstanding structural and electronic properties.« less

Three-dimensional carbon allotropes comprising phenyl rings and acetylenic chains in sp+ sp 2 hybrid networks

DOE PAGES

Wang, Jian -Tao; Chen, Changfeng; Li, Han -Dong; ...

2016-04-18

Here, we here identify by ab initio calculations a new type of three-dimensional (3D) carbon allotropes that consist of phenyl rings connected by linear acetylenic chains in sp+ sp 2 bonding networks. These structures are constructed by inserting acetylenic or diacetylenic bonds into an all sp 2-hybridized rhombohedral polybenzene lattice, and the resulting 3D phenylacetylene and phenyldiacetylene nets comprise a 12-atom and 18-atom rhombohedral primitive unit cells R - 3m symmetry, which are characterized as the 3D chiral crystalline modification of 2D graphyne and graphdiyne, respectively. Simulated phonon spectra reveal that these structures are dynamically stable. Electronic band calculations indicatemore » that phenylacetylene is metallic, while phenyldiacetylene is a semiconductor with an indirect band gap of 0.58 eV. The present results establish a new type of carbon phases and offer insights into their outstanding structural and electronic properties.« less

Relating structure and composition with accessibility of a single catalyst particle using correlative 3-dimensional micro-spectroscopy

DOE PAGES

Liu, Yijin; Meirer, Florian; Krest, Courtney M.; ...

2016-08-30

To understand how hierarchically structured functional materials operate, analytical tools are needed that can reveal small structural and chemical details in large sample volumes. Often, a single method alone is not sufficient to get a complete picture of processes happening at multiple length scales. Here we present a correlative approach combining three-dimensional X-ray imaging techniques at different length scales for the analysis of metal poisoning of an individual catalyst particle. The correlative nature of the data allowed establishing a macro-pore network model that interprets metal accumulations as a resistance to mass transport and can, by tuning the effect of metalmore » deposition, simulate the response of the network to a virtual ageing of the catalyst particle. In conclusion, the developed approach is generally applicable and provides an unprecedented view on dynamic changes in a material’s pore space, which is an essential factor in the rational design of functional porous materials.« less

The Effect of Interactive, Three Dimensional, High Speed Simulations on High School Science Students' Conceptions of the Molecular Structure of Water.

ERIC Educational Resources Information Center

Hakerem, Gita; And Others

The Water and Molecular Networks (WAMNet) Project uses graduate student written Reduced Instruction Set Computing (RISC) computer simulations of the molecular structure of water to assist high school students learn about the nature of water. This study examined: (1) preconceptions concerning the molecular structure of water common among high…

Hierarchical on-surface synthesis and electronic structure of carbonyl-functionalized one- and two-dimensional covalent nanoarchitectures

NASA Astrophysics Data System (ADS)

Steiner, Christian; Gebhardt, Julian; Ammon, Maximilian; Yang, Zechao; Heidenreich, Alexander; Hammer, Natalie; Görling, Andreas; Kivala, Milan; Maier, Sabine

2017-03-01

The fabrication of nanostructures in a bottom-up approach from specific molecular precursors offers the opportunity to create tailored materials for applications in nanoelectronics. However, the formation of defect-free two-dimensional (2D) covalent networks remains a challenge, which makes it difficult to unveil their electronic structure. Here we report on the hierarchical on-surface synthesis of nearly defect-free 2D covalent architectures with carbonyl-functionalized pores on Au(111), which is investigated by low-temperature scanning tunnelling microscopy in combination with density functional theory calculations. The carbonyl-bridged triphenylamine precursors form six-membered macrocycles and one-dimensional (1D) chains as intermediates in an Ullmann-type coupling reaction that are subsequently interlinked to 2D networks. The electronic band gap is narrowed when going from the monomer to 1D and 2D surface-confined π-conjugated organic polymers comprising the same building block. The significant drop of the electronic gap from the monomer to the polymer confirms an efficient conjugation along the triphenylamine units within the nanostructures.

The sea urchin egg jelly coat is a three-dimensional fibrous network as seen by intermediate voltage electron microscopy and deep etching analysis.

PubMed

Bonnell, B S; Larabell, C; Chandler, D E

1993-06-01

The egg jelly (EJ) coat which surrounds the unfertilized sea urchin egg undergoes extensive swelling upon contact with sea water, forming a three-dimensional network of interconnected fibers extending nearly 50 microns from the egg surface. Owing to its solubility, this coat has been difficult to visualize by light and electron microscopy. However, Lytechinus pictus EJ coats remain intact, if the fixation medium is maintained at pH 9. The addition of alcian blue during the final dehydration step of sample preparation stains the EJ for visualization of resin embedded eggs by both light and electron microscopy. Stereo pairs taken of thick sections prepared for intermediate voltage electron microscopy (IVEM) produce a three-dimensional image of the EJ network, consisting of interconnected fibers decorated along their length by globular jelly components. Using scanning electron microscopy (SEM), we have shown that before swelling, EJ exists in a tightly bound network of jelly fibers, 50-60 nm in diameter. In contrast, swollen EJ consists of a greatly extended network whose fibrous components measure 10 to 30 nm in diameter. High resolution stereo images of hydrated jelly produced by the quick-freeze/deep-etch/rotary-shadowing technique (QF/DE/RS) show nearly identical EJ networks, suggesting that dehydration does not markedly alter the structure of this extracellular matrix.

Extracellular matrix structure.

PubMed

Theocharis, Achilleas D; Skandalis, Spyros S; Gialeli, Chrysostomi; Karamanos, Nikos K

2016-02-01

Extracellular matrix (ECM) is a non-cellular three-dimensional macromolecular network composed of collagens, proteoglycans/glycosaminoglycans, elastin, fibronectin, laminins, and several other glycoproteins. Matrix components bind each other as well as cell adhesion receptors forming a complex network into which cells reside in all tissues and organs. Cell surface receptors transduce signals into cells from ECM, which regulate diverse cellular functions, such as survival, growth, migration, and differentiation, and are vital for maintaining normal homeostasis. ECM is a highly dynamic structural network that continuously undergoes remodeling mediated by several matrix-degrading enzymes during normal and pathological conditions. Deregulation of ECM composition and structure is associated with the development and progression of several pathologic conditions. This article emphasizes in the complex ECM structure as to provide a better understanding of its dynamic structural and functional multipotency. Where relevant, the implication of the various families of ECM macromolecules in health and disease is also presented. Copyright © 2015 Elsevier B.V. All rights reserved.

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Quasirandom geometric networks from low-discrepancy sequences

NASA Astrophysics Data System (ADS)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

PubMed Central

Barrett, Louise; Henzi, S. Peter; Lusseau, David

2012-01-01

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution. PMID:22734054

Linking dynamics of the inhibitory network to the input structure

PubMed Central

Komarov, Maxim

2017-01-01

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives. PMID:27650865

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

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

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

Ultra-high aggregate bandwidth two-dimensional multiple-wavelength diode laser arrays

NASA Astrophysics Data System (ADS)

Chang-Hasnain, Connie

1994-04-01

Two-dimensional (2D) multi-wavelength vertical cavity surface emitting laser (VCSEL) arrays is promising for ultrahigh aggregate capacity optical networks. A 2D VCSEL array emitting 140 distinct wavelengths was reported by implementing a spatially graded layer in the VCSEL structure, which in turn creates a wavelength spread. In this program, we concentrated on novel epitaxial growth techniques to make reproducible and repeatable multi-wavelength VCSEL arrays.

Mesoscale assembly of chemically modified graphene into complex cellular networks

PubMed Central

Barg, Suelen; Perez, Felipe Macul; Ni, Na; do Vale Pereira, Paula; Maher, Robert C.; Garcia-Tuñon, Esther; Eslava, Salvador; Agnoli, Stefano; Mattevi, Cecilia; Saiz, Eduardo

2014-01-01

The widespread technological introduction of graphene beyond electronics rests on our ability to assemble this two-dimensional building block into three-dimensional structures for practical devices. To achieve this goal we need fabrication approaches that are able to provide an accurate control of chemistry and architecture from nano to macroscopic levels. Here, we describe a versatile technique to build ultralight (density ≥1 mg cm−3) cellular networks based on the use of soft templates and the controlled segregation of chemically modified graphene to liquid interfaces. These novel structures can be tuned for excellent conductivity; versatile mechanical response (elastic-brittle to elastomeric, reversible deformation, high energy absorption) and organic absorption capabilities (above 600 g per gram of material). The approach can be used to uncover the basic principles that will guide the design of practical devices that by combining unique mechanical and functional performance will generate new technological opportunities. PMID:24999766

Application of ground-penetrating radar imagery for three-dimensional visualisation of near-surface structures in ice-rich permafrost, Barrow, Alaska

USGS Publications Warehouse

Munroe, Jeffrey S.; Doolittle, James A.; Kanevskiy, Mikhail; Hinkel, Kenneth M.; Nelson, Frederick E.; Jones, Benjamin M.; Shur, Yuri; Kimble, John M.

2007-01-01

Three-dimensional ground-penetrating radar (3D GPR) was used to investigate the subsurface structure of ice-wedge polygons and other features of the frozen active layer and near-surface permafrost near Barrow, Alaska. Surveys were conducted at three sites located on landscapes of different geomorphic age. At each site, sediment cores were collected and characterised to aid interpretation of GPR data. At two sites, 3D GPR was able to delineate subsurface ice-wedge networks with high fidelity. Three-dimensional GPR data also revealed a fundamental difference in ice-wedge morphology between these two sites that is consistent with differences in landscape age. At a third site, the combination of two-dimensional and 3D GPR revealed the location of an active frost boil with ataxitic cryostructure. When supplemented by analysis of soil cores, 3D GPR offers considerable potential for imaging, interpreting and 3D mapping of near-surface soil and ice structures in permafrost environments.

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

PubMed Central

Ye, Ye; Cheong, Kang Hao; Cen, Yu-wan; Xie, Neng-gang

2016-01-01

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed. PMID:27845430

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

NASA Astrophysics Data System (ADS)

Ye, Ye; Cheong, Kang Hao; Cen, Yu-Wan; Xie, Neng-Gang

2016-11-01

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.

PubMed

Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán

2014-03-11

While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

New type of chimera and mutual synchronization of spatiotemporal structures in two coupled ensembles of nonlocally interacting chaotic maps

NASA Astrophysics Data System (ADS)

Bukh, Andrei; Rybalova, Elena; Semenova, Nadezhda; Strelkova, Galina; Anishchenko, Vadim

2017-11-01

We study numerically the dynamics of a network made of two coupled one-dimensional ensembles of discrete-time systems. The first ensemble is represented by a ring of nonlocally coupled Henon maps and the second one by a ring of nonlocally coupled Lozi maps. We find that the network of coupled ensembles can realize all the spatio-temporal structures which are observed both in the Henon map ensemble and in the Lozi map ensemble while uncoupled. Moreover, we reveal a new type of spatiotemporal structure, a solitary state chimera, in the considered network. We also establish and describe the effect of mutual synchronization of various complex spatiotemporal patterns in the system of two coupled ensembles of Henon and Lozi maps.

Low-Dimensional Network Formation in Molten Sodium Carbonate

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

Wilding, Martin C.; Wilson, Mark; Alderman, Oliver L. G.

2016-04-15

Molten carbonates are highly inviscid liquids characterized by low melting points and high solubility of rare earth elements and volatile molecules. An understanding of the structure and related properties of these intriguing liquids has been limited to date. We report the results of a study of molten sodium carbonate (Na2CO3) which combines high energy X-ray diffraction, containerless techniques and computer simulation to provide insight into the liquid structure. Total structure factors (F-x(Q)) are collected on the laser-heated carbonate spheres suspended in flowing gases of varying composition in an aerodynamic levitation furnace. The respective partial structure factor contributions to Fx(Q) aremore » obtained by performing molecular dynamics simulations treating the carbonate anions as flexible entities. The carbonate liquid structure is found to be heavily temperature-dependent. At low temperatures a low-dimensional carbonate chain network forms, at T = 1100 K for example similar to 55% of the C atoms form part of a chain. The mean chain lengths decrease as temperature is increased and as the chains become shorter the rotation of the carbonate anions becomes more rapid enhancing the diffusion of Na+ ions.« less

Network marketing on a small-world network

NASA Astrophysics Data System (ADS)

Kim, Beom Jun; Jun, Tackseung; Kim, Jeong-Yoo; Choi, M. Y.

2006-02-01

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts-Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.

Spatiotemporal dynamics in excitable homogeneous random networks composed of periodically self-sustained oscillation.

PubMed

Qian, Yu; Liu, Fei; Yang, Keli; Zhang, Ge; Yao, Chenggui; Ma, Jun

2017-09-19

The collective behaviors of networks are often dependent on the network connections and bifurcation parameters, also the local kinetics plays an important role in contributing the consensus of coupled oscillators. In this paper, we systematically investigate the influence of network structures and system parameters on the spatiotemporal dynamics in excitable homogeneous random networks (EHRNs) composed of periodically self-sustained oscillation (PSO). By using the dominant phase-advanced driving (DPAD) method, the one-dimensional (1D) Winfree loop is exposed as the oscillation source supporting the PSO, and the accurate wave propagation pathways from the oscillation source to the whole network are uncovered. Then, an order parameter is introduced to quantitatively study the influence of network structures and system parameters on the spatiotemporal dynamics of PSO in EHRNs. Distinct results induced by the network structures and the system parameters are observed. Importantly, the corresponding mechanisms are revealed. PSO influenced by the network structures are induced not only by the change of average path length (APL) of network, but also by the invasion of 1D Winfree loop from the outside linking nodes. Moreover, PSO influenced by the system parameters are determined by the excitation threshold and the minimum 1D Winfree loop. Finally, we confirmed that the excitation threshold and the minimum 1D Winfree loop determined PSO will degenerate as the system size is expanded.

Three Dimensional Modeling of the Attenuation Structure in the Part of the Kumaon Himalaya, India Using Strong Motion Data

NASA Astrophysics Data System (ADS)

Joshi, A.; LAL, S.

2017-12-01

Attenuation property of the medium determines the amplitude of seismic waves at different locations during an earthquake. Attenuation can be defined by the inverse of the parameter known as quality factor `Q' (Knopoff, 1964). It has been observed that the peak ground acceleration in the strong motion accelerogram is associated with arrival of S-waves which is controlled mainly by the shear wave attenuation characteristics of the medium. In the present work attenuation structure is obtained using the modified inversion algorithm given by Joshi et al. (2010). The modified inversion algorithm is designed to provide three dimensional attenuation structure of the region at different frequencies. A strong motion network is installed in the Kumaon Himalaya by the Department of Earth Sciences, Indian Institute of Technology Roorkee under a major research project sponsored by the Ministry of Earth Sciences, Government of India. In this work the detailed three dimensional shear wave quality factor has been determined for the Kumaon Himalaya using strong motion data obtained from this network. In the present work 164 records from 26 events recorded at 15 stations located in an area of 129 km x 62 km has been used. The shear wave attenuation structure for the Kumaon Himalaya has been calculated by dividing the study region into 108 three dimensional rectangular blocks of size 22 km x 11 km x 5 km. The input to the inversion algorithm is the acceleration spectra of S wave identified from each record. A total of 164 spectra from equal number of accelerograms with sampling frequency of .024 Hz is used as an input to the algorithms. A total of 2048 three dimensional attenuation structure is obtained upto frequency of 50 Hz. The obtained structure at various frequencies is compared with the existing geological models in the region and it is seen that the obtained model correlated well with the geological model of the region. References: Joshi, A., Mohanty, M., Bansal, A. R., Dimri, V. P. and Chadha, R. K., 2010, Use of spectral acceleration data for determination of three dimensional attenuation structure in the Pithoragarh region of Kumaon Himalaya, J Seismol., 14, 247-272. Knopoff, L., 1964, Q, Reviews of Geophysics, 2, 625-660.

Monolithically integrated two-dimensional arrays of optoelectronic threshold devices for neural network applications

NASA Technical Reports Server (NTRS)

Kim, J. H.; Katz, J.; Lin, S. H.; Psaltis, D.

1989-01-01

A monolithic 10 x 10 two-dimensional array of 'optical neuron' optoelectronic threshold elements for neural network applications has been designed, fabricated, and tested. Overall array dimensions are 5 x 5 mm, while the individual neurons, composed of an LED that is driven by a double-heterojunction bipolar transistor, are 250 x 250 microns. The overall integrated structure exhibited semiconductor-controlled rectifier characteristics, with a breakover voltage of 75 V and a reverse-breakdown voltage of 60 V; this is attributable to the parasitic p-n-p transistor which exists as a result of the sharing of the same n-AlGaAs collector between the transistors and the LED.

Utilization of high resolution computed tomography to visualize the three dimensional structure and function of plant vasculature

USDA-ARS?s Scientific Manuscript database

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique with sub-micron resolution capability that is now being used to evaluate the structure and function of plant xylem network in three dimensions (3D). HRCT imaging is based on the same principles as medi...

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

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

Two-dimensional plasmons in lateral carbon nanotube network structures and their effect on the terahertz radiation detection

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

Ryzhii, V.; Institute of Ultra High Frequency Semiconductor Electronics of RAS, Moscow 117105; Center for Photonics and Infrared Engineering, Bauman Moscow State Technical University, Moscow 111005

2016-07-28

We consider the carrier transport and plasmonic phenomena in the lateral carbon nanotube (CNT) networks forming the device channel with asymmetric electrodes. One electrode is the Ohmic contact to the CNT network and the other contact is the Schottky contact. These structures can serve as detectors of the terahertz (THz) radiation. We develop the device model for collective response of the lateral CNT networks which comprise a mixture of randomly oriented semiconductor CNTs (s-CNTs) and quasi-metal CNTs (m-CNTs). The proposed model includes the concept of the collective two-dimensional (2D) plasmons in relatively dense networks of randomly oriented CNTs (CNT “felt”)more » and predicts the detector responsivity spectral characteristics exhibiting sharp resonant peaks at the signal frequencies corresponding to the 2D plasmonic resonances. The detection mechanism is the rectification of the ac current due the nonlinearity of the Schottky contact current-voltage characteristics under the conditions of a strong enhancement of the potential drop at this contact associated with the plasmon excitation. The detector responsivity depends on the fractions of the s- and m-CNTs. The burning of the near-contact regions of the m-CNTs or destruction of these CNTs leads to a marked increase in the responsivity in agreement with our experimental data. The resonant THz detectors with sufficiently dense lateral CNT networks can compete and surpass other THz detectors using plasmonic effects at room temperatures.« less

Drying Affects the Fiber Network in Low Molecular Weight Hydrogels

PubMed Central

2017-01-01

Low molecular weight gels are formed by the self-assembly of a suitable small molecule gelator into a three-dimensional network of fibrous structures. The gel properties are determined by the fiber structures, the number and type of cross-links and the distribution of the fibers and cross-links in space. Probing these structures and cross-links is difficult. Many reports rely on microscopy of dried gels (xerogels), where the solvent is removed prior to imaging. The assumption is made that this has little effect on the structures, but it is not clear that this assumption is always (or ever) valid. Here, we use small angle neutron scattering (SANS) to probe low molecular weight hydrogels formed by the self-assembly of dipeptides. We compare scattering data for wet and dried gels, as well as following the drying process. We show that the assumption that drying does not affect the network is not always correct. PMID:28631478

Order-disorder transition in a two-dimensional boron-carbon-nitride alloy

NASA Astrophysics Data System (ADS)

Lu, Jiong; Zhang, Kai; Feng Liu, Xin; Zhang, Han; Chien Sum, Tze; Castro Neto, Antonio H.; Loh, Kian Ping

2013-10-01

Two-dimensional boron-carbon-nitride materials exhibit a spectrum of electronic properties ranging from insulating to semimetallic, depending on their composition and geometry. Detailed experimental insights into the phase separation and ordering in such alloy are currently lacking. Here we report the mixing and demixing of boron-nitrogen and carbon phases on ruthenium (0001) and found that energetics for such processes are modified by the metal substrate. The brick-and-mortar patchwork observed of stoichiometrically percolated hexagonal boron-carbon-nitride domains surrounded by a network of segregated graphene nanoribbons can be described within the Blume-Emery-Griffiths model applied to a honeycomb lattice. The isostructural boron nitride and graphene assumes remarkable fluidity and can be exchanged entirely into one another by a catalytically assistant substitution. Visualizing the dynamics of phase separation at the atomic level provides the premise for enabling structural control in a two-dimensional network for broad nanotechnology applications.

Quantum autoencoders for efficient compression of quantum data

NASA Astrophysics Data System (ADS)

Romero, Jonathan; Olson, Jonathan P.; Aspuru-Guzik, Alan

2017-12-01

Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x, to map x to a lower dimensional point y such that x can likely be recovered from y. The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Network Data: Statistical Theory and New Models

DTIC Science & Technology

2016-02-17

SECURITY CLASSIFICATION OF: During this period of review, Bin Yu worked on many thrusts of high-dimensional statistical theory and methodologies. Her...research covered a wide range of topics in statistics including analysis and methods for spectral clustering for sparse and structured networks...2,7,8,21], sparse modeling (e.g. Lasso) [4,10,11,17,18,19], statistical guarantees for the EM algorithm [3], statistical analysis of algorithm leveraging

A Complex Network Analysis of Granular Fabric Evolution in Three-Dimensions

DTIC Science & Technology

2011-01-01

organized pattern formation (e.g., strain localization), and co-evolution of emergent in- ternal structures (e.g., force cycles and force chains) [15...these networks, particularly recurring patterns or motifs, and understanding how these co-evolve are crucial to the robust characterization and...the lead up to and during failure. Since failure patterns and boundaries of flow in three-dimensional specimens can be quite complicated and difficult

Manufacturing of three-dimensionally microstructured nanocomposites through microfluidic infiltration.

PubMed

Dermanaki-Farahani, Rouhollah; Lebel, Louis Laberge; Therriault, Daniel

2014-03-12

Microstructured composite beams reinforced with complex three-dimensionally (3D) patterned nanocomposite microfilaments are fabricated via nanocomposite infiltration of 3D interconnected microfluidic networks. The manufacturing of the reinforced beams begins with the fabrication of microfluidic networks, which involves layer-by-layer deposition of fugitive ink filaments using a dispensing robot, filling the empty space between filaments using a low viscosity resin, curing the resin and finally removing the ink. Self-supported 3D structures with other geometries and many layers (e.g. a few hundreds layers) could be built using this method. The resulting tubular microfluidic networks are then infiltrated with thermosetting nanocomposite suspensions containing nanofillers (e.g. single-walled carbon nanotubes), and subsequently cured. The infiltration is done by applying a pressure gradient between two ends of the empty network (either by applying a vacuum or vacuum-assisted microinjection). Prior to the infiltration, the nanocomposite suspensions are prepared by dispersing nanofillers into polymer matrices using ultrasonication and three-roll mixing methods. The nanocomposites (i.e. materials infiltrated) are then solidified under UV exposure/heat cure, resulting in a 3D-reinforced composite structure. The technique presented here enables the design of functional nanocomposite macroscopic products for microengineering applications such as actuators and sensors.

Manufacturing of Three-dimensionally Microstructured Nanocomposites through Microfluidic Infiltration

PubMed Central

Dermanaki-Farahani, Rouhollah; Lebel, Louis Laberge; Therriault, Daniel

2014-01-01

Microstructured composite beams reinforced with complex three-dimensionally (3D) patterned nanocomposite microfilaments are fabricated via nanocomposite infiltration of 3D interconnected microfluidic networks. The manufacturing of the reinforced beams begins with the fabrication of microfluidic networks, which involves layer-by-layer deposition of fugitive ink filaments using a dispensing robot, filling the empty space between filaments using a low viscosity resin, curing the resin and finally removing the ink. Self-supported 3D structures with other geometries and many layers (e.g. a few hundreds layers) could be built using this method. The resulting tubular microfluidic networks are then infiltrated with thermosetting nanocomposite suspensions containing nanofillers (e.g. single-walled carbon nanotubes), and subsequently cured. The infiltration is done by applying a pressure gradient between two ends of the empty network (either by applying a vacuum or vacuum-assisted microinjection). Prior to the infiltration, the nanocomposite suspensions are prepared by dispersing nanofillers into polymer matrices using ultrasonication and three-roll mixing methods. The nanocomposites (i.e. materials infiltrated) are then solidified under UV exposure/heat cure, resulting in a 3D-reinforced composite structure. The technique presented here enables the design of functional nanocomposite macroscopic products for microengineering applications such as actuators and sensors. PMID:24686754

Three-dimensional inversion for Network-Magnetotelluric data

NASA Astrophysics Data System (ADS)

Siripunvaraporn, W.; Uyeshima, M.; Egbert, G.

2004-09-01

Three-dimensional inversion of Network-Magnetotelluric (MT) data has been implemented. The program is based on a conventional 3-D MT inversion code (Siripunvaraporn et al., 2004), which is a data space variant of the OCCAM approach. In addition to modifications required for computing Network-MT responses and sensitivities, the program makes use of Massage Passing Interface (MPI) software, with allowing computations for each period to be run on separate CPU nodes. Here, we consider inversion of synthetic data generated from simple models consisting of a 1 W-m conductive block buried at varying depths in a 100 W-m background. We focus in particular on inversion of long period (320-40,960 seconds) data, because Network-MT data usually have high coherency in these period ranges. Even with only long period data the inversion recovers shallow and deep structures, as long as these are large enough to affect the data significantly. However, resolution of the inversion depends greatly on the geometry of the dipole network, the range of periods used, and the horizontal size of the conductive anomaly.

Unfolding stabilities of two structurally similar proteins as probed by temperature-induced and force-induced molecular dynamics simulations.

PubMed

Gorai, Biswajit; Prabhavadhni, Arasu; Sivaraman, Thirunavukkarasu

2015-09-01

Unfolding stabilities of two homologous proteins, cardiotoxin III and short-neurotoxin (SNTX) belonging to three-finger toxin (TFT) superfamily, have been probed by means of molecular dynamics (MD) simulations. Combined analysis of data obtained from steered MD and all-atom MD simulations at various temperatures in near physiological conditions on the proteins suggested that overall structural stabilities of the two proteins were different from each other and the MD results are consistent with experimental data of the proteins reported in the literature. Rationalization for the differential structural stabilities of the structurally similar proteins has been chiefly attributed to the differences in the structural contacts between C- and N-termini regions in their three-dimensional structures, and the findings endorse the 'CN network' hypothesis proposed to qualitatively analyse the thermodynamic stabilities of proteins belonging to TFT superfamily of snake venoms. Moreover, the 'CN network' hypothesis has been revisited and the present study suggested that 'CN network' should be accounted in terms of 'structural contacts' and 'structural strengths' in order to precisely describe order of structural stabilities of TFTs.

High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

PubMed

Andras, Peter

2018-02-01

Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

Charge transport in the electrospun nanofiber composite membrane's three-dimensional fibrous structure

NASA Astrophysics Data System (ADS)

DeGostin, Matthew B.; Peracchio, Aldo A.; Myles, Timothy D.; Cassenti, Brice N.; Chiu, Wilson K. S.

2016-03-01

In this paper, a Fiber Network (FN) ion transport model is developed to simulate the three-dimensional fibrous microstructural morphology that results from the electrospinning membrane fabrication process. This model is able to approximate fiber layering within a membrane as well as membrane swelling due to water uptake. The discrete random fiber networks representing membranes are converted to resistor networks and solved for current flow and ionic conductivity. Model predictions are validated by comparison with experimental conductivity data from electrospun anion exchange membranes (AEM) and proton exchange membranes (PEM) for fuel cells as well as existing theories. The model is capable of predicting in-plane and thru-plane conductivity and takes into account detailed membrane characteristics, such as volume fraction, fiber diameter, fiber conductivity, and membrane layering, and as such may be used as a tool for advanced electrode design.

Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

2018-01-01

Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

Online dimensionality reduction using competitive learning and Radial Basis Function network.

PubMed

Tomenko, Vladimir

2011-06-01

The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Three-dimensional nano-heterojunction networks: a highly performing structure for fast visible-blind UV photodetectors.

PubMed

Nasiri, Noushin; Bo, Renheng; Fu, Lan; Tricoli, Antonio

2017-02-02

Visible-blind ultraviolet photodetectors are a promising emerging technology for the development of wide bandgap optoelectronic devices with greatly reduced power consumption and size requirements. A standing challenge is to improve the slow response time of these nanostructured devices. Here, we present a three-dimensional nanoscale heterojunction architecture for fast-responsive visible-blind UV photodetectors. The device layout consists of p-type NiO clusters densely packed on the surface of an ultraporous network of electron-depleted n-type ZnO nanoparticles. This 3D structure can detect very low UV light densities while operating with a near-zero power consumption of ca. 4 × 10 -11 watts and a low bias of 0.2 mV. Most notably, heterojunction formation decreases the device rise and decay times by 26 and 20 times, respectively. These drastic enhancements in photoresponse dynamics are attributed to the stronger surface band bending and improved electron-hole separation of the nanoscale NiO/ZnO interface. These findings demonstrate a superior structural design and a simple, low-cost CMOS-compatible process for the engineering of high-performance wearable photodetectors.

Measures of large-scale structure in the CfA redshift survey slices

NASA Technical Reports Server (NTRS)

De Lapparent, Valerie; Geller, Margaret J.; Huchra, John P.

1991-01-01

Variations of the counts-in-cells with cell size are used here to define two statistical measures of large-scale clustering in three 6 deg slices of the CfA redshift survey. A percolation criterion is used to estimate the filling factor which measures the fraction of the total volume in the survey occupied by the large-scale structures. For the full 18 deg slice of the CfA redshift survey, f is about 0.25 + or - 0.05. After removing groups with more than five members from two of the slices, variations of the counts in occupied cells with cell size have a power-law behavior with a slope beta about 2.2 on scales from 1-10/h Mpc. Application of both this statistic and the percolation analysis to simulations suggests that a network of two-dimensional structures is a better description of the geometry of the clustering in the CfA slices than a network of one-dimensional structures. Counts-in-cells are also used to estimate at 0.3 galaxy h-squared/Mpc the average galaxy surface density in sheets like the Great Wall.

Quantum walks of correlated photon pairs in two-dimensional waveguide arrays.

PubMed

Poulios, Konstantinos; Keil, Robert; Fry, Daniel; Meinecke, Jasmin D A; Matthews, Jonathan C F; Politi, Alberto; Lobino, Mirko; Gräfe, Markus; Heinrich, Matthias; Nolte, Stefan; Szameit, Alexander; O'Brien, Jeremy L

2014-04-11

We demonstrate quantum walks of correlated photons in a two-dimensional network of directly laser written waveguides coupled in a "swiss cross" arrangement. The correlated detection events show high-visibility quantum interference and unique composite behavior: strong correlation and independence of the quantum walkers, between and within the planes of the cross. Violations of a classically defined inequality, for photons injected in the same plane and in orthogonal planes, reveal nonclassical behavior in a nonplanar structure.

High thermal stability and antiferromagnetic properties of a 3D Mn(II)-organic framework with metal carboxylate chains

NASA Astrophysics Data System (ADS)

Han, Lei; Zhou, Yan; Wang, Xiu-Teng; Li, Xing; Tong, Ming-Liang

2009-04-01

A novel three-dimensional metal-organic framework, [Mn 2(hfipbb) 2(bpy)] n ( 1) (H 2hfipbb = 4,4'-(hexafluoroisopropylidene)bis(benzoic acid), bpy = 4,4'-bipyridine), has been hydrothermally synthesized and structurally characterized. The complex consists of metal carboxylate chains, which are cross-linked to six adjacent chains through organic moieties forming extended three-dimensional networks. Complex 1 exhibits high thermal stability (450 °C) and antiferromagnetic properties.

3D Filament Network Segmentation with Multiple Active Contours

NASA Astrophysics Data System (ADS)

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2014-03-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

8-Hy­droxy­quinolin-1-ium hydrogen sulfate monohydrate

PubMed Central

Damous, Maamar; Dénès, George; Bouacida, Sofiane; Hamlaoui, Meriem; Merazig, Hocine; Daran, Jean-Claude

2013-01-01

In the crystal structure of the title salt hydrate, C9H8NO+·HSO4 −·H2O, the quinoline N—H atoms are hydrogen bonded to the bis­ulfate anions. The bis­ulfate anions and water mol­ecules are linked together by O—H⋯O hydrogen-bonding inter­actions. The cations and anions form separate layers alternating along the c axis, which are linked by N—H⋯O and O—H⋯O hydrogen bonds into a two-dimensional network parallel to (100). Further O—H⋯O contacts connect these layers, forming a three-dimensional network, in which two R 4 4(12) rings and C 2 2(13) infinite chains can be identified. PMID:24427083

Beyond context to the skyline: thinking in 3D.

PubMed

Hoagwood, Kimberly; Olin, Serene; Cleek, Andrew

2013-01-01

Sweeping and profound structural, regulatory, and fiscal changes are rapidly reshaping the contours of health and mental health practice. The community-based practice contexts described in the excellent review by Garland and colleagues are being fundamentally altered with different business models, regional networks, accountability standards, and incentive structures. If community-based mental health services are to remain viable, the two-dimensional and flat research and practice paradigm has to be replaced with three-dimensional thinking. Failure to take seriously the changes that are happening to the larger healthcare context and respond actively through significant system redesign will lead to the demise of specialty mental health services.

Effects of the bipartite structure of a network on performance of recommenders

NASA Astrophysics Data System (ADS)

Wang, Qing-Xian; Li, Jian; Luo, Xin; Xu, Jian-Jun; Shang, Ming-Sheng

2018-02-01

Recommender systems aim to predict people's preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender's performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender's performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics.

Organization of the cytokeratin network in an epithelial cell.

PubMed

Portet, Stéphanie; Arino, Ovide; Vassy, Jany; Schoëvaërt, Damien

2003-08-07

The cytoskeleton is a dynamic three-dimensional structure mainly located in the cytoplasm. It is involved in many cell functions such as mechanical signal transduction and maintenance of cell integrity. Among the three cytoskeletal components, intermediate filaments (the cytokeratin in epithelial cells) are the best candidates for this mechanical role. A model of the establishment of the cytokeratin network of an epithelial cell is proposed to study the dependence of its structural organization on extracellular mechanical environment. To implicitly describe the latter and its effects on the intracellular domain, we use mechanically regulated protein synthesis. Our model is a hybrid of a partial differential equation of parabolic type, governing the evolution of the concentration of cytokeratin, and a set of stochastic differential equations describing the dynamics of filaments. Each filament is described by a stochastic differential equation that reflects both the local interactions with the environment and the non-local interactions via the past history of the filament. A three-dimensional simulation model is derived from this mathematical model. This simulation model is then used to obtain examples of cytokeratin network architectures under given mechanical conditions, and to study the influence of several parameters.

Network dimensionality and ligand flexibility in lanthanide terephthalate hydrates

NASA Astrophysics Data System (ADS)

Zehnder, Ralph A.; Renn, Robert A.; Pippin, Ethan; Zeller, Matthias; Wheeler, Kraig A.; Carr, Jason A.; Fontaine, Nick; McMullen, Nathan C.

2011-01-01

Various lanthanide open framework materials incorporating the terephthalate (TP) entity were prepared using hydrothermal synthesis methods at a moderate temperature of 170 °C. The compounds Nd 2(TP) 3(H 2O) 4( 1), Er 2(TP) 3(H 2O) 4( 2), Yb 2(TP) 3(H 2O) 2( 3), Yb 2(TP) 3(H 2O) 6( 4), and Yb 2(TP) 3(H 2O) 8·2H 2O ( 5), were characterized by single crystal structural analysis and FT-IR spectroscopy. While compounds 1 and 2 have been reported before on the basis of powder X-ray diffraction, the structural characterization of any ytterbium terephthalate species is unprecedented. Compounds 1- 5 crystallize in triclinic settings with space group P-1. The compounds are compared with their previously reported Er and Tb-counterparts and the reduction of the dimensionality of the resulting networks from 3D over 2D to 1D with increasing level of hydration is discussed. Compounds 1, 2, and 3 with the lowest water content assemble in three-dimensional network lattices. Compounds 4 and 5, however, form 2D layered systems and 1D rod like chains, respectively, which are held together by hydrogen bonds originating from coordinating H 2O. The crystal lattices of the 3D networks experience higher levels of tension as can be seen by increasing out-of-plane torsion with regard to the terephthalate carboxylate groups. Moreover, there seems to be a correlation between the level of strain on the aromatic ligands and the reduction of the number of carboxylate oxygen atoms that are part of the coordination polyhedra.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Model of lightning strike to a steel reinforce structure using PSpice

NASA Astrophysics Data System (ADS)

Koone, Neil; Condren, Brian

2003-03-01

Surges and arcs from lightning can pose hazards to personnel and sensitive equipment and processes. Steel reinforcement in structures can act as a Faraday cage mitigating lightning effects. Knowing a structure's response to a lightning strike allows hazards associated with lightning to be analyzed. A model of lightning's response in a steel reinforced structure has been developed using PSpice (a commercial circuit simulation). Segments of rebar are modeled as inductors and resistors in series. A program has been written to take architectural information of a steel reinforced structure and "build" a circuit network that is analogous to the network of reinforcement in a facility. A severe current waveform (simulating a 99th percentile lightning strike), modeled as a current source, is introduced in the circuit network, and potential differences within the structure are determined using PSpice. A visual three-dimensional model of the facility displays the voltage distribution across the structure using color to indicate the potential difference relative to the floor. Clear air arcing distances can be calculated from the voltage distribution using a conservative value for the dielectric breakdown strength of air.

Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks.

PubMed

Honegger, Thibault; Thielen, Moritz I; Feizi, Soheil; Sanjana, Neville E; Voldman, Joel

2016-06-22

The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.

Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks

NASA Astrophysics Data System (ADS)

Honegger, Thibault; Thielen, Moritz I.; Feizi, Soheil; Sanjana, Neville E.; Voldman, Joel

2016-06-01

The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.

Super-resolution optical microscopy resolves network morphology of smart colloidal microgels.

PubMed

Bergmann, Stephan; Wrede, Oliver; Huser, Thomas; Hellweg, Thomas

2018-02-14

We present a new method to resolve the network morphology of colloidal particles in an aqueous environment via super-resolution microscopy. By localization of freely diffusing fluorophores inside the particle network we can resolve the three dimensional structure of one species of colloidal particles (thermoresponsive microgels) without altering their chemical composition through copolymerization with fluorescent monomers. Our approach utilizes the interaction of the fluorescent dye rhodamine 6G with the polymer network to achieve an indirect labeling. We calculate the 3D structure from the 2D images and compare the structure to previously published models for the microgel morphology, e.g. the fuzzy sphere model. To describe the differences in the data an extension of this model is suggested. Our method enables the tailor-made fabrication of colloidal particles which are used in various applications, such as paints or cosmetics, and are promising candidates for drug delivery, smart surface coatings, and nanocatalysis. With the precise knowledge of the particle morphology an understanding of the underlying structure-property relationships for various colloidal systems is possible.

Layered structures of organic/inorganic hybrid halide perovskites

NASA Astrophysics Data System (ADS)

Huan, Tran Doan; Tuoc, Vu Ngoc; Minh, Nguyen Viet

2016-03-01

Organic-inorganic hybrid halide perovskites, in which the A cations of an ABX3 perovskite are replaced by organic cations, may be used for photovoltaic and solar thermoelectric applications. In this contribution, we systematically study three lead-free hybrid perovskites, i.e., methylammonium tin iodide CH3NH3SnI3 , ammonium tin iodide NH4SnI3 , and formamidnium tin iodide HC (NH2)2SnI3 by first-principles calculations. We find that in addition to the commonly known motif in which the corner-shared SnI6 octahedra form a three-dimensional network, these materials may also favor a two-dimensional (layered) motif formed by alternating layers of the SnI6 octahedra and the organic cations. These two motifs are nearly equal in free energy and are separated by low barriers. These layered structures features many flat electronic bands near the band edges, making their electronic structures significantly different from those of the structural phases composed of three-dimension networks of SnI6 octahedra. Furthermore, because the electronic structures of HC (NH2)2SnI3 are found to be rather similar to those of CH3NH3SnI3 , formamidnium tin iodide may also be promising for the applications of methylammonium tin iodide.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

Target recognition and scene interpretation in image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-08-01

Vision is only a part of a system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. These mechanisms provide a reliable recognition if the object is occluded or cannot be recognized as a whole. It is hard to split the entire system apart, and reliable solutions to the target recognition problems are possible only within the solution of a more generic Image Understanding Problem. Brain reduces informational and computational complexities, using implicit symbolic coding of features, hierarchical compression, and selective processing of visual information. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, is the most feasible for such models. It converts visual information into relational Network-Symbolic structures, avoiding artificial precise computations of 3-dimensional models. Network-Symbolic Transformations derive abstract structures, which allows for invariant recognition of an object as exemplar of a class. Active vision helps creating consistent models. Attention, separation of figure from ground and perceptual grouping are special kinds of network-symbolic transformations. Such Image/Video Understanding Systems will be reliably recognizing targets.

Recrystallization inhibition in ice due to ice binding protein activity detected by nuclear magnetic resonance.

PubMed

Brown, Jennifer R; Seymour, Joseph D; Brox, Timothy I; Skidmore, Mark L; Wang, Chen; Christner, Brent C; Luo, Bing-Hao; Codd, Sarah L

2014-09-01

Liquid water present in polycrystalline ice at the interstices between ice crystals results in a network of liquid-filled veins and nodes within a solid ice matrix, making ice a low porosity porous media. Here we used nuclear magnetic resonance (NMR) relaxation and time dependent self-diffusion measurements developed for porous media applications to monitor three dimensional changes to the vein network in ices with and without a bacterial ice binding protein (IBP). Shorter effective diffusion distances were detected as a function of increased irreversible ice binding activity, indicating inhibition of ice recrystallization and persistent small crystal structure. The modification of ice structure by the IBP demonstrates a potential mechanism for the microorganism to enhance survivability in ice. These results highlight the potential of NMR techniques in evaluation of the impact of IBPs on vein network structure and recrystallization processes; information useful for continued development of ice-interacting proteins for biotechnology applications.

Generation, Analysis and Characterization of Anisotropic Engineered Meta Materials

NASA Astrophysics Data System (ADS)

Trifale, Ninad T.

