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Sample records for 2-d cellular networks

  1. Topologiacl Models of 2D Fractal Cellular Structures

    NASA Astrophysics Data System (ADS)

    Le Caër, G.; Delannay, R.

    1995-11-01

    In space-filling 2D cellular structures with trivalent vertices and in which each cell is constrained to share at most one side with any cell and no side with itself, the maximum fraction of three-sided cells is produced by a decoration of vertices of any initial structure by three-sided cells. Fractal cellular structures are obtained if the latter decoration process is iterated indefinitely. Other methods of constructions of fractal structures are also described. The probability distribution P(n) of the number n of cell sides and some two-cell topological properties of a 2D fractal cellular structure constructed from the triangular Sierpinski gasket are investigated. On the whole, the repartition of cells in 2D structures with n geq 3 and P(3) ne 0 evolve regularly when topological disorder, conveniently measured by the variance μ2 of P(n), increases. The strong correlations which exist among cells, in particular in natural structures (μ2lesssim 5), decrease progressively when μ2 increases, a cell repartition close to a random one being reached for μ2sim 12. We argue that the structures finally evolve to fractal structures (for which μ2 is infinite) but we have not characterized the latter transition. Dans des structures cellulaires 2D à sommets trivalents qui remplissent l'espace et dans lesquelles une cellule partage au plus un côté avec toute autre cellule et aucun avec elle-même, la proportion maximum admissible de cellules à trois côtés est obtenue par une décoration de tous les sommets d'une structure initiale quelconque par des cellules à trois côtés. Des structures cellulaires “fractales” 2D sont ainsi engendrées si le processus précédent est répété à l'infini. D'autres méthodes de constructions de structures fractales sont également décrites. La distribution de probabilité P(n) du nombre n de côtés des cellules ainsi que des corrélations de paires sont étudiées pour une structure cellulaire fractale construite à partir

  2. 2D-CELL: image processing software for extraction and analysis of 2-dimensional cellular structures

    NASA Astrophysics Data System (ADS)

    Righetti, F.; Telley, H.; Leibling, Th. M.; Mocellin, A.

    1992-01-01

    2D-CELL is a software package for the processing and analyzing of photographic images of cellular structures in a largely interactive way. Starting from a binary digitized image, the programs extract the line network (skeleton) of the structure and determine the graph representation that best models it. Provision is made for manually correcting defects such as incorrect node positions or dangling bonds. Then a suitable algorithm retrieves polygonal contours which define individual cells — local boundary curvatures are neglected for simplicity. Using elementary analytical geometry relations, a range of metric and topological parameters describing the population are then computed, organized into statistical distributions and graphically displayed.

  3. Exact Solution of Ising Model in 2d Shortcut Network

    NASA Astrophysics Data System (ADS)

    Shanker, O.

    We give the exact solution to the Ising model in the shortcut network in the 2D limit. The solution is found by mapping the model to the square lattice model with Brascamp and Kunz boundary conditions.

  4. Dynamics of HIV infection on 2D cellular automata

    NASA Astrophysics Data System (ADS)

    Benyoussef, A.; HafidAllah, N. El; ElKenz, A.; Ez-Zahraouy, H.; Loulidi, M.

    2003-05-01

    We use a cellular automata approach to describe the interactions of the immune system with the human immunodeficiency virus (HIV). We study the evolution of HIV infection, particularly in the clinical latency period. The results we have obtained show the existence of four different behaviours in the plane of death rate of virus-death rate of infected T cell. These regions meet at a critical point, where the virus density and the infected T cell density remain invariant during the evolution of disease. We have introduced two kinds of treatments, the protease inhibitors and the RT inhibitors, in order to study their effects on the evolution of HIV infection. These treatments are powerful in decreasing the density of the virus in the blood and the delay of the AIDS onset.

  5. The role of the cytoskeleton in cellular force generation in 2D and 3D environments

    NASA Astrophysics Data System (ADS)

    Kraning-Rush, Casey M.; Carey, Shawn P.; Califano, Joseph P.; Smith, Brooke N.; Reinhart-King, Cynthia A.