A methodology for a systematic generation of highly anisotropic micro-lattice structures was investigated. Multiple algorithms for generation and validation of engineered structures are developed and evaluated. Set of all possible permutations of structures for an 8-node cubic unit cell were considered and the degree of anisotropy of meta-properties in heat transport and mechanical elasticity were evaluated. Feasibility checks were performed to ensure that the generated unit cell network was repeatable and a continuous lattice structure. Four different strategies for generating permutations of the structures are discussed. Analytical models were developed to predict effective thermal, mechanical and permeability characteristics of these cellular structures.Experimentation and numerical modeling techniques were used to validate the models that are developed. A self-consistent mechanical elasticity model was developed which connects the meso-scale properties to stiffness of individual struts. A three dimensional thermal resistance network analogy was used to evaluate the effective thermal conductivity of the structures. The struts were modeled as a network of one dimensional thermal resistive elements and effective conductivity evaluated. Models were validated against numerical simulations and experimental measurements on 3D printed samples. Model was developed to predict effective permeability of these engineered structures based on Darcy's law. Drag coefficients were evaluated for individual connections in transverse and longitudinal directions and an interaction term was calibrated from the experimental data in literature in order to predict permeability. Generic optimization framework coupled to finite element solver is developed for analyzing any application involving use of porous structures. An objective functions were generated structure to address frequently observed trade-off between the stiffness, thermal conductivity, permeability and porosity. Three application were analyzed for potential use of engineered materials. Heat spreader application involving thermal and mechanical constraints, artificial bone grafts application involving mechanical and permeability constraints and structural materials applications involving mechanical, thermal and porosity constraints is analyzed. Recommendations for optimum topologies for specific operating conditions are provided.

Meso-decorated self-healing gels: network structure and properties

NASA Astrophysics Data System (ADS)

Gong, Jin; Sawamura, Kensuke; Igarashi, Susumu; Furukawa, Hidemitsu

2013-04-01

Gels are a new material having three-dimensional network structures of macromolecules. They possess excellent properties as swellability, high permeability and biocompatibility, and have been applied in various fields of daily life, food, medicine, architecture, and chemistry. In this study, we tried to prepare new multi-functional and high-strength gels by using Meso-Decoration (Meso-Deco), one new method of structure design at intermediate mesoscale. High-performance rigid-rod aromatic polymorphic crystals, and the functional groups of thermoreversible Diels-Alder reaction were introduced into soft gels as crosslinkable pendent chains. The functionalization and strengthening of gels can be realized by meso-decorating the gels' structure using high-performance polymorphic crystals and thermoreversible pendent chains. New gels with good mechanical properties, novel optical properties and thermal properties are expected to be developed.

How mutation alters the evolutionary dynamics of cooperation on networks

NASA Astrophysics Data System (ADS)

Ichinose, Genki; Satotani, Yoshiki; Sayama, Hiroki

2018-05-01

Cooperation is ubiquitous at every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. A recently proposed numerical metric, average gradient of selection (AGoS), a useful tool for interpreting and visualizing evolutionary dynamics on networks, allows simulation results to be visualized on a one-dimensional phase space. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we extend AGoS so that it can analyze the evolution of cooperation where mutation may alter strategies of individuals on networks. We show that our extended AGoS correctly visualizes the final states of cooperation with mutation in the individual-based simulations. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the payoff functions, fraction of cooperators, and network structures. Moreover, we found that scale-free networks are the most vulnerable to mutation and thus the dynamics of cooperation are altered from bistability to coexistence on those networks, undergoing an imperfect pitchfork bifurcation.

Weak signal transmission in complex networks and its application in detecting connectivity.

PubMed

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

Synchronization and spatiotemporal patterns in coupled phase oscillators on a weighted planar network

NASA Astrophysics Data System (ADS)

Kagawa, Yuki; Takamatsu, Atsuko

2009-04-01

To reveal the relation between network structures found in two-dimensional biological systems, such as protoplasmic tube networks in the plasmodium of true slime mold, and spatiotemporal oscillation patterns emerged on the networks, we constructed coupled phase oscillators on weighted planar networks and investigated their dynamics. Results showed that the distribution of edge weights in the networks strongly affects (i) the propensity for global synchronization and (ii) emerging ratios of oscillation patterns, such as traveling and concentric waves, even if the total weight is fixed. In-phase locking, traveling wave, and concentric wave patterns were, respectively, observed most frequently in uniformly weighted, center weighted treelike, and periphery weighted ring-shaped networks. Controlling the global spatiotemporal patterns with the weight distribution given by the local weighting (coupling) rules might be useful in biological network systems including the plasmodial networks and neural networks in the brain.

Three-dimensional P-wave velocity structure of Mt. Etna, Italy

USGS Publications Warehouse

Villasenor, A.; Benz, H.M.; Filippi, L.; De Luca, G.; Scarpa, R.; Patane, G.; Vinciguerra, S.

1998-01-01

The three-dimensional P-wave velocity structure of Mt. Etna is determined to depths of 15 km by tomographic inversion of first arrival times from local earthquakes recorded by a network of 29 permanent and temporary seismographs. Results show a near-vertical low-velocity zone that extends from beneath the central craters to a depth of 10 km. This low-velocity region is coincident with a band of steeply-dipping seismicity, suggesting a magmatic conduit that feeds the summit eruptions. The most prominent structure is an approximately 8-km-diameter high-velocity body located between 2 and 12 km depth below the southeast flank of the volcano. This high-velocity body is interpreted as a remnant mafic intrusion that is an important structural feature influencing both volcanism and east flank slope stability and faulting.

The nature of intermediate-range order in Ge-As-S glasses : results from reverse Monte Carlo modeling.

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

Soyer-Uzun, S.; Benmore, C. J.; Siewenie, J. E.

2010-01-01

The experimental neutron and x-ray diffraction data for stoichiometric and S-deficient Ge{sub x}As{sub x}S{sub 100-2x} glasses with x = 18.2, 25.0, and 33.3 at.% have been modeled simultaneously using the reverse Monte Carlo (RMC) technique. Nearest-neighbor coordination environments, as obtained in previous x-ray absorption spectroscopy and diffraction experiments, have been employed as short-range order constraints in these simulations. The large scale three-dimensional structural models thus obtained from RMC simulation are used to investigate the nature and compositional evolution of intermediate-range structural order in these ternary glasses. The intermediate-range structural order is controlled by (1) a corner-shared three-dimensional network of AsS{submore » 3} pyramids and GeS{sub 4} tetrahedra in the stoichiometric Ge{sub 18.2}As{sub 18.2}S{sub 63.6} glass, (2) a heterogeneous structure that consists of homopolar bonded As-rich regions coexisting with a GeS{sub 2} network in the S-deficient Ge{sub 25}As{sub 25}S{sub 50} glass, and (3) a homogeneous structure resulting from the disruption of the topological continuity of the GeS{sub 2} network and As-rich clusters regions due to the formation of Ge-As bonds in the most S-deficient Ge{sub 33.3}As{sub 33.3}S{sub 33.3} glass. This scenario of the compositional evolution of intermediate-range structural order is consistent with and provides an atomistic explanation of the corresponding evolution in the position, width and intensity of the first sharp diffraction peak and the magnitude of small angle scattering in these glasses.« less

The Use of Decentralized Control in the Design of a Large Segmented Space Reflector

NASA Technical Reports Server (NTRS)

Ryaciotaki-Boussalis, Helen; Mirmirani, Maj; Rad, Khosrow; Morales, Mauricio; Velazquez, Efrain; Chassiakos, Anastasios; Luzardo, Jose-Alberto

1997-01-01

The 3-dimensional model for a segmented reflector telescope is developed using finite element techniques. The structure is decomposed into six subsystems. System control design using neural networks is performed. Performance evaluation is demonstrated via simulation using PRO-MATLAB and SIMULINK.

Covalently bonded networks through surface-confined polymerization

NASA Astrophysics Data System (ADS)

El Garah, Mohamed; MacLeod, Jennifer M.; Rosei, Federico

2013-07-01

The prospect of synthesizing ordered, covalently bonded structures directly on a surface has recently attracted considerable attention due to its fundamental interest and for potential applications in electronics and photonics. This prospective article focuses on efforts to synthesize and characterize epitaxial one- and two-dimensional (1D and 2D, respectively) polymeric networks on single crystal surfaces. Recent studies, mostly performed using scanning tunneling microscopy (STM), demonstrate the ability to induce polymerization based on Ullmann coupling, thermal dehalogenation and dehydration reactions. The 2D polymer networks synthesized to date have exhibited structural limitations and have been shown to form only small domains on the surface. We discuss different approaches to control 1D and 2D polymerization, with particular emphasis on the surface phenomena that are critical to the formation of larger ordered domains.

Three-dimensional supramolecular architecture in imidazolium hydrogen 2,3,5,6-tetrafluoroterephthalate.

PubMed

Yu, Li-Li; Cheng, Mei-Ling; Liu, Qi; Zhang, Zhi-Hui; Chen, Qun

2010-04-01

The asymmetric unit of the title salt formed between 2,3,5,6-tetrafluoroterephthalic acid (H(2)tfbdc) and imidazolium (ImH), C(3)H(5)N(2)(+).C(8)HF(4)O(4)(-), contains one Htfbdc(-) anion and one ImH(2)(+) cation, joined by a classical N-H...O hydrogen bond. The acid and base subunits are further linked by N-H...O and O-H...O hydrogen bonds into infinite two-dimensional layers with R(6)(5)(32) hydrogen-bond motifs. The resulting (4,4) network layers interpenetrate to produce an interlocked three-dimensional structure. The final three-dimensional supramolecular architecture is further stabilized by the linkages of two C-H...O interactions.

Castable three-dimensional stationary phase for electric field-driven applications

DOEpatents

Shepodd, Timothy J.; Whinnery, Jr., Leroy; Even, Jr., William R.

2005-01-25

A polymer material useful as the porous dielectric medium for microfluidic devices generally and electrokinetic pumps in particular. The polymer material is produced from an inverse (water-in-oil) emulsion that creates a 3-dimensional network characterized by small pores and high internal volume, characteristics that are particularly desirable for the dielectric medium for electrokinetic pumps. Further, the material can be cast-to-shape inside a microchannel. The use of bifunctional monomers provides for charge density within the polymer structure sufficient to support electroosmotic flow. The 3-dimensional polymeric material can also be covalently bound to the channel walls thereby making it suitable for high-pressure applications.

Castable three-dimensional stationary phase for electric field-driven applications

DOEpatents

Shepodd, Timothy J [Livermore, CA; Whinnery, Jr., Leroy; Even, Jr., William R.

2009-02-10

A polymer material useful as the porous dielectric medium for microfluidic devices generally and electrokinetic pumps in particular. The polymer material is produced from an inverse (water-in-oil) emulsion that creates a 3-dimensional network characterized by small pores and high internal volume, characteristics that are particularly desirable for the dielectric medium for electrokinetic pumps. Further, the material can be cast-to-shape inside a microchannel. The use of bifunctional monomers provides for charge density within the polymer structure sufficient to support electroosmotic flow. The 3-dimensional polymeric material can also be covalently bound to the channel walls thereby making it suitable for high-pressure applications.

Efficient implementation of a 3-dimensional ADI method on the iPSC/860

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

Van der Wijngaart, R.F.

1993-12-31

A comparison is made between several domain decomposition strategies for the solution of three-dimensional partial differential equations on a MIMD distributed memory parallel computer. The grids used are structured, and the numerical algorithm is ADI. Important implementation issues regarding load balancing, storage requirements, network latency, and overlap of computations and communications are discussed. Results of the solution of the three-dimensional heat equation on the Intel iPSC/860 are presented for the three most viable methods. It is found that the Bruno-Cappello decomposition delivers optimal computational speed through an almost complete elimination of processor idle time, while providing good memory efficiency.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Wavelet analysis of polarization maps of polycrystalline biological fluids networks

NASA Astrophysics Data System (ADS)

Ushenko, Y. A.

2011-12-01

The optical model of human joints synovial fluid is proposed. The statistic (statistic moments), correlation (autocorrelation function) and self-similar (Log-Log dependencies of power spectrum) structure of polarization two-dimensional distributions (polarization maps) of synovial fluid has been analyzed. It has been shown that differentiation of polarization maps of joint synovial fluid with different physiological state samples is expected of scale-discriminative analysis. To mark out of small-scale domain structure of synovial fluid polarization maps, the wavelet analysis has been used. The set of parameters, which characterize statistic, correlation and self-similar structure of wavelet coefficients' distributions of different scales of polarization domains for diagnostics and differentiation of polycrystalline network transformation connected with the pathological processes, has been determined.

Physical Model of the Genotype-to-Phenotype Map of Proteins

NASA Astrophysics Data System (ADS)

Tlusty, Tsvi; Libchaber, Albert; Eckmann, Jean-Pierre

2017-04-01

How DNA is mapped to functional proteins is a basic question of living matter. We introduce and study a physical model of protein evolution which suggests a mechanical basis for this map. Many proteins rely on large-scale motion to function. We therefore treat protein as learning amorphous matter that evolves towards such a mechanical function: Genes are binary sequences that encode the connectivity of the amino acid network that makes a protein. The gene is evolved until the network forms a shear band across the protein, which allows for long-range, soft modes required for protein function. The evolution reduces the high-dimensional sequence space to a low-dimensional space of mechanical modes, in accord with the observed dimensional reduction between genotype and phenotype of proteins. Spectral analysis of the space of 1 06 solutions shows a strong correspondence between localization around the shear band of both mechanical modes and the sequence structure. Specifically, our model shows how mutations are correlated among amino acids whose interactions determine the functional mode.

Robustness and percolation of holes in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Andu; Maletić, Slobodan; Zhao, Yi

2018-07-01

Efficient robustness and fault tolerance of complex network is significantly influenced by its connectivity, commonly modeled by the structure of pairwise relations between network elements, i.e., nodes. Nevertheless, aggregations of nodes build higher-order structures embedded in complex network, which may be more vulnerable when the fraction of nodes is removed. The structure of higher-order aggregations of nodes can be naturally modeled by simplicial complexes, whereas the removal of nodes affects the values of topological invariants, like the number of higher-dimensional holes quantified with Betti numbers. Following the methodology of percolation theory, as the fraction of nodes is removed, new holes appear, which have the role of merger between already present holes. In the present article, relationship between the robustness and homological properties of complex network is studied, through relating the graph-theoretical signatures of robustness and the quantities derived from topological invariants. The simulation results of random failures and intentional attacks on networks suggest that the changes of graph-theoretical signatures of robustness are followed by differences in the distribution of number of holes per cluster under different attack strategies. In the broader sense, the results indicate the importance of topological invariants research for obtaining further insights in understanding dynamics taking place over complex networks.

Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks

PubMed Central

Zachariou, Nicky; Expert, Paul; Takayasu, Misako; Christensen, Kim

2015-01-01

The main finding of this paper is a novel avalanche-size exponent τ ≈ 1.87 when the generalised sandpile dynamics evolves on the real-world Japanese inter-firm network. The topology of this network is non-layered and directed, displaying the typical bow tie structure found in real-world directed networks, with cycles and triangles. We show that one can move from a strictly layered regular lattice to a more fluid structure of the inter-firm network in a few simple steps. Relaxing the regular lattice structure by introducing an interlayer distribution for the interactions, forces the scaling exponent of the avalanche-size probability density function τ out of the two-dimensional directed sandpile universality class τ = 4/3, into the mean field universality class τ = 3/2. Numerical investigation shows that these two classes are the only that exist on the directed sandpile, regardless of the underlying topology, as long as it is strictly layered. Randomly adding a small proportion of links connecting non adjacent layers in an otherwise layered network takes the system out of the mean field regime to produce non-trivial avalanche-size probability density function. Although these do not display proper scaling, they closely reproduce the behaviour observed on the Japanese inter-firm network. PMID:26606143

Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks.

PubMed

Zachariou, Nicky; Expert, Paul; Takayasu, Misako; Christensen, Kim

2015-01-01

The main finding of this paper is a novel avalanche-size exponent τ ≈ 1.87 when the generalised sandpile dynamics evolves on the real-world Japanese inter-firm network. The topology of this network is non-layered and directed, displaying the typical bow tie structure found in real-world directed networks, with cycles and triangles. We show that one can move from a strictly layered regular lattice to a more fluid structure of the inter-firm network in a few simple steps. Relaxing the regular lattice structure by introducing an interlayer distribution for the interactions, forces the scaling exponent of the avalanche-size probability density function τ out of the two-dimensional directed sandpile universality class τ = 4/3, into the mean field universality class τ = 3/2. Numerical investigation shows that these two classes are the only that exist on the directed sandpile, regardless of the underlying topology, as long as it is strictly layered. Randomly adding a small proportion of links connecting non adjacent layers in an otherwise layered network takes the system out of the mean field regime to produce non-trivial avalanche-size probability density function. Although these do not display proper scaling, they closely reproduce the behaviour observed on the Japanese inter-firm network.

Evolution of network architecture in a granular material under compression

NASA Astrophysics Data System (ADS)

Bassett, Danielle

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. However, capturing and characterizing the dynamic nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. Here, we utilize multilayer networks as a framework for directly quantifying the evolution of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and inter-particle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the reconfiguration and evolution of this structure throughout the compression process. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be done by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than consideration of the local inter-particle forces alone. The results discussed throughout this study suggest that these novel network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup. National Science Foundation (BCS-1441502, PHY-1554488, and BCS-1631550).

Analysis of dispersion relation in three-dimensional single gyroid

NASA Astrophysics Data System (ADS)

Jheng, Pei-Lun; Hung, Yu-Chueh

2016-03-01

Gyroid is a type of three-dimensional chiral structures and has been found in many insect species. Besides the photonic crystal properties exhibited by gyroid structures, the chirality and gyroid network morphology also provide unique opportunities for manipulating propagation of light. In this work, we present studies based on finite-difference time domain (FDTD) method for analyzing the dispersion relation characteristics of dielectric single gyroid (SG) metamaterials. The band structures, transmission spectrum, dispersion surfaces, equifrequency contours (EFCs) of SG metamaterials are examined. Some interesting wave guiding characteristics, such as negative refraction and collimation, are presented and discussed. We also show how these optical properties are predicted by analyzing the EFCs at different frequencies. These results are crucial for the design of functional devices at optical frequencies based on dielectric single gyroid metamaterials.

Deformation behaviors of three-dimensional graphene honeycombs under out-of-plane compression: Atomistic simulations and predictive modeling

NASA Astrophysics Data System (ADS)

Meng, Fanchao; Chen, Cheng; Hu, Dianyin; Song, Jun

2017-12-01

Combining atomistic simulations and continuum modeling, a comprehensive study of the out-of-plane compressive deformation behaviors of equilateral three-dimensional (3D) graphene honeycombs was performed. It was demonstrated that under out-of-plane compression, the honeycomb exhibits two critical deformation events, i.e., elastic mechanical instability (including elastic buckling and structural transformation) and inelastic structural collapse. The above events were shown to be strongly dependent on the honeycomb cell size and affected by the local atomic bonding at the cell junction. By treating the 3D graphene honeycomb as a continuum cellular solid, and accounting for the structural heterogeneity and constraint at the junction, a set of analytical models were developed to accurately predict the threshold stresses corresponding to the onset of those deformation events. The present study elucidates key structure-property relationships of 3D graphene honeycombs under out-of-plane compression, and provides a comprehensive theoretical framework to predictively analyze their deformation responses, and more generally, offers critical new knowledge for the rational bottom-up design of 3D networks of two-dimensional nanomaterials.

Synthesis, Structure and Thermal Behavior of Oxalato-Bridged Rb+ and H3O+ Extended Frameworks with Different Dimensionalities

PubMed Central

Kherfi, Hamza; Hamadène, Malika; Guehria-Laïdoudi, Achoura; Dahaoui, Slimane; Lecomte, Claude

2010-01-01

Correlative studies of three oxalato-bridged polymers, obtained under hydrothermal conditions for the two isostructural compounds {Rb(HC2O4)(H2C2O4)(H2O)2}∞1, 1, {H3O(HC2O4)(H2C2O4).2H2O}∞1, 2, and by conventional synthetic method for {Rb(HC2O4)}∞3, 3, allowed the identification of H-bond patterns and structural dimensionality. Ferroïc domain structures are confirmed by electric measurements performed on 3. Although 2 resembles one oxalic acid sesquihydrate, its structure determination doesn’t display any kind of disorder and leads to recognition of a supramolecular network identical to hybrid s-block series, where moreover, unusual H3O+ and NH4+ similarity is brought out. Thermal behaviors show that 1D frameworks with extended H-bonds, whether with or without a metal center, have the same stability. Inversely, despite the dimensionalities, the same metallic intermediate and final compounds are obtained for the two Rb+ ferroïc materials.

An HCG-rich microenvironment contributes to ovarian cancer cell differentiation into endothelioid cells in a three-dimensional culture system.

PubMed

Su, Min; Fan, Chao; Gao, Sainan; Shen, Aiguo; Wang, Xiaoying; Zhang, Yuquan

2015-11-01

We investigated the expression of human chorionic gonadotropin (HCG) and its effects on vasculogenic mimicry (VM) formation in ovarian cancer cells under normoxic and hypoxic conditions in three-dimensional matrices preconditioned by an endothelial-trophoblast cell co-culture system. The co-culture model was established using human umbilical vein endothelial cells (HUVECs) and HTR-8 trophoblast cells in a three-dimensional culture system. The co-cultured cells were removed with NH4OH, and ovarian cancer cells were implanted into the preconditioned matrix. VM was identified morphologically and by detecting vascular markers expressed by cancer cells. The specificity of the effects of exogenous HCG in the microenvironment was assessed by inhibition with a neutralizing anti-HCG antibody. HCG siRNA was used to knock down endogenous HCG expression in OVCAR-3 ovarian cancer cells. HTR-8 cells 'fingerprinted' HUVECs to form capillary-like tube structures in co-cultures. In the preconditioned HCG-rich microenvironment, the number of vessel-like network structures formed by HCG receptor-positive OVCAR-3 cells and the expression levels of CD31, VEGF and factor VIII were significantly increased. The preconditioned HCG-rich microenvironment significantly increased the expression of hypoxia inducible factor-1α (HIF‑1α) and VM formation in OVCAR-3 cells under hypoxic conditions. Treatment with a neutralizing anti-HCG antibody but not HCG siRNA significantly inhibited the formation of vessel-like network structures. HCG in the microenvironment contributes to OVCAR-3 differentiation into endothelioid cells in three-dimensional matrices preconditioned with an endothelial-trophoblast cell co-culture system. HCG may synergistically enhance hypoxia-induced vascular markers and HIF-1α expression. These findings would provide perspectives on new therapeutic targets for ovarian cancer.

Clustering of 3D-Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities.

PubMed

Ohtana, Yuki; Abdullah, Azian Azamimi; Altaf-Ul-Amin, Md; Huang, Ming; Ono, Naoaki; Sato, Tetsuo; Sugiura, Tadao; Horai, Hisayuki; Nakamura, Yukiko; Morita Hirai, Aki; Lange, Klaus W; Kibinge, Nelson K; Katsuragi, Tetsuo; Shirai, Tsuyoshi; Kanaya, Shigehiko

2014-12-01

Developing database systems connecting diverse species based on omics is the most important theme in big data biology. To attain this purpose, we have developed KNApSAcK Family Databases, which are utilized in a number of researches in metabolomics. In the present study, we have developed a network-based approach to analyze relationships between 3D structure and biological activity of metabolites consisting of four steps as follows: construction of a network of metabolites based on structural similarity (Step 1), classification of metabolites into structure groups (Step 2), assessment of statistically significant relations between structure groups and biological activities (Step 3), and 2-dimensional clustering of the constructed data matrix based on statistically significant relations between structure groups and biological activities (Step 4). Applying this method to a data set consisting of 2072 secondary metabolites and 140 biological activities reported in KNApSAcK Metabolite Activity DB, we obtained 983 statistically significant structure group-biological activity pairs. As a whole, we systematically analyzed the relationship between 3D-chemical structures of metabolites and biological activities. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein’s structural organization

PubMed Central

2012-01-01

Background The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein. Results The largest connected component (LCC) of long (LRN)-, short (SRN)- and all-range (ARN) networks within proteins exhibit a transition behaviour when plotted against different interaction strengths of edges among amino acid nodes. While short-range networks having chain like structures exhibit highly cooperative transition; long- and all-range networks, which are more similar to each other, have non-chain like structures and show less cooperativity. Further, the hydrophobic residues subnetworks in long- and all-range networks have similar transition behaviours with all residues all-range networks, but the hydrophilic and charged residues networks don’t. While the nature of transitions of LCC’s sizes is same in SRNs for thermophiles and mesophiles, there exists a clear difference in LRNs. The presence of larger size of interconnected long-range interactions in thermophiles than mesophiles, even at higher interaction strength between amino acids, give extra stability to the tertiary structure of the thermophiles. All the subnetworks at different length scales (ARNs, LRNs and SRNs) show assortativity mixing property of their participating amino acids. While there exists a significant higher percentage of hydrophobic subclusters over others in ARNs and LRNs; we do not find the assortative mixing behaviour of any the subclusters in SRNs. The clustering coefficient of hydrophobic subclusters in long-range network is the highest among types of subnetworks. There exist highly cliquish hydrophobic nodes followed by charged nodes in LRNs and ARNs; on the other hand, we observe the highest dominance of charged residues cliques in short-range networks. Studies on the perimeter of the cliques also show higher occurrences of hydrophobic and charged residues’ cliques. Conclusions The simple framework of protein contact networks and their subnetworks based on London van der Waals force is able to capture several known properties of protein structure as well as can unravel several new features. The thermophiles do not only have the higher number of long-range interactions; they also have larger cluster of connected residues at higher interaction strengths among amino acids, than their mesophilic counterparts. It can reestablish the significant role of long-range hydrophobic clusters in protein folding and stabilization; at the same time, it shed light on the higher communication ability of hydrophobic subnetworks over the others. The results give an indication of the controlling role of hydrophobic subclusters in determining protein’s folding rate. The occurrences of higher perimeters of hydrophobic and charged cliques imply the role of charged residues as well as hydrophobic residues in stabilizing the distant part of primary structure of a protein through London van der Waals interaction. PMID:22720789

What HR-CT imaging can teach us about xylem structure and function

USDA-ARS?s Scientific Manuscript database

It is well established that plant xylem is composed of a complex and interconnected system of vascular elements, but little is known about how the three-dimensional (3D) organization of this network influences properties such as plant hydraulics (Tyree & Zimmermann, 2002), and few studies have measu...

Starch-based aerogels: airy materials from amylose-sodium palmitate inclusion complexes

USDA-ARS?s Scientific Manuscript database

Aerogels are a class of interesting low density porous materials prepared by replacing the water phase contained within a hydrogel with a gas phase while maintaining the three dimensional network structure of the gel. The investigation of starch and hydrocolloid-based aerogels has received attentio...

IM-19: a new flexible microporous gallium based-MOF framework with pressure- and temperature-dependent openings.

PubMed

Chaplais, Gérald; Simon-Masseron, Angélique; Porcher, Florence; Lecomte, Claude; Bazer-Bachi, Delphine; Bats, Nicolas; Patarin, Joël

2009-07-14

Five metal-organic frameworks (MOFs) based on the same three-dimensional gallium terephthalate network (IM-19) are described, and an incommensurate structure (for the as-synthesized form) as well as two remarkable guest-free polymorphs (open and closed) are highlighted.

State transfer in highly connected networks and a quantum Babinet principle

NASA Astrophysics Data System (ADS)

Tsomokos, D. I.; Plenio, M. B.; de Vega, I.; Huelga, S. F.

2008-12-01

The transfer of a quantum state between distant nodes in two-dimensional networks is considered. The fidelity of state transfer is calculated as a function of the number of interactions in networks that are described by regular graphs. It is shown that perfect state transfer is achieved in a network of size N , whose structure is that of an (N/2) -cross polytope graph, if N is a multiple of 4 . The result is reminiscent of the Babinet principle of classical optics. A quantum Babinet principle is derived, which allows for the identification of complementary graphs leading to the same fidelity of state transfer, in analogy with complementary screens providing identical diffraction patterns.

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

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

Elrod, D.W.

1992-01-01

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example ofmore » a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.« less

Commensurate Superstructure of the {Cu(NO3)(H2O)}(HTae)(Bpy) Coordination Polymer: An Example of 2D Hydrogen-Bonding Networks as Magnetic Exchange Pathway.

PubMed

Fernández de Luis, Roberto; Larrea, Edurne S; Orive, Joseba; Lezama, Luis; Arriortua, María I

2016-11-21

The average and commensurate superstructures of the one-dimensional coordination polymer {Cu(NO 3 )(H 2 O)}(HTae)(Bpy) (H 2 Tae = 1,1,2,2-tetraacetylethane, Bpy = 4,4'-bipyridine) were determined by single-crystal X-ray diffraction, and the possible symmetry relations between the space group of the average structure and the superstructure were checked. The crystal structure consists in parallel and oblique {Cu(HTae)(Bpy)} zigzag metal-organic chains stacked along the [100] crystallographic direction. The origin of the fivefold c axis in the commensurate superstructure is ascribed to a commensurate modulation of the coordination environment of the copper atoms. The commensurately ordered nitrate groups and coordinated water molecules establish a two-dimensional hydrogen-bonding network. Moreover, the crystal structure shows a commensurate to incommensurate transition at room temperature. The release of the coordination water molecules destabilizes the crystal framework, and the compound shows an irreversible structure transformation above 100 °C. Despite the loss of crystallinity, the spectroscopic studies indicate that the main building blocks of the crystal framework are retained after the transformation. The hydrogen-bonding network not only plays a crucial role stabilizing the crystal structure but also is an important pathway for magnetic exchange transmission. In fact, the magnetic susceptibility curves indicate that after the loss of coordinated water molecules, and hence the collapse of the hydrogen-bonding network, the weak anti-ferromagnetic coupling observed in the initial compound is broken. The electron paramagnetic resonance spectra are the consequence of the average signals from Cu(II) with different orientations, indicating that the magnetic coupling is effective between them. In fact, X- and Q-band data are reflecting different situations; the X-band spectra show the characteristics of an exchange g-tensor, while the Q-band signals are coming from both the exchange and the molecular g-tensors.

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

PubMed

Deng, Lei; Fan, Chao; Zeng, Zhiwen

2017-12-28

Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

The Zel'dovich approximation: key to understanding cosmic web complexity

NASA Astrophysics Data System (ADS)

Hidding, Johan; Shandarin, Sergei F.; van de Weygaert, Rien

2014-02-01

We describe how the dynamics of cosmic structure formation defines the intricate geometric structure of the spine of the cosmic web. The Zel'dovich approximation is used to model the backbone of the cosmic web in terms of its singularity structure. The description by Arnold et al. in terms of catastrophe theory forms the basis of our analysis. This two-dimensional analysis involves a profound assessment of the Lagrangian and Eulerian projections of the gravitationally evolving four-dimensional phase-space manifold. It involves the identification of the complete family of singularity classes, and the corresponding caustics that we see emerging as structure in Eulerian space evolves. In particular, as it is instrumental in outlining the spatial network of the cosmic web, we investigate the nature of spatial connections between these singularities. The major finding of our study is that all singularities are located on a set of lines in Lagrangian space. All dynamical processes related to the caustics are concentrated near these lines. We demonstrate and discuss extensively how all 2D singularities are to be found on these lines. When mapping this spatial pattern of lines to Eulerian space, we find a growing connectedness between initially disjoint lines, resulting in a percolating network. In other words, the lines form the blueprint for the global geometric evolution of the cosmic web.

Learning multivariate distributions by competitive assembly of marginals.

PubMed

Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

2013-02-01

We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures.

PubMed

Ehrlich, Matthias; Schüffny, René

2013-01-01

One of the major outcomes of neuroscientific research are models of Neural Network Structures (NNSs). Descriptions of these models usually consist of a non-standardized mixture of text, figures, and other means of visual information communication in print media. However, as neuroscience is an interdisciplinary domain by nature, a standardized way of consistently representing models of NNSs is required. While generic descriptions of such models in textual form have recently been developed, a formalized way of schematically expressing them does not exist to date. Hence, in this paper we present Neural Schematics as a concept inspired by similar approaches from other disciplines for a generic two dimensional representation of said structures. After introducing NNSs in general, a set of current visualizations of models of NNSs is reviewed and analyzed for what information they convey and how their elements are rendered. This analysis then allows for the definition of general items and symbols to consistently represent these models as Neural Schematics on a two dimensional plane. We will illustrate the possibilities an agreed upon standard can yield on sampled diagrams transformed into Neural Schematics and an example application for the design and modeling of large-scale NNSs.

Molecule-based magnets formed by bimetallic three-dimensional oxalate networks and chiral tris(bipyridyl) complex cations. The series [ZII(bpy)3][ClO4][MIICrIII(ox)3] (ZII = Ru, Fe, Co, and Ni; MII = Mn, Fe, Co, Ni, Cu, and Zn; ox = oxalate dianion).

PubMed

Coronado, E; Galán-Mascarós, J R; Gómez-García, C J; Martínez-Agudo, J M

2001-01-01

The synthesis, structure, and physical properties of the series of molecular magnets formulated as [ZII(bpy)3][ClO4][MIICrIII(ox)3] (ZII = Ru, Fe, Co, and Ni; MII = Mn, Fe, Co, Ni, Cu, and Zn; ox = oxalate dianion) are presented. All the compounds are isostructural to the [Ru(bpy)3][ClO4][MnCr(ox)3] member whose structure (cubic space group P4(1)32 with a = 15.506(2) A, Z = 4) consists of a three-dimensional bimetallic network formed by alternating MII and CrIII ions connected by oxalate anions. The identical chirality (lambda in the solved crystal) of all the metallic centers determines the 3D chiral structure adopted by these compounds. The anionic 3D sublattice leaves some holes where the chiral [Z(bpy)3]2+ and ClO4- counterions are located. These compounds behave as soft ferromagnets with ordering temperatures up to 6.6 K and coercive fields up to 8 mT.

Combinatorial explosion in model gene networks

NASA Astrophysics Data System (ADS)

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such networks are extremely rich and they offer novel ways to think about how mutations can alter dynamics.

Combinatorial explosion in model gene networks.

PubMed

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such networks are extremely rich and they offer novel ways to think about how mutations can alter dynamics. (c) 2000 American Institute of Physics.

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

NASA Astrophysics Data System (ADS)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Three-dimensional strutted graphene grown by substrate-free sugar blowing for high-power-density supercapacitors

PubMed Central

Wang, Xuebin; Zhang, Yuanjian; Zhi, Chunyi; Wang, Xi; Tang, Daiming; Xu, Yibin; Weng, Qunhong; Jiang, Xiangfen; Mitome, Masanori; Golberg, Dmitri; Bando, Yoshio

2013-01-01

Three-dimensional graphene architectures in the macroworld can in principle maintain all the extraordinary nanoscale properties of individual graphene flakes. However, current 3D graphene products suffer from poor electrical conductivity, low surface area and insufficient mechanical strength/elasticity; the interconnected self-supported reproducible 3D graphenes remain unavailable. Here we report a sugar-blowing approach based on a polymeric predecessor to synthesize a 3D graphene bubble network. The bubble network consists of mono- or few-layered graphitic membranes that are tightly glued, rigidly fixed and spatially scaffolded by micrometre-scale graphitic struts. Such a topological configuration provides intimate structural interconnectivities, freeway for electron/phonon transports, huge accessible surface area, as well as robust mechanical properties. The graphene network thus overcomes the drawbacks of presently available 3D graphene products and opens up a wide horizon for diverse practical usages, for example, high-power high-energy electrochemical capacitors, as highlighted in this work. PMID:24336225

Three-dimensional strutted graphene grown by substrate-free sugar blowing for high-power-density supercapacitors

NASA Astrophysics Data System (ADS)

Wang, Xuebin; Zhang, Yuanjian; Zhi, Chunyi; Wang, Xi; Tang, Daiming; Xu, Yibin; Weng, Qunhong; Jiang, Xiangfen; Mitome, Masanori; Golberg, Dmitri; Bando, Yoshio

2013-12-01

Three-dimensional graphene architectures in the macroworld can in principle maintain all the extraordinary nanoscale properties of individual graphene flakes. However, current 3D graphene products suffer from poor electrical conductivity, low surface area and insufficient mechanical strength/elasticity; the interconnected self-supported reproducible 3D graphenes remain unavailable. Here we report a sugar-blowing approach based on a polymeric predecessor to synthesize a 3D graphene bubble network. The bubble network consists of mono- or few-layered graphitic membranes that are tightly glued, rigidly fixed and spatially scaffolded by micrometre-scale graphitic struts. Such a topological configuration provides intimate structural interconnectivities, freeway for electron/phonon transports, huge accessible surface area, as well as robust mechanical properties. The graphene network thus overcomes the drawbacks of presently available 3D graphene products and opens up a wide horizon for diverse practical usages, for example, high-power high-energy electrochemical capacitors, as highlighted in this work.

Programmable disorder in random DNA tilings

NASA Astrophysics Data System (ADS)

Tikhomirov, Grigory; Petersen, Philip; Qian, Lulu

2017-03-01

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

Network community-based model reduction for vortical flows

NASA Astrophysics Data System (ADS)

Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya G.; Taira, Kunihiko

2018-06-01

A network community-based reduced-order model is developed to capture key interactions among coherent structures in high-dimensional unsteady vortical flows. The present approach is data-inspired and founded on network-theoretic techniques to identify important vortical communities that are comprised of vortical elements that share similar dynamical behavior. The overall interaction-based physics of the high-dimensional flow field is distilled into the vortical community centroids, considerably reducing the system dimension. Taking advantage of these vortical interactions, the proposed methodology is applied to formulate reduced-order models for the inter-community dynamics of vortical flows, and predict lift and drag forces on bodies in wake flows. We demonstrate the capabilities of these models by accurately capturing the macroscopic dynamics of a collection of discrete point vortices, and the complex unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap. The present formulation is found to be robust against simulated experimental noise and turbulence due to its integrating nature of the system reduction.

Diverse Supramolecular Nanofiber Networks Assembled by Functional Low-Complexity Domains.

PubMed

An, Bolin; Wang, Xinyu; Cui, Mengkui; Gui, Xinrui; Mao, Xiuhai; Liu, Yan; Li, Ke; Chu, Cenfeng; Pu, Jiahua; Ren, Susu; Wang, Yanyi; Zhong, Guisheng; Lu, Timothy K; Liu, Cong; Zhong, Chao

2017-07-25

Self-assembling supramolecular nanofibers, common in the natural world, are of fundamental interest and technical importance to both nanotechnology and materials science. Despite important advances, synthetic nanofibers still lack the structural and functional diversity of biological molecules, and the controlled assembly of one type of molecule into a variety of fibrous structures with wide-ranging functional attributes remains challenging. Here, we harness the low-complexity (LC) sequence domain of fused in sarcoma (FUS) protein, an essential cellular nuclear protein with slow kinetics of amyloid fiber assembly, to construct random copolymer-like, multiblock, and self-sorted supramolecular fibrous networks with distinct structural features and fluorescent functionalities. We demonstrate the utilities of these networks in the templated, spatially controlled assembly of ligand-decorated gold nanoparticles, quantum dots, nanorods, DNA origami, and hybrid structures. Owing to the distinguishable nanoarchitectures of these nanofibers, this assembly is structure-dependent. By coupling a modular genetic strategy with kinetically controlled complex supramolecular self-assembly, we demonstrate that a single type of protein molecule can be used to engineer diverse one-dimensional supramolecular nanostructures with distinct functionalities.

Highly cytocompatible and flexible three-dimensional graphene/polydimethylsiloxane composite for culture and electrochemical detection of L929 fibroblast cells.

PubMed

Waiwijit, Uraiwan; Maturos, Thitima; Pakapongpan, Saithip; Phokharatkul, Ditsayut; Wisitsoraat, Anurat; Tuantranont, Adisorn

2016-08-01

Recently, three-dimensional graphene interconnected network has attracted great interest as a scaffold structure for tissue engineering due to its high biocompatibility, high electrical conductivity, high specific surface area and high porosity. However, free-standing three-dimensional graphene exhibits poor flexibility and stability due to ease of disintegration during processing. In this work, three-dimensional graphene is composited with polydimethylsiloxane to improve the structural flexibility and stability by a new simple two-step process comprising dip coating of polydimethylsiloxane on chemical vapor deposited graphene/Ni foam and wet etching of nickel foam. Structural characterizations confirmed an interconnected three-dimensional multi-layer graphene structure with thin polydimethylsiloxane scaffold. The composite was employed as a substrate for culture of L929 fibroblast cells and its cytocompatibility was evaluated by cell viability (Alamar blue assay), reactive oxygen species production and vinculin immunofluorescence imaging. The result revealed that cell viability on three-dimensional graphene/polydimethylsiloxane composite increased with increasing culture time and was slightly different from a polystyrene substrate (control). Moreover, cells cultured on three-dimensional graphene/polydimethylsiloxane composite generated less ROS than the control at culture times of 3-6 h. The results of immunofluorescence staining demonstrated that fibroblast cells expressed adhesion protein (vinculin) and adhered well on three-dimensional graphene/polydimethylsiloxane surface. Good cell adhesion could be attributed to suitable surface properties of three-dimensional graphene/polydimethylsiloxane with moderate contact angle and small negative zeta potential in culture solution. The results of electrochemical study by cyclic voltammetry showed that an oxidation current signal with no apparent peak was induced by fibroblast cells and the oxidation current at an oxidation potential of +0.9 V increased linearly with increasing cell number. Therefore, the three-dimensional graphene/polydimethylsiloxane composite exhibits high cytocompatibility and can potentially be used as a conductive substrate for cell-based electrochemical sensing. © The Author(s) 2016.

Adaptive Control of Truss Structures for Gossamer Spacecraft

NASA Technical Reports Server (NTRS)

Yang, Bong-Jun; Calise, Anthony J.; Craig, James I.; Whorton, Mark S.

2007-01-01

Neural network-based adaptive control is considered for active control of a highly flexible truss structure which may be used to support solar sail membranes. The objective is to suppress unwanted vibrations in SAFE (Solar Array Flight Experiment) boom, a test-bed located at NASA. Compared to previous tests that restrained truss structures in planar motion, full three dimensional motions are tested. Experimental results illustrate the potential of adaptive control in compensating for nonlinear actuation and modeling error, and in rejecting external disturbances.

Node-Based Learning of Multiple Gaussian Graphical Models

PubMed Central

Mohan, Karthik; London, Palma; Fazel, Maryam; Witten, Daniela; Lee, Su-In

2014-01-01

We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of recovering transcriptional regulatory networks on the basis of gene expression data containing heterogeneous samples, such as different disease states, multiple species, or different developmental stages. We assume that most aspects of the conditional dependence networks are shared, but that there are some structured differences between them. Rather than assuming that similarities and differences between networks are driven by individual edges, we take a node-based approach, which in many cases provides a more intuitive interpretation of the network differences. We consider estimation under two distinct assumptions: (1) differences between the K networks are due to individual nodes that are perturbed across conditions, or (2) similarities among the K networks are due to the presence of common hub nodes that are shared across all K networks. Using a row-column overlap norm penalty function, we formulate two convex optimization problems that correspond to these two assumptions. We solve these problems using an alternating direction method of multipliers algorithm, and we derive a set of necessary and sufficient conditions that allows us to decompose the problem into independent subproblems so that our algorithm can be scaled to high-dimensional settings. Our proposal is illustrated on synthetic data, a webpage data set, and a brain cancer gene expression data set. PMID:25309137

On the strength of random fiber networks

NASA Astrophysics Data System (ADS)

Deogekar, S.; Picu, R. C.

2018-07-01

Damage accumulation and failure in random fiber networks is of importance in a variety of applications, from design of synthetic materials, such as paper and non-wovens, to accidental tearing of biological tissues. In this work we study these processes using three-dimensional models of athermal fiber networks, focusing attention on the modes of failure and on the relationship between network strength and network structural parameters. We consider network failure at small and large strains associated with the rupture of inter-fiber bonds. It is observed that the strength increases linearly with the network volume fraction and with the bond strength, while the stretch at peak stress is inversely related to these two parameters. A small fraction of the bonds rupture before peak stress and this fraction increases with increasing failure stretch. Rendering the bond strength stochastic causes a reduction of the network strength. However, heterogeneity retards damage localization and increases the stretch at peak stress, therefore promoting ductility.

DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts.

PubMed

Czerwinska, Urszula; Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

2015-08-14

Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases. We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network. DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/.

Connectivity of glass structure. Oxygen number

NASA Astrophysics Data System (ADS)

Medvedev, E. F.; Min'ko, N. I.

2018-03-01

With reference to mathematics, crystal chemistry and chemical technology of synthesis of glass structures in the solution (sol-gel technology), the paper is devoted to the study of the degree of connectivity of a silicon-oxygen backbone (fSi) and the oxygen number (R) [1]. It reveals logical contradictions and uncertainty of mathematical expressions of parameters, since fSi is not similar to the oxygen number. The connectivity of any structure is a result of various types of bonds: ion-covalent, donor-acceptor, hydrogen bonds, etc. Besides, alongside with SiO2, many glass compositions contain other glass-forming elements due to tetrahedral sites thus formed. The connectivity function of a glassy network with any set of glass-forming elements is roughly ensured by connectivity factor Y [2], which has monovalent elements loosening a glassy network. The paper considers the existence of various structural motives in hydrogen-impermeable glasses containing B2O3, Al2O3, PbO, Na2O, K2O and rare-earth elements. Hence, it also describes gradual nucleation, change of crystal forms, and structure consolidation in the process of substance intake from a matrix solution according to sol-gel technology. The crystal form varied from two-dimensional plates to three-dimensional and dendritical ones [3]. Alternative parameters, such as the oxygen number (O) and the structure connectivity factor (Y), were suggested. Functional dependence of Y=f(O) to forecast the generated structures was obtained for two- and multicomponent glass compositions.

Two-dimensional plasmons in the random impedance network model of disordered thin film nanocomposites

NASA Astrophysics Data System (ADS)

Olekhno, N. A.; Beltukov, Y. M.

2018-05-01

Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric nanocomposites. Two-dimensional networks are applied when considering thin films despite the fact that such networks correspond to the two-dimensional electrodynamics [Clerc et al., J. Phys. A 29, 4781 (1996), 10.1088/0305-4470/29/16/006]. In the present work, we propose a model of two-dimensional systems with the three-dimensional Coulomb interaction and show that this model is equivalent to the planar network with long-range capacitive links between distant sites. In the case of a metallic film, we obtain the well-known dispersion of two-dimensional plasmons ω ∝√{k } . We study the evolution of resonances with a decrease in the metal filling factor within the framework of the proposed model. In the subcritical region with the metal filling p lower than the percolation threshold pc, we observe a gap with Lifshitz tails in the spectral density of states (DOS). In the supercritical region p >pc , the DOS demonstrates a crossover between plane-wave two-dimensional plasmons and resonances of finite clusters.

Nonequilibrium phase transition in a self-activated biological network.

PubMed

Berry, Hugues

2003-03-01

We present a lattice model for a two-dimensional network of self-activated biological structures with a diffusive activating agent. The model retains basic and simple properties shared by biological systems at various observation scales, so that the structures can consist of individuals, tissues, cells, or enzymes. Upon activation, a structure emits a new mobile activator and remains in a transient refractory state before it can be activated again. Varying the activation probability, the system undergoes a nonequilibrium second-order phase transition from an active state, where activators are present, to an absorbing, activator-free state, where each structure remains in the deactivated state. We study the phase transition using Monte Carlo simulations and evaluate the critical exponents. As they do not seem to correspond to known values, the results suggest the possibility of a separate universality class.

PSpice Model of Lightning Strike to a Steel Reinforced Structure

NASA Astrophysics Data System (ADS)

Koone, Neil; Condren, Brian

2003-12-01

Surges and arcs from lightning can pose hazards to personnel and sensitive equipment, and processes. Steel reinforcement in structures can act as a Faraday cage mitigating lightning effects. Knowing a structure's response to a lightning strike allows hazards associated with lightning to be analyzed. A model of lightning's response in a steel reinforced structure has been developed using PSpice (a commercial circuit simulation). Segments of rebar are modeled as inductors and resistors in series. A program has been written to take architectural information of a steel reinforced structure and "build" a circuit network that is analogous to the network of reinforcement in a facility. A severe current waveform (simulating a 99th percentile lightning strike), modeled as a current source, is introduced in the circuit network, and potential differences within the structure are determined using PSpice. A visual three-dimensional model of the facility displays the voltage distribution across the structure using color to indicate the potential difference relative to the floor. Clear air arcing distances can be calculated from the voltage distribution using a conservative value for the dielectric breakdown strength of air. Potential validation tests for the model will be presented.

An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure.

PubMed

Yenilmez, Firdes; Düzgün, Sebnem; Aksoy, Aysegül

2015-01-01

In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

Polymer-based platform for microfluidic systems

DOEpatents

Benett, William [Livermore, CA; Krulevitch, Peter [Pleasanton, CA; Maghribi, Mariam [Livermore, CA; Hamilton, Julie [Tracy, CA; Rose, Klint [Boston, MA; Wang, Amy W [Oakland, CA

2009-10-13

A method of forming a polymer-based microfluidic system platform using network building blocks selected from a set of interconnectable network building blocks, such as wire, pins, blocks, and interconnects. The selected building blocks are interconnectably assembled and fixedly positioned in precise positions in a mold cavity of a mold frame to construct a three-dimensional model construction of a microfluidic flow path network preferably having meso-scale dimensions. A hardenable liquid, such as poly (dimethylsiloxane) is then introduced into the mold cavity and hardened to form a platform structure as well as to mold the microfluidic flow path network having channels, reservoirs and ports. Pre-fabricated elbows, T's and other joints are used to interconnect various building block elements together. After hardening the liquid the building blocks are removed from the platform structure to make available the channels, cavities and ports within the platform structure. Microdevices may be embedded within the cast polymer-based platform, or bonded to the platform structure subsequent to molding, to create an integrated microfluidic system. In this manner, the new microfluidic platform is versatile and capable of quickly generating prototype systems, and could easily be adapted to a manufacturing setting.

A one-dimensional ice structure built from pentagons

NASA Astrophysics Data System (ADS)

Carrasco, Javier; Michaelides, Angelos

2010-03-01

Heterogeneous nucleation of water plays a key role in fields as diverse as atmospheric chemistry, astrophysics, and biology. Ice nucleation on metal surfaces offers an opportunity to watch this process unfold, providing a molecular-scale description at a well-defined, planar interface. We discuss a density-functional theory study on a metal surface specifically designed to understand such phenomena. Together with our colleges at the University of Liverpool, we found that the nanometer wide water-ice chains experimentally observed to nucleate and grow on Cu(110) are built from a face sharing arrangement of water pentagons [1]. The novel one-dimensional pentagon structure maximizes the water-metal bonding whilst simultaneously maintaining a strong hydrogen bonding network. These results reveal an unanticipated structural adaptability of water-ice films, demonstrating that the presence of the substrate can be sufficient to favor non-conventional structural units. [4pt] [1] J. Carrasco et al., Nature Mater. 8, 427 (2009).

Function approximation using combined unsupervised and supervised learning.

PubMed

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

PubMed

Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

2018-02-05

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Developing a method of fabricating microchannels using plant root structure

NASA Astrophysics Data System (ADS)

Nakashima, Shota; Tokumaru, Kazuki; Tsumori, Fujio

2018-06-01

Complicated three-dimensional (3D) microchannels are expected to be applied to a lab-on-a-chip, especially an organ-on-a-chip. There are fine microchannel networks such as blood vessels in a living organ. However, it is difficult to recreate the complicated 3D microchannels of real living structures. Plant roots have a similar structure to blood vessels. They spread radially and three-dimensionally, and become thinner as they branch. In this research, we propose a method of fabricating microchannels using a live plant root as a template to mimic a blood vessel structure. We grew a plant in ceramic slurry instead of soil. The slurry consists of ceramic powder, binder and water, so it plays a similar role to soil consisting of fine particles in water. After growing the plant, the roots inside the slurry were burned and a sintered ceramic body with channel structures was obtained by heating. We used two types of slurry with different composition ratios, and compared the internal channel structures before and after sintering.

Validating two-dimensional leadership models on three-dimensionally structured fish schools

PubMed Central

Nagy, Máté; Holbrook, Robert I.; Biro, Dora; Burt de Perera, Theresa

2017-01-01

Identifying leader–follower interactions is crucial for understanding how a group decides where or when to move, and how this information is transferred between members. Although many animal groups have a three-dimensional structure, previous studies investigating leader–follower interactions have often ignored vertical information. This raises the question of whether commonly used two-dimensional leader–follower analyses can be used justifiably on groups that interact in three dimensions. To address this, we quantified the individual movements of banded tetra fish (Astyanax mexicanus) within shoals by computing the three-dimensional trajectories of all individuals using a stereo-camera technique. We used these data firstly to identify and compare leader–follower interactions in two and three dimensions, and secondly to analyse leadership with respect to an individual's spatial position in three dimensions. We show that for 95% of all pairwise interactions leadership identified through two-dimensional analysis matches that identified through three-dimensional analysis, and we reveal that fish attend to the same shoalmates for vertical information as they do for horizontal information. Our results therefore highlight that three-dimensional analyses are not always required to identify leader–follower relationships in species that move freely in three dimensions. We discuss our results in terms of the importance of taking species' sensory capacities into account when studying interaction networks within groups. PMID:28280582

New 3-D coordination polymers based on semi-rigid V-shape tetracarboxylates

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

Huang, Jing-Jing; Xu, Wei; Wang, Yan-Ning

Under the hydrothermal conditions, the reactions of transition-metal salts, tetracarboxylic acids and N,N′-donor ligands yielded three new coordination polymers as [Cu{sub 4}(fph){sub 2}(bpe){sub 3}(H{sub 2}O){sub 2}]·2H{sub 2}O (fph=4,4′-(hexafluoroisopropylidene)diphthalate, bpe=1,2-bis(pyridyl)ethylene) 1, [Co{sub 2}(fph)(bpa){sub 2}(H{sub 2}O){sub 2}]·3H{sub 2}O (bpa=1,2-bis(pyridyl)ethylane) 2, and [Ni(H{sub 2}O)(H{sub 2}oph)(bpa)] (oph=4,4′-oxydiphthalate) 3. X-ray single-crystal diffraction analysis revealed that the title three compounds all possess the three-dimensional (3-D) network structures. For compound 1, the fph molecules first link the Cu{sup 2+} ions into a two-dimensional (2-D) wave-like layer with a (4,4) topology. The bpe molecules act as the second linkers, extending the 2-D layers into a 3-D network. Formore » compound 2, the fph molecules still serve as the first connectors, linking the Co{sup 2+} ions into a one-dimensional (1-D) tube-like chain. Then the bpa molecules propagate the chains into a 3-D (4,4,4)-connected network. In the formation of the 3-D network of compound 3, the oph molecule does not play a role. The bpa molecules as well as the water molecules act as a mixed bridge. Only a kind of 4-connected metal node is observed in compound 3. The magnetic properties of compounds 1–3 were investigated and all exhibit the predominant antiferromegnetic magnetic behaviors. - Graphical abstract: Structures of three semi-rigid V-shape tetracarboxylate-based coordination polymers were reported, and their magnetic properties were investigated. - Highlights: • Structures of three tetracarboxylate-based coordination polymers were reported. • Role of organic bases in metal–tetracarboxylate compounds was discussed. • Characters of V-shape and semi-rigidity for tetracarboxylate play a key role in crystal growth. • Their magnetic properties were investigated.« less

A three-dimensional collagen-fiber network model of the extracellular matrix for the simulation of the mechanical behaviors and micro structures.

PubMed

Dong, Shoubin; Huang, Zetao; Tang, Liqun; Zhang, Xiaoyang; Zhang, Yongrou; Jiang, Yi

2017-07-01

The extracellular matrix (ECM) provides structural and biochemical support to cells and tissues, which is a critical factor for modulating cell dynamic behavior and intercellular communication. In order to further understand the mechanisms of the interactive relationship between cell and the ECM, we developed a three-dimensional (3D) collagen-fiber network model to simulate the micro structure and mechanical behaviors of the ECM and studied the stress-strain relationship as well as the deformation of the ECM under tension. In the model, the collagen-fiber network consists of abundant random distributed collagen fibers and some crosslinks, in which each fiber is modeled as an elastic beam and a crosslink is modeled as a linear spring with tensile limit, it means crosslinks will fail while the tensile forces exceed the limit of spring. With the given parameters of the beam and the spring, the simulated tensile stress-strain relation of the ECM highly matches the experimental results including damaged and failed behaviors. Moreover, by applying the maximal inscribed sphere method, we measured the size distribution of pores in the fiber network and learned the variation of the distribution with deformation. We also defined the alignment of the collagen-fibers to depict the orientation of fibers in the ECM quantitatively. By the study of changes of the alignment and the damaged crosslinks against the tensile strain, this paper reveals the comprehensive mechanisms of four stages of 'toe', 'linear', 'damage' and 'failure' in the tensile stress-strain relation of the ECM which can provide further insight in the study of cell-ECM interaction.

Achieving high strength and high ductility in metal matrix composites reinforced with a discontinuous three-dimensional graphene-like network.

PubMed

Zhang, Xiang; Shi, Chunsheng; Liu, Enzuo; He, Fang; Ma, Liying; Li, Qunying; Li, Jiajun; Bacsa, Wolfgang; Zhao, Naiqin; He, Chunnian

2017-08-24

Graphene or graphene-like nanosheets have been emerging as an attractive reinforcement for composites due to their unique mechanical and electrical properties as well as their fascinating two-dimensional structure. It is a great challenge to efficiently and homogeneously disperse them within a metal matrix for achieving metal matrix composites with excellent mechanical and physical performance. In this work, we have developed an innovative in situ processing strategy for the fabrication of metal matrix composites reinforced with a discontinuous 3D graphene-like network (3D GN). The processing route involves the in situ synthesis of the encapsulation structure of 3D GN powders tightly anchored with Cu nanoparticles (NPs) (3D GN@Cu) to ensure mixing at the molecular level between graphene-like nanosheets and metal, coating of Cu on the 3D GN@Cu (3D GN@Cu@Cu), and consolidation of the 3D GN@Cu@Cu powders. This process can produce GN/Cu composites on a large scale, in which the in situ synthesized 3D GN not only maintains the perfect 3D network structure within the composites, but also has robust interfacial bonding with the metal matrix. As a consequence, the as-obtained 3D GN/Cu composites exhibit exceptionally high strength and superior ductility (the uniform and total elongation to failure of the composite are even much higher than the unreinforced Cu matrix). To the best of our knowledge, this work is the first report validating that a discontinuous 3D graphene-like network can simultaneously remarkably enhance the strength and ductility of the metal matrix.

Low-dimensional ordering and fluctuations in methanol-{beta}-hydroquinone clathrate studied by x-ray and neutron diffraction

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

Rheinstaedter, Maikel C.; Enderle, Mechthild; Kloepperpieper, Axel

2005-01-01

Methanol-{beta}-hydroquinone clathrate has been established as a model system for dielectric ordering and fluctuations and is conceptually close to magnetic spin systems. In x-ray and neutron diffraction experiments, we investigated the ordered structure, the one-dimensional (1D) and the three-dimensional critical scattering in the paraelectric phase, and the temperature dependence of the lattice constants. Our results can be explained by microscopic models of the methanol pseudospin in the hydroquinone cage network, in consistency with previous dielectric investigations. A coupling of the 1D fluctuations to local strains leads to an anomalous temperature dependence of the 1D lattice parameter in the paraelectric regime.

Statistical Analysis of Big Data on Pharmacogenomics

PubMed Central

Fan, Jianqing; Liu, Han

2013-01-01

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

All-printed thin-film transistors from networks of liquid-exfoliated nanosheets

NASA Astrophysics Data System (ADS)

Kelly, Adam G.; Hallam, Toby; Backes, Claudia; Harvey, Andrew; Esmaeily, Amir Sajad; Godwin, Ian; Coelho, João; Nicolosi, Valeria; Lauth, Jannika; Kulkarni, Aditya; Kinge, Sachin; Siebbeles, Laurens D. A.; Duesberg, Georg S.; Coleman, Jonathan N.

2017-04-01

All-printed transistors consisting of interconnected networks of various types of two-dimensional nanosheets are an important goal in nanoscience. Using electrolytic gating, we demonstrate all-printed, vertically stacked transistors with graphene source, drain, and gate electrodes, a transition metal dichalcogenide channel, and a boron nitride (BN) separator, all formed from nanosheet networks. The BN network contains an ionic liquid within its porous interior that allows electrolytic gating in a solid-like structure. Nanosheet network channels display on:off ratios of up to 600, transconductances exceeding 5 millisiemens, and mobilities of >0.1 square centimeters per volt per second. Unusually, the on-currents scaled with network thickness and volumetric capacitance. In contrast to other devices with comparable mobility, large capacitances, while hindering switching speeds, allow these devices to carry higher currents at relatively low drive voltages.

Capacity of Heterogeneous Mobile Wireless Networks with D-Delay Transmission Strategy.

PubMed

Wu, Feng; Zhu, Jiang; Xi, Zhipeng; Gao, Kai

2016-03-25

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks.

The Impact of The Fractal Paradigm on Geography

NASA Astrophysics Data System (ADS)

De Cola, L.

2001-12-01

Being itself somewhat fractal, Benoit Mandelbrot's magnum opus THE FRACTAL GEOMETRY OF NATURE may be deconstructed in many ways, including geometrically, systematically, and epistemologically. Viewed as a work of geography it may be used to organize the major topics of interest to scientists preoccupied with the understanding of real-world space in astronomy, geology, meteorology, hydrology, and biology. We shall use it to highlight such recent geographic accomplishments as automated feature detection, understanding urban growth, and modeling the spread of disease in space and time. However, several key challenges remain unsolved, among them: 1. It is still not possible to move continuously from one map scale to another so that objects change their dimension smoothly. I.e. as a viewer zooms in on a map the zero-dimensional location of a city should gradually become a 2-dimensional polygon, then a network of 1-dimensional streets, then 3-dimensional buildings, etc. 2. Spatial autocorrelation continues to be regarded more as an econometric challenge than as a problem of scaling. Similarities of values among closely-spaced observation is not so much a problem to be overcome as a source of information about spatial structure. 3. Although the fractal paradigm is a powerful model for data analysis, its ideas and techniques need to be brought to bear on the problems of understanding such hierarchies as ecosystems (the flow networks of energy and matter), taxonomies (biological classification), and knowledge (hierarchies of bureaucratic information, networks of linked data, etc).

Large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

Neural network computer simulation of medical aerosols.

PubMed

Richardson, C J; Barlow, D J

1996-06-01

Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols.

Music Signal Processing Using Vector Product Neural Networks

NASA Astrophysics Data System (ADS)

Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

2017-05-01

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

Revisiting the Al/Al₂O₃ interface: coherent interfaces and misfit accommodation.

PubMed

Pilania, Ghanshyam; Thijsse, Barend J; Hoagland, Richard G; Lazić, Ivan; Valone, Steven M; Liu, Xiang-Yang

2014-03-27

We study the coherent and semi-coherent Al/α-Al2O3 interfaces using molecular dynamics simulations with a mixed, metallic-ionic atomistic model. For the coherent interfaces, both Al-terminated and O-terminated nonstoichiometric interfaces have been studied and their relative stability has been established. To understand the misfit accommodation at the semi-coherent interface, a 1-dimensional (1D) misfit dislocation model and a 2-dimensional (2D) dislocation network model have been studied. For the latter case, our analysis reveals an interface dislocation structure with a network of three sets of parallel dislocations, each with pure-edge character, giving rise to a pattern of coherent and stacking-fault-like regions at the interface. Structural relaxation at elevated temperatures leads to a further change of the dislocation pattern, which can be understood in terms of a competition between the stacking fault energy and the dislocation interaction energy at the interface. Our results are expected to serve as an input for the subsequent dislocation dynamics models to understand and predict the macroscopic mechanical behavior of Al/α-Al2O3 composite heterostructures.

Three-dimensional MoO2 nanotextiles assembled from elongated nanowires as advanced anode for Li ion batteries

NASA Astrophysics Data System (ADS)

Xu, Guoqing; Liu, Ping; Ren, Yurong; Huang, Xiaobing; Peng, Zhiguang; Tang, Yougen; Wang, Haiyan

2017-09-01

The fabrication of an ideal electrode architecture consisting of robust three dimensional (3D) nanowire networks have gained special interest for energy storage applications owing to the integrated advantages of nanostructures and microstructures. In this work, 3D MoO2 nanotextiles assembled from highly interconnected elongated nanowires are successfully prepared by a facile stirring assisted hydrothermal method and followed by an annealing process. In addition, a methylbenzene/water biphasic reaction system is involved in the hydrothermal process. When used as an anode material in Li ion batteries (LIBs), this robust MoO2 nanotextiles exhibit a high reversible capacity (860.4 mAh g-1 at 300 mA g-1), excellent cycling performance (89% capacity retention after 160 cycles) and rate capability (577 mAh g-1 at 2000 mA g-1). Various synthetic factors to the fabrication of 3D nanotextiles structure are discussed here and this design of 3D network structures may be extended to the preparation of other functional nanomaterials.

A three-dimensionally chitin nanofiber/carbon nanotube hydrogel network for foldable conductive paper.

PubMed

Chen, Chuchu; Yang, Chuang; Li, Suiyi; Li, Dagang

2015-12-10

We reported a highly conductive nanocomposite made with multiwalled carbon nanotubes (MWCNTs) and chitin nanofibers (ChNFs). The MWCNTs were dispersed into ChNFs by the simple process of vacuum-filtration, forming a three-dimensional network structure. In this approach, MWCNT acted as a filler to introduce electron channel paths throughout the ChNF skeleton. And then, a hybrid hydrogel system (20 wt.% NaOH, -18 °C) was applied to prepare the ChNF/MWCNT gel-film followed with drying process. It is found that the resultant ChNF/MWCNT gel-film exposed much more MWCNT areas forming denser structure due to the shrinking of ChNFs after the gelation treatment. Compared with ChNF/MWCNT film, the one treated under hydrogel system (ChNF/MWCNT gel-film) exhibited almost twice higher conductivity (9.3S/cm for 50 wt.% MWCNTs in gel-film; whereas 4.7S/cm for 50 wt.% MWCNTs in film). Moreover, the facile and low-cost of this conductive paper may have great potential in development of foldable electronic devices. Copyright © 2015 Elsevier Ltd. All rights reserved.

Probing hydrogen bond networks in half-sandwich Ru(II) building blocks by a combined 1H DQ CRAMPS solid-state NMR, XRPD, and DFT approach.

PubMed

Chierotti, Michele R; Gobetto, Roberto; Nervi, Carlo; Bacchi, Alessia; Pelagatti, Paolo; Colombo, Valentina; Sironi, Angelo

2014-01-06

The hydrogen bond network of three polymorphs (1α, 1β, and 1γ) and one solvate form (1·H2O) arising from the hydration-dehydration process of the Ru(II) complex [(p-cymene)Ru(κN-INA)Cl2] (where INA is isonicotinic acid), has been ascertained by means of one-dimensional (1D) and two-dimensional (2D) double quantum (1)H CRAMPS (Combined Rotation and Multiple Pulses Sequences) and (13)C CPMAS solid-state NMR experiments. The resolution improvement provided by homonuclear decoupling pulse sequences, with respect to fast MAS experiments, has been highlighted. The solid-state structure of 1γ has been fully characterized by combining X-ray powder diffraction (XRPD), solid-state NMR, and periodic plane-wave first-principles calculations. None of the forms show the expected supramolecular cyclic dimerization of the carboxylic functions of INA, because of the presence of Cl atoms as strong hydrogen bond (HB) acceptors. The hydration-dehydration process of the complex has been discussed in terms of structure and HB rearrangements.

Methane dissociation on Ni(111): A fifteen-dimensional potential energy surface using neural network method

NASA Astrophysics Data System (ADS)

Shen, Xiangjian; Chen, Jun; Zhang, Zhaojun; Shao, Kejie; Zhang, Dong H.

2015-10-01

In the present work, we develop a highly accurate, fifteen-dimensional potential energy surface (PES) of CH4 interacting on a rigid flat Ni(111) surface with the methodology of neural network (NN) fit to a database consisted of about 194 208 ab initio density functional theory (DFT) energy points. Some careful tests of the accuracy of the fitting PES are given through the descriptions of the fitting quality, vibrational spectrum of CH4 in vacuum, transition state (TS) geometries as well as the activation barriers. Using a 25-60-60-1 NN structure, we obtain one of the best PESs with the least root mean square errors: 10.11 meV for the entrance region and 17.00 meV for the interaction and product regions. Our PES can reproduce the DFT results very well in particular for the important TS structures. Furthermore, we present the sticking probability S0 of ground state CH4 at the experimental surface temperature using some sudden approximations by Jackson's group. An in-depth explanation is given for the underestimated sticking probability.

Muscle networks: Connectivity analysis of EMG activity during postural control

NASA Astrophysics Data System (ADS)

Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

2015-12-01

Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

Two- and three-dimensional networks of gadolinium(III) with dicarboxylate ligands: synthesis, crystal structure, and magnetic properties.

PubMed

Cañadillas-Delgado, Laura; Pasan, Jorge; Fabelo, Oscar; Hernandez-Molina, María; Lloret, Francesc; Julve, Miguel; Ruiz-Pérez, Catalina

2006-12-25

Four gadolinium(III) complexes with dicarboxylate ligands of formulas [Gd2(mal)3(H2O)5]n.2nH2O (1), [Gd2(mal)3(H2O)6]n (2), [NaGd(mal)(ox)(H2O)3]n (3), and [Gd2(ox)3(H2O)6]n.2.5nH2O (4) (mal = malonate; ox = oxalate) have been prepared, and their magnetic properties have been investigated as a function of the temperature. The structures of 1-3 have been determined by X-ray diffraction methods. The crystal structure of 4 was already known, and it is made of hexagonal layers of Gd atoms that are bridged by bis-bidentate oxalate. Compound 1 is isostructural with the europium(III) malonate complex [Eu2(mal)3(H2O)5]n.2nH2O,1 whose structure was reported elsewhere. The Gd atoms in 1 define a two-dimensional network where a terminal bidentate and bridging bidentate/bis-monodentate and tris-bidentate coordination modes of malonate occur. Compound 2 has a three-dimensional structure with a structural phase transition at 226 K, which involves a change of the space group from I2/a to Ia. Although its structure at room temperature was already known, that below 226 K was not. Pairs of Gd atoms with a double oxo-carboxylate bridge occur in both phases, and the main differences between both structures deal with the Gd environment and the H-bond pattern. 3 is also a three-dimensional compound, and it was obtained by reacting Gd(III) ions with malonic acid in a silica gel medium. Oxalic acid results as an oxidized product of the malonic acid, and single crystals of the heteroleptic complex were produced. The Gd atoms in 3 are connected through bis-bidentate oxalate and carboxylate-malonate bridges in the anti-anti and anti-syn coordination modes. Compounds 1 and 2 exhibit weak but significant ferromagnetic couplings between the Gd(III) ions through the single (1) and double (2) oxo-carboxylate bridges, whereas antiferromagnetic interactions across the bis-bidentate oxalate account for the overall antiferromagnetic behavior observed in 3 and 4.

Accurate Prediction of Protein Contact Maps by Coupling Residual Two-Dimensional Bidirectional Long Short-Term Memory with Convolutional Neural Networks.

PubMed

Hanson, Jack; Paliwal, Kuldip; Litfin, Thomas; Yang, Yuedong; Zhou, Yaoqi

2018-06-19

Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12, (Schaarschmidt et al., 2018)) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC)>0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data is available at Bioinformatics online.

Efficient tree tensor network states (TTNS) for quantum chemistry: Generalizations of the density matrix renormalization group algorithm

NASA Astrophysics Data System (ADS)

Nakatani, Naoki; Chan, Garnet Kin-Lic

2013-04-01

We investigate tree tensor network states for quantum chemistry. Tree tensor network states represent one of the simplest generalizations of matrix product states and the density matrix renormalization group. While matrix product states encode a one-dimensional entanglement structure, tree tensor network states encode a tree entanglement structure, allowing for a more flexible description of general molecules. We describe an optimal tree tensor network state algorithm for quantum chemistry. We introduce the concept of half-renormalization which greatly improves the efficiency of the calculations. Using our efficient formulation we demonstrate the strengths and weaknesses of tree tensor network states versus matrix product states. We carry out benchmark calculations both on tree systems (hydrogen trees and π-conjugated dendrimers) as well as non-tree molecules (hydrogen chains, nitrogen dimer, and chromium dimer). In general, tree tensor network states require much fewer renormalized states to achieve the same accuracy as matrix product states. In non-tree molecules, whether this translates into a computational savings is system dependent, due to the higher prefactor and computational scaling associated with tree algorithms. In tree like molecules, tree network states are easily superior to matrix product states. As an illustration, our largest dendrimer calculation with tree tensor network states correlates 110 electrons in 110 active orbitals.

Evolution of network architecture in a granular material under compression

NASA Astrophysics Data System (ADS)

Papadopoulos, Lia; Puckett, James G.; Daniels, Karen E.; Bassett, Danielle S.

2016-09-01

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup.

Biomimetic oral mucin from polymer micelle networks

NASA Astrophysics Data System (ADS)

Authimoolam, Sundar Prasanth

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

Anhydrous 1:1 proton-transfer compounds of isonipecotamide with picric acid and 3,5-dinitrosalicylic acid: 4-carbamoylpiperidinium 2,4,6-trinitrophenolate and two polymorphs of 4-carbamoylpiperidinium 2-carboxy-4,6-dinitrophenolate.

PubMed

Smith, Graham; Wermuth, Urs D

2010-12-01

The structures of the anhydrous 1:1 proton-transfer compounds of isonipecotamide (piperidine-4-carboxamide) with picric acid and 3,5-dinitrosalicylic acid, namely 4-carbamoylpiperidinium 2,4,6-trinitrophenolate, C(6)H(13)N(2)O(+)·C(6)H(2)N(3)O(7)(-), (I), and 4-carbamoylpiperidinium 2-carboxy-4,6-dinitrophenolate [two forms of which were found, the monoclinic α-polymorph, (II), and the triclinic β-polymorph, (III)], C(6)H(13)N(2)O(+)·C(7)H(3)N(2)O(7)(-), have been determined at 200 K. All three compounds form hydrogen-bonded structures, viz. one-dimensional in (II), two-dimensional in (I) and three-dimensional in (III). In (I), the cations form centrosymmetric cyclic head-to-tail hydrogen-bonded homodimers [graph set R(2)(2)(14)] through lateral duplex piperidinium-amide N-H...O interactions. These dimers are extended into a two-dimensional network structure through further interactions with phenolate and nitro O-atom acceptors, including a direct symmetric piperidinium-phenol/nitro N-H...O,O cation-anion association [graph set R(1)(2)(6)]. The monoclinic polymorph, (II), has a similar R(1)(2)(6) cation-anion hydrogen-bonding interaction to (I) but with an additional conjoint symmetrical R(1)(2)(4) interaction as well as head-to-tail piperidinium-amide N-H...O,O hydrogen bonds and amide-carboxyl N-H...O hydrogen bonds, giving a network structure which includes large R(4)(3)(20) rings. The hydrogen bonding in the triclinic polymorph, (III), is markedly different from that of monoclinic (II). The asymmetric unit contains two independent cation-anion pairs which associate through cyclic piperidinium-carboxyl N-H...O,O' interactions [graph set R(1)(2)(4)]. The cations also show the zigzag head-to-tail piperidinium-amide N-H...O hydrogen-bonded chain substructures found in (II), but in addition feature amide-nitro and amide-phenolate N-H...O associations. As well, there is a centrosymmetric double-amide N-H...O(carboxyl) bridged bis(cation-anion) ring system [graph set R(4)(2)(8)] in the three-dimensional framework. The structures reported here demonstrate the utility of the isonipecotamide cation as a synthon with previously unrecognized potential for structure assembly applications. Furthermore, the structures of the two polymorphic 3,5-dinitrosalicylic acid salts show an unusual dissimilarity in hydrogen-bonding characteristics, considering that both were obtained from identical solvent systems.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

Three-Dimensional Networked Metal-Organic Frameworks with Conductive Polypyrrole Tubes for Flexible Supercapacitors.

PubMed

Xu, Xingtao; Tang, Jing; Qian, Huayu; Hou, Shujin; Bando, Yoshio; Hossain, Md Shahriar A; Pan, Likun; Yamauchi, Yusuke

2017-11-08

Metal-organic frameworks (MOFs) with high porosity and a regular porous structure have emerged as a promising electrode material for supercapacitors, but their poor electrical conductivity limits their utilization efficiency and capacitive performance. To increase the overall electrical conductivity as well as the efficiency of MOF particles, three-dimensional networked MOFs are developed via using preprepared conductive polypyrrole (PPy) tubes as the support for in situ growth of MOF particles. As a result, the highly conductive PPy tubes that run through the MOF particles not only increase the electron transfer between MOF particles and maintain the high effective porosity of the MOFs but also endow the MOFs with flexibility. Promoted by such elaborately designed MOF-PPy networks, the specific capacitance of MOF particles has been increased from 99.2 F g -1 for pristine zeolitic imidazolate framework (ZIF)-67 to 597.6 F g -1 for ZIF-PPy networks, indicating the importance of the design of the ZIF-PPy continuous microstructure. Furthermore, a flexible supercapacitor device based on ZIF-PPy networks shows an outstanding areal capacitance of 225.8 mF cm -2 , which is far above other MOFs-based supercapacitors reported up to date, confirming the significance of in situ synthetic chemistry as well as the importance of hybrid materials on the nanoscale.

Simulation of Electromigration Based on Resistor Networks

NASA Astrophysics Data System (ADS)

Patrinos, Anthony John

A two dimensional computer simulation of electromigration based on resistor networks was designed and implemented. The model utilizes a realistic grain structure generated by the Monte Carlo method and takes specific account of the local effects through which electromigration damage progresses. The dynamic evolution of the simulated thin film is governed by the local current and temperature distributions. The current distribution is calculated by superimposing a two dimensional electrical network on the lattice whose nodes correspond to the particles in the lattice and the branches to interparticle bonds. Current is assumed to flow from site to site via nearest neighbor bonds. The current distribution problem is solved by applying Kirchhoff's rules on the resulting electrical network. The calculation of the temperature distribution in the lattice proceeds by discretizing the partial differential equation for heat conduction, with appropriate material parameters chosen for the lattice and its defects. SEReNe (for Simulation of Electromigration using Resistor Networks) was tested by applying it to common situations arising in experiments with real films with satisfactory results. Specifically, the model successfully reproduces the expected grain size, line width and bamboo effects, the lognormal failure time distribution and the relationship between current density exponent and current density. It has also been modified to simulate temperature ramp experiments but with mixed, in this case, results.

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

PubMed Central

Srivastava, Ashutosh; Sinha, Somdatta

2014-01-01

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

Polyimide Aerogels with Three-Dimensional Cross-Linked Structure

NASA Technical Reports Server (NTRS)

Panek, John

2010-01-01

Polyimide aerogels with three-dimensional cross-linked structure are made using linear oligomeric segments of polyimide, and linked with one of the following into a 3D structure: trifunctional aliphatic or aromatic amines, latent reactive end caps such as nadic anhydride or phenylethynylphenyl amine, and silica or silsesquioxane cage structures decorated with amine. Drying the gels supercritically maintains the solid structure of the gel, creating a polyimide aerogel with improved mechanical properties over linear polyimide aerogels. Lightweight, low-density structures are desired for acoustic and thermal insulation for aerospace structures, habitats, astronaut equipment, and aeronautic applications. Aerogels are a unique material for providing such properties because of their extremely low density and small pore sizes. However, plain silica aerogels are brittle. Reinforcing the aerogel structure with a polymer (X-Aerogel) provides vast improvements in strength while maintaining low density and pore structure. However, degradation of polymers used in cross-linking tends to limit use temperatures to below 150 C. Organic aerogels made from linear polyimide have been demonstrated, but gels shrink substantially during supercritical fluid extraction and may have lower use temperature due to lower glass transition temperatures. The purpose of this innovation is to raise the glass transition temperature of all organic polyimide aerogel by use of tri-, tetra-, or poly-functional units in the structure to create a 3D covalently bonded network. Such cross-linked polyimides typically have higher glass transition temperatures in excess of 300 400 C. In addition, the reinforcement provided by a 3D network should improve mechanical stability, and prevent shrinkage on supercritical fluid extraction. The use of tri-functional aromatic or aliphatic amine groups in the polyimide backbone will provide such a 3D structure.

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

Equivalence between a generalized dendritic network and a set of one-dimensional networks as a ground of linear dynamics.

PubMed

Koda, Shin-ichi

2015-05-28

It has been shown by some existing studies that some linear dynamical systems defined on a dendritic network are equivalent to those defined on a set of one-dimensional networks in special cases and this transformation to the simple picture, which we call linear chain (LC) decomposition, has a significant advantage in understanding properties of dendrimers. In this paper, we expand the class of LC decomposable system with some generalizations. In addition, we propose two general sufficient conditions for LC decomposability with a procedure to systematically realize the LC decomposition. Some examples of LC decomposable linear dynamical systems are also presented with their graphs. The generalization of the LC decomposition is implemented in the following three aspects: (i) the type of linear operators; (ii) the shape of dendritic networks on which linear operators are defined; and (iii) the type of symmetry operations representing the symmetry of the systems. In the generalization (iii), symmetry groups that represent the symmetry of dendritic systems are defined. The LC decomposition is realized by changing the basis of a linear operator defined on a dendritic network into bases of irreducible representations of the symmetry group. The achievement of this paper makes it easier to utilize the LC decomposition in various cases. This may lead to a further understanding of the relation between structure and functions of dendrimers in future studies.

Histomorphometric study and three-dimensional reconstruction of the osteocyte lacuno-canalicular network one hour after applying tensile and compressive forces.

PubMed

Bozal, Carola B; Sánchez, Luciana M; Mandalunis, Patricia M; Ubios, Ángela M

2013-01-01

The occurrence of very early morphological changes in the osteocyte lacuno-canalicular network following application of tensile and/or compressive forces remains unknown to date. Thus, the aim of this study was to perform a morphological and morphometric evaluation of the changes in the three-dimensional structure of the lacuno-canalicular network and the osteocyte network of alveolar bone that take place very early after applying tensile and compressive forces in vivo, conducting static histomorphometry on bright-field microscopy and confocal laser scanning microscopy images. Our results showed that both the tensile and compressive forces induced early changes in osteocytes and their lacunae, which manifested as an increase in lacunar volume and changes in lacunar shape and orientation. An increase in canalicular width and a decrease in the width and an increase in the length of cytoplasmic processes were also observed. The morphological changes in the lacuno-canalicular and osteocyte networks that occur in vivo very early after application of tensile and compressive forces would be an indication of an increase in permeability within the system. Thus, both compressive and tensile forces would cause fluid displacement very soon after being applied; the latter would in turn rapidly activate alveolar bone osteocytes, enhancing transmission of the signals to the entire osteocyte network and the effector cells located at the bone surface. Copyright © 2013 S. Karger AG, Basel.

Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations.

PubMed

Wu, Shuang; Liu, Zhi-Ping; Qiu, Xing; Wu, Hulin

2014-01-01

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

Pressure induced metallization with absence of structural transition in layered molybdenum diselenide

PubMed Central

Zhao, Zhao; Zhang, Haijun; Yuan, Hongtao; Wang, Shibing; Lin, Yu; Zeng, Qiaoshi; Xu, Gang; Liu, Zhenxian; Solanki, G. K.; Patel, K. D.; Cui, Yi; Hwang, Harold Y.; Mao, Wendy L.

2015-01-01

Layered transition-metal dichalcogenides have emerged as exciting material systems with atomically thin geometries and unique electronic properties. Pressure is a powerful tool for continuously tuning their crystal and electronic structures away from the pristine states. Here, we systematically investigated the pressurized behavior of MoSe2 up to ∼60 GPa using multiple experimental techniques and ab-initio calculations. MoSe2 evolves from an anisotropic two-dimensional layered network to a three-dimensional structure without a structural transition, which is a complete contrast to MoS2. The role of the chalcogenide anions in stabilizing different layered patterns is underscored by our layer sliding calculations. MoSe2 possesses highly tunable transport properties under pressure, determined by the gradual narrowing of its band-gap followed by metallization. The continuous tuning of its electronic structure and band-gap in the range of visible light to infrared suggest possible energy-variable optoelectronics applications in pressurized transition-metal dichalcogenides. PMID:26088416

Pressure induced metallization with absence of structural transition in layered molybdenum diselenide

DOE PAGES

Zhao, Zhao; Zhang, Haijun; Yuan, Hongtao; ...