    2011-02-01

    To adhere and migrate, cells generate forces through the cytoskeleton that are transmitted to the surrounding matrix. While cellular force generation has been studied on 2D substrates, less is known about cytoskeletal-mediated traction forces of cells embedded in more in vivo-like 3D matrices. Recent studies have revealed important differences between the cytoskeletal structure, adhesion, and migration of cells in 2D and 3D. Because the cytoskeleton mediates force, we sought to directly compare the role of the cytoskeleton in modulating cell force in 2D and 3D. MDA-MB-231 cells were treated with agents that perturbed actin, microtubules, or myosin, and analyzed for changes in cytoskeletal organization and force generation in both 2D and 3D. To quantify traction stresses in 2D, traction force microscopy was used; in 3D, force was assessed based on single cell-mediated collagen fibril reorganization imaged using confocal reflectance microscopy. Interestingly, even though previous studies have observed differences in cell behaviors like migration in 2D and 3D, our data indicate that forces generated on 2D substrates correlate with forces within 3D matrices. Disruption of actin, myosin or microtubules in either 2D or 3D microenvironments disrupts cell-generated force. These data suggest that despite differences in cytoskeletal organization in 2D and 3D, actin, microtubules and myosin contribute to contractility and matrix reorganization similarly in both microenvironments.

  6. Phononic Band Gaps in 2D Quadratic and 3D Cubic Cellular Structures

    PubMed Central

    Warmuth, Franziska; Körner, Carolin

    2015-01-01

    The static and dynamic mechanical behaviour of cellular materials can be designed by the architecture of the underlying unit cell. In this paper, the phononic band structure of 2D and 3D cellular structures is investigated. It is shown how the geometry of the unit cell influences the band structure and eventually leads to full band gaps. The mechanism leading to full band gaps is elucidated. Based on this knowledge, a 3D cellular structure with a broad full band gap is identified. Furthermore, the dependence of the width of the gap on the geometry parameters of the unit cell is presented. PMID:28793713

  7. Connectivity, formation factor and permeability of 2D fracture network

    NASA Astrophysics Data System (ADS)

    Tang, Y. B.; Li, M.; Li, X. F.

    2017-10-01

    The purpose of this paper is to investigate the effects of fracture connectivity and length distributions on the electrical formation factor, F, of random fracture network using percolation theory. We assumed that the matrix was homogeneous and low-permeable, but the connectivity and length distributions of fracture system were randomly variable. F of fracture network is analyzed via finite element method. The main result is that: different from the classical percolation ;universal; power law for porous-type rocks, F of fracture network obeys a normalized ;universal; scaling relation using the length-scale < l > / L (< l > is fracture mean length, and L is the domain size). Our proposed formation factor model, derived from the normalized ;universal; scaling relationship, is valid in fracture network with constant fracture length and length distributions, showing that the normalized ;universal; scaling law is independent of fracture patterns. The normalized scaling relation is also successfully used to derive the permeability model of 2D random fracture network using the previously published dataset, which obtained better fitting results than before.

  8. MSAT and cellular hybrid networking

    NASA Technical Reports Server (NTRS)

    Baranowsky, Patrick W., II

    1993-01-01

    Westinghouse Electric Corporation is developing both the Communications Ground Segment and the Series 1000 Mobile Phone for American Mobile Satellite Corporation's (AMSC's) Mobile Satellite (MSAT) system. The success of the voice services portion of this system depends, to some extent, upon the interoperability of the cellular network and the satellite communication circuit switched communication channels. This paper will describe the set of user-selectable cellular interoperable modes (cellular first/satellite second, etc.) provided by the Mobile Phone and described how they are implemented with the ground segment. Topics including roaming registration and cellular-to-satellite 'seamless' call handoff will be discussed, along with the relevant Interim Standard IS-41 Revision B Cellular Radiotelecommunications Intersystem Operations and IOS-553 Mobile Station - Land Station Compatibility Specification.

  9. 2D reentrant auxetic structures of graphene/CNT networks for omnidirectionally stretchable supercapacitors.