2015-06-19

Layered transition-metal dichalcogenides have emerged as exciting material systems with atomically thin geometries and unique electronic properties. Pressure is a powerful tool for continuously tuning their crystal and electronic structures away from the pristine states. Here, we systematically investigated the pressurized behavior of MoSe 2 up to ~60 GPa using multiple experimental techniques and ab-initio calculations. MoSe 2 evolves from an anisotropic two-dimensional layered network to a three-dimensional structure without a structural transition, which is a complete contrast to MoS 2. The role of the chalcogenide anions in stabilizing different layered patterns is underscored by our layer sliding calculations. MoSemore » 2 possesses highly tunable transport properties under pressure, determined by the gradual narrowing of its band-gap followed by metallization. The continuous tuning of its electronic structure and band-gap in the range of visible light to infrared suggest possible energy-variable optoelectronics applications in pressurized transition-metal dichalcogenides.« less

Structure determination and characterization of two rare-earth molybdenum borate compounds: LnMoBO(6) (Ln = La, Ce).

PubMed

Zhao, Dan; Cheng, Wen-Dan; Zhang, Hao; Hang, Shu-Ping; Fang, Ming

2008-07-28

The structural, optical, and electronic properties of two rare-earth molybdenum borate compounds, LnMoBO(6) (Ln = La, Ce), have been investigated by means of single-crystal X-ray diffraction, elemental analyses, and spectral measurements, as well as calculations of energy band structures, density of states, and optical response functions by the density functional method. The title compounds, which crystallize in monoclinic space group P2(1)/c, possess a similar network of interconnected [Ce(2)(MoO(4))(2)](2+) chains and [BO(2)](-) wavy chains. Novel 1D molybdenum oxide chains are contained in their three-dimensional (3D) networks. The calculated results of crystal energy band structure by the density functional theory (DFT) method show that the solid-state compound LaMoBO(6) is a semiconductor with indirect band gaps.

Nanocarbon networks for advanced rechargeable lithium batteries.

PubMed

Xin, Sen; Guo, Yu-Guo; Wan, Li-Jun

2012-10-16

Carbon is one of the essential elements in energy storage. In rechargeable lithium batteries, researchers have considered many types of nanostructured carbons, such as carbon nanoparticles, carbon nanotubes, graphene, and nanoporous carbon, as anode materials and, especially, as key components for building advanced composite electrode materials. Nanocarbons can form efficient three-dimensional conducting networks that improve the performance of electrode materials suffering from the limited kinetics of lithium storage. Although the porous structure guarantees a fast migration of Li ions, the nanocarbon network can serve as an effective matrix for dispersing the active materials to prevent them from agglomerating. The nanocarbon network also affords an efficient electron pathway to provide better electrical contacts. Because of their structural stability and flexibility, nanocarbon networks can alleviate the stress and volume changes that occur in active materials during the Li insertion/extraction process. Through the elegant design of hierarchical electrode materials with nanocarbon networks, researchers can improve both the kinetic performance and the structural stability of the electrode material, which leads to optimal battery capacity, cycling stability, and rate capability. This Account summarizes recent progress in the structural design, chemical synthesis, and characterization of the electrochemical properties of nanocarbon networks for Li-ion batteries. In such systems, storage occurs primarily in the non-carbon components, while carbon acts as the conductor and as the structural buffer. We emphasize representative nanocarbon networks including those that use carbon nanotubes and graphene. We discuss the role of carbon in enhancing the performance of various electrode materials in areas such as Li storage, Li ion and electron transport, and structural stability during cycling. We especially highlight the use of graphene to construct the carbon conducting network for alloy anodes, such as Si and Ge, to accelerate electron transport, alleviate volume change, and prevent the agglomeration of active nanoparticles. Finally, we describe the power of nanocarbon networks for the next generation rechargeable lithium batteries, including Li-S, Li-O(2), and Li-organic batteries, and provide insights into the design of ideal nanocarbon networks for these devices. In addition, we address the ways in which nanocarbon networks can expand the applications of rechargeable lithium batteries into the emerging fields of stationary energy storage and transportation.

Syntheses, crystal structures, and properties of four complexes based on polycarboxylate and imidazole ligands

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

Qiao, Rui; Chen, Shui-Sheng, E-mail: chenss@fync.edu.cn; Coordination Chemistry Institute, State Key Laboratory of Coordination Chemistry, Nanjing University, Nanjing 210093

2015-08-15

Four metal–organic coordination polymers [Zn(HL)(H{sub 2}O)]·4H{sub 2}O (1), [Zn(HL)(L{sub 1})]·4H{sub 2}O (2), [Cu(HL)(H{sub 2}O)]·3H{sub 2}O (3) and [Cu(HL)(L{sub 1})]·5H{sub 2}O (4) were synthesized by reactions of the corresponding metal(II) salts with semirigid polycarboxylate ligand (5-((4-carboxypiperidin-1-yl)methyl)isophthalic acid hydrochloride, H{sub 3}L·HCl) or auxiliary ligand (1,4-di(1H-imidazol-4-yl)benzene, L{sub 1}). The structures of the compounds were characterized by elemental analysis, FT-IR spectroscopy and single-crystal X-ray diffraction. The use of auxiliary ligand L{sub 1} has great influence on the structures of two pairs of complexes 1, 2 and 3, 4. Complex 1 is a uninodal 3-connected rare 2-fold interpenetrating ZnSc net with a Point (Schlafli) symbolmore » of (10{sup 3}) while 2 is a one-dimensional (1D) ladder structure. Compound 3 features a two-dimensional (2D) honeycomb network with typical 6{sup 3}-hcb topology, while 4 is 2D network with (4, 4) sql topology based on binuclear Cu{sup II} subunits. The non-covalent bonding interactions such as hydrogen bonds, π···π stacking and C–H···π exist in complexes 1–4, which contributes to stabilize crystal structure and extend the low-dimensional entities into high-dimensional frameworks. And the photoluminescent property of 1 and 2 and gas sorption property of 4 have been investigated. - Graphical abstract: Four new coordination polymers have been obtained and their photoluminescent and gas sorption properties have also been investigated. - Highlights: • Two pairs of Zn{sup II}/ Cu{sup II} compounds have been synthesized. • Auxiliary ligand-controlled assembly of the complexes is reported. • The luminescent properties of complexes 1–2 were investigated. • The gas sorption property of 4 has been investigated.« less

Air and moisture stable covalently-bonded tin(ii) coordination polymers.

PubMed

de Lima, G M; Walton, R I; Clarkson, G J; Bitzer, R S; Ardisson, J D

2018-06-05

Four covalently-bonded tin(ii) coordination polymers, (1)-(4), were hydrothermally prepared in aqueous alkaline media by the reactions of SnSO4 with 1,2,4,5-benzenetetracarboxylic acid (1), 1,3,5-benzenetricarboxylic acid (2), 4-hydroxypyridine-2,6-dicarboxylic acid (3), and 1,3,5-cyclohexanetricarboxylic acid (4). All products were structurally authenticated by single-crystal X-ray diffraction, and the number of different tin centres and their oxidation states were confirmed by 119Sn Mössbauer spectroscopy. In addition, the comparison between experimental and simulated X-ray powder diffraction patterns confirmed the authenticity of the samples. Our crystallographic results for (1)-(4) show that the Sn(ii) centres are tetracoordinated and exhibit distorted disphenoidal geometries, corroborating the presence of one stereochemically active lone electron pair at each metal site. Products (1) and (2) display bi-dimensional polymeric structures, (3) exhibits a one-dimensional architecture, whereas (4) shows a remarkable three-dimensional coordination network. Hirshfeld surface and supramolecular analyses for the repeating units of (1)-(4) were also performed in order to identify structurally important non-covalent interactions.

Electron percolation in realistic models of carbon nanotube networks

NASA Astrophysics Data System (ADS)

Simoneau, Louis-Philippe; Villeneuve, Jérémie; Rochefort, Alain

2015-09-01

The influence of penetrable and curved carbon nanotubes (CNT) on the charge percolation in three-dimensional disordered CNT networks have been studied with Monte-Carlo simulations. By considering carbon nanotubes as solid objects but where the overlap between their electron cloud can be controlled, we observed that the structural characteristics of networks containing lower aspect ratio CNT are highly sensitive to the degree of penetration between crossed nanotubes. Following our efficient strategy to displace CNT to different positions to create more realistic statistical models, we conclude that the connectivity between objects increases with the hard-core/soft-shell radii ratio. In contrast, the presence of curved CNT in the random networks leads to an increasing percolation threshold and to a decreasing electrical conductivity at saturation. The waviness of CNT decreases the effective distance between the nanotube extremities, hence reducing their connectivity and degrading their electrical properties. We present the results of our simulation in terms of thickness of the CNT network from which simple structural parameters such as the volume fraction or the carbon nanotube density can be accurately evaluated with our more realistic models.

Network representation of protein interactions: Theory of graph description and analysis.

PubMed

Kurzbach, Dennis

2016-09-01

A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin. © 2016 The Protein Society.

Evolution of the three-dimensional collagen structure in vascular walls during deformation: an in situ mechanical testing under multiphoton microscopy observation.

PubMed

Nierenberger, Mathieu; Fargier, Guillaume; Ahzi, Saïd; Rémond, Yves

2015-08-01

The collagen fibers' three-dimensional architecture has a strong influence on the mechanical behavior of biological tissues. To accurately model this behavior, it is necessary to get some knowledge about the structure of the collagen network. In the present paper, we focus on the in situ characterization of the collagenous structure, which is present in porcine jugular vein walls. An observation of the vessel wall is first proposed in an unloaded configuration. The vein is then put into a mechanical tensile testing device. As the vein is stretched, three-dimensional images of its collagenous structure are acquired using multiphoton microscopy. Orientation analyses are provided for the multiple images recorded during the mechanical test. From these analyses, the reorientation of the two families of collagen fibers existing in the vein wall is quantified. We noticed that the reorientation of the fibers stops as the tissue stiffness starts decreasing, corresponding to the onset of damage. Besides, no relevant evolutions of the out of plane collagen orientations were observed. Due to the applied loading, our analysis also allowed for linking the stress relaxation within the tissue to its internal collagenous structure. Finally, this analysis constitutes the first mechanical test performed under a multiphoton microscope with a continuous three-dimensional observation of the tissue structure all along the test. It allows for a quantitative evaluation of microstructural parameters combined with a measure of the global mechanical behavior. Such data are useful for the development of structural mechanical models for living tissues.

Frequency mode excitations in two-dimensional Hindmarsh-Rose neural networks

NASA Astrophysics Data System (ADS)

Tabi, Conrad Bertrand; Etémé, Armand Sylvin; Mohamadou, Alidou

2017-05-01

In this work, we explicitly show the existence of two frequency regimes in a two-dimensional Hindmarsh-Rose neural network. Each of the regimes, through the semi-discrete approximation, is shown to be described by a two-dimensional complex Ginzburg-Landau equation. The modulational instability phenomenon for the two regimes is studied, with consideration given to the coupling intensities among neighboring neurons. Analytical solutions are also investigated, along with their propagation in the two frequency regimes. These waves, depending on the coupling strength, are identified as breathers, impulses and trains of soliton-like structures. Although the waves in two regimes appear in some common regions of parameters, some phase differences are noticed and the global dynamics of the system is highly influenced by the values of the coupling terms. For some values of such parameters, the high-frequency regime displays modulated trains of waves, while the low-frequency dynamics keeps the original asymmetric character of action potentials. We argue that in a wide range of pathological situations, strong interactions among neurons can be responsible for some pathological states, including schizophrenia and epilepsy.

A neural-network potential through charge equilibration for WS2: From clusters to sheets

NASA Astrophysics Data System (ADS)

Hafizi, Roohollah; Ghasemi, S. Alireza; Hashemifar, S. Javad; Akbarzadeh, Hadi

2017-12-01

In the present work, we use a machine learning method to construct a high-dimensional potential for tungsten disulfide using a charge equilibration neural-network technique. A training set of stoichiometric WS2 clusters is prepared in the framework of density functional theory. After training the neural-network potential, the reliability and transferability of the potential are verified by performing a crystal structure search on bulk phases of WS2 and by plotting energy-area curves of two different monolayers. Then, we use the potential to investigate various triangular nano-clusters and nanotubes of WS2. In the case of nano-structures, we argue that 2H atomic configurations with sulfur rich edges are thermodynamically more stable than the other investigated configurations. We also studied a number of WS2 nanotubes which revealed that 1T tubes with armchair chirality exhibit lower bending stiffness.

Reconfigurable origami-inspired acoustic waveguides

PubMed Central

Babaee, Sahab; Overvelde, Johannes T. B.; Chen, Elizabeth R.; Tournat, Vincent; Bertoldi, Katia

2016-01-01

We combine numerical simulations and experiments to design a new class of reconfigurable waveguides based on three-dimensional origami-inspired metamaterials. Our strategy builds on the fact that the rigid plates and hinges forming these structures define networks of tubes that can be easily reconfigured. As such, they provide an ideal platform to actively control and redirect the propagation of sound. We design reconfigurable systems that, depending on the externally applied deformation, can act as networks of waveguides oriented along one, two, or three preferential directions. Moreover, we demonstrate that the capability of the structure to guide and radiate acoustic energy along predefined directions can be easily switched on and off, as the networks of tubes are reversibly formed and disrupted. The proposed designs expand the ability of existing acoustic metamaterials and exploit complex waveguiding to enhance control over propagation and radiation of acoustic energy, opening avenues for the design of a new class of tunable acoustic functional systems. PMID:28138527

Three Dimensional Mesoscale Analysis of Translamellar Cross-Bridge Morphologies in the Annulus Fibrosus using Optical Coherence Tomography

PubMed Central

Han, Sang Kuy; Chen, Chao-Wei; Wierwille, Jerry; Chen, Yu; Hsieh, Adam H.

2014-01-01

The defining characteristic of the annulus fibrosus (AF) of the intervertebral disc (IVD) has long been the lamellar structures that consist of highly ordered collagen fibers arranged in alternating oblique angles from one layer to the next. However, a series of recent histologic studies have demonstrated that AF lamellae contain elastin- and type VI collagen-rich secondary “cross-bridge” structures across lamellae. In this study, we use optical coherence tomography (OCT) to elucidate the three-dimensional (3D) morphologies of these translamellar cross-bridge in AF tissues. Mesoscale volumetric images by OCT reveal a highly heterogeneous spatial network and distribution of 3-D translamellar cross-bridges. The results of this study confirm the translamellar cross-bridge is identified as a distinguishable structure, which is laid in the interbundle space of adjacent lamellae and crisscrosses multiple lamellae in the radial direction. In contrast to previously proposed models extrapolated from 2-D sections, results from this current study show that translamellar cross-bridges exist as a complex, interconnected network. We also found much greater variation in lengths of cross-bridges within the interbundle space of lamellae (0.8-1.4 mm from the current study versus 0.3-0.6 mm from 2-D sections). OCT-based 3-D morphology of translamellar cross-bridge provides novel insight into the AF structure. PMID:25564974

DNA-nanoparticle assemblies go organic: macroscopic polymeric materials with nanosized features.

PubMed

Mentovich, Elad D; Livanov, Konstantin; Prusty, Deepak K; Sowwan, Mukules; Richter, Shachar

2012-05-30

One of the goals in the field of structural DNA nanotechnology is the use of DNA to build up 2- and 3-D nanostructures. The research in this field is motivated by the remarkable structural features of DNA as well as by its unique and reversible recognition properties. Nucleic acids can be used alone as the skeleton of a broad range of periodic nanopatterns and nanoobjects and in addition, DNA can serve as a linker or template to form DNA-hybrid structures with other materials. This approach can be used for the development of new detection strategies as well as nanoelectronic structures and devices. Here we present a new method for the generation of unprecedented all-organic conjugated-polymer nanoparticle networks guided by DNA, based on a hierarchical self-assembly process. First, microphase separation of amphiphilic block copolymers induced the formation of spherical nanoobjects. As a second ordering concept, DNA base pairing has been employed for the controlled spatial definition of the conjugated-polymer particles within the bulk material. These networks offer the flexibility and the diversity of soft polymeric materials. Thus, simple chemical methodologies could be applied in order to tune the network's electrical, optical and mechanical properties. One- two- and three-dimensional networks have been successfully formed. Common to all morphologies is the integrity of the micelles consisting of DNA block copolymer (DBC), which creates an all-organic engineered network.

Neural constraints on learning.

PubMed

Sadtler, Patrick T; Quick, Kristin M; Golub, Matthew D; Chase, Steven M; Ryu, Stephen I; Tyler-Kabara, Elizabeth C; Yu, Byron M; Batista, Aaron P

2014-08-28

Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

[Establishment of sprouting embryoid body model mimicking early embryonic vasculogenesis in human embryo].

PubMed

Jiang, Hua; Feng, You-Ji; Xie, Yi; Han, Jin-Lan; Wang, Zack; Chen, Tong

2008-10-14

To establish a sprouting embryoid body model mimicking early embryonic vasculogenesis in human embryo. Human embryonic stem were (hESCs) were cultured on the mouse embryo fibroblasts and then were induced to differentiate to form three-dimensional EB. The hEBs were cultured in media containing various angiogenesis-related factors: vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), endostatin, angiostatin, and platelet factor (PF)-4 of different concentrations for 3 days to observe the sprouting of the hEBs. 3, 3, 3', 3'-tetramethylindo-carbocyanine perchlorate labeled acetylated low density lipoprotein (Dil-AcLDL) was added onto the hEBs foe 4 h Immunofluorescence assay was used to observe if Dil-AcLDL was absorbed and if CD31 was expressed so as to determine the existence of embryonic endothelial cells in the sprouting structures. The ideal culturing condition was analyzed. The differentiated EBs formed sprouting structures in the collagen I matrix containing VEGF and FGF. The sprouts among individual EBs were able to link to each other and form vascular network-like structures. In the presence of VEGF and FGF, the sprouts branching from the EBs assimilated Dil-AcLDL, expressed CD31 and formed a 3-dimensional cylindrical organization. The concentrations of growth factors ideally stimulating sprouting growth were 100 ng/ml of VEGF and 50 ng/ml of FGF. The networks among the EBs were abolished by the angiostatin, endostatin, and PF4. The sprouting from hEBs accumulates embryonic endothelial cells and the sprouting network-like structures are indeed endothelial in nature. Inducing of sprouting EBs is an ideal model that mimics early embryonic vasculogenesis in humans.

Syntheses, structures and properties of four Cd(II) coordination polymers induced by the pH regulator

NASA Astrophysics Data System (ADS)

Xu, Yun; Ding, Fang; Liu, Dong; Yang, Pei-Pei; Zhu, Li-Li

2018-03-01

Four new coordination polymers [Cd2(CHDC)2(APYZ)(H2O)2](H2O) (1), [Cd(HCHDC)2(APYZ) (H2O)] (2), [Cd2(CHDC)2(PYZ)(H2O)2](H2O) (3), and [Cd(HCHDC)2(PYZ)(H2O)] (4) (H2CHDC = 1,4-cyclohexanedicarboxylic acid, APYZ = 2-aminopyrazine, PYZ = pyrazine) have been synthesized under the hydrothermal conditions by changing the pH regulator and the N-containing ligands. The pH regulator impacted on the degree of deprotonation of the 1,4-cyclohexanedicarboxylic acid ligand and resulted in the formation of the two pairs of different networks. Polymers 1 and 3 crystallize in monoclinic, space group P21/c, exhibit two dimensional 63 net, which further formed three-dimensional supramolecular structure by the Csbnd H⋯O hydrogen bond interactions. While polymers 2 and 4 possess one dimensional chain structures and further link into two dimensional layered supramolecular structures by intermolecular hydrogen bonding interactions. From all three conformers of H2CHDC, e,a-cis is consistently present in the Cd coordination polymers. Furthermore, photoluminescence properties of four polymers are also investigated, the luminescent intensity of polymer 1 (or 2) with amino group in pyrazine is dramatically stronger than that of the similar structure of polymer 3 (or 4) without amino group in pyrazine, the results shown that the presence of the amino group from 2-aminopyrazine play a key role in increasing the luminescence properties.

A series of Cadmium(II) complexes with 2-substituted terephthalate building block and N-Donor co-ligands: Structural diversity and fluorescence properties

NASA Astrophysics Data System (ADS)

Ren, Yixia; Zhou, Shanhong; Wang, Zhixiang; Zhang, Meili; Wang, Jijiang; Cao, Jia

2017-11-01

Four new Cd(II) complexes have been prepared based on 1,2,4-trimellitic acid (H3tma) and monosodium 2-sulfoterephthalate (2-NaH2stp), formulated as [Cd2(Htma)2 (dpp)2(H2O)] (1), [Cd3 (tma)2 (2,4-bipy)4(H2O)2] (2), [Cd (2-Hstp) (2,2'-bipy)2]·2H2O (3) and [Cd (2-Hstp) (2,4-bipy) (H2O)2] (4) (dpp = dipyrido [3,2-a:2‧,3'-c] phenazine, 2,4-bipy = 2,4-bipyridine, 2,2'-bipy = 2,2'- bipyridine) by hydrothermal method. X-ray diffraction structural analyses show all these complexes crystallized in triclinic crystal system of Pī space group, but their structures are diverse. Complex 1 exhibits an infinite one-dimensional chain featuring the left- and right-handed stranded chains interweaved each other. For 2, the two-dimensional network is constructed by one-dimensional ladder-like chain linked by Cd2 ions. In complex 3, the cadmium ion is surrounded with one 2-Hstp2- anion and two 2,2'-bipy molecules. Complex 4 is also a discrete structure based on a metallic dimer unit. In all these complexes, the N-donor co-ligands take the important roles in the assembly of three-dimensional supramolecular structures. The fluorescence properties of complexes 1-4 could be assigned to the π - π* transition of organic ligands.

Cellulose nanofiber-templated three-dimension TiO 2 hierarchical nanowire network for photoelectrochemical photoanode

Treesearch

Zhaodong Li; Chunhua Yao; Fei Wang; Zhiyong Cai; Xudong Wang

2014-01-01

Three dimensional (3D) nanostructures with extremely large porosity possess a great promise for the development of high-performance energy harvesting storage devices. In this paper, we developed a high-density 3D TiO2 fiber-nanorod (NR) heterostructure for photoelectrochemical (PEC) water splitting. The hierarchical structure was synthesized on a...

Hierarchical structural health monitoring system combining a fiber optic spinal cord network and distributed nerve cell devices

NASA Astrophysics Data System (ADS)

Minakuchi, Shu; Tsukamoto, Haruka; Takeda, Nobuo

2009-03-01

This study proposes novel hierarchical sensing concept for detecting damages in composite structures. In the hierarchical system, numerous three-dimensionally structured sensor devices are distributed throughout the whole structural area and connected with the optical fiber network through transducing mechanisms. The distributed "sensory nerve cell" devices detect the damage, and the fiber optic "spinal cord" network gathers damage signals and transmits the information to a measuring instrument. This study began by discussing the basic concept of the hierarchical sensing system thorough comparison with existing fiber optic based systems and nerve systems in the animal kingdom. Then, in order to validate the proposed sensing concept, impact damage detection system for the composite structure was proposed. The sensor devices were developed based on Comparative Vacuum Monitoring (CVM) system and the Brillouin based distributed strain sensing was utilized to gather the damage signals from the distributed devices. Finally a verification test was conducted using prototype devices. Occurrence of barely visible impact damage was successfully detected and it was clearly indicated that the hierarchical system has better repairability, higher robustness, and wider monitorable area compared to existing systems utilizing embedded optical fiber sensors.

The DIMA web resource--exploring the protein domain network.

PubMed

Pagel, Philipp; Oesterheld, Matthias; Stümpflen, Volker; Frishman, Dmitrij

2006-04-15

Conserved domains represent essential building blocks of most known proteins. Owing to their role as modular components carrying out specific functions they form a network based both on functional relations and direct physical interactions. We have previously shown that domain interaction networks provide substantially novel information with respect to networks built on full-length protein chains. In this work we present a comprehensive web resource for exploring the Domain Interaction MAp (DIMA), interactively. The tool aims at integration of multiple data sources and prediction techniques, two of which have been implemented so far: domain phylogenetic profiling and experimentally demonstrated domain contacts from known three-dimensional structures. A powerful yet simple user interface enables the user to compute, visualize, navigate and download domain networks based on specific search criteria. http://mips.gsf.de/genre/proj/dima

Mechanisms of Seizure Propagation in 2-Dimensional Centre-Surround Recurrent Networks

PubMed Central

Hall, David; Kuhlmann, Levin

2013-01-01

Understanding how seizures spread throughout the brain is an important problem in the treatment of epilepsy, especially for implantable devices that aim to avert focal seizures before they spread to, and overwhelm, the rest of the brain. This paper presents an analysis of the speed of propagation in a computational model of seizure-like activity in a 2-dimensional recurrent network of integrate-and-fire neurons containing both excitatory and inhibitory populations and having a difference of Gaussians connectivity structure, an approximation to that observed in cerebral cortex. In the same computational model network, alternative mechanisms are explored in order to simulate the range of seizure-like activity propagation speeds (0.1–100 mm/s) observed in two animal-slice-based models of epilepsy: (1) low extracellular , which creates excess excitation and (2) introduction of gamma-aminobutyric acid (GABA) antagonists, which reduce inhibition. Moreover, two alternative connection topologies are considered: excitation broader than inhibition, and inhibition broader than excitation. It was found that the empirically observed range of propagation velocities can be obtained for both connection topologies. For the case of the GABA antagonist model simulation, consistent with other studies, it was found that there is an effective threshold in the degree of inhibition below which waves begin to propagate. For the case of the low extracellular model simulation, it was found that activity-dependent reductions in inhibition provide a potential explanation for the emergence of slowly propagating waves. This was simulated as a depression of inhibitory synapses, but it may also be achieved by other mechanisms. This work provides a localised network understanding of the propagation of seizures in 2-dimensional centre-surround networks that can be tested empirically. PMID:23967201

On characterizing population commonalities and subject variations in brain networks.

PubMed

Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan; Shankar, Varsha; Roberts, Timothy P L; Edgar, J Christopher; Schultz, Robert T; Verma, Ragini

2017-05-01

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. Copyright © 2015 Elsevier B.V. All rights reserved.

de Sitter space as a tensor network: Cosmic no-hair, complementarity, and complexity

NASA Astrophysics Data System (ADS)

Bao, Ning; Cao, ChunJun; Carroll, Sean M.; Chatwin-Davies, Aidan

2017-12-01

We investigate the proposed connection between de Sitter spacetime and the multiscale entanglement renormalization ansatz (MERA) tensor network, and ask what can be learned via such a construction. We show that the quantum state obeys a cosmic no-hair theorem: the reduced density operator describing a causal patch of the MERA asymptotes to a fixed point of a quantum channel, just as spacetimes with a positive cosmological constant asymptote to de Sitter space. The MERA is potentially compatible with a weak form of complementarity (local physics only describes single patches at a time, but the overall Hilbert space is infinite dimensional) or, with certain specific modifications to the tensor structure, a strong form (the entire theory describes only a single patch plus its horizon, in a finite-dimensional Hilbert space). We also suggest that de Sitter evolution has an interpretation in terms of circuit complexity, as has been conjectured for anti-de Sitter space.

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

The Virtual Pelvic Floor, a tele-immersive educational environment.

PubMed Central

Pearl, R. K.; Evenhouse, R.; Rasmussen, M.; Dech, F.; Silverstein, J. C.; Prokasy, S.; Panko, W. B.

1999-01-01

This paper describes the development of the Virtual Pelvic Floor, a new method of teaching the complex anatomy of the pelvic region utilizing virtual reality and advanced networking technology. Virtual reality technology allows improved visualization of three-dimensional structures over conventional media because it supports stereo vision, viewer-centered perspective, large angles of view, and interactivity. Two or more ImmersaDesk systems, drafting table format virtual reality displays, are networked together providing an environment where teacher and students share a high quality three-dimensional anatomical model, and are able to converse, see each other, and to point in three dimensions to indicate areas of interest. This project was realized by the teamwork of surgeons, medical artists and sculptors, computer scientists, and computer visualization experts. It demonstrates the future of virtual reality for surgical education and applications for the Next Generation Internet. Images Figure 1 Figure 2 Figure 3 PMID:10566378

Neural-Network Object-Recognition Program

NASA Technical Reports Server (NTRS)

Spirkovska, L.; Reid, M. B.

1993-01-01

HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

Cell-ECM Interactions During Cancer Invasion

NASA Astrophysics Data System (ADS)

Jiang, Yi

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

Evolution of robustness to damage in artificial 3-dimensional development.

PubMed

Joachimczak, Michał; Wróbel, Borys

2012-09-01

GReaNs is an Artificial Life platform we have built to investigate the general principles that guide evolution of multicellular development and evolution of artificial gene regulatory networks. The embryos develop in GReaNs in a continuous 3-dimensional (3D) space with simple physics. The developmental trajectories are indirectly encoded in linear genomes. The genomes are not limited in size and determine the topology of gene regulatory networks that are not limited in the number of nodes. The expression of the genes is continuous and can be modified by adding environmental noise. In this paper we evolved development of structures with a specific shape (an ellipsoid) and asymmetrical pattering (a 3D pattern inspired by the French flag problem), and investigated emergence of the robustness to damage in development and the emergence of the robustness to noise. Our results indicate that both types of robustness are related, and that including noise during evolution promotes higher robustness to damage. Interestingly, we have observed that some evolved gene regulatory networks rely on noise for proper behaviour. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

PubMed

de la Vega de León, Antonio; Bajorath, Jürgen

2016-09-01

The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation. A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space. TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional chemical space.

Connections of intermediate filaments with the nuclear lamina and the cell periphery.

PubMed

Katsuma, Y; Swierenga, S H; Marceau, N; French, S W

1987-01-01

We investigated the relationship between intermediate filaments (IFs) and other detergent- and nuclease-resistant filamentous structures of cultured liver epithelial cells (T51B cell line) using whole mount unembedded preparations which were sequentially extracted with Triton X-100 and nucleases. Immunogold labelling and stereoscopic observation facilitated the examination of each filamentous structure and their three-dimensional relationships to each other. After solubilizing phospholipid, nucleic acid and soluble cellular protein, the resulting cytoskeleton preparation consisted of a network of cytokeratin and vimentin IFs linked by 3 nm filaments. The IFs were anchored to and determined the position of the nuclear lamina filaments (NLF) network and the centrioles. The NLF was composed of the nuclear lamina filaments measuring 3-6 nm in diameter which radiated from and anchored to the skeleton of the nuclear pores. The IFs located in the nuclear region appeared to be interwoven with the NLF. At the cell surface, the IFs seemed to be attached to the putative actin filament network. They formed a focally interrupted plexus-like structure at the cell periphery. Fragments of vimentin filaments were found among the filamentous network located at the cell surface, and some filaments terminated blindly there.

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

PubMed

Kowalski, Piotr A; Kusy, Maciej

2018-05-01

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Constructed uninterrupted charge-transfer pathways in three-dimensional micro/nanointerconnected carbon-based electrodes for high energy-density ultralight flexible supercapacitors.

PubMed

He, Yongmin; Chen, Wanjun; Zhou, Jinyuan; Li, Xiaodong; Tang, Pengyi; Zhang, Zhenxing; Fu, Jiecai; Xie, Erqing

2014-01-08

A type of freestanding three-dimensional (3D) micro/nanointerconnected structure, with a conjunction of microsized 3D graphene networks, nanosized 3D carbon nanofiber (CNF) forests, and consequently loaded MnO2 nanosheets, has been designed as the electrodes of an ultralight flexible supercapacitor. The resulting 3D graphene/CNFs/MnO2 composite networks exhibit remarkable flexibility and highly mechanical properties due to good and intimate contacts among them, without current collectors and binders. Simultaneously, this designed 3D micro/nanointerconnected structure can provide an uninterrupted double charges freeway network for both electron and electrolyte ion to minimize electron accumulation and ion-diffusing resistance, leading to an excellent electrochemical performance. The ultrahigh specific capacitance of 946 F/g from cyclic voltammetry (CV) (or 920 F/g from galvanostatic charging/discharging (GCD)) were obtained, which is superior to that of the present electrode materials based on 3D graphene/MnO2 hybrid structure (482 F/g). Furthermore, we have also investigated the superior electrochemical performances of an asymmetric supercapacitor device (weight of less than 12 mg/cm(2) and thickness of ~0.8 mm), showing a total capacitance of 0.33 F/cm(2) at a window voltage of 1.8 V and a maximum energy density of 53.4 W h/kg for driving a digital clock for 42 min. These inspiring performances would make our designed supercapacitors become one of the most promising candidates for the future flexible and lightweight energy storage systems.

Cellulose as an Architectural Element in Spatially Structured Escherichia coli Biofilms

PubMed Central

Serra, Diego O.; Richter, Anja M.

2013-01-01

Morphological form in multicellular aggregates emerges from the interplay of genetic constitution and environmental signals. Bacterial macrocolony biofilms, which form intricate three-dimensional structures, such as large and often radially oriented ridges, concentric rings, and elaborate wrinkles, provide a unique opportunity to understand this interplay of “nature and nurture” in morphogenesis at the molecular level. Macrocolony morphology depends on self-produced extracellular matrix components. In Escherichia coli, these are stationary phase-induced amyloid curli fibers and cellulose. While the widely used “domesticated” E. coli K-12 laboratory strains are unable to generate cellulose, we could restore cellulose production and macrocolony morphology of E. coli K-12 strain W3110 by “repairing” a single chromosomal SNP in the bcs operon. Using scanning electron and fluorescence microscopy, cellulose filaments, sheets and nanocomposites with curli fibers were localized in situ at cellular resolution within the physiologically two-layered macrocolony biofilms of this “de-domesticated” strain. As an architectural element, cellulose confers cohesion and elasticity, i.e., tissue-like properties that—together with the cell-encasing curli fiber network and geometrical constraints in a growing colony—explain the formation of long and high ridges and elaborate wrinkles of wild-type macrocolonies. In contrast, a biofilm matrix consisting of the curli fiber network only is brittle and breaks into a pattern of concentric dome-shaped rings separated by deep crevices. These studies now set the stage for clarifying how regulatory networks and in particular c-di-GMP signaling operate in the three-dimensional space of highly structured and “tissue-like” bacterial biofilms. PMID:24097954

Cellulose as an architectural element in spatially structured Escherichia coli biofilms.

PubMed

Serra, Diego O; Richter, Anja M; Hengge, Regine

2013-12-01

Morphological form in multicellular aggregates emerges from the interplay of genetic constitution and environmental signals. Bacterial macrocolony biofilms, which form intricate three-dimensional structures, such as large and often radially oriented ridges, concentric rings, and elaborate wrinkles, provide a unique opportunity to understand this interplay of "nature and nurture" in morphogenesis at the molecular level. Macrocolony morphology depends on self-produced extracellular matrix components. In Escherichia coli, these are stationary phase-induced amyloid curli fibers and cellulose. While the widely used "domesticated" E. coli K-12 laboratory strains are unable to generate cellulose, we could restore cellulose production and macrocolony morphology of E. coli K-12 strain W3110 by "repairing" a single chromosomal SNP in the bcs operon. Using scanning electron and fluorescence microscopy, cellulose filaments, sheets and nanocomposites with curli fibers were localized in situ at cellular resolution within the physiologically two-layered macrocolony biofilms of this "de-domesticated" strain. As an architectural element, cellulose confers cohesion and elasticity, i.e., tissue-like properties that-together with the cell-encasing curli fiber network and geometrical constraints in a growing colony-explain the formation of long and high ridges and elaborate wrinkles of wild-type macrocolonies. In contrast, a biofilm matrix consisting of the curli fiber network only is brittle and breaks into a pattern of concentric dome-shaped rings separated by deep crevices. These studies now set the stage for clarifying how regulatory networks and in particular c-di-GMP signaling operate in the three-dimensional space of highly structured and "tissue-like" bacterial biofilms.

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

A new approach for solving seismic tomography problems and assessing the uncertainty through the use of graph theory and direct methods

NASA Astrophysics Data System (ADS)

Bogiatzis, P.; Ishii, M.; Davis, T. A.

2016-12-01

Seismic tomography inverse problems are among the largest high-dimensional parameter estimation tasks in Earth science. We show how combinatorics and graph theory can be used to analyze the structure of such problems, and to effectively decompose them into smaller ones that can be solved efficiently by means of the least squares method. In combination with recent high performance direct sparse algorithms, this reduction in dimensionality allows for an efficient computation of the model resolution and covariance matrices using limited resources. Furthermore, we show that a new sparse singular value decomposition method can be used to obtain the complete spectrum of the singular values. This procedure provides the means for more objective regularization and further dimensionality reduction of the problem. We apply this methodology to a moderate size, non-linear seismic tomography problem to image the structure of the crust and the upper mantle beneath Japan using local deep earthquakes recorded by the High Sensitivity Seismograph Network stations.

Poly[mu2-(N-hydroxypyridine-2-carboxamidine)-mu2-nitrato-silver(I)].

PubMed

Cui, Ai-Li; Han, Peng; Yang, Hui-Juan; Wang, Ru-Ji; Kou, Hui-Zhong

2007-12-01

In the title complex, [Ag(NO3)(C6H7N3O)]n or [Ag(NO3)(pyaoxH2)] (pyaoxH2 is N-hydroxypyridine-2-carboxamidine), the Ag+ ion is bridged by the pyaoxH2 ligands and nitrate anions, giving rise to a two-dimensional molecular structure. Each pyaoxH2 ligand coordinates to two Ag+ ions using its pyridyl and carboxamidine N atoms, and the OH and the NH2 groups are uncoordinated. Each nitrate anion uses two O atoms to coordinate to two Ag+ ions. The Ag...Ag separation via the pyaoxH2 bridge is 2.869 (1) A, markedly shorter than that of 6.452 (1) A via the nitrate bridge. The two-dimensional structure is fishscale-like, and can be described as pyaoxH2-bridged Ag2 nodes that are further linked by nitrate anions. Hydrogen bonding between the amidine groups and the nitrate O atoms connects adjacent layers into a three-dimensional network.

A new series of two-dimensional silicon crystals with versatile electronic properties

NASA Astrophysics Data System (ADS)

Chae, Kisung; Kim, Duck Young; Son, Young-Woo

2018-04-01

Silicon (Si) is one of the most extensively studied materials owing to its significance to semiconductor science and technology. While efforts to find a new three-dimensional (3D) Si crystal with unusual properties have made some progress, its two-dimensional (2D) phases have not yet been explored as much. Here, based on a newly developed systematic ab initio materials searching strategy, we report a series of novel 2D Si crystals with unprecedented structural and electronic properties. The new structures exhibit perfectly planar outermost surface layers of a distorted hexagonal network with their thicknesses varying with the atomic arrangement inside. Dramatic changes in electronic properties ranging from semimetal to semiconducting with indirect energy gaps and even to one with direct energy gaps are realized by varying thickness as well as by surface oxidation. Our predicted 2D Si crystals with flat surfaces and tunable electronic properties will shed light on the development of silicon-based 2D electronics technology.

A one-dimensional nickel(II) coordination polymer containing 2,6-dipicolinate and dipyrido[3,2-a:2',3'-c]phenazine.

PubMed

Ma, Yi; Zhang, Li-Tian; Wang, Xiao-Fang; He, Yong-Ke; Han, Zheng-Bo

2007-12-01

A new coordination polymer, catena-poly[[(dipyrido[3,2-a:2',3'-c]phenazine-kappa(2)N,N')nickel(II)]-mu-2,6-dipicolinato-kappa(4)O(2),N,O(6):O(2')], [Ni(C7H3NO4)(C18H10N4)]n, exhibits a one-dimensional structure in which 2,6-dipicolinate acts as a bridging ligand interconnecting adjacent nickel(II) centers to form a chain structure. The asymmetric unit contains one Ni(II) center, one dipyrido[3,2-a:2',3'-c]phenazine ligand and one 2,6-dipicolinate ligand. Each Ni(II) center is six-coordinated and surrounded by three N atoms and three O atoms from one dipyrido[3,2-a:2',3'-c]phenazine ligand and two different 2,6-dipicolinate ligands, leading to a distorted octahedral geometry. Adjacent chains are linked by pi-pi stacking interactions and weak interactions to form a three-dimensional supramolecular network.

Quantitative Analysis of Filament Branch Orientation in Listeria Actin Comet Tails.

PubMed

Jasnin, Marion; Crevenna, Alvaro H

2016-02-23

Several bacterial and viral pathogens hijack the host actin cytoskeleton machinery to facilitate spread and infection. In particular, Listeria uses Arp2/3-mediated actin filament nucleation at the bacterial surface to generate a branched network that will help propel the bacteria. However, the mechanism of force generation remains elusive due to the lack of high-resolution three-dimensional structural data on the spatial organization of the actin mother and daughter (i.e., branch) filaments within this network. Here, we have explored the three-dimensional structure of Listeria actin tails in Xenopus laevis egg extracts using cryo-electron tomography. We found that the architecture of Listeria actin tails is shared between those formed in cells and in cell extracts. Both contained nanoscopic bundles along the plane of the substrate, where the bacterium lies, and upright filaments (also called Z filaments), both oriented tangentially to the bacterial cell wall. Here, we were able to identify actin filament intersections, which likely correspond to branches, within the tails. A quantitative analysis of putative Arp2/3-mediated branches in the actin network showed that mother filaments lie on the plane of the substrate, whereas daughter filaments have random deviations out of this plane. Moreover, the analysis revealed that branches are randomly oriented with respect to the bacterial surface. Therefore, the actin filament network does not push directly toward the surface but rather accumulates, building up stress around the Listeria surface. Our results favor a mechanism of force generation for Listeria movement where the stress is released into propulsive motion. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Three-dimensional gas exchange pathways in pome fruit characterized by synchrotron x-ray computed tomography.

PubMed

Verboven, Pieter; Kerckhofs, Greet; Mebatsion, Hibru Kelemu; Ho, Quang Tri; Temst, Kristiaan; Wevers, Martine; Cloetens, Peter; Nicolaï, Bart M

2008-06-01

Our understanding of the gas exchange mechanisms in plant organs critically depends on insights in the three-dimensional (3-D) structural arrangement of cells and voids. Using synchrotron radiation x-ray tomography, we obtained for the first time high-contrast 3-D absorption images of in vivo fruit tissues of high moisture content at 1.4-microm resolution and 3-D phase contrast images of cell assemblies at a resolution as low as 0.7 microm, enabling visualization of individual cell morphology, cell walls, and entire void networks that were previously unknown. Intercellular spaces were always clear of water. The apple (Malus domestica) cortex contains considerably larger parenchyma cells and voids than pear (Pyrus communis) parenchyma. Voids in apple often are larger than the surrounding cells and some cells are not connected to void spaces. The main voids in apple stretch hundreds of micrometers but are disconnected. Voids in pear cortex tissue are always smaller than parenchyma cells, but each cell is surrounded by a tight and continuous network of voids, except near brachyssclereid groups. Vascular and dermal tissues were also measured. The visualized network architecture was consistent over different picking dates and shelf life. The differences in void fraction (5.1% for pear cortex and 23.0% for apple cortex) and in gas network architecture helps explain the ability of tissues to facilitate or impede gas exchange. Structural changes and anisotropy of tissues may eventually lead to physiological disorders. A combined tomography and internal gas analysis during growth are needed to make progress on the understanding of void formation in fruit.

Neural network approach for characterizing structural transformations by X-ray absorption fine structure

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

Timoshenko, Janis; Frenkel, Anatoly I.; Cintins, Arturs

The knowledge of coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use artificial neural network approach to extract the information on the local structure and its in-situ changes directly from the X-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic andmore » austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from body-centered to face-centered cubic arrangement of iron atoms. Furthermore, this method is attractive for a broad range of materials and experimental conditions« less

Viscoelastic and Functional Properties of Cod-Bone Gelatin in the Presence of Xylitol and Stevioside

NASA Astrophysics Data System (ADS)

Nian, Linyu; Cao, Ailing; Wang, Jing; Tian, Hongyu; Liu, Yongguo; Gong, Lingxiao; Cai, Luyun; Wang, Yuhao

2018-05-01

The physical, rheological, structural and functional properties of cod bone gelatin (CBG) with various concentrations (0, 2, 4, 6, 10 and 15%) of low-calorie sweeteners (xylitol (X) and stevioside (S)) to form gels were investigated. The gel strength of CBGX increased with increased xylitol due presumably to hydrogen bonds between xylitol and gelatin, but with CBGS the highest gel strength occurred when S concentration was 4%. Viscosity of CBGS samples were higher than CBGX due to S’s high molecular mass. The viscoelasticity (G' and G″), foaming capacity and fat binding capacity of CBGX were higher while foam stability was lower. The emulsion activity and emulsion stability of CBGX were a little lower than CBGS at the same concentration. The structure of X is linear making it easier to form a dense three-dimensional network structure, while the complex cyclic structure of S had more difficulty forming a network structure with cod bone gelatin. Therefore, X may be a better choice for sweetening gelatin gels.

Neural network approach for characterizing structural transformations by X-ray absorption fine structure

DOE PAGES

Timoshenko, Janis; Frenkel, Anatoly I.; Cintins, Arturs; ...

2018-05-25

The knowledge of coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use artificial neural network approach to extract the information on the local structure and its in-situ changes directly from the X-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic andmore » austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from body-centered to face-centered cubic arrangement of iron atoms. Furthermore, this method is attractive for a broad range of materials and experimental conditions« less

Mapping population-based structural connectomes.

PubMed

Zhang, Zhengwu; Descoteaux, Maxime; Zhang, Jingwen; Girard, Gabriel; Chamberland, Maxime; Dunson, David; Srivastava, Anuj; Zhu, Hongtu

2018-05-15

Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects. Copyright © 2018 Elsevier Inc. All rights reserved.

Viscoelastic and Functional Properties of Cod-Bone Gelatin in the Presence of Xylitol and Stevioside.

PubMed

Nian, Linyu; Cao, Ailing; Wang, Jing; Tian, Hongyu; Liu, Yongguo; Gong, Lingxiao; Cai, Luyun; Wang, Yuhao

2018-01-01

The physical, rheological, structural and functional properties of cod bone gelatin (CBG) with various concentrations (0, 2, 4, 6, 10, and 15%) of low-calorie sweeteners [xylitol (X) and stevioside (S)] to form gels were investigated. The gel strength of CBGX increased with increased xylitol due presumably to hydrogen bonds between xylitol and gelatin, but with CBGS the highest gel strength occurred when S concentration was 4%. Viscosity of CBGS samples were higher than CBGX due to S's high molecular mass. The viscoelasticity (G' and G''), foaming capacity and fat binding capacity of CBGX were higher while foam stability was lower. The emulsion activity and emulsion stability of CBGX were a little lower than CBGS at the same concentration. The structure of X is linear making it easier to form a dense three-dimensional network structure, while the complex cyclic structure of S had more difficulty forming a network structure with cod bone gelatin. Therefore, X may be a better choice for sweetening gelatin gels.

Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy

NASA Astrophysics Data System (ADS)

Timoshenko, Janis; Anspoks, Andris; Cintins, Arturs; Kuzmin, Alexei; Purans, Juris; Frenkel, Anatoly I.

2018-06-01

The knowledge of the coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use an artificial neural network approach to extract the information on the local structure and its in situ changes directly from the x-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic and austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from a body-centered to a face-centered cubic arrangement of iron atoms. This method is attractive for a broad range of materials and experimental conditions.

Crystal structure of tin(IV) chloride octa­hydrate

PubMed Central

Hennings, Erik; Schmidt, Horst; Voigt, Wolfgang

2014-01-01

The title compound, [SnCl4(H2O)2]·6H2O, was crystallized according to the solid–liquid phase diagram at lower temperatures. It is built-up of SnCl4(H2O)2 octa­hedral units (point group symmetry 2) and lattice water mol­ecules. An intricate three-dimensional network of O—H⋯O and O—H⋯Cl hydrogen bonds between the complex molecules and the lattice water molecules is formed in the crystal structure. PMID:25552971

Design Graphics

NASA Technical Reports Server (NTRS)

1990-01-01

A mathematician, David R. Hedgley, Jr. developed a computer program that considers whether a line in a graphic model of a three-dimensional object should or should not be visible. Known as the Hidden Line Computer Code, the program automatically removes superfluous lines and displays an object from a specific viewpoint, just as the human eye would see it. An example of how one company uses the program is the experience of Birdair which specializes in production of fabric skylights and stadium covers. The fabric called SHEERFILL is a Teflon coated fiberglass material developed in cooperation with DuPont Company. SHEERFILL glazed structures are either tension structures or air-supported tension structures. Both are formed by patterned fabric sheets supported by a steel or aluminum frame or cable network. Birdair uses the Hidden Line Computer Code, to illustrate a prospective structure to an architect or owner. The program generates a three- dimensional perspective with the hidden lines removed. This program is still used by Birdair and continues to be commercially available to the public.

Self-penetration--a structural compromise between single networks and interpenetration: magnetic properties and crystal structures of

PubMed

Jensen; Price; Batten; Moubaraki; Murray

2000-09-01

The three-dimensional coordination polymers [Mn(dca)2(H2O)] (1) and [M(dca)(tcm)], M =Co (2), Ni (3), Cu (4), dca =dicyanamide, N(CN)2-, tcm = tricyanomethanide, C(CN)3-, have isomorphous structures. In 1 half the dca ligands coordinate directly (through all three nitrogen atoms) to three Mn atoms (all metal atoms are six-coordinate), while the other half coordinate to two Mn atoms (through the nitrile nitrogens) and hydrogen bond to water molecules coordinated to a third Mn atom (through the amide nitrogen). This dca. H2O structural moiety is disordered over a mirror plane, and is replaced by the structurally equivalent tcm ligand in compounds 2-4. The resulting structures display a new self-penetrating 3,6-connected (2:1) network topology that can be related to, but is different from, the rutile net. The self-penetrating [M(dca)(tcm)] network can be viewed as a structural compromise between the two interpenetrating rutile-like networks of [M(tcm)2] and the single rutile-like network of alpha-[M(dca)2]. The temperature and field dependence of the DC and AC magnetic susceptibilities and magnetisations has been measured for complexes 1-4. Compounds 1-3 exhibit long-range magnetic order with critical temperatures of 6.3 K for 1, 3.5 K for 2 and 8.0 K for 3. The Cu11 compound 4 does not order and is essentially a paramagnet. Hysteresis measurements of coercive field and remnant magnetisation show that 1, 2 and 3 are soft magnets, 1 being a canted-spin antiferromagnet (weak ferromagnet), while 2 and 3 are ferromagnets that display some unusual features in their high-field magnetisation isotherms in comparison to their related alpha-[M(dca)2] phases.

Influence of Landscape Coverage on Measuring Spatial and Length Properties of Rock Fracture Networks: Insights from Numerical Simulation

NASA Astrophysics Data System (ADS)

Cao, Wenzhuo; Lei, Qinghua

2018-01-01

Natural fractures are ubiquitous in the Earth's crust and often deeply buried in the subsurface. Due to the difficulty in accessing to their three-dimensional structures, the study of fracture network geometry is usually achieved by sampling two-dimensional (2D) exposures at the Earth's surface through outcrop mapping or aerial photograph techniques. However, the measurement results can be considerably affected by the coverage of forests and other plant species over the exposed fracture patterns. We quantitatively study such effects using numerical simulation. We consider the scenario of nominally isotropic natural fracture systems and represent them using 2D discrete fracture network models governed by fractal and length scaling parameters. The groundcover is modelled as random patches superimposing onto the 2D fracture patterns. The effects of localisation and total coverage of landscape patches are further investigated. The fractal dimension and length exponent of the covered fracture networks are measured and compared with those of the original non-covered patterns. The results show that the measured length exponent increases with the reduced localisation and increased coverage of landscape patches, which is more evident for networks dominated by very large fractures (i.e. small underlying length exponent). However, the landscape coverage seems to have a minor impact on the fractal dimension measurement. The research findings of this paper have important implications for field survey and statistical analysis of geological systems.

Synthesis, structure and topological analysis of glycine templated highly stable cadmium sulfate framework: A New Lewis Acid catalyst

NASA Astrophysics Data System (ADS)

Paul, Avijit Kumar

2018-04-01

One new open-framework two-dimensional layer, [Cd(NH3CH2COO)(SO4)], I, has been synthesized using amino acid as templating agent. Single crystal structural analysis shows that the compound crystallizes in monoclinic cell with non-centrosymmetric space group P21, a = 4.9513(1) Å, b = 7.9763(2) Å, c = 8.0967(2) Å, β = 105.917(1)° and V = 307.504(12) Å3. The compound has connectivity between the Cd-centers and the sulfate units forming a two-dimensional layer structure. Sulfate unit is coordinated to metal center with η3, μ4 mode possessing a coordination free oxygen atom. The zwitterionic form of glycine molecule is present in the structure bridging with two metal centers through μ2-mode by carboxylate oxygens. The topological analysis reveals that the two-dimensional network is formed with a novel 4- and 6-connected binodal net of (32,42,52)(34,44,54,63) topology. Although one end of the glycine molecule is free from coordination, the structure is highly stable up to 350 °C. Strong N-H⋯ O hydrogen bonding interactions play an important role in the stabilization and formation of three-dimensional supramolecular structure. The cyanosilylation of imines using the present compounds as heterogeneous catalyst indicates good catalytic behavior. The present study illustrates the usefulness of the amino acid for the structure building in less studied sulfate based framework materials as well as designing of new heterogeneous catalysts for the broad application. The compound has also been characterized through elemental analysis, PXRD, IR, SEM and TG-DT studies.

Three-Dimensional Microvascular Fiber-Reinforced Composites

DTIC Science & Technology

2011-03-01

are varied to meet the desired design criteria. The interstitial pore space between fi bers is infi ltrated with a low- viscosity thermosetting resin...depolymerization and monomer vaporization results in a 3D microvascular network integrated into a structural composite; d) fl uid (yellow) fi lls...VaSC method uses commercially available materials and can be seamlessly integrated with conventional fi ber-reinforced composite manufacturing

Three-Dimensional Array of TiN@Pt3Cu Nanowires as an Efficient Porous Electrode for the Lithium-Oxygen Battery.

PubMed

Luo, Wen-Bin; Pham, Thien Viet; Guo, Hai-Peng; Liu, Hua-Kun; Dou, Shi-Xue

2017-02-28

The nonaqueous lithium-oxygen battery is a promising candidate as a next-generation energy storage system because of its potentially high energy density (up to 2-3 kW kg -1 ), exceeding that of any other existing energy storage system for storing sustainable and clean energy to reduce greenhouse gas emissions and the consumption of nonrenewable fossil fuels. To achieve high round-trip efficiency and satisfactory cycling stability, the air electrode structure and the electrocatalysts play important roles. Here, a 3D array composed of one-dimensional TiN@Pt 3 Cu nanowires was synthesized and employed as a whole porous air electrode in a lithium-oxygen battery. The TiN nanowire was primarily used as an air electrode frame and catalyst support to provide a high electronic conductivity network because of the high-orientation one-dimensional crystalline structure. Meanwhile, deposited icosahedral Pt 3 Cu nanocrystals exhibit highly efficient catalytic activity owing to the abundant {111} active lattice facets and multiple twin boundaries. This porous air electrode comprises a one-dimensional TiN@Pt 3 Cu nanowire array that demonstrates excellent energy conversion efficiency and rate performance in full discharge and charge modes. The discharge capacity is up to 4600 mAh g -1 along with an 84% conversion efficiency at a current density of 0.2 mA cm -2 , and when the current density increased to 0.8 mA cm -2 , the discharge capacity is still greater than 3500 mAh g -1 together with a nearly 70% efficiency. This designed array is a promising bifunctional porous air electrode for lithium-oxygen batteries, forming a continuous conductive and high catalytic activity network to facilitate rapid gas and electrolyte diffusion and catalytic reaction throughout the whole energy conversion process.

Revisiting the Al/Al 2O 3 Interface: Coherent Interfaces and Misfit Accommodation

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

Pilania, Ghanshyam; Thijsse, Barend J.; Hoagland, Richard G.

We report the coherent and semi-coherent Al/α-Al 2O 3 interfaces using molecular dynamics simulations with a mixed, metallic-ionic atomistic model. For the coherent interfaces, both Al-terminated and O-terminated nonstoichiometric interfaces have been studied and their relative stability has been established. To understand the misfit accommodation at the semi-coherent interface, a 1-dimensional (1D) misfit dislocation model and a 2-dimensional (2D) dislocation network model have been studied. For the latter case, our analysis reveals an interface dislocation structure with a network of three sets of parallel dislocations, each with pure-edge character, giving rise to a pattern of coherent and stacking-fault-like regions atmore » the interface. Structural relaxation at elevated temperatures leads to a further change of the dislocation pattern, which can be understood in terms of a competition between the stacking fault energy and the dislocation interaction energy at the interface. In conclusion, our results are expected to serve as an input for the subsequent dislocation dynamics models to understand and predict the macroscopic mechanical behavior of Al/α-Al 2O 3 composite heterostructures.« less

Revisiting the Al/Al 2O 3 Interface: Coherent Interfaces and Misfit Accommodation

DOE PAGES

Pilania, Ghanshyam; Thijsse, Barend J.; Hoagland, Richard G.; ...

2014-03-27

We report the coherent and semi-coherent Al/α-Al 2O 3 interfaces using molecular dynamics simulations with a mixed, metallic-ionic atomistic model. For the coherent interfaces, both Al-terminated and O-terminated nonstoichiometric interfaces have been studied and their relative stability has been established. To understand the misfit accommodation at the semi-coherent interface, a 1-dimensional (1D) misfit dislocation model and a 2-dimensional (2D) dislocation network model have been studied. For the latter case, our analysis reveals an interface dislocation structure with a network of three sets of parallel dislocations, each with pure-edge character, giving rise to a pattern of coherent and stacking-fault-like regions atmore » the interface. Structural relaxation at elevated temperatures leads to a further change of the dislocation pattern, which can be understood in terms of a competition between the stacking fault energy and the dislocation interaction energy at the interface. In conclusion, our results are expected to serve as an input for the subsequent dislocation dynamics models to understand and predict the macroscopic mechanical behavior of Al/α-Al 2O 3 composite heterostructures.« less

Non-triglyceride components modulate the fat crystal network of palm kernel oil and coconut oil.

PubMed

Chai, Xiuhang; Meng, Zong; Jiang, Jiang; Cao, Peirang; Liang, Xinyu; Piatko, Michael; Campbell, Shawn; Lo, Seong Koon; Liu, Yuanfa

2018-03-01

PKO and CNO are composed of 97-98% triacylglycerols and 2-3% minor non-triglyceride components (FFA, DAG and MAG). Triglycerides were separated from minor components by chromatographic method. The lipid composition, thermal properties, polymorphism, isothermal crystallization behavior, nanostructure and microstructure of PKO, PKO-TAG, CNO and CNO-TAG were evaluated. Removal of minor components had no effect on lipid composition and equilibrium solid fat contents. However, presence of minor components did increase the slip melting point and promoted the onset of crystallization from DSC crystallization profiles. The thickness of the nanoscale crystals increased with no polymorphic transformation after removing the minor components. Crystallization kinetics revealed that minor components decreased crystal growth rate with higher t 1/2 . Sharp changes in the values of the Avrami constant k and exponent n were observed for all fats around 10°C. Increases in n around 10°C indicated a change from one-dimensional to multi-dimensional growth . From the results of polarized light micrographs, the transformation from the coarser crystal structure to tiny crystal structure occurred in microstructure networks at the action of minor components. Copyright © 2017 Elsevier Ltd. All rights reserved.

Crystal structure and physicochemical characterization of ambazone monohydrate, anhydrous, and acetate salt solvate.

PubMed

Muresan-Pop, Marieta; Braga, Dario; Pop, Mihaela M; Borodi, Gheorghe; Kacso, Irina; Maini, Lucia

2014-11-01

The crystal structures of the monohydrate and anhydrous forms of ambazone were determined by single-crystal X-ray diffraction (SC-XRD). Ambazone monohydrate is characterized by an infinite three-dimensional network involving the water molecules, whereas anhydrous ambazone forms a two-dimensional network via hydrogen bonds. The reversible transformation between the monohydrate and anhydrous forms of ambazone was evidenced by thermal analysis, temperature-dependent X-ray powder diffraction and accelerated stability at elevated temperature, and relative humidity (RH). Additionally, a novel ambazone acetate salt solvate form was obtained and its nature was elucidated by SC-XRD. Powder dissolution measurements revealed a substantial solubility and dissolution rate improvement of acetate salt solvated form in water and physiological media compared with ambazone forms. Also, the acetate salt solvate displayed good thermal and solution stability but it transformed to the monohydrate on storage at elevated temperature and RH. Our study shows that despite the requirement for controlled storage conditions, the acetate salt solvated form could be an alternative to ambazone when solubility and bioavailability improvement is critical for the clinical efficacy of the drug product. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Characterizing complex networks through statistics of Möbius transformations

NASA Astrophysics Data System (ADS)

Jaćimović, Vladimir; Crnkić, Aladin

2017-04-01

It is well-known now that dynamics of large populations of globally (all-to-all) coupled oscillators can be reduced to low-dimensional submanifolds (WS transformation and OA ansatz). Marvel et al. (2009) described an intriguing algebraic structure standing behind this reduction: oscillators evolve by the action of the group of Möbius transformations. Of course, dynamics in complex networks of coupled oscillators is highly complex and not reducible. Still, closer look unveils that even in complex networks some (possibly overlapping) groups of oscillators evolve by Möbius transformations. In this paper, we study properties of the network by identifying Möbius transformations in the dynamics of oscillators. This enables us to introduce some new (statistical) concepts that characterize the network. In particular, the notion of coherence of the network (or subnetwork) is proposed. This conceptual approach is meaningful for the broad class of networks, including those with time-delayed, noisy or mixed interactions. In this paper, several simple (random) graphs are studied illustrating the meaning of the concepts introduced in the paper.

Comparative analysis of two discretizations of Ricci curvature for complex networks.

PubMed

Samal, Areejit; Sreejith, R P; Gu, Jiao; Liu, Shiping; Saucan, Emil; Jost, Jürgen

2018-06-05

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks. Moreover, we show that if one considers the augmented Forman-Ricci curvature which also accounts for the two-dimensional simplicial complexes arising in graphs, the observed correlation between the two discretizations is even higher, especially, in real networks. Besides the potential theoretical implications of these observations, the close relationship between the two discretizations has practical implications whereby Forman-Ricci curvature can be employed in place of Ollivier-Ricci curvature for faster computation in larger real-world networks whenever coarse analysis suffices.

Online service for monitoring the ionosphere based on data from the global navigation satellite system

NASA Astrophysics Data System (ADS)

Aleshin, I. M.; Alpatov, V. V.; Vasil'ev, A. E.; Burguchev, S. S.; Kholodkov, K. I.; Budnikov, P. A.; Molodtsov, D. A.; Koryagin, V. N.; Perederin, F. V.

2014-07-01

A service is described that makes possible the effective construction of a three-dimensional ionospheric model based on the data of ground receivers of signals from global navigation satellite positioning systems (GNSS). The obtained image has a high resolution, mainly because data from the IPG GNSS network of the Federal Service for Hydrometeorology and Environmental Monitoring (Rosgidromet) are used. A specially developed format and its implementation in the form of SQL structures are used to collect, transmit, and store data. The method of high-altitude radio tomography is used to construct the three-dimensional model. The operation of all system components (from registration point organization to the procedure for constructing the electron density three-dimensional distribution and publication of the total electron content map on the Internet) has been described in detail. The three-dimensional image of the ionosphere, obtained automatically, is compared with the ionosonde measurements, calculated using the two-dimensional low-altitude tomography method and averaged by the ionospheric model.

Network evolution induced by asynchronous stimuli through spike-timing-dependent plasticity.

PubMed

Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

2013-01-01

In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR

NASA Astrophysics Data System (ADS)

Baek, Andrew; Farhat, Nabil H.

1998-10-01

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Disordered configurations of the Glauber model in two-dimensional networks

NASA Astrophysics Data System (ADS)

Bačić, Iva; Franović, Igor; Perc, Matjaž

2017-12-01

We analyze the ordering efficiency and the structure of disordered configurations for the zero-temperature Glauber model on Watts-Strogatz networks obtained by rewiring 2D regular square lattices. In the small-world regime, the dynamics fails to reach the ordered state in the thermodynamic limit. Due to the interplay of the perturbed regular topology and the energy neutral stochastic state transitions, the stationary state consists of two intertwined domains, manifested as multiclustered states on the original lattice. Moreover, for intermediate rewiring probabilities, one finds an additional source of disorder due to the low connectivity degree, which gives rise to small isolated droplets of spins. We also examine the ordering process in paradigmatic two-layer networks with heterogeneous rewiring probabilities. Comparing the cases of a multiplex network and the corresponding network with random inter-layer connectivity, we demonstrate that the character of the final state qualitatively depends on the type of inter-layer connections.

Molecular mechanics of mineralized collagen fibrils in bone

PubMed Central

Nair, Arun K.; Gautieri, Alfonso; Chang, Shu-Wei; Buehler, Markus J.

2013-01-01

Bone is a natural composite of collagen protein and the mineral hydroxyapatite. The structure of bone is known to be important to its load-bearing characteristics, but relatively little is known about this structure or the mechanism that govern deformation at the molecular scale. Here we perform full-atomistic calculations of the three-dimensional molecular structure of a mineralized collagen protein matrix to try to better understand its mechanical characteristics under tensile loading at various mineral densities. We find that as the mineral density increases, the tensile modulus of the network increases monotonically and well beyond that of pure collagen fibrils. Our results suggest that the mineral crystals within this network bears up to four times the stress of the collagen fibrils, whereas the collagen is predominantly responsible for the material’s deformation response. These findings reveal the mechanism by which bone is able to achieve superior energy dissipation and fracture resistance characteristics beyond its individual constituents. PMID:23591891

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

Highly stable multi-wall carbon nanotubes@poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) core-shell composites with three-dimensional porous nano-network for electrochemical capacitors

NASA Astrophysics Data System (ADS)

Zhou, Haihan; Han, Gaoyi; Chang, Yunzhen; Fu, Dongying; Xiao, Yaoming

2015-01-01

A facile and feasible electrochemical polymerization method has been used to construct the multi-wall carbon nanotubes@poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) (MWCNTs@PEDOT/PSS) core-shell composites with three-dimensional (3D) porous nano-network microstructure. The composites are characterized with Fourier transform infrared spectroscopy, scanning electron microscope, and transmission electron microscopy. This special core-shell nanostructure can significantly reduce the ions diffusion distance and the 3D porous nano-network microstructure effectively enlarges the electrode/electrolyte interface. The electrochemical tests including cyclic voltammetry, galvanostatic charge/discharge measurements, and electrochemical impedance spectroscopy tests are performed, the results manifest the MWCNTs@PEDOT/PSS core-shell composites have superior capacitive behaviors and excellent cyclic stability, and a high areal capacitance of 98.1 mF cm-2 is achieved at 5 mV s-1 cyclic voltammetry scan. Furthermore, the MWCNTs@PEDOT/PSS composites exhibit obviously superior capacitive performance than that of PEDOT/PSS and PEDOT/Cl electrodes, indicating the effective composite of MWCNTs and PEDOT noticeably boosts the capacitive performance of PEDOT-based electrodes for electrochemical energy storage. Such a highly stable core-shell 3D network structural composite is very promising to be used as electrode materials for the high-performance electrochemical capacitors.

Geometric characterization and simulation of planar layered elastomeric fibrous biomaterials

PubMed Central

Carleton, James B.; D'Amore, Antonio; Feaver, Kristen R.; Rodin, Gregory J.; Sacks, Michael S.

2014-01-01

Many important biomaterials are composed of multiple layers of networked fibers. While there is a growing interest in modeling and simulation of the mechanical response of these biomaterials, a theoretical foundation for such simulations has yet to be firmly established. Moreover, correctly identifying and matching key geometric features is a critically important first step for performing reliable mechanical simulations. The present work addresses these issues in two ways. First, using methods of geometric probability we develop theoretical estimates for the mean linear and areal fiber intersection densities for two-dimensional fibrous networks. These densities are expressed in terms of the fiber density and the orientation distribution function, both of which are relatively easy-to-measure properties. Secondly, we develop a random walk algorithm for geometric simulation of two-dimensional fibrous networks which can accurately reproduce the prescribed fiber density and orientation distribution function. Furthermore, the linear and areal fiber intersection densities obtained with the algorithm are in agreement with the theoretical estimates. Both theoretical and computational results are compared with those obtained by post-processing of SEM images of actual scaffolds. These comparisons reveal difficulties inherent to resolving fine details of multilayered fibrous networks. The methods provided herein can provide a rational means to define and generate key geometric features from experimentally measured or prescribed scaffold structural data. PMID:25311685

Photogrammetric measurement to one part in a million

NASA Astrophysics Data System (ADS)

Fraser, Clive S.

1992-03-01

Industrial photogrammetric measurement to accuracies of 1 part in 1,000,000 of the size of the object is discussed. Network design concepts are reviewed, especially with regard both to the relationships between the first- and second-order design phases and to minimization of the influences of uncompensated systematic error. Photogrammetric system aspects are also briefly touched upon. The network optimization process for the measurement of a large compact range reflector is described and results of successive alignment surveys of this structure are summarized. These photogrammetric measurements yielded three dimensional (3D) coordinate accuracies surpassing one part in a million.

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

PubMed Central

Bolser, Dan; Dafas, Panos; Harrington, Richard; Park, Jong; Schroeder, Michael

2003-01-01

Background Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Conclusions Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level. PMID:14531933

A three-dimensional carbon nano-network for high performance lithium ion batteries

DOE PAGES

Tian, Miao; Wang, Wei; Liu, Yang; ...

2014-11-20

Three-dimensional (3D) network structure has been envisioned as a superior architecture for lithium ion battery (LIB) electrodes, which enhances both ion and electron transport to significantly improve battery performance. Herein, a 3D carbon nano-network is fabricated through chemical vapor deposition of carbon on a scalably manufactured 3D porous anodic alumina (PAA) template. As a demonstration on the applicability of 3D carbon nano-network for LIB electrodes, the low conductivity active material, TiO 2, is then uniformly coated on the 3D carbon nano-network using atomic layer deposition. High power performance is demonstrated in the 3D C/TiO 2 electrodes, where the parallel tubesmore » and gaps in the 3D carbon nano-network facilitates fast Li ion transport. A large areal capacity of ~0.37 mAh·cm –2 is achieved due to the large TiO 2 mass loading in the 60 µm-thick 3D C/TiO 2 electrodes. At a test rate of C/5, the 3D C/TiO 2 electrode with 18 nm-thick TiO 2 delivers a high gravimetric capacity of ~240 mAh g –1, calculated with the mass of the whole electrode. A long cycle life of over 1000 cycles with a capacity retention of 91% is demonstrated at 1C. In this study, the effects of the electrical conductivity of carbon nano-network, ion diffusion, and the electrolyte permeability on the rate performance of these 3D C/TiO 2 electrodes are systematically studied.« less

Hierarchical MoS2-coated three-dimensional graphene network for enhanced supercapacitor performances

NASA Astrophysics Data System (ADS)

Zhou, Rui; Han, Cheng-jie; Wang, Xiao-min

2017-06-01

Layered molybdenum disulfide (MoS2) owns graphene-like two-dimensional structure, and when used as the electrode material for energy storage devices, the intercalation of electrolyte ions is permitted. Herein, a simple dipping and drying method is employed to stack few-layered MoS2 nanosheets on a three-dimensional graphene network (3DGN). The structure measurement results indicate that the assembled hierarchical MoS2 nanosheets own expanded interlayer spacing (∼0.75 nm) and are stacked on the surface of 3DGN uncontinuously. The composite can achieve 110.57% capacitance retention after 4000 cycles of galvanostatic charge/discharge tests and 76.73% capacitance retention with increasing the current density from 1 A g-1 to 100 A g-1. Moreover, the asymmetric coin cell supercapacitor using MoS2@3DGN and active carbon as electrode materials is assembled. This device could achieve a working voltage window of 1.6 V along with the power and energy densities of 400.0-8001.6 W kg-1 and 36.43-1.12 Wh kg-1 respectively. The enhanced electrochemical performance can be attributed to: (1) the expanded interlayer spacing of hierarchical MoS2 nanosheets which can facilitate the fast intercalation/deintercalation of electrolyte cations, (2) the uncontinuous deposition of hierarchical MoS2 nanosheets which facilitates more contact between electrolyte and the section of MoS2 nanosheets to provide more gates for the intercalation/deintercalation.

Synthesis, growth, structural and optical studies of a new organic three dimensional framework: 4-(aminocarbonyl)pyridine 4-(aminocarbonyl)pyridinium hydrogen L-malate

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

Vijayalakshmi, A.; Vidyavathy, B., E-mail: vidyavathybalraj@gmail.com; Peramaiyan, G.

2017-02-15

4-(aminocarbonyl)pyridine 4-(aminocarbonyl)pyridinium hydrogen L-malate [(4ACP)(4ACP).(LM)] a new organic nonlinear optical (NLO) crystal was grown by the slow evaporation method. Single crystal X-ray diffraction analysis revealed that the [(4ACP)(4ACP).(LM)] crystal belongs to monoclinic crystal system, space group P2{sub 1}/n, with a three dimensional network. Thermogravimetry (TG) and differential thermal (DT) analyses showed that [(4ACP)(4ACP).(LM)] is thermally stable up to 165 °C. The optical transmittance window and the lower cut-off wavelength of [(4ACP)(4ACP).(LM)] were found out by UV–vis–NIR spectral study. The molecular structure of [(4ACP)(4ACP).(LM)] was further confirmed by FTIR spectral studies. The relative dielectric permittivity and dielectric loss were determined asmore » function of frequency and temperature. The third order nonlinear optical property of [(4ACP)(4ACP).(LM)] was studied by the Z-scan technique using a 532 nm diode pumped CW Nd:YAG laser. Nonlinear refractive index, nonlinear absorption coefficient and third order nonlinear susceptibility of the grown crystal were found to be 7.38×10{sup −8} cm{sup 2}/W, 0.08×10{sup −4} cm/W and 5.36×10{sup −6} esu, respectively. The laser damage threshold value is found to be 1.75 GW/cm{sup 2} - Graphical abstract: In the crystal structure of the title complex, the asymmetric unit contains one hydrogen L-malate anion, 4-(aminocarbonyl)pyridinium cation and a neutral isonicotinamide molecule. It is stabilized by intermolecular N-H…O, C-H…O and O-H…O hydrogen bonds which generate a three dimensional network.« less

Structure design and characteristic analysis of micro-nano probe based on six dimensional micro-force measuring principle

NASA Astrophysics Data System (ADS)

Yang, Hong-tao; Cai, Chun-mei; Fang, Chuan-zhi; Wu, Tian-feng

2013-10-01

In order to develop micro-nano probe having error self-correcting function and good rigidity structure, a new micro-nano probe system was developed based on six-dimensional micro-force measuring principle. The structure and working principle of the probe was introduced in detail. The static nonlinear decoupling method was established with BP neural network to do the static decoupling for the dimension coupling existing in each direction force measurements. The optimal parameters of BP neural network were selected and the decoupling simulation experiments were done. The maximum probe coupling rate after decoupling is 0.039% in X direction, 0.025% in Y direction and 0.027% in Z direction. The static measurement sensitivity of the probe can reach 10.76μɛ / mN in Z direction and 14.55μɛ / mN in X and Y direction. The modal analysis and harmonic response analysis under three dimensional harmonic load of the probe were done by using finite element method. The natural frequencies under different vibration modes were obtained and the working frequency of the probe was determined, which is higher than 10000 Hz . The transient response analysis of the probe was done, which indicates that the response time of the probe can reach 0.4 ms. From the above results, it is shown that the developed micro-nano probe meets triggering requirements of micro-nano probe. Three dimension measuring force can be measured precisely by the developed probe, which can be used to predict and correct the force deformation error and the touch error of the measuring ball and the measuring rod.

DNA-nanoparticle assemblies go organic: Macroscopic polymeric materials with nanosized features

PubMed Central

2012-01-01

Background One of the goals in the field of structural DNA nanotechnology is the use of DNA to build up 2- and 3-D nanostructures. The research in this field is motivated by the remarkable structural features of DNA as well as by its unique and reversible recognition properties. Nucleic acids can be used alone as the skeleton of a broad range of periodic nanopatterns and nanoobjects and in addition, DNA can serve as a linker or template to form DNA-hybrid structures with other materials. This approach can be used for the development of new detection strategies as well as nanoelectronic structures and devices. Method Here we present a new method for the generation of unprecedented all-organic conjugated-polymer nanoparticle networks guided by DNA, based on a hierarchical self-assembly process. First, microphase separation of amphiphilic block copolymers induced the formation of spherical nanoobjects. As a second ordering concept, DNA base pairing has been employed for the controlled spatial definition of the conjugated-polymer particles within the bulk material. These networks offer the flexibility and the diversity of soft polymeric materials. Thus, simple chemical methodologies could be applied in order to tune the network's electrical, optical and mechanical properties. Results and conclusions One- two- and three-dimensional networks have been successfully formed. Common to all morphologies is the integrity of the micelles consisting of DNA block copolymer (DBC), which creates an all-organic engineered network. PMID:22646980

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

PubMed

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

Design of nano- and microfiber combined scaffolds by electrospinning of collagen onto starch-based fiber meshes: a man-made equivalent of natural extracellular matrix.

PubMed

Tuzlakoglu, Kadriye; Santos, Marina I; Neves, Nuno; Reis, Rui L

2011-02-01

Mimicking the structural organization and biologic function of natural extracellular matrix has been one of the main goals of tissue engineering. Nevertheless, the majority of scaffolding materials for bone regeneration highlights biochemical functionality in detriment of mechanical properties. In this work we present a rather innovative construct that combines in the same structure electrospun type I collagen nanofibers with starch-based microfibers. These combined structures were obtained by a two-step methodology and structurally consist in a type I collagen nano-network incorporated on a macro starch-based support. The morphology of the developed structures was assessed by several microscopy techniques and the collagenous nature of the nano-network was confirmed by immunohistochemistry. In addition, and especially regarding the requirements of large bone defects, we also successfully introduced the concept of layer by layer, as a way to produce thicker structures. In an attempt to recreate bone microenvironment, the design and biochemical composition of the combined structures also envisioned bone-forming cells and endothelial cells (ECs). The inclusion of a type I collagen nano-network induced a stretched morphology and improved the metabolic activity of osteoblasts. Regarding ECs, the presence of type I collagen on the combined structures provided adhesive support and obviated the need of precoating with fibronectin. It was also importantly observed that ECs on the nano-network organized into circular structures, a three-dimensional arrangement distinct from that observed for osteoblasts and resembling the microcappillary-like organizations formed during angiogenesis. By providing simultaneously physical and chemical cues for cells, the herein-proposed combined structures hold a great potential in bone regeneration as a man-made equivalent of extracellular matrix.

Nonequilibrium dynamics of probe filaments in actin-myosin networks

NASA Astrophysics Data System (ADS)

Gladrow, J.; Broedersz, C. P.; Schmidt, C. F.

2017-08-01

Active dynamic processes of cells are largely driven by the cytoskeleton, a complex and adaptable semiflexible polymer network, motorized by mechanoenzymes. Small dimensions, confined geometries, and hierarchical structures make it challenging to probe dynamics and mechanical response of such networks. Embedded semiflexible probe polymers can serve as nonperturbing multiscale probes to detect force distributions in active polymer networks. We show here that motor-induced forces transmitted to the probe polymers are reflected in nonequilibrium bending dynamics, which we analyze in terms of spatial eigenmodes of an elastic beam under steady-state conditions. We demonstrate how these active forces induce correlations among the mode amplitudes, which furthermore break time-reversal symmetry. This leads to a breaking of detailed balance in this mode space. We derive analytical predictions for the magnitude of resulting probability currents in mode space in the white-noise limit of motor activity. We relate the structure of these currents to the spatial profile of motor-induced forces along the probe polymers and provide a general relation for observable currents on two-dimensional hyperplanes.

Coupling Network Computing Applications in Air-cooled Turbine Blades Optimization

NASA Astrophysics Data System (ADS)

Shi, Liang; Yan, Peigang; Xie, Ming; Han, Wanjin

2018-05-01

Through establishing control parameters from blade outside to inside, the parametric design of air-cooled turbine blade based on airfoil has been implemented. On the basis of fast updating structure features and generating solid model, a complex cooling system has been created. Different flow units are modeled into a complex network topology with parallel and serial connection. Applying one-dimensional flow theory, programs have been composed to get pipeline network physical quantities along flow path, including flow rate, pressure, temperature and other parameters. These inner units parameters set as inner boundary conditions for external flow field calculation program HIT-3D by interpolation, thus to achieve full field thermal coupling simulation. Referring the studies in literatures to verify the effectiveness of pipeline network program and coupling algorithm. After that, on the basis of a modified design, and with the help of iSIGHT-FD, an optimization platform had been established. Through MIGA mechanism, the target of enhancing cooling efficiency has been reached, and the thermal stress has been effectively reduced. Research work in this paper has significance for rapid deploying the cooling structure design.

Deep learning and the electronic structure problem

NASA Astrophysics Data System (ADS)

Mills, Kyle; Spanner, Michael; Tamblyn, Isaac

In the past decade, the fields of artificial intelligence and computer vision have progressed remarkably. Supported by the enthusiasm of large tech companies, as well as significant hardware advances and the utilization of graphical processing units to accelerate computations, deep neural networks (DNN) are gaining momentum as a robust choice for many diverse machine learning applications. We have demonstrated the ability of a DNN to solve a quantum mechanical eigenvalue equation directly, without the need to compute a wavefunction, and without knowledge of the underlying physics. We have trained a convolutional neural network to predict the total energy of an electron in a confining, 2-dimensional electrostatic potential. We numerically solved the one-electron Schrödinger equation for millions of electrostatic potentials, and used this as training data for our neural network. Four classes of potentials were assessed: the canonical cases of the harmonic oscillator and infinite well, and two types of randomly generated potentials for which no analytic solution is known. We compare the performance of the neural network and consider how these results could lead to future advances in electronic structure theory.

Hierarchical lattice models of hydrogen-bond networks in water

NASA Astrophysics Data System (ADS)

Dandekar, Rahul; Hassanali, Ali A.

2018-06-01

We develop a graph-based model of the hydrogen-bond network in water, with a view toward quantitatively modeling the molecular-level correlational structure of the network. The networks formed are studied by the constructing the model on two infinite-dimensional lattices. Our models are built bottom up, based on microscopic information coming from atomistic simulations, and we show that the predictions of the model are consistent with known results from ab initio simulations of liquid water. We show that simple entropic models can predict the correlations and clustering of local-coordination defects around tetrahedral waters observed in the atomistic simulations. We also find that orientational correlations between bonds are longer ranged than density correlations, determine the directional correlations within closed loops, and show that the patterns of water wires within these structures are also consistent with previous atomistic simulations. Our models show the existence of density and compressibility anomalies, as seen in the real liquid, and the phase diagram of these models is consistent with the singularity-free scenario previously proposed by Sastry and coworkers [Phys. Rev. E 53, 6144 (1996), 10.1103/PhysRevE.53.6144].

Nanocomposite Hydrogels: 3D Polymer-Nanoparticle Synergies for On-Demand Drug Delivery.

PubMed

Merino, Sonia; Martín, Cristina; Kostarelos, Kostas; Prato, Maurizio; Vázquez, Ester

2015-05-26

Considerable progress in the synthesis and technology of hydrogels makes these materials attractive structures for designing controlled-release drug delivery systems. In particular, this review highlights the latest advances in nanocomposite hydrogels as drug delivery vehicles. The inclusion/incorporation of nanoparticles in three-dimensional polymeric structures is an innovative means for obtaining multicomponent systems with diverse functionality within a hybrid hydrogel network. Nanoparticle-hydrogel combinations add synergistic benefits to the new 3D structures. Nanogels as carriers for cancer therapy and injectable gels with improved self-healing properties have also been described as new nanocomposite systems.