    PubMed

    Kim, Byoung Soo; Lee, Kangsuk; Kang, Seulki; Lee, Soyeon; Pyo, Jun Beom; Choi, In Suk; Char, Kookheon; Park, Jong Hyuk; Lee, Sang-Soo; Lee, Jonghwi; Son, Jeong Gon

    2017-09-14

    Stretchable energy storage systems are essential for the realization of implantable and epidermal electronics. However, high-performance stretchable supercapacitors have received less attention because currently available processing techniques and material structures are too limited to overcome the trade-off relationship among electrical conductivity, ion-accessible surface area, and stretchability of electrodes. Herein, we introduce novel 2D reentrant cellular structures of porous graphene/CNT networks for omnidirectionally stretchable supercapacitor electrodes. Reentrant structures, with inwardly protruded frameworks in porous networks, were fabricated by the radial compression of vertically aligned honeycomb-like rGO/CNT networks, which were prepared by a directional crystallization method. Unlike typical porous graphene structures, the reentrant structure provided structure-assisted stretchability, such as accordion and origami structures, to otherwise unstretchable materials. The 2D reentrant structures of graphene/CNT networks maintained excellent electrical conductivities under biaxial stretching conditions and showed a slightly negative or near-zero Poisson's ratio over a wide strain range because of their structural uniqueness. For practical applications, we fabricated all-solid-state supercapacitors based on 2D auxetic structures. A radial compression process up to 1/10(th) densified the electrode, significantly increasing the areal and volumetric capacitances of the electrodes. Additionally, vertically aligned graphene/CNT networks provided a plentiful surface area and induced sufficient ion transport pathways for the electrodes. Therefore, they exhibited high gravimetric and areal capacitance values of 152.4 F g(-1) and 2.9 F cm(-2), respectively, and had an excellent retention ratio of 88% under a biaxial strain of 100%. Auxetic cellular and vertically aligned structures provide a new strategy for the preparation of robust platforms for stretchable

  10. Automated Quantification and Integrative Analysis of 2D and 3D Mitochondrial Shape and Network Properties

    PubMed Central

    Nikolaisen, Julie; Nilsson, Linn I. H.; Pettersen, Ina K. N.; Willems, Peter H. G. M.; Lorens, James B.; Koopman, Werner J. H.; Tronstad, Karl J.

    2014-01-01

    Mitochondrial morphology and function are coupled in healthy cells, during pathological conditions and (adaptation to) endogenous and exogenous stress. In this sense mitochondrial shape can range from small globular compartments to complex filamentous networks, even within the same cell. Understanding how mitochondrial morphological changes (i.e. “mitochondrial dynamics”) are linked to cellular (patho) physiology is currently the subject of intense study and requires detailed quantitative information. During the last decade, various computational approaches have been developed for automated 2-dimensional (2D) analysis of mitochondrial morphology and number in microscopy images. Although these strategies are well suited for analysis of adhering cells with a flat morphology they are not applicable for thicker cells, which require a three-dimensional (3D) image acquisition and analysis procedure. Here we developed and validated an automated image analysis algorithm allowing simultaneous 3D quantification of mitochondrial morphology and network properties in human endothelial cells (HUVECs). Cells expressing a mitochondria-targeted green fluorescence protein (mitoGFP) were visualized by 3D confocal microscopy and mitochondrial morphology was quantified using both the established 2D method and the new 3D strategy. We demonstrate that both analyses can be used to characterize and discriminate between various mitochondrial morphologies and network properties. However, the results from 2D and 3D analysis were not equivalent when filamentous mitochondria in normal HUVECs were compared with circular/spherical mitochondria in metabolically stressed HUVECs treated with rotenone (ROT). 2D quantification suggested that metabolic stress induced mitochondrial fragmentation and loss of biomass. In contrast, 3D analysis revealed that the mitochondrial network structure was dissolved without affecting the amount and size of the organelles. Thus, our results demonstrate that 3D

  11. Structure and Reversibility of 2D von Neumann Cellular Automata Over Triangular Lattice

    NASA Astrophysics Data System (ADS)