An inter-residue network model to identify mutational-constrained regions on the Ebola coat glycoprotein

PubMed Central

Quinlan, Devin S.; Raman, Rahul; Tharakaraman, Kannan; Subramanian, Vidya; del Hierro, Gabriella; Sasisekharan, Ram

2017-01-01

Recently, progress has been made in the development of vaccines and monoclonal antibody cocktails that target the Ebola coat glycoprotein (GP). Based on the mutation rates for Ebola virus given its natural sequence evolution, these treatment strategies are likely to impose additional selection pressure to drive acquisition of mutations in GP that escape neutralization. Given the high degree of sequence conservation among GP of Ebola viruses, it would be challenging to determine the propensity of acquiring mutations in response to vaccine or treatment with one or a cocktail of monoclonal antibodies. In this study, we analyzed the mutability of each residue using an approach that captures the structural constraints on mutability based on the extent of its inter-residue interaction network within the three-dimensional structure of the trimeric GP. This analysis showed two distinct clusters of highly networked residues along the GP1-GP2 interface, part of which overlapped with epitope surfaces of known neutralizing antibodies. This network approach also permitted us to identify additional residues in the network of the known hotspot residues of different anti-Ebola antibodies that would impact antibody-epitope interactions. PMID:28397835

Structural features of silver-doped phosphate glasses in zone of femtosecond laser-induced modification

NASA Astrophysics Data System (ADS)

Vasileva, A. A.; Nazarov, I. A.; Olshin, P. K.; Povolotskiy, A. V.; Sokolov, I. A.; Manshina, A. A.

2015-10-01

Femtosecond (fs) laser writing of two-dimensional microstructures (waveguides) is demonstrated in bulk phosphate glasses doped with silver ions. Silver-content phosphate and silver-content niobium-phosphate glasses with high concentration of silver oxide 55 mol% were used as samples for fs laser writing. The chemical network structure of the synthesized samples is analyzed through Raman spectroscopy and was found to be strongly sensitive to Nb incorporation. It was found that the direct laser writing process enables not only reorganization of glass network, but also formation of color centers and silver nanoparticles that are revealed in appearance of luminescence signal and plasmon absorption. The process of NPs' formation is more efficient for Nb-phosphate glass, while color centers are preferably formed in phosphate glass.

Station corrections for the Katmai Region Seismic Network

USGS Publications Warehouse

Searcy, Cheryl K.

2003-01-01

Most procedures for routinely locating earthquake hypocenters within a local network are constrained to using laterally homogeneous velocity models to represent the Earth's crustal velocity structure. As a result, earthquake location errors may arise due to actual lateral variations in the Earth's velocity structure. Station corrections can be used to compensate for heterogeneous velocity structure near individual stations (Douglas, 1967; Pujol, 1988). The HYPOELLIPSE program (Lahr, 1999) used by the Alaska Volcano Observatory (AVO) to locate earthquakes in Cook Inlet and the Aleutian Islands is a robust and efficient program that uses one-dimensional velocity models to determine hypocenters of local and regional earthquakes. This program does have the capability of utilizing station corrections within it's earthquake location proceedure. The velocity structures of Cook Inlet and Aleutian volcanoes very likely contain laterally varying heterogeneities. For this reason, the accuracy of earthquake locations in these areas will benefit from the determination and addition of station corrections. In this study, I determine corrections for each station in the Katmai region. The Katmai region is defined to lie between latitudes 57.5 degrees North and 59.00 degrees north and longitudes -154.00 and -156.00 (see Figure 1) and includes Mount Katmai, Novarupta, Mount Martin, Mount Mageik, Snowy Mountain, Mount Trident, and Mount Griggs volcanoes. Station corrections were determined using the computer program VELEST (Kissling, 1994). VELEST inverts arrival time data for one-dimensional velocity models and station corrections using a joint hypocenter determination technique. VELEST can also be used to locate single events.

Counting and classifying attractors in high dimensional dynamical systems.

PubMed

Bagley, R J; Glass, L

1996-12-07

Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.

Assessing Social Networks in Patients with Psychotic Disorders: A Systematic Review of Instruments.

PubMed

Siette, Joyce; Gulea, Claudia; Priebe, Stefan

2015-01-01

Evidence suggests that social networks of patients with psychotic disorders influence symptoms, quality of life and treatment outcomes. It is therefore important to assess social networks for which appropriate and preferably established instruments should be used. To identify instruments assessing social networks in studies of patients with psychotic disorders and explore their properties. A systematic search of electronic databases was conducted to identify studies that used a measure of social networks in patients with psychotic disorders. Eight instruments were identified, all of which had been developed before 1991. They have been used in 65 studies (total N of patients = 8,522). They assess one or more aspects of social networks such as their size, structure, dimensionality and quality. Most instruments have various shortcomings, including questionable inter-rater and test-retest reliability. The assessment of social networks in patients with psychotic disorders is characterized by a variety of approaches which may reflect the complexity of the construct. Further research on social networks in patients with psychotic disorders would benefit from advanced and more precise instruments using comparable definitions of and timescales for social networks across studies.

Representing perturbed dynamics in biological network models

NASA Astrophysics Data System (ADS)

Stoll, Gautier; Rougemont, Jacques; Naef, Felix

2007-07-01

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Δ , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

Structure of force networks in tapped particulate systems of disks and pentagons. I. Clusters and loops.

PubMed

Pugnaloni, Luis A; Carlevaro, C Manuel; Kramár, M; Mischaikow, K; Kondic, L

2016-06-01

The force network of a granular assembly, defined by the contact network and the corresponding contact forces, carries valuable information about the state of the packing. Simple analysis of these networks based on the distribution of force strengths is rather insensitive to the changes in preparation protocols or to the types of particles. In this and the companion paper [Kondic et al., Phys. Rev. E 93, 062903 (2016)10.1103/PhysRevE.93.062903], we consider two-dimensional simulations of tapped systems built from frictional disks and pentagons, and study the structure of the force networks of granular packings by considering network's topology as force thresholds are varied. We show that the number of clusters and loops observed in the force networks as a function of the force threshold are markedly different for disks and pentagons if the tangential contact forces are considered, whereas they are surprisingly similar for the network defined by the normal forces. In particular, the results indicate that, overall, the force network is more heterogeneous for disks than for pentagons. Such differences in network properties are expected to lead to different macroscale response of the considered systems, despite the fact that averaged measures (such as force probability density function) do not show any obvious differences. Additionally, we show that the states obtained by tapping with different intensities that display similar packing fraction are difficult to distinguish based on simple topological invariants.

Plasmonic Three-Dimensional Transparent Conductor Based on Al-Doped Zinc Oxide-Coated Nanostructured Glass Using Atomic Layer Deposition

DOE PAGES

Malek, Gary A.; Aytug, Tolga; Liu, Qingfeng; ...

2015-04-02

Transparent nanostructured glass coatings, fabricated on glass substrates, with a unique three-dimensional (3D) architecture were utilized as the foundation for the design of plasmonic 3D transparent conductors. Transformation of the non-conducting 3D structure to a conducting 3D network was accomplished through atomic layer deposition of aluminum-doped zinc oxide (AZO). After AZO growth, gold nanoparticles (AuNPs) were deposited by electronbeam evaporation to enhance light trapping and decrease the overall sheet resistance. Field emission scanning electron microscopy and atomic force microcopy images revealed the highly porous, nanostructured morphology of the AZO coated glass surface along with the in-plane dimensions of the depositedmore » AuNPs. Sheet resistance measurements conducted on the coated samples verified that the electrical properties of the 3D network are comparable to that of the untextured two-dimensional AZO coated glass substrates. In addition, transmittance measurements of the glass samples coated with various AZO thicknesses showed preservation of the highly transparent nature of each sample, while the AuNPs demonstrated enhanced light scattering as well as light-trapping capability.« less

Plasmonic Three-Dimensional Transparent Conductor Based on Al-Doped Zinc Oxide-Coated Nanostructured Glass Using Atomic Layer Deposition

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

Malek, Gary A.; Aytug, Tolga; Liu, Qingfeng

Transparent nanostructured glass coatings, fabricated on glass substrates, with a unique three-dimensional (3D) architecture were utilized as the foundation for the design of plasmonic 3D transparent conductors. Transformation of the non-conducting 3D structure to a conducting 3D network was accomplished through atomic layer deposition of aluminum-doped zinc oxide (AZO). After AZO growth, gold nanoparticles (AuNPs) were deposited by electronbeam evaporation to enhance light trapping and decrease the overall sheet resistance. Field emission scanning electron microscopy and atomic force microcopy images revealed the highly porous, nanostructured morphology of the AZO coated glass surface along with the in-plane dimensions of the depositedmore » AuNPs. Sheet resistance measurements conducted on the coated samples verified that the electrical properties of the 3D network are comparable to that of the untextured two-dimensional AZO coated glass substrates. In addition, transmittance measurements of the glass samples coated with various AZO thicknesses showed preservation of the highly transparent nature of each sample, while the AuNPs demonstrated enhanced light scattering as well as light-trapping capability.« less

Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.

PubMed

Teli, Mohammad Nayeem; Anderson, Charles

2009-01-01

Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.

Kinetic products in coordination networks: ab initio X-ray powder diffraction analysis.

PubMed

Martí-Rujas, Javier; Kawano, Masaki

2013-02-19

Porous coordination networks are materials that maintain their crystal structure as molecular "guests" enter and exit their pores. They are of great research interest with applications in areas such as catalysis, gas adsorption, proton conductivity, and drug release. As with zeolite preparation, the kinetic states in coordination network preparation play a crucial role in determining the final products. Controlling the kinetic state during self-assembly of coordination networks is a fundamental aspect of developing further functionalization of this class of materials. However, unlike for zeolites, there are few structural studies reporting the kinetic products made during self-assembly of coordination networks. Synthetic routes that produce the necessary selectivity are complex. The structural knowledge obtained from X-ray crystallography has been crucial for developing rational strategies for design of organic-inorganic hybrid networks. However, despite the explosive progress in the solid-state study of coordination networks during the last 15 years, researchers still do not understand many chemical reaction processes because of the difficulties in growing single crystals suitable for X-ray diffraction: Fast precipitation can lead to kinetic (metastable) products, but in microcrystalline form, unsuitable for single crystal X-ray analysis. X-ray powder diffraction (XRPD) routinely is used to check phase purity, crystallinity, and to monitor the stability of frameworks upon guest removal/inclusion under various conditions, but rarely is used for structure elucidation. Recent advances in structure determination of microcrystalline solids from ab initio XRPD have allowed three-dimensional structure determination when single crystals are not available. Thus, ab initio XRPD structure determination is becoming a powerful method for structure determination of microcrystalline solids, including porous coordination networks. Because of the great interest across scientific disciplines in coordination networks, especially porous coordination networks, the ability to determine crystal structures when the crystals are not suitable for single crystal X-ray analysis is of paramount importance. In this Account, we report the potential of kinetic control to synthesize new coordination networks and we describe ab initio XRPD structure determination to characterize these networks' crystal structures. We describe our recent work on selective instant synthesis to yield kinetically controlled porous coordination networks. We demonstrate that instant synthesis can selectively produce metastable networks that are not possible to synthesize by conventional solution chemistry. Using kinetic products, we provide mechanistic insights into thermally induced (573-723 K) (i.e., annealing method) structural transformations in porous coordination networks as well as examples of guest exchange/inclusion reactions. Finally, we describe a memory effect that allows the transfer of structural information from kinetic precursor structures to thermally stable structures through amorphous intermediate phases. We believe that ab initio XRPD structure determination will soon be used to investigate chemical processes that lead intrinsically to microcrystalline solids, which up to now have not been fully understood due to the unavailability of single crystals. For example, only recently have researchers used single-crystal X-ray diffraction to elucidate crystal-to-crystal chemical reactions taking place in the crystalline scaffold of coordination networks. The potential of ab initio X-ray powder diffraction analysis goes beyond single-crystal-to-single-crystal processes, potentially allowing members of this field to study intriguing in situ reactions, such as reactions within pores.

Water-network percolation transitions in hydrated yeast

NASA Astrophysics Data System (ADS)

Sokołowska, Dagmara; Król-Otwinowska, Agnieszka; Mościcki, Józef K.

2004-11-01

We discovered two percolation processes in succession in dc conductivity of bulk baker’s yeast in the course of dehydration. Critical exponents characteristic for the three-dimensional network for heavily hydrated system, and two dimensions in the light hydration limit, evidenced a dramatic change of the water network dimensionality in the dehydration process.

Synchronization in oscillator networks with delayed coupling: a stability criterion.

PubMed

Earl, Matthew G; Strogatz, Steven H

2003-03-01

We derive a stability criterion for the synchronous state in networks of identical phase oscillators with delayed coupling. The criterion applies to any network (whether regular or random, low dimensional or high dimensional, directed or undirected) in which each oscillator receives delayed signals from k others, where k is uniform for all oscillators.

Tertiary network in mammalian mitochondrial tRNAAsp revealed by solution probing and phylogeny

PubMed Central

Messmer, Marie; Pütz, Joern; Suzuki, Takeo; Suzuki, Tsutomu; Sauter, Claude; Sissler, Marie; Catherine, Florentz

2009-01-01

Primary and secondary structures of mammalian mitochondrial (mt) tRNAs are divergent from canonical tRNA structures due to highly skewed nucleotide content and large size variability of D- and T-loops. The nonconservation of nucleotides involved in the expected network of tertiary interactions calls into question the rules governing a functional L-shaped three-dimensional (3D) structure. Here, we report the solution structure of human mt-tRNAAsp in its native post-transcriptionally modified form and as an in vitro transcript. Probing performed with nuclease S1, ribonuclease V1, dimethylsulfate, diethylpyrocarbonate and lead, revealed several secondary structures for the in vitro transcribed mt-tRNAAsp including predominantly the cloverleaf. On the contrary, the native tRNAAsp folds into a single cloverleaf structure, highlighting the contribution of the four newly identified post-transcriptional modifications to correct folding. Reactivities of nucleotides and phosphodiester bonds in the native tRNA favor existence of a full set of six classical tertiary interactions between the D-domain and the variable region, forming the core of the 3D structure. Reactivities of D- and T-loop nucleotides support an absence of interactions between these domains. According to multiple sequence alignments and search for conservation of Leontis–Westhof interactions, the tertiary network core building rules apply to all tRNAAsp from mammalian mitochondria. PMID:19767615

Assembly of Triblock Amphiphilic Peptides into One-Dimensional Aggregates and Network Formation.

PubMed

Ozgur, Beytullah; Sayar, Mehmet

2016-10-06

Peptide assembly plays a key role in both neurological diseases and development of novel biomaterials with well-defined nanostructures. Synthetic model peptides provide a unique platform to explore the role of intermolecular interactions in the assembly process. A triblock peptide architecture designed by the Hartgerink group is a versatile system which relies on Coulomb interactions, hydrogen bonding, and hydrophobicity to guide these peptides' assembly at three different length scales: β-sheets, double-wall ribbon-like aggregates, and finally a highly porous network structure which can support gels with ≤1% by weight peptide concentration. In this study, by using molecular dynamics simulations of a structure based implicit solvent coarse grained model, we analyzed this hierarchical assembly process. Parametrization of our CG model is based on multiple-state points from atomistic simulations, which enables this model to represent the conformational adaptability of the triblock peptide molecule based on the surrounding medium. Our results indicate that emergence of the double-wall β-sheet packing mechanism, proposed in light of the experimental evidence, strongly depends on the subtle balance of the intermolecular forces. We demonstrate that, even though backbone hydrogen bonding dominates the early nucleation stages, depending on the strength of the hydrophobic and Coulomb forces, alternative structures such as zero-dimensional aggregates with two β-sheets oriented orthogonally (which we refer to as a cross-packed structure) and β-sheets with misoriented hydrophobic side chains are also feasible. We discuss the implications of these competing structures for the three different length scales of assembly by systematically investigating the influence of density, counterion valency, and hydrophobicity.

Dynamics of a bilayer membrane coupled to a two-dimensional cytoskeleton: Scale transfers of membrane deformations

NASA Astrophysics Data System (ADS)

Okamoto, Ryuichi; Komura, Shigeyuki; Fournier, Jean-Baptiste

2017-07-01

We theoretically investigate the dynamics of a floating lipid bilayer membrane coupled with a two-dimensional cytoskeleton network, taking into account explicitly the intermonolayer friction, the discrete lattice structure of the cytoskeleton, and its prestress. The lattice structure breaks lateral continuous translational symmetry and couples Fourier modes with different wave vectors. It is shown that within a short time interval a long-wavelength deformation excites a collection of modes with wavelengths shorter than the lattice spacing. These modes relax slowly with a common renormalized rate originating from the long-wavelength mode. As a result, and because of the prestress, the slowest relaxation is governed by the intermonolayer friction. Conversely, and most interestingly, forces applied at the scale of the cytoskeleton for a sufficiently long time can cooperatively excite large-scale modes.

On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.

PubMed

Li, Bing; Chun, Hyonho; Zhao, Hongyu

2014-09-01

We introduce a nonparametric method for estimating non-gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a parallel structure to the gaussian graphical model that replaces the precision matrix by an additive precision operator. The estimators derived from additive conditional independence cover the recently introduced nonparanormal graphical model as a special case, but outperform it when the gaussian copula assumption is violated. We compare the new method with existing ones by simulations and in genetic pathway analysis.

Assembly of micro/nanomaterials into complex, three-dimensional architectures by compressive buckling

NASA Astrophysics Data System (ADS)

Xu, Sheng; Yan, Zheng; Jang, Kyung-In; Huang, Wen; Fu, Haoran; Kim, Jeonghyun; Wei, Zijun; Flavin, Matthew; McCracken, Joselle; Wang, Renhan; Badea, Adina; Liu, Yuhao; Xiao, Dongqing; Zhou, Guoyan; Lee, Jungwoo; Chung, Ha Uk; Cheng, Huanyu; Ren, Wen; Banks, Anthony; Li, Xiuling; Paik, Ungyu; Nuzzo, Ralph G.; Huang, Yonggang; Zhang, Yihui; Rogers, John A.

2015-01-01

Complex three-dimensional (3D) structures in biology (e.g., cytoskeletal webs, neural circuits, and vasculature networks) form naturally to provide essential functions in even the most basic forms of life. Compelling opportunities exist for analogous 3D architectures in human-made devices, but design options are constrained by existing capabilities in materials growth and assembly. We report routes to previously inaccessible classes of 3D constructs in advanced materials, including device-grade silicon. The schemes involve geometric transformation of 2D micro/nanostructures into extended 3D layouts by compressive buckling. Demonstrations include experimental and theoretical studies of more than 40 representative geometries, from single and multiple helices, toroids, and conical spirals to structures that resemble spherical baskets, cuboid cages, starbursts, flowers, scaffolds, fences, and frameworks, each with single- and/or multiple-level configurations.

Directed assembly of bio-inspired hierarchical materials with controlled nanofibrillar architectures

NASA Astrophysics Data System (ADS)

Tseng, Peter; Napier, Bradley; Zhao, Siwei; Mitropoulos, Alexander N.; Applegate, Matthew B.; Marelli, Benedetto; Kaplan, David L.; Omenetto, Fiorenzo G.

2017-05-01

In natural systems, directed self-assembly of structural proteins produces complex, hierarchical materials that exhibit a unique combination of mechanical, chemical and transport properties. This controlled process covers dimensions ranging from the nano- to the macroscale. Such materials are desirable to synthesize integrated and adaptive materials and systems. We describe a bio-inspired process to generate hierarchically defined structures with multiscale morphology by using regenerated silk fibroin. The combination of protein self-assembly and microscale mechanical constraints is used to form oriented, porous nanofibrillar networks within predesigned macroscopic structures. This approach allows us to predefine the mechanical and physical properties of these materials, achieved by the definition of gradients in nano- to macroscale order. We fabricate centimetre-scale material geometries including anchors, cables, lattices and webs, as well as functional materials with structure-dependent strength and anisotropic thermal transport. Finally, multiple three-dimensional geometries and doped nanofibrillar constructs are presented to illustrate the facile integration of synthetic and natural additives to form functional, interactive, hierarchical networks.

Data-driven confounder selection via Markov and Bayesian networks.

PubMed

Häggström, Jenny

2018-06-01

To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder selection criteria can be used to select subsets of the observed pretreatment covariates, X, sufficient for unconfoundedness, if such subsets exist. Here, estimation of these target subsets is considered when the underlying causal structure is unknown. The proposed method is to model the causal structure by a probabilistic graphical model, for example, a Markov or Bayesian network, estimate this graph from observed data and select the target subsets given the estimated graph. The approach is evaluated by simulation both in a high-dimensional setting where unconfoundedness holds given X and in a setting where unconfoundedness only holds given subsets of X. Several common target subsets are investigated and the selected subsets are compared with respect to accuracy in estimating the average causal effect. The proposed method is implemented with existing software that can easily handle high-dimensional data, in terms of large samples and large number of covariates. The results from the simulation study show that, if unconfoundedness holds given X, this approach is very successful in selecting the target subsets, outperforming alternative approaches based on random forests and LASSO, and that the subset estimating the target subset containing all causes of outcome yields smallest MSE in the average causal effect estimation. © 2017, The International Biometric Society.

Silver(I) coordination polymers assembled from flexible cyclotriphosphazene ligand: structures, topologies and investigation of the counteranion effects.

PubMed

Davarcı, Derya; Gür, Rüştü; Beşli, Serap; Şenkuytu, Elif; Zorlu, Yunus

2016-06-01

The reactions of a flexible ligand hexakis(3-pyridyloxy)cyclotriphosphazene (HPCP) with a variety of silver(I) salts (AgX; X = NO3(-), PF6(-), ClO4(-), CH3PhSO3(-), BF4(-) and CF3SO3(-)) afforded six silver(I) coordination polymers, namely {[Ag2(HPCP)]·(NO3)2·H2O}n (1), {[Ag2(HPCP)(CH3CN)]·(PF6)2}n (2), {[Ag2(HPCP)(CH3CN)]·(ClO4)2}n (3), [Ag3(HPCP)(CH3PhSO3)3]n (4), [Ag2(HPCP)(CH3CN)(BF4)2]n (5) and {[Ag(HPCP)]·(CF3SO3)}n (6). All of the isolated crystalline compounds were structurally determined by X-ray crystallography. Changing the counteranions in the reactions, which were conducted under similar conditions of M/L ratio (1:1), temperature and solvent, resulted in structures with different types of topologies. In complexes (1)-(6), the ligand HPCP shows different coordination modes with Ag(I) ions giving two-dimensional layered structures and three-dimensional frameworks with different topologies. Complex (1) displays a new three-dimensional framework adopting a (3,3,6)-connected 3-nodal net with point symbol {4.6(2)}2{4(2).6(10).8(3)}. Complexes (2) and (3) are isomorphous and have a two-dimensional layered structure showing the same 3,6L60 topology with point symbol {4.2(6)}2{4(8).6(6).8}. Complex (4) is a two-dimensional structure incorporating short Ag...Ag argentophilic interactions and has a uninodal 4-connected sql/Shubnikov tetragonal plane net with {4(4).6(2)} topology. Complex (5) exhibits a novel three-dimensional framework and more suprisingly contains twofold interpenetrated honeycomb-like networks, in which the single net has a trinodal (2,3,5)-connected 3-nodal net with point symbol {6(3).8(6).12}{6(3)}{8}. Complex (6) crystallizes in a trigonal crystal system with the space group R\\bar 3 and possesses a three-dimensional polymeric structure showing a binodal (4,6)-connected fsh net with the point symbol (4(3).6(3))2.(4(6).6(6).8(3)). The effect of the counteranions on the formation of coordination polymers is discussed in this study.

Crystal structure and Hirshfeld surface analysis of (2E,2'E)-3,3'-(1,4-phenyl-ene)bis-[1-(2,4-di-fluoro-phen-yl)prop-2-en-1-one].

PubMed

Kwong, Huey Chong; Sim, Aijia; Chidan Kumar, C S; Then, Li Yee; Win, Yip-Foo; Quah, Ching Kheng; Naveen, S; Warad, Ismail

2017-12-01

The asymmetric unit of the title compound, C 24 H 14 F 4 O 2 , comprises of one and a half mol-ecules; the half-mol-ecule is completed by crystallographic inversion symmetry. In the crystal, mol-ecules are linked into a three-dimensional network by C-H⋯F and C-H⋯O hydrogen bonds. Some of the C-H⋯F links are unusually short (< 2.20 Å). Hirshfeld surface analyses ( d norm surfaces and two-dimensional fingerprint plots) for the title compound are presented and discussed.

Drainage networks after wildfire

USGS Publications Warehouse

Kinner, D.A.; Moody, J.A.

2005-01-01

Predicting runoff and erosion from watersheds burned by wildfires requires an understanding of the three-dimensional structure of both hillslope and channel drainage networks. We investigate the small-and large-scale structures of drainage networks using field studies and computer analysis of 30-m digital elevation model. Topologic variables were derived from a composite 30-m DEM, which included 14 order 6 watersheds within the Pikes Peak batholith. Both topologic and hydraulic variables were measured in the field in two smaller burned watersheds (3.7 and 7.0 hectares) located within one of the order 6 watersheds burned by the 1996 Buffalo Creek Fire in Central Colorado. Horton ratios of topologic variables (stream number, drainage area, stream length, and stream slope) for small-scale and large-scale watersheds are shown to scale geometrically with stream order (i.e., to be scale invariant). However, the ratios derived for the large-scale drainage networks could not be used to predict the rill and gully drainage network structure. Hydraulic variables (width, depth, cross-sectional area, and bed roughness) for small-scale drainage networks were found to be scale invariant across 3 to 4 stream orders. The relation between hydraulic radius and cross-sectional area is similar for rills and gullies, suggesting that their geometry can be treated similarly in hydraulic modeling. Additionally, the rills and gullies have relatively small width-to-depth ratios, implying sidewall friction may be important to the erosion and evolutionary process relative to main stem channels.

TiO2 nanowire-templated hierarchical nanowire network as water-repelling coating

NASA Astrophysics Data System (ADS)

Hang, Tian; Chen, Hui-Jiuan; Xiao, Shuai; Yang, Chengduan; Chen, Meiwan; Tao, Jun; Shieh, Han-ping; Yang, Bo-ru; Liu, Chuan; Xie, Xi

2017-12-01

Extraordinary water-repelling properties of superhydrophobic surfaces make them novel candidates for a great variety of potential applications. A general approach to achieve superhydrophobicity requires low-energy coating on the surface and roughness on nano- and micrometre scale. However, typical construction of superhydrophobic surfaces with micro-nano structure through top-down fabrication is restricted by sophisticated fabrication techniques and limited choices of substrate materials. Micro-nanoscale topographies templated by conventional microparticles through surface coating may produce large variations in roughness and uncontrollable defects, resulting in poorly controlled surface morphology and wettability. In this work, micro-nanoscale hierarchical nanowire network was fabricated to construct self-cleaning coating using one-dimensional TiO2 nanowires as microscale templates. Hierarchical structure with homogeneous morphology was achieved by branching ZnO nanowires on the TiO2 nanowire backbones through hydrothermal reaction. The hierarchical nanowire network displayed homogeneous micro/nano-topography, in contrast to hierarchical structure templated by traditional microparticles. This hierarchical nanowire network film exhibited high repellency to both water and cell culture medium after functionalization with fluorinated organic molecules. The hierarchical structure templated by TiO2 nanowire coating significantly increased the surface superhydrophobicity compared to vertical ZnO nanowires with nanotopography alone. Our results demonstrated a promising strategy of using nanowires as microscale templates for the rational design of hierarchical coatings with desired superhydrophobicity that can also be applied to various substrate materials.

TiO2 nanowire-templated hierarchical nanowire network as water-repelling coating

PubMed Central

Hang, Tian; Chen, Hui-Jiuan; Xiao, Shuai; Yang, Chengduan; Chen, Meiwan; Tao, Jun; Shieh, Han-ping; Yang, Bo-ru; Liu, Chuan

2017-01-01

Extraordinary water-repelling properties of superhydrophobic surfaces make them novel candidates for a great variety of potential applications. A general approach to achieve superhydrophobicity requires low-energy coating on the surface and roughness on nano- and micrometre scale. However, typical construction of superhydrophobic surfaces with micro-nano structure through top-down fabrication is restricted by sophisticated fabrication techniques and limited choices of substrate materials. Micro-nanoscale topographies templated by conventional microparticles through surface coating may produce large variations in roughness and uncontrollable defects, resulting in poorly controlled surface morphology and wettability. In this work, micro-nanoscale hierarchical nanowire network was fabricated to construct self-cleaning coating using one-dimensional TiO2 nanowires as microscale templates. Hierarchical structure with homogeneous morphology was achieved by branching ZnO nanowires on the TiO2 nanowire backbones through hydrothermal reaction. The hierarchical nanowire network displayed homogeneous micro/nano-topography, in contrast to hierarchical structure templated by traditional microparticles. This hierarchical nanowire network film exhibited high repellency to both water and cell culture medium after functionalization with fluorinated organic molecules. The hierarchical structure templated by TiO2 nanowire coating significantly increased the surface superhydrophobicity compared to vertical ZnO nanowires with nanotopography alone. Our results demonstrated a promising strategy of using nanowires as microscale templates for the rational design of hierarchical coatings with desired superhydrophobicity that can also be applied to various substrate materials. PMID:29308265

Syntheses, crystal structures, and properties of four coordination polymers based on mixed multi-N donor and polycarboxylate ligands

NASA Astrophysics Data System (ADS)

Chen, Shui-Sheng; Guo, Xing-Zhe; Zhao, Yue; Li, Wei-Dong

2018-02-01

Four new coordination polymers [Ni2(HL1)2(L1)3(BTC)2]·6H2O (1), [Ni2(L1)3(HBTC)2]·4H2O (2), [Cd2(L2)(BTC)(H2O)3]·2H2O (3) and [Cd2(HL2)(BTCA)] (4) were synthesized by reactions of nickel(II)/ cadmium(II) salts with rigid ligands of 1,4-di(1H-imidazol-4-yl)benzene (L1), 1,3-di(1-imidazolyl)-5-(4H-tetrazol-5-yl)benzene (HL2) and polycarboxylic acids of 1,3,5-benzenetricarboxylic acid (H3BTC), 1,2,4,5-benzenetetracarboxylic acid (H4BTCA), respectively. The structures of the complexes were determined by single crystal X-ray diffraction analysis. The complex 1 is one-dimensional (1D) chain while 2 is a (4, 4)-connected two-dimensional (2D) layered structure with 2D → 2D parallel interpenetration. Complex 3 is a rare tetranodal (3,4)-connected three-dimensional (3D) CrVTiSc architecture with Point (Schläfli) symbol of (4·82)(4·84·10)(42·82·102)(83), and compound 4 has the 2D network with (4,4) topology based on the [Cd2(COO)4] SBUs. The weak interactions such as hydrogen bonds and π···π stacking contribute to stabilize crystal structure and extend the low-dimensional entities into high-dimensional frameworks. The UV-vis absorption spectra of 1 - 4 are discussed. Moreover, the photo luminescent properties of 3 and 4 and gas sorption property of 2 have been investigated.

Multispectral embedding-based deep neural network for three-dimensional human pose recovery

NASA Astrophysics Data System (ADS)

Yu, Jialin; Sun, Jifeng

2018-01-01

Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

An advanced NMR protocol for the structural characterization of aluminophosphate glasses.

PubMed

van Wüllen, Leo; Tricot, Grégory; Wegner, Sebastian

2007-10-01

In this work a combination of complementary advanced solid-state nuclear magnetic resonance (NMR) strategies is employed to analyse the network organization in aluminophosphate glasses to an unprecedented level of detailed insight. The combined results from MAS, MQMAS and (31)P-{(27)Al}-CP-heteronuclear correlation spectroscopy (HETCOR) NMR experiments allow for a detailed speciation of the different phosphate and aluminate species present in the glass. The interconnection of these local building units to an extended three-dimensional network is explored employing heteronuclear dipolar and scalar NMR approaches to quantify P-O-Al connectivity by (31)P{(27)Al}-heteronuclear multiple quantum coherence (HMQC), -rotational echo adiabatic passage double resonance (REAPDOR) and -HETCOR NMR as well as (27)Al{(31)P}-rotational echo double resonance (REDOR) NMR experiments, complemented by (31)P-2D-J-RESolved MAS NMR experiments to probe P-O-P connectivity utilizing the through bond scalar J-coupling. The combination of the results from the various NMR approaches enables us to not only quantify the phosphate units present in the glass but also to identify their respective structural environments within the three-dimensional network on a medium length scale employing a modified Q notation, Q(n)(m),(AlO)(x), where n denotes the number of connected tetrahedral phosphate, m gives the number of aluminate species connected to a central phosphate unit and x specifies the nature of the bonded aluminate species (i.e. 4, 5 or 6 coordinate aluminium).

Syntheses, structures and properties of two new coordination polymers based on D-camphoric acid and 2-phenyl-4,6-diamino-1,3,5-triazine

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

Lun, Huijie; Yang, Jinghe; Jin, Linyu

2015-05-15

By hydrothermal method, two new coordination polymers [Co(ca)(phdat)]{sub n} (1), [Ni(ca)(phdat).0.125H{sub 2}O]{sub n} (2) (H{sub 2}ca=D-camphoric acid, phdat=2-phenyl-4,6-diamino-1,3,5-triazine) have been achieved and structurally characterized by IR, elemental analyses, X-ray single-crystal diffraction and TGA. The X-ray single-crystal diffraction reveals that compounds 1 and 2 are isostructural, both of which exhibit two-dimensional layered network built up from paddle-wheel Co{sub 2}(CO{sub 2}){sub 4}/Ni{sub 2}(CO{sub 2}){sub 4} SBUs by ca{sup 2−} ligand. In the existence of π…π stacking interactions between triazine rings and phenyl rings, the 3D networks are constructed with the hanging phdat filled between the neighboring layers. Furthermore, compounds 1–2 exhibit antiferromagneticmore » behavior and compound 2 displays a good activity for methanol oxidation. - Graphical abstract: Two new coordination compounds 1–2 have been synthesized and characterized by single-crystal X-ray diffractions, IR spectra, elemental analyses, thermogravimetric analyses, magnetic and electrochemical measurement. - Highlights: • This paper reports two new coordination polymers based on D-camphoric acid. • Both the compounds feather two-dimensional layered networks built up from paddle-wheel SBUs. • The magnetism and electrochemical property are investigated.« less

Three-dimensional neural cultures produce networks that mimic native brain activity.

PubMed

Bourke, Justin L; Quigley, Anita F; Duchi, Serena; O'Connell, Cathal D; Crook, Jeremy M; Wallace, Gordon G; Cook, Mark J; Kapsa, Robert M I

2018-02-01

Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity. Copyright © 2017 John Wiley & Sons, Ltd.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

Organosilicon Polymers as Precursors for Silicon Containing Ceramics: Recent Developments.

DTIC Science & Technology

1987-08-14

the polymer to a ceramic material, hopefully of the desired composition . In the latter alternative, shrinkage during pyrolysis should not be great...carbon-carbon composite materials. In order to have a useful preceramic polymer . considerations of structure and reactivitv are of paramount importance...process so that on pyrolysis non-volatile, three-dimensional networks (which lead to maximum weight retention) are formed. Thus. preceramic polymer

Three-dimensional graphene networks as a new substrate for immobilization of laccase and dopamine and its application in glucose/O2 biofuel cell.

PubMed

Zhang, Yijia; Chu, Mi; Yang, Lu; Tan, Yueming; Deng, Wenfang; Ma, Ming; Su, Xiaoli; Xie, Qingji

2014-08-13

We report here three-dimensional graphene networks (3D-GNs) as a novel substrate for the immobilization of laccase (Lac) and dopamine (DA) and its application in glucose/O2 biofuel cell. 3D-GNs were synthesized with an Ni(2+)-exchange/KOH activation combination method using a 732-type sulfonic acid ion-exchange resin as the carbon precursor. The 3D-GNs exhibited an interconnected network structure and a high specific surface area. DA was noncovalently functionalized on the surface of 3D-GNs with 3,4,9,10-perylene tetracarboxylic acid (PTCA) as a bridge and used as a novel immobilized mediating system for Lac-based bioelectrocatalytic reduction of oxygen. The 3D-GNs-PTCA-DA nanocomposite modified glassy carbon electrode (GCE) showed stable and well-defined redox current peaks for the catechol/o-quinone redox couple. Due to the mediated electron transfer by the 3D-GNs-PTCA-DA nanocomposite, the Nafion/Lac/3D-GNs-PTCA-DA/GCE exhibited high catalytic activity for oxygen reduction. The 3D-GNs are proven to be a better substrate for Lac and its mediator immobilization than 2D graphene nanosheets (2D-GNs) due to the interconnected network structure and high specific surface area of 3D-GNs. A glucose/O2 fuel cell using Nafion/Lac/3D-GNs-PTCA-DA/GCE as the cathode and Nafion/glucose oxidase/ferrocence/3D-GNs/GCE as the anode can output a maximum power density of 112 μW cm(-2) and a short-circuit current density of 0.96 mA cm(-2). This work may be helpful for exploiting the popular 3D-GNs as an efficient electrode material for many other biotechnology applications.

Three-Dimensional Gas Exchange Pathways in Pome Fruit Characterized by Synchrotron X-Ray Computed Tomography1[C][W][OA

PubMed Central

Verboven, Pieter; Kerckhofs, Greet; Mebatsion, Hibru Kelemu; Ho, Quang Tri; Temst, Kristiaan; Wevers, Martine; Cloetens, Peter; Nicolaï, Bart M.

2008-01-01

Our understanding of the gas exchange mechanisms in plant organs critically depends on insights in the three-dimensional (3-D) structural arrangement of cells and voids. Using synchrotron radiation x-ray tomography, we obtained for the first time high-contrast 3-D absorption images of in vivo fruit tissues of high moisture content at 1.4-μm resolution and 3-D phase contrast images of cell assemblies at a resolution as low as 0.7 μm, enabling visualization of individual cell morphology, cell walls, and entire void networks that were previously unknown. Intercellular spaces were always clear of water. The apple (Malus domestica) cortex contains considerably larger parenchyma cells and voids than pear (Pyrus communis) parenchyma. Voids in apple often are larger than the surrounding cells and some cells are not connected to void spaces. The main voids in apple stretch hundreds of micrometers but are disconnected. Voids in pear cortex tissue are always smaller than parenchyma cells, but each cell is surrounded by a tight and continuous network of voids, except near brachyssclereid groups. Vascular and dermal tissues were also measured. The visualized network architecture was consistent over different picking dates and shelf life. The differences in void fraction (5.1% for pear cortex and 23.0% for apple cortex) and in gas network architecture helps explain the ability of tissues to facilitate or impede gas exchange. Structural changes and anisotropy of tissues may eventually lead to physiological disorders. A combined tomography and internal gas analysis during growth are needed to make progress on the understanding of void formation in fruit. PMID:18417636

A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series

NASA Astrophysics Data System (ADS)

Selvi, Eşref; Selver, M. Alper; Güzeliş, Cüneyt; Dicle, Oǧuz

2014-03-01

Segmentation of anatomical structures from medical image series is an ongoing field of research. Although, organs of interest are three-dimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme. To be able to use slice-by-slice techniques effectively, adjacent slice information, which represents likelihood of a region to be the structure of interest, plays critical role. Recent studies focus on using distance transform directly as a feature or to increase the feature values at the vicinity of the search area. This study presents a novel approach by constructing a higher order neural network, the input layer of which receives features together with their multiplications with the distance transform. This allows higher-order interactions between features through the non-linearity introduced by the multiplication. The application of the proposed method to 9 CT datasets for segmentation of the liver shows higher performance than well-known higher order classification neural networks.

Clustering of neural code words revealed by a first-order phase transition

NASA Astrophysics Data System (ADS)

Huang, Haiping; Toyoizumi, Taro

2016-06-01

A network of neurons in the central nervous system collectively represents information by its spiking activity states. Typically observed states, i.e., code words, occupy only a limited portion of the state space due to constraints imposed by network interactions. Geometrical organization of code words in the state space, critical for neural information processing, is poorly understood due to its high dimensionality. Here, we explore the organization of neural code words using retinal data by computing the entropy of code words as a function of Hamming distance from a particular reference codeword. Specifically, we report that the retinal code words in the state space are divided into multiple distinct clusters separated by entropy-gaps, and that this structure is shared with well-known associative memory networks in a recallable phase. Our analysis also elucidates a special nature of the all-silent state. The all-silent state is surrounded by the densest cluster of code words and located within a reachable distance from most code words. This code-word space structure quantitatively predicts typical deviation of a state-trajectory from its initial state. Altogether, our findings reveal a non-trivial heterogeneous structure of the code-word space that shapes information representation in a biological network.

Micro-MRI-based image acquisition and processing system for assessing the response to therapeutic intervention

NASA Astrophysics Data System (ADS)

Vasilić, B.; Ladinsky, G. A.; Saha, P. K.; Wehrli, F. W.

2006-03-01

Osteoporosis is the cause of over 1.5 million bone fractures annually. Most of these fractures occur in sites rich in trabecular bone, a complex network of bony struts and plates found throughout the skeleton. The three-dimensional structure of the trabecular bone network significantly determines mechanical strength and thus fracture resistance. Here we present a data acquisition and processing system that allows efficient noninvasive assessment of trabecular bone structure through a "virtual bone biopsy". High-resolution MR images are acquired from which the trabecular bone network is extracted by estimating the partial bone occupancy of each voxel. A heuristic voxel subdivision increases the effective resolution of the bone volume fraction map and serves a basis for subsequent analysis of topological and orientational parameters. Semi-automated registration and segmentation ensure selection of the same anatomical location in subjects imaged at different time points during treatment. It is shown with excerpts from an ongoing clinical study of early post-menopausal women, that significant reduction in network connectivity occurs in the control group while the structural integrity is maintained in the hormone replacement group. The system described should be suited for large-scale studies designed to evaluate the efficacy of therapeutic intervention in subjects with metabolic bone disease.

Graphical models for optimal power flow

DOE PAGES

Dvijotham, Krishnamurthy; Chertkov, Michael; Van Hentenryck, Pascal; ...

2016-09-13

Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithmmore » for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. In conclusion, numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.« less

Neural Connectivity Evidence for a Categorical-Dimensional Hybrid Model of Autism Spectrum Disorder.

PubMed

Elton, Amanda; Di Martino, Adriana; Hazlett, Heather Cody; Gao, Wei

2016-07-15

Autism spectrum disorder (ASD) encompasses a complex manifestation of symptoms that include deficits in social interaction and repetitive or stereotyped interests and behaviors. In keeping with the increasing recognition of the dimensional characteristics of ASD symptoms and the categorical nature of a diagnosis, we sought to delineate the neural mechanisms of ASD symptoms based on the functional connectivity of four known neural networks (i.e., default mode network, dorsal attention network, salience network, and executive control network). We leveraged an open data resource (Autism Brain Imaging Data Exchange) providing resting-state functional magnetic resonance imaging data sets from 90 boys with ASD and 95 typically developing boys. This data set also included the Social Responsiveness Scale as a dimensional measure of ASD traits. Seed-based functional connectivity was paired with linear regression to identify functional connectivity abnormalities associated with categorical effects of ASD diagnosis, dimensional effects of ASD-like behaviors, and their interaction. Our results revealed the existence of dimensional mechanisms of ASD uniquely affecting each network based on the presence of connectivity-behavioral relationships; these were independent of diagnostic category. However, we also found evidence of categorical differences (i.e., diagnostic group differences) in connectivity strength for each network as well as categorical differences in connectivity-behavioral relationships (i.e., diagnosis-by-behavior interactions), supporting the coexistence of categorical mechanisms of ASD. Our findings support a hybrid model for ASD characterization that includes a combination of categorical and dimensional brain mechanisms and provide a novel understanding of the neural underpinnings of ASD. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

3D printing of layered brain-like structures using peptide modified gellan gum substrates.