    Uguz, Selman; Redjepov, Shovkat; Acar, Ecem; Akin, Hasan

    2017-06-01

    Even though the fundamental main structure of cellular automata (CA) is a discrete special model, the global behaviors at many iterative times and on big scales could be a close, nearly a continuous, model system. CA theory is a very rich and useful phenomena of dynamical model that focuses on the local information being relayed to the neighboring cells to produce CA global behaviors. The mathematical points of the basic model imply the computable values of the mathematical structure of CA. After modeling the CA structure, an important problem is to be able to move forwards and backwards on CA to understand their behaviors in more elegant ways. A possible case is when CA is to be a reversible one. In this paper, we investigate the structure and the reversibility of two-dimensional (2D) finite, linear, triangular von Neumann CA with null boundary case. It is considered on ternary field ℤ3 (i.e. 3-state). We obtain their transition rule matrices for each special case. For given special triangular information (transition) rule matrices, we prove which triangular linear 2D von Neumann CAs are reversible or not. It is known that the reversibility cases of 2D CA are generally a much challenged problem. In the present study, the reversibility problem of 2D triangular, linear von Neumann CA with null boundary is resolved completely over ternary field. As far as we know, there is no structure and reversibility study of von Neumann 2D linear CA on triangular lattice in the literature. Due to the main CA structures being sufficiently simple to investigate in mathematical ways, and also very complex to obtain in chaotic systems, it is believed that the present construction can be applied to many areas related to these CA using any other transition rules.

  12. Nanostructured 2D cellular materials in silicon by sidewall transfer lithography NEMS

    NASA Astrophysics Data System (ADS)

    Syms, Richard R. A.; Liu, Dixi; Ahmad, Munir M.

    2017-07-01

    Sidewall transfer lithography (STL) is demonstrated as a method for parallel fabrication of 2D nanostructured cellular solids in single-crystal silicon. The linear mechanical properties of four lattices (perfect and defected diamond; singly and doubly periodic honeycomb) with low effective Young’s moduli and effective Poisson’s ratio ranging from positive to negative are modelled using analytic theory and the matrix stiffness method with an emphasis on boundary effects. The lattices are fabricated with a minimum feature size of 100 nm and an aspect ratio of 40:1 using single- and double-level STL and deep reactive ion etching of bonded silicon-on-insulator. Nanoelectromechanical systems (NEMS) containing cellular materials are used to demonstrate stretching, bending and brittle fracture. Predicted edge effects are observed, theoretical values of Poisson’s ratio are verified and failure patterns are described.

  13. Adaptation algorithms for 2-D feedforward neural networks.

    PubMed

    Kaczorek, T

    1995-01-01

    The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).

  14. Micromechanics of cellularized biopolymer networks

    PubMed Central

    Jones, Christopher A. R.; Cibula, Matthew; Feng, Jingchen; Krnacik, Emma A.; McIntyre, David H.; Levine, Herbert; Sun, Bo

    2015-01-01

    Collagen gels are widely used in experiments on cell mechanics because they mimic the extracellular matrix in physiological conditions. Collagen gels are often characterized by their bulk rheology; however, variations in the collagen fiber microstructure and cell adhesion forces cause the mechanical properties to be inhomogeneous at the cellular scale. We study the mechanics of type I collagen on the scale of tens to hundreds of microns by using holographic optical tweezers to apply pN forces to microparticles embedded in the collagen fiber network. We find that in response to optical forces, particle displacements are inhomogeneous, anisotropic, and asymmetric. Gels prepared at 21 °C and 37 °C show qualitative difference in their micromechanical characteristics. We also demonstrate that contracting cells remodel the micromechanics of their surrounding extracellular matrix in a strain- and distance-dependent manner. To further understand the micromechanics of cellularized extracellular matrix, we have constructed a computational model which reproduces the main experiment findings. PMID:26324923

  15. 2D Carbon Nanotube Network: A New material for Electronics

    NASA Astrophysics Data System (ADS)

    Gruner, George

    2006-03-01

    This talk will focus on the electronic properties of two dimensional carbon nanotube networks, and on their application potential. Percolation issues, together with the frequency, and temperature dependent activity will be discussed. The network can be tuned from having semiconducting to metallic like behavior, and doping with electron withdrawing and donating species leads to networks with tailor-made electronic properties. The network is also highly transparent in the visible spectral range, this attribute -- together with simple room temperature fab processes -- opens up application opportunities in the area of electronics, opto-electronics, photovoltaics and sensors. Recent results on solar cells, OLEDs and smart windows will be reviewed. Field effect transistors that incorporate nanotube network conducting channels, together with complex functional devices that incorporate networks and functional molecules will also be discussed. Finally a comparison will be made with conventional and emerging materials that compete area of disposable, flexible and printable electronics.

  16. 2D pattern evolution constrained by complex network dynamics

    NASA Astrophysics Data System (ADS)

    da Rocha, L. E. C.; Costa, L. da F.

    2007-03-01

    Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling several complex natural and artificial systems. In the same time in which the structural intricacies of such networks are being revealed and understood, efforts have also been directed at investigating how such connectivity properties define and constrain the dynamics of systems unfolding on such structures. However, less attention has been focused on hybrid systems, i.e. involving more than one type of network and/or dynamics. Several real systems present such an organization, e.g. the dynamics of a disease coexisting with the dynamics of the immune system. The current paper investigates a specific system involving diffusive (linear and nonlinear) dynamics taking place in a regular network while interacting with a complex network of defensive agents following Erdös Rényi (ER) and Barabási Albert (BA) graph models with moveable nodes. More specifically, the complex network is expected to control, and if possible, to extinguish the diffusion of some given unwanted process (e.g. fire, oil spilling, pest dissemination, and virus or bacteria reproduction during an infection). Two types of pattern evolution are considered: Fick and Gray Scott. The nodes of the defensive network then interact with the diffusing patterns and communicate between themselves in order to control the diffusion. The main findings include the identification of higher efficiency for the BA control networks and the presence of relapses in the case of the ER model.

  17. 2D photonic crystal complete band gap search using a cyclic cellular automaton refination

    NASA Astrophysics Data System (ADS)

    González-García, R.; Castañón, G.; Hernández-Figueroa, H. E.

    2014-11-01

    We present a refination method based on a cyclic cellular automaton (CCA) that simulates a crystallization-like process, aided with a heuristic evolutionary method called differential evolution (DE) used to perform an ordered search of full photonic band gaps (FPBGs) in a 2D photonic crystal (PC). The solution is proposed as a combinatorial optimization of the elements in a binary array. These elements represent the existence or absence of a dielectric material surrounded by air, thus representing a general geometry whose search space is defined by the number of elements in such array. A block-iterative frequency-domain method was used to compute the FPBGs on a PC, when present. DE has proved to be useful in combinatorial problems and we also present an implementation feature that takes advantage of the periodic nature of PCs to enhance the convergence of this algorithm. Finally, we used this methodology to find a PC structure with a 19% bandgap-to-midgap ratio without requiring previous information of suboptimal configurations and we made a statistical study of how it is affected by disorder in the borders of the structure compared with a previous work that uses a genetic algorithm.

  18. Magnetic anisotropy of metal functionalized phthalocyanine 2D networks

    NASA Astrophysics Data System (ADS)

    Zhu, Guojun; Zhang, Yun; Xiao, Huaping; Cao, Juexian

    2016-06-01

    The magnetic anisotropy of metal including Cr, Mn, Fe, Co, Mo, Tc, Ru, Rh, W, Re, Os, Ir atoms functionalized phthalocyanine networks have been investigated with first-principles calculations. The magnetic moments can be expressed as 8-n μB with n the electronic number of outmost d shell in the transition metals. The huge magnetocrystalline anisotropy energy (MAE) is obtained by torque method. Especially, the MAE of Re functionalized phthalocyanine network is about 20 meV with an easy axis perpendicular to the plane of phthalocyanine network. The MAE is further manipulated by applying the external biaxial strain. It is found that the MAE is linear increasing with the external strain in the range of -2% to 2%. Our results indicate an effective approach to modulate the MAE for practical application.

  19. Cellular neuron and large wireless neural network

    NASA Astrophysics Data System (ADS)

    Jannson, Tomasz; Forrester, Thomas; Ambrose, Barry; Kazantzidis, Matheos; Lin, Freddie

    2006-05-01

    A new approach to neural networks is proposed, based on wireless interconnects (synapses) and cellular neurons, both software and hardware; with the capacity of 10 10 neurons, almost fully connected. The core of the system is Spatio-Temporal-Variant (STV) kernel and cellular axon with synaptic plasticity variable in time and space. The novel large neural network hardware is based on two established wireless technologies: RF-cellular and IR-wireless.