PubMed

Lozano, Rodrigo; Stevens, Leo; Thompson, Brianna C; Gilmore, Kerry J; Gorkin, Robert; Stewart, Elise M; in het Panhuis, Marc; Romero-Ortega, Mario; Wallace, Gordon G

2015-10-01

The brain is an enormously complex organ structured into various regions of layered tissue. Researchers have attempted to study the brain by modeling the architecture using two dimensional (2D) in vitro cell culturing methods. While those platforms attempt to mimic the in vivo environment, they do not truly resemble the three dimensional (3D) microstructure of neuronal tissues. Development of an accurate in vitro model of the brain remains a significant obstacle to our understanding of the functioning of the brain at the tissue or organ level. To address these obstacles, we demonstrate a new method to bioprint 3D brain-like structures consisting of discrete layers of primary neural cells encapsulated in hydrogels. Brain-like structures were constructed using a bio-ink consisting of a novel peptide-modified biopolymer, gellan gum-RGD (RGD-GG), combined with primary cortical neurons. The ink was optimized for a modified reactive printing process and developed for use in traditional cell culturing facilities without the need for extensive bioprinting equipment. Furthermore the peptide modification of the gellan gum hydrogel was found to have a profound positive effect on primary cell proliferation and network formation. The neural cell viability combined with the support of neural network formation demonstrated the cell supportive nature of the matrix. The facile ability to form discrete cell-containing layers validates the application of this novel printing technique to form complex, layered and viable 3D cell structures. These brain-like structures offer the opportunity to reproduce more accurate 3D in vitro microstructures with applications ranging from cell behavior studies to improving our understanding of brain injuries and neurodegenerative diseases. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cusps enable line attractors for neural computation

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

Xiao, Zhuocheng; Zhang, Jiwei; Sornborger, Andrew T.

Here, line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyzemore » system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.« less

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Cusps enable line attractors for neural computation

NASA Astrophysics Data System (ADS)

Xiao, Zhuocheng; Zhang, Jiwei; Sornborger, Andrew T.; Tao, Louis

2017-11-01

Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.

Cusps enable line attractors for neural computation

DOE PAGES

Xiao, Zhuocheng; Zhang, Jiwei; Sornborger, Andrew T.; ...

2017-11-07

Here, line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyzemore » system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.« less

High resolution structural characterisation of laser-induced defect clusters inside diamond

NASA Astrophysics Data System (ADS)

Salter, Patrick S.; Booth, Martin J.; Courvoisier, Arnaud; Moran, David A. J.; MacLaren, Donald A.

2017-08-01

Laser writing with ultrashort pulses provides a potential route for the manufacture of three-dimensional wires, waveguides, and defects within diamond. We present a transmission electron microscopy study of the intrinsic structure of the laser modifications and reveal a complex distribution of defects. Electron energy loss spectroscopy indicates that the majority of the irradiated region remains as sp3 bonded diamond. Electrically conductive paths are attributed to the formation of multiple nano-scale, sp2-bonded graphitic wires and a network of strain-relieving micro-cracks.

Travelling wave effects in large space structures

NASA Technical Reports Server (NTRS)

Vonflotow, A.

1983-01-01

Several aspects of travelling waves in Large Space Structures(LSS) are discussed. The dynamic similarity among LSS's, electric power systems, microwave circuits and communications network is noted. The existence of time lag between actuation and response is illuminated with the aid of simple examples, and their prediction is demonstrated. To prevent echoes, communications lines have matched terminations; this idea is applied to the design of dampers of one dimensional structures. Periodic structures act as mechanical band pass filters. Implications of this behavior are examined on a simple example. It is noted that the implication is twofold; continuum models of periodic lattice structures may err considerably; on the other hand, it is possible to design favorable transmission (and resonance) characteristics into the structure.

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

PubMed Central

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative. PMID:22860044

Three-dimensional core-shell Fe2O3 @ carbon/carbon cloth as binder-free anode for the high-performance lithium-ion batteries

NASA Astrophysics Data System (ADS)

Wang, Xiaohua; Zhang, Miao; Liu, Enzuo; He, Fang; Shi, Chunsheng; He, Chunnian; Li, Jiajun; Zhao, Naiqin

2016-12-01

A facile and scalable strategy is developed to fabricate three dimensional core-shell Fe2O3 @ carbon/carbon cloth structure by simple hydrothermal route as binder-free lithium-ion battery anode. In the unique structure, carbon coated Fe2O3 nanorods uniformly disperse on carbon cloth which forms the conductive carbon network. The hierarchical porous Fe2O3 nanorods in situ grown on the carbon cloth can effectively shorten the transfer paths of lithium ions and reduce the contact resistance. The carbon coating significantly inhibits pulverization of active materials during the repeated Li-ion insertion/extraction, as well as the direct exposure of Fe2O3 to the electrolyte. Benefiting from the structural integrity and flexibility, the nanocomposites used as binder-free anode for lithium-ion batteries, demonstrate high reversible capacity and excellent cyclability. Moreover, this kind of material represents an alternative promising candidate for flexible, cost-effective, and binder-free energy storage devices.

2-(4-Hy-droxy-phen-yl)-1H-benzimidazol-3-ium chloride monohydrate.

PubMed

González-Padilla, Jazmin E; Rosales-Hernández, Martha Cecila; Padilla-Martínez, Itzia I; García-Báez, Efren V; Rojas-Lima, Susana

2013-01-01

The title mol-ecular salt, C13H11N2O(+)·Cl(-)·H2O, crystallizes as a monohydrate. In the cation, the phenol and benzimidazole rings are almost coplanar, making a dihedral angle of 3.18 (4)°. The chloride anion and benzimidazole cation are linked by two N(+)-H⋯Cl(-) hydrogen bonds, forming chains propagating along [010]. These chains are linked through O-H⋯Cl hydrogen bonds involving the water mol-ecule and the chloride anion, which form a diamond core, giving rise to the formation of two-dimensional networks lying parallel to (10-2). Two π-π inter-actions involving the imidazolium ring with the benzene and phenol rings [centroid-centroid distances = 3.859 (3) and 3.602 (3) Å, respectively], contribute to this second dimension. A strong O-H⋯O hydrogen bond involving the water mol-ecule and the phenol substituent on the benzimidazole unit links the networks, forming a three-dimensional structure.

Linear topology in amorphous metal oxide electrochromic networks obtained via low-temperature solution processing

NASA Astrophysics Data System (ADS)

Llordés, Anna; Wang, Yang; Fernandez-Martinez, Alejandro; Xiao, Penghao; Lee, Tom; Poulain, Agnieszka; Zandi, Omid; Saez Cabezas, Camila A.; Henkelman, Graeme; Milliron, Delia J.

2016-12-01

Amorphous transition metal oxides are recognized as leading candidates for electrochromic window coatings that can dynamically modulate solar irradiation and improve building energy efficiency. However, their thin films are normally prepared by energy-intensive sputtering techniques or high-temperature solution methods, which increase manufacturing cost and complexity. Here, we report on a room-temperature solution process to fabricate electrochromic films of niobium oxide glass (NbOx) and `nanocrystal-in-glass’ composites (that is, tin-doped indium oxide (ITO) nanocrystals embedded in NbOx glass) via acid-catalysed condensation of polyniobate clusters. A combination of X-ray scattering and spectroscopic characterization with complementary simulations reveals that this strategy leads to a unique one-dimensional chain-like NbOx structure, which significantly enhances the electrochromic performance, compared to a typical three-dimensional NbOx network obtained from conventional high-temperature thermal processing. In addition, we show how self-assembled ITO-in-NbOx composite films can be successfully integrated into high-performance flexible electrochromic devices.

Investigation of Supramolecular Coordination Self-Assembly and Polymerization Confined on Metal Surfaces Using Scanning Tunneling Microscopy

NASA Astrophysics Data System (ADS)

Lin, Tao

Organic molecules are envisioned as the building blocks for design and fabrication of functional devices in future, owing to their versatility, low cost and flexibility. Although some devices such as organic light-emitting diode (OLED) have been already applied in our daily lives, the field is still in its infancy and numerous challenges still remain. In particular, fundamental understanding of the process of organic material fabrication at a molecular level is highly desirable. This thesis focuses on the design and fabrication of supramolecular and macromolecular nanostructures on a Au(111) surface through self-assembly, polymerization and a combination of two. We used scanning tunneling microscopy (STM) as an experimental tool and Monte Carlo (MC) and kinetic Monte Carlo (KMC) simulations as theoretical tools to characterize the structures of these systems and to investigate the mechanisms of the self-assembly and polymerization processes at a single-molecular level. The results of this thesis consist of four parts as below: Part I addresses the mechanisms of two-dimensional multicomponent supramolecular self-assembly via pyridyl-Fe-terpyridyl coordination. Firstly, we studied four types of self-assembled metal-organic systems exhibiting different dimensionalities using specifically-designed molecular building blocks. We found that the two-dimensional system is under thermodynamic controls while the systems of lower dimension are under kinetic controls. Secondly, we studied the self-assembly of a series of cyclic supramolecular polygons. Our results indicate that the yield of on-surface cyclic polygon structures is very low independent of temperature and concentration and this phenomenon can be attributed to a subtle competition between kinetic and thermodynamic controls. These results shed light on thermodynamic and kinetic controls in on-surface coordination self-assembly. Part II addresses the two-dimensional supramolecular self-assembly of porphyrin derivatives. Firstly, we investigated the coordination self-assembly of a series of peripheral bromo-phenyl and pyridyl substituted porphyrins with Fe. The self-assembly of the porphyrin derivatives in which phenyl groups are substituted by bromo-phenyl results in coordination networks exhibiting identical structures to that of the parent compounds, but contained nanopores that are functionalized by bromine substitutes. Secondly, we studied a two-dimensional coordination networks formed by 5,10,15,20-tetra(4-pyridyl)porphyrin and Fe. We discovered a novel coordination motif in which a pair of vertically aligned Fe atoms is ligated by four equatorial pyridyl groups. Lateral manipulation, vertical manipulation and tunneling spectroscopy were employed to characterize the networks. These novel coordination networks decorated with Br or vertically aligned Fe atoms may provide potential functions as nano-receptor, molecular magnetism or catalyst. Part III addresses the mechanism of on-surface Ullmann coupling reaction. We studied Pd- and Cu-catalyzed Ullmann coupling reactions between phenyl bromide functionalized porphyrin derivatives. We discovered that the reactions catalyzed by Pd or Cu can be described as a two-phase process that involves an initial activation followed by C-C bond formation. Analysis of rate constants of the Pd-catalyzed reactions allowed us to determine its activation energy as (0.41 +/- 0.03) eV. These results provide a quantitative understanding of on-surface Ullmann coupling reaction. Part IV addresses the on-surface self-assembly driven by a combination of coordination bonds and covalent bonds. Firstly, we utilized metal-directed template to control the on-surface polymerization process. Taking advantage of efficient topochemical enhancement owing to the conformation flexibility of the Cu-pyridyl bonds, macromolecular porphyrin structures that exhibit a narrow size distribution were synthesized. The results reveal that the polymerization process profited from the rich chemistry of Cu which catalyzed the C-C bond formation, controlled the size of the macromolecular products, and organized the macromolecules in a highly ordered manner on the surface. Secondly, we demonstrated a two-step approach for assembling metal-organic coordination network exhibiting very large pores. The first step involves obtaining one kind of building blocks via on-surface Ullmann coupling and the second step is coordination self-assembly. Moreover, the modulation of the surface-state electrons in the network was studied. These results provide new approaches to design and fabricate on-surface nanostructures. In summary, we resolved the structures and studied the on-surface assembly and reaction mechanisms of supramolecular and macromolecular nanostructures at a sub-molecular level. These fundamental studies may shed lights on design and fabrication of low-dimensional organic materials.

Supramolecular architectures constructed by lanthanum, amino acids and 1,10-phenanthroline via non-covalent bond interactions

NASA Astrophysics Data System (ADS)

Zheng, Xiang-Jun; Jin, Lin-Pei

2003-07-01

Three supramolecular lanthanum coordination compounds of amino acids, with 1,10-phenanthroline (phen), [La 2(APA) 6(phen) 2(H 2O) 2](ClO 4) 6(phen) 4·2H 2O ( 1), [La 2(ABA) 6(phen) 2(H 2O) 2](ClO 4) 6 (phen) 6·4H 2O ( 2), and [La 2(AHA) 4(phen) 4](ClO 4) 6(phen) 4·2H 2O ( 3) (APA=3-aminopropionic acid; ABA=4-aminobutanoic acid; AHA=6-aminohexanoic acid) were synthesized and characterized by single crystal X-ray diffraction. The results show that the three coordination compounds are all composed of binuclear coordination cations built by metal-ligand coordination. Through hydrogen bonding and π-π stacking interactions, complex 1 forms a two-dimensional supramolecular sheet structure extending in the (001) plane, complex 2 forms a three-dimensional supramolecular network with many cavities occupied by ClO 4- and lattice H 2O molecules, and complex 3 forms a two-dimensional supramolecular lamellar structure in the (100) plane.

Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

PubMed

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2016-01-01

To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

Multistability in bidirectional associative memory neural networks

NASA Astrophysics Data System (ADS)

Huang, Gan; Cao, Jinde

2008-04-01

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

PubMed

Glaser, Joshua I; Zamft, Bradley M; Church, George M; Kording, Konrad P

2015-01-01

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

Polymorphism and metal-induced structural transformation in 5,5'-bis(4-pyridyl)(2,2'-bispyrimidine) adlayers on Au(111).

PubMed

Hötger, Diana; Carro, Pilar; Gutzler, Rico; Wurster, Benjamin; Chandrasekar, Rajadurai; Klyatskaya, Svetlana; Ruben, Mario; Salvarezza, Roberto C; Kern, Klaus; Grumelli, Doris

2018-05-31

Metal-organic coordination networks self-assembled on surfaces have emerged as functional low-dimensional architectures with potential applications ranging from the fabrication of functional nanodevices to electrocatalysis. Among them, bis-pyridyl-bispyrimidine (PBP) and Fe-PBP on noble metal surfaces appear as interesting systems in revealing the details of the molecular self-assembly and the effect of metal incorporation on the organic network arrangement. Herein, we report a combined STM, XPS, and DFT study revealing polymorphism in bis-pyridyl-bispyrimidine adsorbed adlayers on the reconstructed Au(111) surface. The polymorphic structures are converted by the addition of Fe adatoms into one unique Fe-PBP surface structure. DFT calculations show that while all PBP phases exhibit a similar thermodynamic stability, metal incorporation selects the PBP structure that maximizes the number of metal-N close contacts. Charge transfer from the Fe adatoms to the Au substrate and N-Fe interactions stabilize the Fe-PBP adlayer. The increased thermodynamic stability of the metal-stabilized structure leads to its sole expression on the surface.

Mapping uncharted territory in ice from zeolite networks to ice structures.

PubMed

Engel, Edgar A; Anelli, Andrea; Ceriotti, Michele; Pickard, Chris J; Needs, Richard J

2018-06-05

Ice is one of the most extensively studied condensed matter systems. Yet, both experimentally and theoretically several new phases have been discovered over the last years. Here we report a large-scale density-functional-theory study of the configuration space of water ice. We geometry optimise 74,963 ice structures, which are selected and constructed from over five million tetrahedral networks listed in the databases of Treacy, Deem, and the International Zeolite Association. All prior knowledge of ice is set aside and we introduce "generalised convex hulls" to identify configurations stabilised by appropriate thermodynamic constraints. We thereby rediscover all known phases (I-XVII, i, 0 and the quartz phase) except the metastable ice IV. Crucially, we also find promising candidates for ices XVIII through LI. Using the "sketch-map" dimensionality-reduction algorithm we construct an a priori, navigable map of configuration space, which reproduces similarity relations between structures and highlights the novel candidates. By relating the known phases to the tractably small, yet structurally diverse set of synthesisable candidate structures, we provide an excellent starting point for identifying formation pathways.

The discovery of structural form

PubMed Central

Kemp, Charles; Tenenbaum, Joshua B.

2008-01-01

Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., “bigger than” or “better than”) respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development. PMID:18669663

Optimization of Turbine Blade Design for Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Shyy, Wei

1998-01-01

To facilitate design optimization of turbine blade shape for reusable launching vehicles, appropriate techniques need to be developed to process and estimate the characteristics of the design variables and the response of the output with respect to the variations of the design variables. The purpose of this report is to offer insight into developing appropriate techniques for supporting such design and optimization needs. Neural network and polynomial-based techniques are applied to process aerodynamic data obtained from computational simulations for flows around a two-dimensional airfoil and a generic three- dimensional wing/blade. For the two-dimensional airfoil, a two-layered radial-basis network is designed and trained. The performances of two different design functions for radial-basis networks, one based on the accuracy requirement, whereas the other one based on the limit on the network size. While the number of neurons needed to satisfactorily reproduce the information depends on the size of the data, the neural network technique is shown to be more accurate for large data set (up to 765 simulations have been used) than the polynomial-based response surface method. For the three-dimensional wing/blade case, smaller aerodynamic data sets (between 9 to 25 simulations) are considered, and both the neural network and the polynomial-based response surface techniques improve their performance as the data size increases. It is found while the relative performance of two different network types, a radial-basis network and a back-propagation network, depends on the number of input data, the number of iterations required for radial-basis network is less than that for the back-propagation network.

Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network.

PubMed

Dornay, M; Sanger, T D

1993-01-01

A planar 17 muscle model of the monkey's arm based on realistic biomechanical measurements was simulated on a Symbolics Lisp Machine. The simulator implements the equilibrium point hypothesis for the control of arm movements. Given initial and final desired positions, it generates a minimum-jerk desired trajectory of the hand and uses the backdriving algorithm to determine an appropriate sequence of motor commands to the muscles (Flash 1987; Mussa-Ivaldi et al. 1991; Dornay 1991b). These motor commands specify a temporal sequence of stable (attractive) equilibrium positions which lead to the desired hand movement. A strong disadvantage of the simulator is that it has no memory of previous computations. Determining the desired trajectory using the minimum-jerk model is instantaneous, but the laborious backdriving algorithm is slow, and can take up to one hour for some trajectories. The complexity of the required computations makes it a poor model for biological motor control. We propose a computationally simpler and more biologically plausible method for control which achieves the benefits of the backdriving algorithm. A fast learning, tree-structured network (Sanger 1991c) was trained to remember the knowledge obtained by the backdriving algorithm. The neural network learned the nonlinear mapping from a 2-dimensional cartesian planar hand position (x,y) to a 17-dimensional motor command space (u1, . . ., u17). Learning 20 training trajectories, each composed of 26 sample points [[x,y], [u1, . . ., u17] took only 20 min on a Sun-4 Sparc workstation. After the learning stage, new, untrained test trajectories as well as the original trajectories of the hand were given to the neural network as input. The network calculated the required motor commands for these movements. The resulting movements were close to the desired ones for both the training and test cases.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impact of network topology on self-organized criticality

NASA Astrophysics Data System (ADS)

Hoffmann, Heiko

2018-02-01

The general mechanisms behind self-organized criticality (SOC) are still unknown. Several microscopic and mean-field theory approaches have been suggested, but they do not explain the dependence of the exponents on the underlying network topology of the SOC system. Here, we first report the phenomena that in the Bak-Tang-Wiesenfeld (BTW) model, sites inside an avalanche area largely return to their original state after the passing of an avalanche, forming, effectively, critically arranged clusters of sites. Then, we hypothesize that SOC relies on the formation process of these clusters, and present a model of such formation. For low-dimensional networks, we show theoretically and in simulation that the exponent of the cluster-size distribution is proportional to the ratio of the fractal dimension of the cluster boundary and the dimensionality of the network. For the BTW model, in our simulations, the exponent of the avalanche-area distribution matched approximately our prediction based on this ratio for two-dimensional networks, but deviated for higher dimensions. We hypothesize a transition from cluster formation to the mean-field theory process with increasing dimensionality. This work sheds light onto the mechanisms behind SOC, particularly, the impact of the network topology.

Discontinuities in effective permeability due to fracture percolation

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

Hyman, Jeffrey De'Haven; Karra, Satish; Carey, James William

Motivated by a triaxial coreflood experiment with a sample of Utica shale where an abrupt jump in permeability was observed, possibly due to the creation of a percolating fracture network through the sample, we perform numerical simulations based on the experiment to characterize how the effective permeability of otherwise low-permeability porous media depends on fracture formation, connectivity, and the contrast between the fracture and matrix permeabilities. While a change in effective permeability due to fracture formation is expected, the dependence of its magnitude upon the contrast between the matrix permeability and fracture permeability and the fracture network structure is poorlymore » characterized. We use two different high-fidelity fracture network models to characterize how effective permeability changes as percolation occurs. The first is a dynamic two-dimensional fracture propagation model designed to mimic the laboratory settings of the experiment. The second is a static three-dimensional discrete fracture network (DFN) model, whose fracture and network statistics are based on the fractured sample of Utica shale. Once the network connects the inflow and outflow boundaries, the effective permeability increases non-linearly with network density. In most networks considered, a jump in the effective permeability was observed when the embedded fracture network percolated. We characterize how the magnitude of the jump, should it occur, depends on the contrast between the fracture and matrix permeabilities. For small contrasts between the matrix and fracture permeabilities the change is insignificant. However, for larger contrasts, there is a substantial jump whose magnitude depends non-linearly on the difference between matrix and fracture permeabilities. A power-law relationship between the size of the jump and the difference between the matrix and fracture permeabilities is observed. In conclusion, the presented results underscore the importance of fracture network topology on the upscaled properties of the porous medium in which it is embedded.« less

Discontinuities in effective permeability due to fracture percolation

DOE PAGES

Hyman, Jeffrey De'Haven; Karra, Satish; Carey, James William; ...

2018-01-31

Motivated by a triaxial coreflood experiment with a sample of Utica shale where an abrupt jump in permeability was observed, possibly due to the creation of a percolating fracture network through the sample, we perform numerical simulations based on the experiment to characterize how the effective permeability of otherwise low-permeability porous media depends on fracture formation, connectivity, and the contrast between the fracture and matrix permeabilities. While a change in effective permeability due to fracture formation is expected, the dependence of its magnitude upon the contrast between the matrix permeability and fracture permeability and the fracture network structure is poorlymore » characterized. We use two different high-fidelity fracture network models to characterize how effective permeability changes as percolation occurs. The first is a dynamic two-dimensional fracture propagation model designed to mimic the laboratory settings of the experiment. The second is a static three-dimensional discrete fracture network (DFN) model, whose fracture and network statistics are based on the fractured sample of Utica shale. Once the network connects the inflow and outflow boundaries, the effective permeability increases non-linearly with network density. In most networks considered, a jump in the effective permeability was observed when the embedded fracture network percolated. We characterize how the magnitude of the jump, should it occur, depends on the contrast between the fracture and matrix permeabilities. For small contrasts between the matrix and fracture permeabilities the change is insignificant. However, for larger contrasts, there is a substantial jump whose magnitude depends non-linearly on the difference between matrix and fracture permeabilities. A power-law relationship between the size of the jump and the difference between the matrix and fracture permeabilities is observed. In conclusion, the presented results underscore the importance of fracture network topology on the upscaled properties of the porous medium in which it is embedded.« less

The tensor network theory library

NASA Astrophysics Data System (ADS)

Al-Assam, S.; Clark, S. R.; Jaksch, D.

2017-09-01

In this technical paper we introduce the tensor network theory (TNT) library—an open-source software project aimed at providing a platform for rapidly developing robust, easy to use and highly optimised code for TNT calculations. The objectives of this paper are (i) to give an overview of the structure of TNT library, and (ii) to help scientists decide whether to use the TNT library in their research. We show how to employ the TNT routines by giving examples of ground-state and dynamical calculations of one-dimensional bosonic lattice system. We also discuss different options for gaining access to the software available at www.tensornetworktheory.org.

Alcohol expectancy multiaxial assessment: a memory network-based approach.

PubMed

Goldman, Mark S; Darkes, Jack

2004-03-01

Despite several decades of activity, alcohol expectancy research has yet to merge measurement approaches with developing memory theory. This article offers an expectancy assessment approach built on a conceptualization of expectancy as an information processing network. The authors began with multidimensional scaling models of expectancy space, which served as heuristics to suggest confirmatory factor analytic dimensional models for entry into covariance structure predictive models. It is argued that this approach permits a relatively thorough assessment of the broad range of potential expectancy dimensions in a format that is very flexible in terms of instrument length and specificity versus breadth of focus. ((c) 2004 APA, all rights reserved)

Three-dimensional structure of the lithostathine protofibril, a protein involved in Alzheimer's disease.

PubMed

Grégoire, C; Marco, S; Thimonier, J; Duplan, L; Laurine, E; Chauvin, J P; Michel, B; Peyrot, V; Verdier, J M

2001-07-02

Neurodegenerative diseases are characterized by the presence of filamentous aggregates of proteins. We previously established that lithostathine is a protein overexpressed in the pre-clinical stages of Alzheimer's disease. Furthermore, it is present in the pathognomonic lesions associated with Alzheimer's disease. After self-proteolysis, the N-terminally truncated form of lithostathine leads to the formation of fibrillar aggregates. Here we observed using atomic force microscopy that these aggregates consisted of a network of protofibrils, each of which had a twisted appearance. Electron microscopy and image analysis showed that this twisted protofibril has a quadruple helical structure. Three-dimensional X-ray structural data and the results of biochemical experiments showed that when forming a protofibril, lithostathine was first assembled via lateral hydrophobic interactions into a tetramer. Each tetramer then linked up with another tetramer as the result of longitudinal electrostatic interactions. All these results were used to build a structural model for the lithostathine protofibril called the quadruple-helical filament (QHF-litho). In conclusion, lithostathine strongly resembles the prion protein in its dramatic proteolysis and amyloid proteins in its ability to form fibrils.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

PubMed

Chen Peng; Ao Li

2017-01-01

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

A network model for characterizing brine channels in sea ice

NASA Astrophysics Data System (ADS)

Lieblappen, Ross M.; Kumar, Deip D.; Pauls, Scott D.; Obbard, Rachel W.

2018-03-01

The brine pore space in sea ice can form complex connected structures whose geometry is critical in the governance of important physical transport processes between the ocean, sea ice, and surface. Recent advances in three-dimensional imaging using X-ray micro-computed tomography have enabled the visualization and quantification of the brine network morphology and variability. Using imaging of first-year sea ice samples at in situ temperatures, we create a new mathematical network model to characterize the topology and connectivity of the brine channels. This model provides a statistical framework where we can characterize the pore networks via two parameters, depth and temperature, for use in dynamical sea ice models. Our approach advances the quantification of brine connectivity in sea ice, which can help investigations of bulk physical properties, such as fluid permeability, that are key in both global and regional sea ice models.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

PubMed

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Monitoring Coral Growth - the Dichotomy Between Underwater Photogrammetry and Geodetic Control Network

NASA Astrophysics Data System (ADS)

Neyer, F.; Nocerino, E.; Gruen, A.

2018-05-01

Creating 3-dimensional (3D) models of underwater scenes has become a common approach for monitoring coral reef changes and its structural complexity. Also in underwater archeology, 3D models are often created using underwater optical imagery. In this paper, we focus on the aspect of detecting small changes in the coral reef using a multi-temporal photogrammetric modelling approach, which requires a high quality control network. We show that the quality of a good geodetic network limits the direct change detection, i.e., without any further registration process. As the photogrammetric accuracy is expected to exceed the geodetic network accuracy by at least one order of magnitude, we suggest to do a fine registration based on a number of signalized points. This work is part of the Moorea Island Digital Ecosystem Avatar (IDEA) project that has been initiated in 2013 by a group of international researchers (https://mooreaidea.ethz.ch/).

Aromatic carboxylate effect on dimensionality of three bis(benzimidazole)-based cobalt(II) coordination polymers: Syntheses, structures and properties

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

Zhang, Ju-Wen; Gong, Chun-Hua; Hou, Li-Li

2013-09-15

Three new metal-organic coordination polymers [Co(4-bbc){sub 2}(bbbm)] (1), [Co(3,5-pdc)(bbbm)]·2H{sub 2}O (2) and [Co(1,4-ndc)(bbbm)] (3) (4-Hbbc=4-bromobenzoic acid, 3,5-H{sub 2}pdc=3,5-pyridinedicarboxylic acid, 1,4-H{sub 2}ndc=1,4-naphthalenedicarboxylic acid and bbbm=1,1-(1,4-butanediyl)bis-1H-benzimidazole) were hydrothermally synthesized and structurally characterized. Polymer 1 is a 1D chain formed by the bbbm ligands and Co{sup II} ions. Polymer 2 exhibits a 2D network with a (3·4·5)(3{sup 2}·4·5·6{sup 2}·7{sup 4}) topology. Polymer 3 possesses a 3D three-fold interpenetrating framework. The versatile structures of title polymers indicate that the aromatic carboxylates have an important influence on the dimensionality of 1–3. Moreover, the thermal stability, electrochemical and luminescent properties of 1–3 were investigated. - graphicalmore » abstract: Three bis(benzimidazole)-based cobalt(II) coordination polymers tuned by aromatic carboxylates were hydrothermally synthesized and structurally characterized. The aromatic carboxylates play a key role in the dimensionality of three polymers. The electrochemical and luminescent properties of three polymers were investigated. Display Omitted - Highlights: • Three bis(benzimidazole)-based cobalt(II) coordination polymers tuned by aromatic carboxylates were obtained. • The aromatic carboxylates have an important influence on the dimensionality of three polymers. • The electrochemical and luminescent properties of three polymers were investigated.« less

Role of Hydrophobic Clusters and Long-Range Contact Networks in the Folding of (α/β)8 Barrel Proteins

PubMed Central

Selvaraj, S.; Gromiha, M. Michael

2003-01-01

Analysis on the three dimensional structures of (α/β)8 barrel proteins provides ample light to understand the factors that are responsible for directing and maintaining their common fold. In this work, the hydrophobically enriched clusters are identified in 92% of the considered (α/β)8 barrel proteins. The residue segments with hydrophobic clusters have high thermal stability. Further, these clusters are formed and stabilized through long-range interactions. Specifically, a network of long-range contacts connects adjacent β-strands of the (α/β)8 barrel domain and the hydrophobic clusters. The implications of hydrophobic clusters and long-range networks in providing a feasible common mechanism for the folding of (α/β)8 barrel proteins are proposed. PMID:12609894

Meso-Decorated Switching-Knot Gels

NASA Astrophysics Data System (ADS)

Gong, Jin; Sawamura, Kensuke; Makino, Masato; Kabir, M. H.; Furukawa, Hidemitsu

Gels are a new material having three-dimensional network structures of macromolecules. They possess excellent properties as swellability, high permeability and biocompatibility, and have been applied in various fields of daily life, food, medicine, architecture, and chemistry .In this study, we tried to prepare new multi-functional and high-strength gels by using Meso-Decoration (Meso-Deco), one new method of structure design at intermediate mesoscale. High-performance rigid-rod aromatic polymorphic crystals. The strengthening of gels can be realized by meso-decorating the gels' structure using high-performance polymorphic crystals. New gels with good mechanical properties, novel optical properties and thermal properties are expected to be developed.

Diamond network: template-free fabrication and properties.

PubMed

Zhuang, Hao; Yang, Nianjun; Fu, Haiyuan; Zhang, Lei; Wang, Chun; Huang, Nan; Jiang, Xin

2015-03-11

A porous diamond network with three-dimensionally interconnected pores is of technical importance but difficult to be produced. In this contribution, we demonstrate a simple, controllable, and "template-free" approach to fabricate diamond networks. It combines the deposition of diamond/β-SiC nanocomposite film with a wet-chemical selective etching of the β-SiC phase. The porosity of these networks was tuned from 15 to 68%, determined by the ratio of the β-SiC phase in the composite films. The electrochemical working potential and the reactivity of redox probes on the diamond networks are similar to those of a flat nanocrystalline diamond film, while their surface areas are hundreds of times larger than that of a flat diamond film (e.g., 490-fold enhancement for a 3 μm thick diamond network). The marriage of the unprecedented physical/chemical features of diamond with inherent advantages of the porous structure makes the diamond network a potential candidate for various applications such as water treatment, energy conversion (batteries or fuel cells), and storage (capacitors), as well as electrochemical and biochemical sensing.

Defining an essence of structure determining residue contacts in proteins.

PubMed

Sathyapriya, R; Duarte, Jose M; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

2009-12-01

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this "structural essence" has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts-such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed "cone-peeling" that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 A Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This "structural essence" opens new avenues in the fields of structure prediction, empirical potentials and docking.

Defining an Essence of Structure Determining Residue Contacts in Proteins

PubMed Central

Sathyapriya, R.; Duarte, Jose M.; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

2009-01-01

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this “structural essence” has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts—such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed “cone-peeling” that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 Å Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This “structural essence” opens new avenues in the fields of structure prediction, empirical potentials and docking. PMID:19997489

Carbon Microfibers with Hierarchical Porous Structure from Electrospun Fiber-Like Natural Biopolymer

NASA Astrophysics Data System (ADS)

Liang, Yeru; Wu, Dingcai; Fu, Ruowen

2013-01-01

Electrospinning offers a powerful route for building one-dimensional (1D) micro/nanostructures, but a common requirement for toxic or corrosive organic solvents during the preparation of precursor solution has limited their large scale synthesis and broad applications. Here we report a facile and low-cost way to prepare 1D porous carbon microfibers by using an electrospun fiber-like natural product, i.e., silk cocoon, as precursor. We surprisingly found that by utilizing a simple carbonization treatment, the cocoon microfiber can be directly transformed into 1D carbon microfiber of ca. 6 μm diameter with a unique three-dimensional porous network structure composed of interconnected carbon nanoparticles of 10~40 nm diameter. We further showed that the as-prepared carbon product presents superior electrochemical performance as binder-free electrodes of supercapacitors and good adsorption property toward organic vapor.

Materials science. Assembly of micro/nanomaterials into complex, three-dimensional architectures by compressive buckling.

PubMed

Xu, Sheng; Yan, Zheng; Jang, Kyung-In; Huang, Wen; Fu, Haoran; Kim, Jeonghyun; Wei, Zijun; Flavin, Matthew; McCracken, Joselle; Wang, Renhan; Badea, Adina; Liu, Yuhao; Xiao, Dongqing; Zhou, Guoyan; Lee, Jungwoo; Chung, Ha Uk; Cheng, Huanyu; Ren, Wen; Banks, Anthony; Li, Xiuling; Paik, Ungyu; Nuzzo, Ralph G; Huang, Yonggang; Zhang, Yihui; Rogers, John A

2015-01-09

Complex three-dimensional (3D) structures in biology (e.g., cytoskeletal webs, neural circuits, and vasculature networks) form naturally to provide essential functions in even the most basic forms of life. Compelling opportunities exist for analogous 3D architectures in human-made devices, but design options are constrained by existing capabilities in materials growth and assembly. We report routes to previously inaccessible classes of 3D constructs in advanced materials, including device-grade silicon. The schemes involve geometric transformation of 2D micro/nanostructures into extended 3D layouts by compressive buckling. Demonstrations include experimental and theoretical studies of more than 40 representative geometries, from single and multiple helices, toroids, and conical spirals to structures that resemble spherical baskets, cuboid cages, starbursts, flowers, scaffolds, fences, and frameworks, each with single- and/or multiple-level configurations. Copyright © 2015, American Association for the Advancement of Science.

A Depth-Adjustment Deployment Algorithm Based on Two-Dimensional Convex Hull and Spanning Tree for Underwater Wireless Sensor Networks.

PubMed

Jiang, Peng; Liu, Shuai; Liu, Jun; Wu, Feng; Zhang, Le

2016-07-14

Most of the existing node depth-adjustment deployment algorithms for underwater wireless sensor networks (UWSNs) just consider how to optimize network coverage and connectivity rate. However, these literatures don't discuss full network connectivity, while optimization of network energy efficiency and network reliability are vital topics for UWSN deployment. Therefore, in this study, a depth-adjustment deployment algorithm based on two-dimensional (2D) convex hull and spanning tree (NDACS) for UWSNs is proposed. First, the proposed algorithm uses the geometric characteristics of a 2D convex hull and empty circle to find the optimal location of a sleep node and activate it, minimizes the network coverage overlaps of the 2D plane, and then increases the coverage rate until the first layer coverage threshold is reached. Second, the sink node acts as a root node of all active nodes on the 2D convex hull and then forms a small spanning tree gradually. Finally, the depth-adjustment strategy based on time marker is used to achieve the three-dimensional overall network deployment. Compared with existing depth-adjustment deployment algorithms, the simulation results show that the NDACS algorithm can maintain full network connectivity with high network coverage rate, as well as improved network average node degree, thus increasing network reliability.

A Depth-Adjustment Deployment Algorithm Based on Two-Dimensional Convex Hull and Spanning Tree for Underwater Wireless Sensor Networks

PubMed Central

Jiang, Peng; Liu, Shuai; Liu, Jun; Wu, Feng; Zhang, Le

2016-01-01

Most of the existing node depth-adjustment deployment algorithms for underwater wireless sensor networks (UWSNs) just consider how to optimize network coverage and connectivity rate. However, these literatures don’t discuss full network connectivity, while optimization of network energy efficiency and network reliability are vital topics for UWSN deployment. Therefore, in this study, a depth-adjustment deployment algorithm based on two-dimensional (2D) convex hull and spanning tree (NDACS) for UWSNs is proposed. First, the proposed algorithm uses the geometric characteristics of a 2D convex hull and empty circle to find the optimal location of a sleep node and activate it, minimizes the network coverage overlaps of the 2D plane, and then increases the coverage rate until the first layer coverage threshold is reached. Second, the sink node acts as a root node of all active nodes on the 2D convex hull and then forms a small spanning tree gradually. Finally, the depth-adjustment strategy based on time marker is used to achieve the three-dimensional overall network deployment. Compared with existing depth-adjustment deployment algorithms, the simulation results show that the NDACS algorithm can maintain full network connectivity with high network coverage rate, as well as improved network average node degree, thus increasing network reliability. PMID:27428970

1-[6-(1H-Indol-1-yl)pyridin-2-yl]-1H-indole-3-carbaldehyde.

PubMed

Ramathilagam, C; Umarani, P R; Venkatesan, N; Rajakumar, P; Gunasekaran, B; Manivannan, V

2014-02-01

In the title compound, C22H15N3O, the dihedral angle between the two indole units is 33.72 (3)°. The mol-ecular structure features a weak intra-molecular C-H⋯N inter-action. In the crystal, weak C-H⋯O and C-H⋯π inter-actions, forming a two-dimensional network parallel to the bc plane.

Guiding of Plasmons and Phonons in Complex Three Dimensional Structures

DTIC Science & Technology

2013-01-01

typical sample. We employed X - ray diffraction (XRD) to measure the average grain size across the entire depth of the sample over spot sizes Figure...propagation distance L as the 1/e decay length of the field intensity along x ...as well as the network layout with subwavelegth gap size and internode distance on the order of the effective wavelength, a small 2 x 2 resonant

Supramolecular Hydrogels Based on DNA Self-Assembly.

PubMed

Shao, Yu; Jia, Haoyang; Cao, Tianyang; Liu, Dongsheng

2017-04-18

Extracellular matrix (ECM) provides essential supports three dimensionally to the cells in living organs, including mechanical support and signal, nutrition, oxygen, and waste transportation. Thus, using hydrogels to mimic its function has attracted much attention in recent years, especially in tissue engineering, cell biology, and drug screening. However, a hydrogel system that can merit all parameters of the natural ECM is still a challenge. In the past decade, deoxyribonucleic acid (DNA) has arisen as an outstanding building material for the hydrogels, as it has unique properties compared to most synthetic or natural polymers, such as sequence designability, precise recognition, structural rigidity, and minimal toxicity. By simple attachment to polymers as a side chain, DNA has been widely used as cross-links in hydrogel preparation. The formed secondary structures could confer on the hydrogel designable responsiveness, such as response to temperature, pH, metal ions, proteins, DNA, RNA, and small signal molecules like ATP. Moreover, single or multiple DNA restriction enzyme sites could be incorporated into the hydrogels by sequence design and greatly expand the latitude of their responses. Compared with most supramolecular hydrogels, these DNA cross-linked hydrogels could be relatively strong and easily adjustable via sequence variation, but it is noteworthy that these hydrogels still have excellent thixotropic properties and could be easily injected through a needle. In addition, the quick formation of duplex has also enabled the multilayer three-dimensional injection printing of living cells with the hydrogel as matrix. When the matrix is built purely by DNA assembly structures, the hydrogel inherits all the previously described characteristics; however, the long persistence length of DNA structures excluded the small size meshes of the network and made the hydrogel permeable to nutrition for cell proliferation. This unique property greatly expands the cell viability in the three-dimensional matrix to several weeks and also provides an easy way to prepare interpenetrating double network materials. In this Account, we outline the stream of hydrogels based on DNA self-assembly and discuss the mechanism that brings outstanding properties to the materials. Unlike most reported hydrogel systems, the all-in-one character of the DNA hydrogel avoids the "cask effect" in the properties. We believe the hydrogel will greatly benefit cell behavior studies especially in the following aspects: (1) stem cell differentiation can be studied with solely tunable mechanical strength of the matrix; (2) the dynamic nature of the network can allow cell migration through the hydrogel, which will help to build a more realistic model to observe the migration of cancer cells in vivo; (3) combination with rapidly developing three-dimension printing technology, the hydrogel will boost the construction of three-dimensional tissues and artificial organs.