  20. Magnetic anisotropy of metal functionalized phthalocyanine 2D networks

    SciTech Connect

    Zhu, Guojun; Zhang, Yun; Xiao, Huaping; Cao, Juexian

    2016-06-15

    The magnetic anisotropy of metal including Cr, Mn, Fe, Co, Mo, Tc, Ru, Rh, W, Re, Os, Ir atoms functionalized phthalocyanine networks have been investigated with first-principles calculations. The magnetic moments can be expressed as 8-n μ{sub B} with n the electronic number of outmost d shell in the transition metals. The huge magnetocrystalline anisotropy energy (MAE) is obtained by torque method. Especially, the MAE of Re functionalized phthalocyanine network is about 20 meV with an easy axis perpendicular to the plane of phthalocyanine network. The MAE is further manipulated by applying the external biaxial strain. It is found that the MAE is linear increasing with the external strain in the range of −2% to 2%. Our results indicate an effective approach to modulate the MAE for practical application. - Graphical abstract: The charge density redistribution (ρ{sub MPc}-ρ{sub M}-ρ{sub Pc}) and spin density of the CoPc molecule, from top- and side-views. Purple and green isosurfaces indicate charge depletion and accumulation, respectively. Display Omitted.

  1. Construction and repair of highly ordered 2D covalent networks by chemical equilibrium regulation.

    PubMed

    Guan, Cui-Zhong; Wang, Dong; Wan, Li-Jun

    2012-03-21

    The construction of well-ordered 2D covalent networks via the dehydration of di-borate aromatic molecules was successfully realized through introducing a small amount of water into a closed reaction system to regulate the chemical equilibrium.

  2. Probabilistic Cellular Automata for Low-Temperature 2-d Ising Model

    NASA Astrophysics Data System (ADS)

    Procacci, Aldo; Scoppola, Benedetto; Scoppola, Elisabetta

    2016-12-01

    We construct a parallel stochastic dynamics with invariant measure converging to the Gibbs measure of the 2-d low-temperature Ising model. The proof of such convergence requires a polymer expansion based on suitably defined Peierls-type contours.

  3. The Transition Rules of 2D Linear Cellular Automata Over Ternary Field and Self-Replicating Patterns

    NASA Astrophysics Data System (ADS)

    Sahin, Uḡur; Uguz, Selman; Akin, Hasan

    In this paper we start with two-dimensional (2D) linear cellular automata (CA) in relation with basic mathematical structure. We investigate uniform linear 2D CA over ternary field, i.e. ℤ3. Present work is related to theoretical and imaginary investigations of 2D linear CA. Even though the basic construction of a CA is a discrete model, its macroscopic level behavior at large times and on large scales could be a close approximation to a continuous system. Considering some statistical properties, someone may also study geometrical aspects of patterns generated by cellular automaton evolution. After iteratively applying the linear rules, CA have been shown capable of producing interesting complex behaviors. Some examples of CA produce remarkably regular behavior on finite configurations. Using some simple initial configurations, the produced pattern can be self-replicating regarding some linear rules. Here we deal with the theory 2D uniform periodic, adiabatic and reflexive boundary CA (2D PB, AB and RB) over the ternary field ℤ3 and the applications of image processing for patterns generation. From the visual appearance of the patterns, it is seen that some rules display sensitive dependence on boundary conditions and their rule numbers.

  4. Complexity, dynamic cellular network, and tumorigenesis.

    PubMed

    Waliszewski, P

    1997-01-01

    A holistic approach to tumorigenesis is proposed. The main element of the model is the existence of dynamic cellular network. This network comprises a molecular and an energetistic structure of a cell connected through the multidirectional flow of information. The interactions within dynamic cellular network are complex, stochastic, nonlinear, and also involve quantum effects. From this non-reductionist perspective, neither tumorigenesis can be limited to the genetic aspect, nor the initial event must be of molecular nature, nor mutations and epigenetic factors are mutually exclusive, nor a link between cause and effect can be established. Due to complexity, an unstable stationary state of dynamic cellular network rather than a group of unrelated genes determines the phenotype of normal and transformed cells. This implies relativity of tumor suppressor genes and oncogenes. A bifurcation point is defined as an unstable state of dynamic cellular network leading to the other phenotype-stationary state. In particular, the bifurcation point may be determined by a change of expression of a single gene. Then, the gene is called bifurcation point gene. The unstable stationary state facilitates the chaotic dynamics. This may result in a fractal dimension of both normal and tumor tissues. The co-existence of chaotic dynamics and complexity is the essence of cellular processes and shapes differentiation, morphogenesis, and tumorigenesis. In consequence, tumorigenesis is a complex, unpredictable process driven by the interplay between self-organisation and selection.

  5. Inferring cellular networks – a review

    PubMed Central

    Markowetz, Florian; Spang, Rainer

    2007-01-01

    In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations. PMID:17903286

  6. 2D spatially controlled polymer micro patterning for cellular behavior studies

    NASA Astrophysics Data System (ADS)

    Dinca, V.; Palla-Papavlu, A.; Paraico, I.; Lippert, T.; Wokaun, A.; Dinescu, M.

    2011-04-01

    A simple and effective method to functionalize glass surfaces that enable polymer micropatterning and subsequent spatially controlled adhesion of cells is reported in this paper. The method involves the application of laser induced forward transfer (LIFT) to achieve polymer patterning in a single step onto cell repellent substrates (i.e. polyethyleneglycol (PEG)). This approach was used to produce micron-size polyethyleneimine (PEI)-patterns alternating with cell-repellent areas. The focus of this work is the ability of SH-SY5Y human neuroblastoma cells to orient, migrate, and produce organized cellular arrangements on laser generated PEI patterns.

  7. Intracellular ROS mediates gas plasma-facilitated cellular transfection in 2D and 3D cultures

    PubMed Central

    Xu, Dehui; Wang, Biqing; Xu, Yujing; Chen, Zeyu; Cui, Qinjie; Yang, Yanjie; Chen, Hailan; Kong, Michael G.

    2016-01-01

    This study reports the potential of cold atmospheric plasma (CAP) as a versatile tool for delivering oligonucleotides into mammalian cells. Compared to lipofection and electroporation methods, plasma transfection showed a better uptake efficiency and less cell death in the transfection of oligonucleotides. We demonstrated that the level of extracellular aqueous reactive oxygen species (ROS) produced by gas plasma is correlated with the uptake efficiency and that this is achieved through an increase of intracellular ROS levels and the resulting increase in cell membrane permeability. This finding was supported by the use of ROS scavengers, which reduced CAP-based uptake efficiency. In addition, we found that cold atmospheric plasma could transfer oligonucleotides such as siRNA and miRNA into cells even in 3D cultures, thus suggesting the potential for unique applications of CAP beyond those provided by standard transfection techniques. Together, our results suggest that cold plasma might provide an efficient technique for the delivery of siRNA and miRNA in 2D and 3D culture models. PMID:27296089

  8. Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Reid, Max B.

    1994-01-01

    A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

  9. Wilson punctured network defects in 2D q-deformed Yang-Mills theory

    NASA Astrophysics Data System (ADS)

    Watanabe, Noriaki

    2016-12-01

    In the context of class S theories and 4D/2D duality relations there, we discuss the skein relations of general topological defects on the 2D side which are expected to be counterparts of composite surface-line operators in 4D class S theory. Such defects are geometrically interpreted as networks in a three dimensional space. We also propose a conjectural computational procedure for such defects in two dimensional SU( N ) topological q-deformed Yang-Mills theory by interpreting it as a statistical mechanical system associated with ideal triangulations.

  10. Automatic angle measurement of a 2D object using optical correlator-neural networks hybrid system

    NASA Astrophysics Data System (ADS)

    Manivannan, N.; Neil, M. A. A.

    2011-04-01

    In this paper a novel method is proposed and demonstrated for automatic rotation angle measurement of a 2D object using a hybrid architecture, consisting of a 4f optical correlator with a binary phase only multiplexed matched filter and a single layer neural network. The hybrid set-up can be considered as a two-layer perceptron-like neural network; an optical correlator is the first layer and the standard single layer neural network is the second layer. The training scheme used to train the hybrid architecture is a combination of a Direct Binary Search algorithm, to train the optical correlator, and an Error Back Propagation algorithm, to train the neural network. The aim is to perform the major information processing by the optical correlator with a small additional processing by the neural network stage. This allows the system to be used for real-time applications as optics has the inherent ability to process information in a parallel manner at high speed. The neural network stage gives an extra dimension of freedom so that complicated tasks like automatic rotation angle measurement can be achieved. Results of both computer simulation and experimental set-up are presented for rotation angle measurement of an English alphabetic character as a 2D object. The experimental set-up consists of a real optical correlator using two spatial light modulators for both input and frequency plane representations and a PC based model of a single layer network.