Small-sized PdCu nanocapsules on 3D graphene for high-performance ethanol oxidation

NASA Astrophysics Data System (ADS)

HuThese Authors Contributed Equally To This Work., Chuangang; Zhai, Xiangquan; Zhao, Yang; Bian, Ke; Zhang, Jing; Qu, Liangti; Zhang, Huimin; Luo, Hongxia

2014-02-01

A one-pot solvothermal process has been developed for direct preparation of PdCu nanocapsules (with a size of ca. 10 nm) on three-dimensional (3D) graphene. Due to the 3D pore-rich network of graphene and the unique hollow structure of PdCu nanocapsules with a wall thickness of ca. 3 nm, the newly-prepared PdCu/3D graphene hybrids activated electrochemically have great electrocatalytic activity towards ethanol oxidation in alkaline media, much better than single-phase Pd and commercial E-TEK 20% Pt/C catalysts promising for application in direct ethanol fuel cells.A one-pot solvothermal process has been developed for direct preparation of PdCu nanocapsules (with a size of ca. 10 nm) on three-dimensional (3D) graphene. Due to the 3D pore-rich network of graphene and the unique hollow structure of PdCu nanocapsules with a wall thickness of ca. 3 nm, the newly-prepared PdCu/3D graphene hybrids activated electrochemically have great electrocatalytic activity towards ethanol oxidation in alkaline media, much better than single-phase Pd and commercial E-TEK 20% Pt/C catalysts promising for application in direct ethanol fuel cells. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr05722d

A novel three-dimensional carbonized PANI1600@CNTs network for enhanced enzymatic biofuel cell.

PubMed

Kang, Zepeng; Jiao, Kailong; Cheng, Jin; Peng, Ruiyun; Jiao, Shuqiang; Hu, Zongqian

2018-03-15

A novel three-dimensional (3D) carbon composite of PANI 1600 @CNTs with rhizobium-like structure is prepared by in-situ polymerization of aniline monomers around and along the functionalized carbon nanotubes (CNTs) and then carbonized at 1600°C for enzymatic biofuel cells (EBFCs). The SEM and TEM images clearly show that the carbonized PANI grew seamlessly on the surface of CNTs and presented the rhizobium-like structure. The carbonized PANI acts like conductive "glue" and connects the adjacent tubes together, which can assemble the CNTs into a 3D network. The PANI 1600 @CNTs composite modified glassy carbon electrodes based on glucose oxidase (GOx) and laccase (Lac) exhibit high electrochemical performance. A glucose//O 2 EBFC constitutes of the fabricated anode and cathode performs a maximum power density of 1.12mWcm -2 at 0.45V. Furthermore, three of the fabricated EBFCs in series are able to lightening up a yellow light-emitting diode (LED) whose turn-on voltage is about at 1.8V. This work may be helpful for exploiting novel substrates by carbonizing the composites of conducting polymer with nano materials at high-temperature for immobilization of enzymes in the EBFCs or biosensor fields. Copyright © 2017 Elsevier B.V. All rights reserved.

Ultrastructural networks in growth cones and neurites of cultured central nervous system neurons.

PubMed

Tsui, H C; Ris, H; Klein, W L

1983-09-01

We have examined growth cones and neurites of cultured central nervous system neurons by high-voltage electron microscopy. Embryonic chicken retina cells were cultured on polylysine-treated and Formvar-coated gold grids for 2-6 days, fixed, and critical point dried. Growth cones and neurites were examined as unembedded whole mounts. Three-dimensional images from stereo-pair electron micrographs of these regions showed a high degree of ultrastructural articulation, with distinct, non-tapering filaments (5-9 nm in diameter) joining both cytoskeletal and membranous components. In the central regions of growth cones, interconnected structures included microtubules, large membranous sacs (up to 400 nm), and irregular vesicles (25-75 nm). A denser filamentous network was prevalent at the edges of growth cones. This network, which frequently adjoined the surface membrane, linked vesicles of uniform size (35-40 nm). Such vesicles often were seen densely packed in growth cone protrusions that were about the size of small synaptic boutons. Prevalent structural interconnections within growth cones conceivably could play a logistic role in specific membrane assembly, intracellular transport, endocytosis, and secretion. Because such processes are not unique to growth cones, the extensive linkages we have observed may have implications for cytoplasmic structure in general.

On the synthesis and structure of resorcinol-formaldehyde polymeric networks – Precursors to 3D-carbon macroassemblies

DOE PAGES

Lewicki, James P.; Fox, Christina A.; Worsley, Marcus A.

2015-05-15

With the new impetus towards the development of hierarchical graphene and CNT macro-assemblies for application in fields such as advanced energy storage, catalysis and electronics; there is much renewed interest in organic carbon-based sol–gel processes as a synthetically convenient and versatile means of forming three dimensional, covalently bonded organic/inorganic networks. Such matrices can act as highly effective precursors, scaffolds or molecular ‘glues’ for the assembly of a wide variety of functional carbon macro-assemblies. However, despite the utility and broad use of organic sol–gel processes – such as the ubiquitous resorcinol-formaldehyde (RF) reaction, there are details of the reaction chemistries ofmore » these important sol–gel processes that remain poorly understood at present. It is therefore both timely and necessary to examine these reactions in more detail using modern analytical techniques in order to gain a more rigorous understanding of the mechanisms by which these organic networks form. The goal of such studies is to obtain improved and rational control over the organic network structure, in order to better direct and tailor the architecture of the final inorganic carbon matrix. In this study we have investigated in detail, the mechanism of the organic sol–gel network forming reaction of resorcinol and formaldehyde from a structural and kinetic standpoint, by using a combination of real-time high field solution state nuclear magnetic resonance (NMR), low field NMR relaxometry and differential scanning calorimetry (DSC). These investigations have allowed us to track the network formation processes in real-time, gain both detailed structural information on the mechanisms of the RF sol–gel process and a quantitative assessment of the kinetics of the global network formation process. Here, it has been shown that the mechanism, by which the RF organic network forms, proceeds via an initial exothermic step correlated to the formation of a free aromatic aldehyde. The network growth reaction then proceeds in a statistical manner following a first order Arrhenius type kinetic relationship – characteristic of a typical thermoset network poly-condensation process. Finally, despite the relative complexity and ill-defined nature of the formaldehyde staring material, the final network structure is to a large extent, governed by the substitution pattern of the resorcinol molecule.« less

Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators

NASA Astrophysics Data System (ADS)

Jia, Yanxin; Kiss, István Z.

2017-04-01

The analysis of network interactions among dynamical units and the impact of the coupling on self-organized structures is a challenging task with implications in many biological and engineered systems. We explore the coupling topology that arises through the potential drops in a flow channel in a lab-on-chip device that accommodates chemical reactions on electrode arrays. The networks are revealed by analysis of the synchronization patterns with the use of an oscillatory chemical reaction (nickel electrodissolution) and are further confirmed by direct decoding using phase model analysis. In dual electrode configuration, a variety coupling schemes, (uni- or bidirectional positive or negative) were identified depending on the relative placement of the reference and counter electrodes (e.g., placed at the same or the opposite ends of the flow channel). With three electrodes, the network consists of a superposition of a localized (upstream) and global (all-to-all) coupling. With six electrodes, the unique, position dependent coupling topology resulted spatially organized partial synchronization such that there was a synchrony gradient along the quasi-one-dimensional spatial coordinate. The networked, electrode potential (current) spike generating electrochemical reactions hold potential for construction of an in-situ information processing unit to be used in electrochemical devices in sensors and batteries.

New patterns in human biogeography revealed by networks of contacts between linguistic groups.

PubMed

Capitán, José A; Bock Axelsen, Jacob; Manrubia, Susanna

2015-03-07

Human languages differ broadly in abundance and are distributed highly unevenly on the Earth. In many qualitative and quantitative aspects, they strongly resemble biodiversity distributions. An intriguing and previously unexplored issue is the architecture of the neighbouring relationships between human linguistic groups. Here we construct and characterize these networks of contacts and show that they represent a new kind of spatial network with uncommon structural properties. Remarkably, language networks share a meaningful property with food webs: both are quasi-interval graphs. In food webs, intervality is linked to the existence of a niche space of low dimensionality; in language networks, we show that the unique relevant variable is the area occupied by the speakers of a language. By means of a range model analogous to niche models in ecology, we show that a geometric restriction of perimeter covering by neighbouring linguistic domains explains the structural patterns observed. Our findings may be of interest in the development of models for language dynamics or regarding the propagation of cultural innovations. In relation to species distribution, they pose the question of whether the spatial features of species ranges share architecture, and eventually generating mechanism, with the distribution of human linguistic groups. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Application of Two-Dimensional AWE Algorithm in Training Multi-Dimensional Neural Network Model

DTIC Science & Technology

2003-07-01

hybrid scheme . the general neural network method (Table 3.1). The training process of the software- ACKNOWLEDGMENT "Neuralmodeler" is shown in Fig. 3.2...engineering. Artificial neural networks (ANNs) have emerged Training a neural network model is the key of as a powerful technique for modeling general neural...coefficients am, the derivatives method of moments (MoM). The variables in the of matrix I have to be generated . A closed form model are frequency

Phase synchronization of bursting neurons in clustered small-world networks

NASA Astrophysics Data System (ADS)

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

2012-07-01

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

Assessing Social Networks in Patients with Psychotic Disorders: A Systematic Review of Instruments

PubMed Central

Priebe, Stefan

2015-01-01

Background Evidence suggests that social networks of patients with psychotic disorders influence symptoms, quality of life and treatment outcomes. It is therefore important to assess social networks for which appropriate and preferably established instruments should be used. Aims To identify instruments assessing social networks in studies of patients with psychotic disorders and explore their properties. Method A systematic search of electronic databases was conducted to identify studies that used a measure of social networks in patients with psychotic disorders. Results Eight instruments were identified, all of which had been developed before 1991. They have been used in 65 studies (total N of patients = 8,522). They assess one or more aspects of social networks such as their size, structure, dimensionality and quality. Most instruments have various shortcomings, including questionable inter-rater and test-retest reliability. Conclusions The assessment of social networks in patients with psychotic disorders is characterized by a variety of approaches which may reflect the complexity of the construct. Further research on social networks in patients with psychotic disorders would benefit from advanced and more precise instruments using comparable definitions of and timescales for social networks across studies. PMID:26709513

The topology of large Open Connectome networks for the human brain.

PubMed

Gastner, Michael T; Ódor, Géza

2016-06-07

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

The topology of large Open Connectome networks for the human brain

NASA Astrophysics Data System (ADS)

Gastner, Michael T.; Ódor, Géza

2016-06-01

The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

Dimensionality Alteration and Intra- versus Inter-SBU Void Encapsulation in Zinc Phosphate Frameworks.

PubMed

Dar, Aijaz A; Bhat, Gulzar A; Murugavel, Ramaswamy

2016-06-06

4,4'-Bipyridine-N-oxide (BIPYMO, 1), a less commonly employed coordination polymer linker, has been used as a ditopic spacer to bridge double-four-ring (D4R) zinc phosphate clusters to form novel framework coordination polymers. Zinc phosphate framework compounds [Zn4(X-dipp)4(BIPYMO)2]n·2MeOH [X = H (2), Cl (3), Br (4), I (5); dipp = 2,6-diisopropylphenyl phosphate] have been obtained by treating a methanol solution of zinc acetate with X-dippH2 and BIPYMO (in a 1:1:1 molar ratio) at ambient conditions. Framework phosphates 2-5 can also be obtained by treating the preformed D4R cubanes [Zn(X-dipp)(DMSO)]4 with required quantities of BIPYMO in methanol. Single-crystal X-ray diffraction studies reveal that these framework solids are two-dimensional (2D) networks as opposed to the diamondoid networks obtained when the parent unoxidized 4,4'-bipyridine is used as the linker (Inorg. Chem. 2014, 53, 8959). The two types of voids (viz., smaller intra-D4R and larger inter-D4R) present in these framework solids can be utilized for different types of encapsulation processes. For example, the in situ generated 2D framework 2 encapsulates fluoride ions accompanied by a change in the dimensionality of the framework to yield {[(nC4H9)4N][F@(Zn4(dipp)4(BIPYMO)2)]}n (6). The three-dimensional framework 6 represents the first structurally characterized example of a fluoride-ion-encapsulated polymeric coordination compound or a metal-organic framework. The possibility of utilizing inter-D4R voids as hosts for small organic molecules has been explored by treating in situ generated 2 with a series of organic molecules of appropriate size. Framework 2 has been found to be a selective host for benzil and not for other structurally similar molecules such as benzoquinone, benzidine, anthracene, naphthalene, α-pyridoin, etc. The benzil-occluded isolated framework [benzil@{Zn4(dipp)4(BIPYMO)2}]n (7) has been isolated as single crystals, and its crystal structure determination revealed the binding of benzil molecules to the framework through strong π-π interactions.

ePlant and the 3D data display initiative: integrative systems biology on the world wide web.

PubMed

Fucile, Geoffrey; Di Biase, David; Nahal, Hardeep; La, Garon; Khodabandeh, Shokoufeh; Chen, Yani; Easley, Kante; Christendat, Dinesh; Kelley, Lawrence; Provart, Nicholas J

2011-01-10

Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed "ePlant" (http://bar.utoronto.ca/eplant) - a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the "3D Data Display Initiative" (http://3ddi.org).

Development of human nervous tissue upon differentiation of embryonic stem cells in three-dimensional culture.

PubMed

Preynat-Seauve, Olivier; Suter, David M; Tirefort, Diderik; Turchi, Laurent; Virolle, Thierry; Chneiweiss, Herve; Foti, Michelangelo; Lobrinus, Johannes-Alexander; Stoppini, Luc; Feki, Anis; Dubois-Dauphin, Michel; Krause, Karl Heinz

2009-03-01

Researches on neural differentiation using embryonic stem cells (ESC) require analysis of neurogenesis in conditions mimicking physiological cellular interactions as closely as possible. In this study, we report an air-liquid interface-based culture of human ESC. This culture system allows three-dimensional cell expansion and neural differentiation in the absence of added growth factors. Over a 3-month period, a macroscopically visible, compact tissue developed. Histological coloration revealed a dense neural-like neural tissue including immature tubular structures. Electron microscopy, immunochemistry, and electrophysiological recordings demonstrated a dense network of neurons, astrocytes, and oligodendrocytes able to propagate signals. Within this tissue, tubular structures were niches of cells resembling germinal layers of human fetal brain. Indeed, the tissue contained abundant proliferating cells expressing markers of neural progenitors. Finally, the capacity to generate neural tissues on air-liquid interface differed for different ESC lines, confirming variations of their neurogenic potential. In conclusion, this study demonstrates in vitro engineering of a human neural-like tissue with an organization that bears resemblance to early developing brain. As opposed to previously described methods, this differentiation (a) allows three-dimensional organization, (b) yields dense interconnected neural tissue with structurally and functionally distinct areas, and (c) is spontaneously guided by endogenous developmental cues.

ER sheet persistence is coupled to myosin 1c–regulated dynamic actin filament arrays

PubMed Central

Joensuu, Merja; Belevich, Ilya; Rämö, Olli; Nevzorov, Ilya; Vihinen, Helena; Puhka, Maija; Witkos, Tomasz M.; Lowe, Martin; Vartiainen, Maria K.; Jokitalo, Eija

2014-01-01

The endoplasmic reticulum (ER) comprises a dynamic three-dimensional (3D) network with diverse structural and functional domains. Proper ER operation requires an intricate balance within and between dynamics, morphology, and functions, but how these processes are coupled in cells has been unclear. Using live-cell imaging and 3D electron microscopy, we identify a specific subset of actin filaments localizing to polygons defined by ER sheets and tubules and describe a role for these actin arrays in ER sheet persistence and, thereby, in maintenance of the characteristic network architecture by showing that actin depolymerization leads to increased sheet fluctuation and transformations and results in small and less abundant sheet remnants and a defective ER network distribution. Furthermore, we identify myosin 1c localizing to the ER-associated actin filament arrays and reveal a novel role for myosin 1c in regulating these actin structures, as myosin 1c manipulations lead to loss of the actin filaments and to similar ER phenotype as observed after actin depolymerization. We propose that ER-associated actin filaments have a role in ER sheet persistence regulation and thus support the maintenance of sheets as a stationary subdomain of the dynamic ER network. PMID:24523293

ER sheet persistence is coupled to myosin 1c-regulated dynamic actin filament arrays.

PubMed

Joensuu, Merja; Belevich, Ilya; Rämö, Olli; Nevzorov, Ilya; Vihinen, Helena; Puhka, Maija; Witkos, Tomasz M; Lowe, Martin; Vartiainen, Maria K; Jokitalo, Eija

2014-04-01

The endoplasmic reticulum (ER) comprises a dynamic three-dimensional (3D) network with diverse structural and functional domains. Proper ER operation requires an intricate balance within and between dynamics, morphology, and functions, but how these processes are coupled in cells has been unclear. Using live-cell imaging and 3D electron microscopy, we identify a specific subset of actin filaments localizing to polygons defined by ER sheets and tubules and describe a role for these actin arrays in ER sheet persistence and, thereby, in maintenance of the characteristic network architecture by showing that actin depolymerization leads to increased sheet fluctuation and transformations and results in small and less abundant sheet remnants and a defective ER network distribution. Furthermore, we identify myosin 1c localizing to the ER-associated actin filament arrays and reveal a novel role for myosin 1c in regulating these actin structures, as myosin 1c manipulations lead to loss of the actin filaments and to similar ER phenotype as observed after actin depolymerization. We propose that ER-associated actin filaments have a role in ER sheet persistence regulation and thus support the maintenance of sheets as a stationary subdomain of the dynamic ER network.

3D Actin Network Centerline Extraction with Multiple Active Contours

PubMed Central

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2013-01-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels. PMID:24316442

Networks as Renormalized Models for Emergent Behavior in Physical Systems

NASA Astrophysics Data System (ADS)

Paczuski, Maya

2005-09-01

Networks are paradigms for describing complex biological, social and technological systems. Here I argue that networks provide a coherent framework to construct coarsegrained models for many different physical systems. To elucidate these ideas, I discuss two long-standing problems. The first concerns the structure and dynamics of magnetic fields in the solar corona, as exemplified by sunspots that startled Galileo almost 400 years ago. We discovered that the magnetic structure of the corona embodies a scale free network, with spots at all scales. A network model representing the three-dimensional geometry of magnetic fields, where links rewire and nodes merge when they collide in space, gives quantitative agreement with available data, and suggests new measurements. Seismicity is addressed in terms of relations between events without imposing space-time windows. A metric estimates the correlation between any two earthquakes. Linking strongly correlated pairs, and ignoring pairs with weak correlation organizes the spatio-temporal process into a sparse, directed, weighted network. New scaling laws for seismicity are found. For instance, the aftershock decay rate decreases as ~ 1/t in time up to a correlation time, tomori. An estimate from the data gives tomori to be about one year for small magnitude 3 earthquakes, about 1400 years for the Landers event, and roughly 26,000 years for the earthquake causing the 2004 Asian tsunami. Our results confirm Kagan's conjecture that aftershocks can rumble on for centuries.

Copper coordination polymers constructed from thiazole-5-carboxylic acid: Synthesis, crystal structures, and structural transformation

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

Meundaeng, Natthaya; Rujiwatra, Apinpus; Prior, Timothy J., E-mail: t.prior@hull.ac.uk

2017-01-15

We have successfully prepared crystals of thiazole-5-carboxylic acid (5-Htza) (L) and three new thiazole-5-carboxylate-based Cu{sup 2+} coordination polymers with different dimensionality, namely, 1D [Cu{sub 2}(5-tza){sub 2}(1,10-phenanthroline){sub 2}(NO{sub 3}){sub 2}] (1), 2D [Cu(5-tza){sub 2}(MeOH){sub 2}] (2), and 3D [Cu(5-tza){sub 2}]·H{sub 2}O (3). These have been characterized by single crystal X-ray diffraction and thermogravimetry. Interestingly, the 2D network structure of 2 can directly transform into the 3D framework of 3 upon removal of methanol molecules at room temperature. 2 can also undergo structural transformation to produce the same 2D network present in the known [Cu(5-tza){sub 2}]·1.5H{sub 2}O upon heat treatment for 2more » h. This 2D network can adsorb water and convert to 3 upon exposure to air. - Highlights: • Rare examples of coordination polymers of thiazole-5-carboxylic acid were prepared. • Non-covalent interactions play a key role on the assembly of the complexes in solid state. • Structural transformation of a 2D framework to a 3D upon removal of methanol is observed.« less

Metal Flux Growth, Structural Relations, and Physical Properties of EuCu2Ge2 and Eu3T2In9 (T = Cu and Ag).

PubMed

Subbarao, Udumula; Roy, Soumyabrata; Sarma, Saurav Ch; Sarkar, Sumanta; Mishra, Vidyanshu; Khulbe, Yatish; Peter, Sebastian C

2016-10-17

Single crystals (SCs) of the compounds Eu 3 Ag 2 In 9 and EuCu 2 Ge 2 were synthesized through the reactions run in liquid indium. Eu 3 Ag 2 In 9 crystallizes in the La 3 Al 11 structure type [orthorhombic space group (SG) Immm] with the lattice parameters: a = 4.8370(1) Å, b = 10.6078(3) Å, and c = 13.9195(4) Å. EuCu 2 Ge 2 crystallizes in the tetragonal ThCr 2 Si 2 structure type (SG I4/mmm) with the lattice parameters: a = b = 4.2218(1) Å, and c = 10.3394(5) Å. The crystal structure of Eu 3 Ag 2 In 9 is comprised of edge-shared hexagonal rings consisting of indium. The one-dimensional chains of In 6 rings are shared through the edges, which are further interconnected with other six-membered rings forming a three-dimensional (3D) stable crystal structure along the bc plane. The crystal structure of EuCu 2 Ge 2 can be explained as the complex [CuGe] (2+δ)- polyanionic network embedded with Eu ions. These polyanionic networks present in the crystal structure of EuCu 2 Ge 2 are shared through the edges of the 011 plane containing Cu and Ge atoms, resulting in a 3D network. The structural relationship between Eu 3 T 2 In 9 and EuCu 2 Ge 2 has been discussed in detail, and we conclude that Eu 3 T 2 In 9 is the metal deficient variant of EuCu 2 Ge 2 . The magnetic susceptibilities of Eu 3 T 2 In 9 (T = Cu and Ag) and EuCu 2 Ge 2 were measured between 2 and 300 K. In all cases, magnetic susceptibility data followed Curie-Weiss law above 150 K. Magnetic moment values obtained from the measurements indicate the probable mixed/intermediate valent behavior of the europium atoms, which was further confirmed by X-ray absorption studies and bond distances around the Eu atoms. Electrical resistivity measurements suggest that Eu 3 T 2 In 9 and EuCu 2 Ge 2 are metallic in nature.

Small Artery Elastin Distribution and Architecture-Focus on Three Dimensional Organization.

PubMed

Hill, Michael A; Nourian, Zahra; Ho, I-Lin; Clifford, Philip S; Martinez-Lemus, Luis; Meininger, Gerald A

2016-11-01

The distribution of ECM proteins within the walls of resistance vessels is complex both in variety of proteins and structural arrangement. In particular, elastin exists as discrete fibers varying in orientation across the adventitia and media as well as often resembling a sheet-like structure in the case of the IEL. Adding to the complexity is the tissue heterogeneity that exists in these structural arrangements. For example, small intracranial cerebral arteries lack adventitial elastin while similar sized arteries from skeletal muscle and intestinal mesentery exhibit a complex adventitial network of elastin fibers. With regard to the IEL, several vascular beds exhibit an elastin sheet with punctate holes/fenestrae while in others the IEL is discontinuous and fibrous in appearance. Importantly, these structural patterns likely sub-serve specific functional properties, including mechanosensing, control of external forces, mechanical properties of the vascular wall, cellular positioning, and communication between cells. Of further significance, these processes are altered in vascular disorders such as hypertension and diabetes mellitus where there is modification of ECM. This brief report focuses on the three-dimensional wall structure of small arteries and considers possible implications with regard to mechanosensing under physiological and pathophysiological conditions. © 2016 John Wiley & Sons Ltd.

Three-dimensional structure of thymidine phosphorylase from E. coli in complex with 3'-azido-2'-fluoro-2',3'-dideoxyuridine

NASA Astrophysics Data System (ADS)

Timofeev, V. I.; Abramchik, Yu. A.; Fateev, I. V.; Zhukhlistova, N. E.; Murav'eva, T. I.; Kuranova, I. P.; Esipov, R. S.

2013-11-01

The three-dimensional structures of thymidine phosphorylase from E. coli containing the bound sulfate ion in the phosphate-binding site and of the complex of thymidine phosphorylase with sulfate in the phosphate-binding site and the inhibitor 3'-azido-2'-fluoro-2',3'-dideoxyuridine (N3F-ddU) in the nucleoside-binding site were determined at 1.55 and 1.50 Å resolution, respectively. The amino-acid residues involved in the ligand binding and the hydrogen-bond network in the active site occupied by a large number of bound water molecules are described. A comparison of the structure of thymidine phosphorylase in complex with N3F-ddU with the structure of pyrimidine nucleoside phosphorylase from St. Aureus in complex with the natural substrate thymidine (PDB_ID: 3H5Q) shows that the substrate and the inhibitor in the nucleoside-binding pocket have different orientations. It is suggested that the position of N3F-ddU can be influenced by the presence of the azido group, which prefers a hydrophobic environment. In both structures, the active sites of the subunits are in the open conformation.

Structure of alkali tellurite glasses from neutron diffraction and molecular orbital calculations

NASA Astrophysics Data System (ADS)

Niida, Haruki; Uchino, Takashi; Jin, Jisun; Kim, Sae-Hoon; Fukunaga, Toshiharu; Yoko, Toshinobu

2001-01-01

The structure of pure TeO2 and alkali tellurite glasses has been examined by neutron diffraction and ab initio molecular orbital methods. The experimental radial distribution functions along with the calculated results have demonstrated that the basic structural units in tellurite glasses change from highly strained TeO4 trigonal bipyramids to more regular TeO3 trigonal pyramids with increasing alkali content. It has also been shown that the TeO3 trigonal pyramids do not exist in the form of isolated units in the glass network but interact with each other to form intertrigonal Te⋯O linkages. The present results suggest that nonbridging oxygen (NBO) atoms in tellurite glasses do not exist in their "pure" form; that is, all the NBO atoms in TeO3 trigonal bipyramids will interact with the first- and/or second-neighbor Te atoms, resulting in the three-dimensional continuous random network even in tellurite glasses with over 30 mol % of alkali oxides.

Modeling and Reconstruction of Micro-structured 3D Chitosan/Gelatin Porous Scaffolds Using Micro-CT

NASA Astrophysics Data System (ADS)

Gong, Haibo; Li, Dichen; He, Jiankang; Liu, Yaxiong; Lian, Qin; Zhao, Jinna

2008-09-01

Three dimensional (3D) channel networks are the key to promise the uniform distribution of nutrients inside 3D hepatic tissue engineering scaffolds and prompt elimination of metabolic products out of the scaffolds. 3D chitosan/gelatin porous scaffolds with predefined internal channels were fabricated and a combination of light microscope, laser confocal microscopy and micro-CT were employed to characterize the structure of porous scaffolds. In order to evaluate the flow field distribution inside the micro-structured 3D scaffolds, a computer reconstructing method based on Micro-CT was proposed. According to this evaluating method, a contrast between 3D porous scaffolds with and without predefined internal channels was also performed to assess scaffolds' fluid characters. Results showed that the internal channel of the 3D scaffolds formed the 3D fluid channel network; the uniformity of flow field distribution of the scaffolds fabricated in this paper was better than the simple porous scaffold without micro-fluid channels.

Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

NASA Technical Reports Server (NTRS)

Schallhorn, Paul; Majumdar, Alok

2012-01-01

This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.

Simulation studies of ionic liquids: Orientational correlations and static dielectric properties

NASA Astrophysics Data System (ADS)

Schröder, C.; Rudas, T.; Steinhauser, O.

2006-12-01

The ionic liquids BMIM+I-, BMIM+BF4-, and BMIM+PF6- were simulated by means of the molecular dynamics method over a time period of more than 100ns. Besides the common structural analysis, e.g., radial distribution functions and three dimensional occupancy plots, a more sophisticated orientational analysis was performed. The angular correlation functions g00110(r) and g00101(r) are the first distance dependent coefficients of the pairwise orientational distribution function g(rij,Ω1,Ω2,Ω12). These functions help to interpret the three dimensional plot and reveal interesting insights into the local structure of the analyzed ionic liquids. Furthermore, the collective network of ionic liquids can be characterized by the Kirkwood factor Gκ(r ) [J. Chem. Phys. 7, 911 (1939)]. The short-range behavior (r<10Å) of this factor may be suitable to predict the water miscibility of the ionic liquid. The long-range limit of Gk∞ is below 1 which demonstrates the strongly coupled nature of the ionic liquid networks. In addition, this factor relates the orientational structure and the dielectric properties of the ionic liquids. The static dielectric constant ɛ(ω =0) for the simulated system is 8.9-9.5. Since in ionic liquids the very same molecule contributes to the total dipole moment as well as carries a net charge, a small, but significant contribution of the cross term between the total dipole moment and the electric current to ɛ(ω =0) is observed.

Three-dimensional micro-electrode array for recording dissociated neuronal cultures.

PubMed

Musick, Katherine; Khatami, David; Wheeler, Bruce C

2009-07-21

This work demonstrates the design, fabrication, packaging, characterization, and functionality of an electrically and fluidically active three-dimensional micro-electrode array (3D MEA) for use with neuronal cell cultures. The successful function of the device implies that this basic concept-construction of a 3D array with a layered approach-can be utilized as the basis for a new family of neural electrode arrays. The 3D MEA prototype consists of a stack of individually patterned thin films that form a cell chamber conducive to maintaining and recording the electrical activity of a long-term three-dimensional network of rat cortical neurons. Silicon electrode layers contain a polymer grid for neural branching, growth, and network formation. Along the walls of these electrode layers lie exposed gold electrodes which permit recording and stimulation of the neuronal electrical activity. Silicone elastomer micro-fluidic layers provide a means for loading dissociated neurons into the structure and serve as the artificial vasculature for nutrient supply and aeration. The fluidic layers also serve as insulation for the micro-electrodes. Cells have been shown to survive in the 3D MEA for up to 28 days, with spontaneous and evoked electrical recordings performed in that time. The micro-fluidic capability was demonstrated by flowing in the drug tetrotodoxin to influence the activity of the culture.

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

PubMed

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Direct growth of 2D nickel hydroxide nanosheets intercalated with polyoxovanadate anions as a binder-free supercapacitor electrode.

PubMed

Gunjakar, Jayavant L; Inamdar, Akbar I; Hou, Bo; Cha, SeungNam; Pawar, S M; Abu Talha, A A; Chavan, Harish S; Kim, Jongmin; Cho, Sangeun; Lee, Seongwoo; Jo, Yongcheol; Kim, Hyungsang; Im, Hyunsik

2018-05-17

A mesoporous nanoplate network of two-dimensional (2D) layered nickel hydroxide Ni(OH)2 intercalated with polyoxovanadate anions (Ni(OH)2-POV) was built using a chemical solution deposition method. This approach will provide high flexibility for controlling the chemical composition and the pore structure of the resulting Ni(OH)2-POV nanohybrids. The layer-by-layer ordered growth of the Ni(OH)2-POV is demonstrated by powder X-ray diffraction and cross-sectional high-resolution transmission electron microscopy. The random growth of the intercalated Ni(OH)2-POV nanohybrids leads to the formation of an interconnected network morphology with a highly porous stacking structure whose porosity is controlled by changing the ratio of Ni(OH)2 and POV. The lateral size and thickness of the Ni(OH)2-POV nanoplates are ∼400 nm and from ∼5 nm to 7 nm, respectively. The obtained thin films are highly active electrochemical capacitor electrodes with a maximum specific capacity of 1440 F g-1 at a current density of 1 A g-1, and they withstand up to 2000 cycles with a capacity retention of 85%. The superior electrochemical performance of the Ni(OH)2-POV nanohybrids is attributed to the expanded mesoporous surface area and the intercalation of the POV anions. The experimental findings highlight the outstanding electrochemical functionality of the 2D Ni(OH)2-POV nanoplate network that will provide a facile route for the synthesis of low-dimensional hybrid nanomaterials for a highly active supercapacitor electrode.

Structures of cage, prism, and book isomers of water hexamer from broadband rotational spectroscopy.

PubMed

Pérez, Cristóbal; Muckle, Matt T; Zaleski, Daniel P; Seifert, Nathan A; Temelso, Berhane; Shields, George C; Kisiel, Zbigniew; Pate, Brooks H

2012-05-18

Theory predicts the water hexamer to be the smallest water cluster with a three-dimensional hydrogen-bonding network as its minimum energy structure. There are several possible low-energy isomers, and calculations with different methods and basis sets assign them different relative stabilities. Previous experimental work has provided evidence for the cage, book, and cyclic isomers, but no experiment has identified multiple coexisting structures. Here, we report that broadband rotational spectroscopy in a pulsed supersonic expansion unambiguously identifies all three isomers; we determined their oxygen framework structures by means of oxygen-18-substituted water (H(2)(18)O). Relative isomer populations at different expansion conditions establish that the cage isomer is the minimum energy structure. Rotational spectra consistent with predicted heptamer and nonamer structures have also been identified.

Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Ullmann-like reactions for the synthesis of complex two-dimensional materials

NASA Astrophysics Data System (ADS)

Quardokus, Rebecca C.; Tewary, V. K.; DelRio, Frank W.

2016-11-01

Engineering two-dimensional materials through surface-confined synthetic techniques is a promising avenue for designing new materials with tailored properties. Developing and understanding reaction mechanisms for surface-confined synthesis of two-dimensional materials requires atomic-level characterization and chemical analysis. Beggan et al (2015 Nanotechnology 26 365602) used scanning tunneling microscopy and x-ray photoelectron spectroscopy to elucidate the formation mechanism of surface-confined Ullmann-like coupling of thiophene substituted porphyrins on Ag(111). Upon surface deposition, bromine is dissociated and the porphyrins couple with surface adatoms to create linear strands and hexagonally packed molecules. Annealing the sample results in covalently-bonded networks of thienylporphyrin derivatives. A deeper understanding of surface-confined Ullmann-like coupling has the potential to lead to precision-engineered nano-structures through synthetic techniques. Contribution of the National Institute of Standards and Technology, not subject to copyright in the United States of America.

Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization

PubMed Central

Glaser, Joshua I.; Zamft, Bradley M.; Church, George M.; Kording, Konrad P.

2015-01-01

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, “puzzle imaging,” that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples. PMID:26192446

Freeze-drying synthesis of three-dimensional porous LiFePO4 modified with well-dispersed nitrogen-doped carbon nanotubes for high-performance lithium-ion batteries

NASA Astrophysics Data System (ADS)

Tu, Xiaofeng; Zhou, Yingke; Song, Yijie

2017-04-01

The three-dimensional porous LiFePO4 modified with uniformly dispersed nitrogen-doped carbon nanotubes has been successfully prepared by a freeze-drying method. The morphology and structure of the porous composites are characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), and the electrochemical performances are evaluated using the constant current charge/discharge tests, cyclic voltammetry and electrochemical impedance spectroscopy. The nitrogen-doped carbon nanotubes are uniformly dispersed inside the porous LiFePO4 to construct a superior three-dimensional conductive network, which remarkably increases the electronic conductivity and accelerates the diffusion of lithium ion. The porous composite displays high specific capacity, good rate capability and excellent cycling stability, rendering it a promising positive electrode material for high-performance lithium-ion batteries.

Three-dimensional imaging and photostimulation by remote-focusing and holographic light patterning

PubMed Central

Anselmi, Francesca; Ventalon, Cathie; Bègue, Aurélien; Ogden, David; Emiliani, Valentina

2011-01-01

Access to three-dimensional structures in the brain is fundamental to probe signal processing at multiple levels, from integration of synaptic inputs to network activity mapping. Here, we present an optical method for independent three-dimensional photoactivation and imaging by combination of digital holography with remote-focusing. We experimentally demonstrate compensation of spherical aberration for out-of-focus imaging in a range of at least 300 μm, as well as scanless imaging along oblique planes. We apply this method to perform functional imaging along tilted dendrites of hippocampal pyramidal neurons in brain slices, after photostimulation by multiple spots glutamate uncaging. By bringing extended portions of tilted dendrites simultaneously in-focus, we monitor the spatial extent of dendritic calcium signals, showing a shift from a widespread to a spatially confined response upon blockage of voltage-gated Na+ channels. PMID:22074779

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

Bie Haiying; Lu Jing; Yu Jiehui

Three novel thiocyanate supramolecular compounds have been synthesized and characterized by X-ray diffraction and fluorescent spectra. Compound [pipH]{sub 2}[Co(NCS){sub 4}] (pip=piperazine) 1 possesses a two-dimensional layer connected by the combination of N-H...N hydrogen bonds and weak S...S contacts. Under the same conditions, using nickel salt instead of cobalt salt as a starting material, we obtained a different two-dimensional supramolecular layer [pipH]{sub 2}[Ni(NCS){sub 4}] 2 connected by unusual N-H...S hydrogen bonds and weak S...S contacts. In order to observe the influence of the dimension of ligand on the self-assembly structure, dabco was used for substituting pip, and compound [dabcoH]{sub 2}[Ni(NCS){sub 4}]more » (dabco=1,4-Diazabicyclo[2.2.2] octane) 3 was gained, which constructed two-dimensional, highly wavy network with hourglass-shaped cavities only through N-H...S hydrogen bonds.« less

Toward two-dimensional search engines

NASA Astrophysics Data System (ADS)

Ermann, L.; Chepelianskii, A. D.; Shepelyansky, D. L.

2012-07-01

We study the statistical properties of various directed networks using ranking of their nodes based on the dominant vectors of the Google matrix known as PageRank and CheiRank. On average PageRank orders nodes proportionally to a number of ingoing links, while CheiRank orders nodes proportionally to a number of outgoing links. In this way, the ranking of nodes becomes two dimensional which paves the way for the development of two-dimensional search engines of a new type. Statistical properties of information flow on the PageRank-CheiRank plane are analyzed for networks of British, French and Italian universities, Wikipedia, Linux Kernel, gene regulation and other networks. A special emphasis is done for British universities networks using the large database publicly available in the UK. Methods of spam links control are also analyzed.

Self-Assembly of Phenylalanine Oligopeptides: Insights from Experiments and Simulations

PubMed Central

Tamamis, Phanourios; Adler-Abramovich, Lihi; Reches, Meital; Marshall, Karen; Sikorski, Pawel; Serpell, Louise; Gazit, Ehud; Archontis, Georgios

2009-01-01

Abstract Studies of peptide-based nanostructures provide general insights into biomolecular self-assembly and can lead material engineering toward technological applications. The diphenylalanine peptide (FF) self-assembles into discrete, hollow, well ordered nanotubes, and its derivatives form nanoassemblies of various morphologies. Here we demonstrate for the first time, to our knowledge, the formation of planar nanostructures with β-sheet content by the triphenylalanine peptide (FFF). We characterize these structures using various microscopy and spectroscopy techniques. We also obtain insights into the interactions and structural properties of the FF and FFF nanostructures by 0.4-μs, implicit-solvent, replica-exchange, molecular-dynamics simulations of aqueous FF and FFF solutions. In the simulations the peptides form aggregates, which often contain open or ring-like peptide networks, as well as elementary and network-containing structures with β-sheet characteristics. The networks are stabilized by polar and nonpolar interactions, and by the surrounding aggregate. In particular, the charged termini of neighbor peptides are involved in hydrogen-bonding interactions and their aromatic side chains form “T-shaped” contacts, as in three-dimensional FF crystals. These interactions may assist the FF and FFF self-assembly at the early stage, and may also stabilize the mature nanostructures. The FFF peptides have higher network propensities and increased aggregate stabilities with respect to FF, which can be interpreted energetically. PMID:19527662

Spatial development of transport structures in apple (Malus × domestica Borkh.) fruit

PubMed Central

Herremans, Els; Verboven, Pieter; Hertog, Maarten L. A. T. M.; Cantre, Dennis; van Dael, Mattias; De Schryver, Thomas; Van Hoorebeke, Luc; Nicolaï, Bart M.

2015-01-01

The void network and vascular system are important pathways for the transport of gases, water and solutes in apple fruit (Malus × domestica Borkh). Here we used X-ray micro-tomography at various spatial resolutions to investigate the growth of these transport structures in 3D during fruit development of “Jonagold” apple. The size of the void space and porosity in the cortex tissue increased considerably. In the core tissue, the porosity was consistently lower, and seemed to decrease toward the end of the maturation period. The voids in the core were more narrow and fragmented than the voids in the cortex. Both the void network in the core and in the cortex changed significantly in terms of void morphology. An automated segmentation protocol underestimated the total vasculature length by 9–12% in comparison to manually processed images. Vascular networks increased in length from a total of 5 m at 9 weeks after full bloom, to more than 20 m corresponding to 5 cm of vascular tissue per cubic centimeter of apple tissue. A high degree of branching in both the void network and vascular system and a complex three-dimensional pattern was observed across the whole fruit. The 3D visualizations of the transport structures may be useful for numerical modeling of organ growth and transport processes in fruit. PMID:26388883

A two-dimensional polymer synthesized at the air/water interface.

PubMed

Schlüter, A Dieter; Müller, Vivian; Hinaut, Antoine; Moradi, Mina; Baljozovic, Milos; Jung, Thomas; Shahgaldian, Patrick; Möhwald, Helmuth; Hofer, Gregor; Kröger, Martin; King, Benjamin; Meyer, Ernst; Glatzel, Thilo

2018-06-11

A trifunctional, partially fluorinated anthracene-substituted triptycene monomer is spread at the air/water interface into a monolayer, which is transformed into a long-range ordered 2D polymer by irradiation with a standard ultraviolet lamp using 365 nm light. The polymer is analyzed by Brewster angle microscopy directly at this interface and by scanning tunneling microscopy measurements and non-contact atomic force microscopy (nc-AFM), both after transfer from below the interface onto highly oriented pyrolytic graphite and then into ultra-high vacuum. Both methods confirm a network structure, the lattice parameters of which are virtually identical to a structural model network based on X-ray diffractometry of a closely related 2D polymer unequivocally established in a single crystal. The nc-AFM images are obtained with unprecedentedly high resolution and prove long-range order over areas of at least 300 × 300 nm2. As required for a 2D polymer, the pore sizes are monodisperse, except for the regions, where the network is somewhat stretched because it spans over protrusions. Together with a previous report on the nature of the cross-links in this network, the structural information provided here leaves no doubt that a 2D polymer has been synthesized under ambient conditions at an air/water interface. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.