  11. Using economy of means to evolve transition rules within 2D cellular automata.

    PubMed

    Ripps, David L

    2010-01-01

    Running a cellular automaton (CA) on a rectangular lattice is a time-honored method for studying artificial life on a digital computer. Commonly, the researcher wishes to investigate some specific or general mode of behavior, say, the ability of a coherent pattern of points to glide within the lattice, or to generate copies of itself. This technique has a problem: how to design the transitions table-the set of distinct rules that specify the next content of a cell from its current content and that of its near neighbors. Often the table is painstakingly designed manually, rule by rule. The problem is exacerbated by the potentially vast number of individual rules that need be specified to cover all combinations of center and neighbors when there are several symbols in the alphabet of the CA. In this article a method is presented to have the set of rules evolve automatically while running the CA. The transition table is initially empty, with rules being added as the need arises. A novel principle drives the evolution: maximum economy of means-maximizing the reuse of rules introduced on previous cycles. This method may not be a panacea applicable to all CA studies. Nevertheless, it is sufficiently potent to evolve sets of rules and associated patterns of points that glide (periodically regenerate themselves at another location) and to generate gliding "children" that then "mate" by collision.

  12. Engineering the extracellular environment: Strategies for building 2D and 3D cellular structures.

    PubMed

    Guillame-Gentil, Orane; Semenov, Oleg; Roca, Ana Sala; Groth, Thomas; Zahn, Raphael; Vörös, Janos; Zenobi-Wong, Marcy

    2010-12-21

    Cell fate is regulated by extracellular environmental signals. Receptor specific interaction of the cell with proteins, glycans, soluble factors as well as neighboring cells can steer cells towards proliferation, differentiation, apoptosis or migration. In this review, approaches to build cellular structures by engineering aspects of the extracellular environment are described. These methods include non-specific modifications to control the wettability and stiffness of surfaces using self-assembled monolayers (SAMs) and polyelectrolyte multilayers (PEMs) as well as methods where the temporal activation and spatial distribution of adhesion ligands is controlled. Building on these techniques, construction of two-dimensional cell sheets using temperature sensitive polymers or electrochemical dissolution is described together with current applications of these grafts in the clinical arena. Finally, methods to pattern cells in three-dimensions as well as to functionalize the 3D environment with biologic motifs take us one step closer to being able to engineer multicellular tissues and organs. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Cellular and synaptic network defects in autism.

    PubMed

    Peça, João; Feng, Guoping

    2012-10-01

    Many candidate genes are now thought to confer susceptibility to autism spectrum disorders (ASDs). Here we review four interrelated complexes, each composed of multiple families of genes that functionally coalesce on common cellular pathways. We illustrate a common thread in the organization of glutamatergic synapses and suggest a link between genes involved in Tuberous Sclerosis Complex, Fragile X syndrome, Angelman syndrome and several synaptic ASD candidate genes. When viewed in this context, progress in deciphering the molecular architecture of cellular protein-protein interactions together with the unraveling of synaptic dysfunction in neural networks may prove pivotal to advancing our understanding of ASDs. Copyright © 2012. Published by Elsevier Ltd.

  14. Adaptive reorganization of 2D molecular nanoporous network induced by coadsorbed guest molecule.

    PubMed

    Zheng, Qing-Na; Wang, Lei; Zhong, Yu-Wu; Liu, Xuan-He; Chen, Ting; Yan, Hui-Juan; Wang, Dong; Yao, Jian-Nian; Wan, Li-Jun

    2014-03-25

    The ordered array of nanovoids in nanoporous networks, such as honeycomb, Kagome, and square, provides a molecular template for the accommodation of "guest molecules". Compared with the commonly studied guest molecules featuring high symmetry evenly incorporated into the template, guest molecules featuring lower symmetry are rare to report. Herein, we report the formation of a distinct patterned superlattice of guest molecules by selective trapping of guest molecules into the honeycomb network of trimesic acid (TMA). Two distinct surface patterns have been achieved by the guest inclusion induced adaptive reconstruction of a 2D molecular nanoporous network. The honeycomb networks can synergetically tune the arrangement upon inclusion of the guest molecules with different core size but similar peripherals groups, resulting in a trihexagonal Kagome or triangular patterns.

  15. Network motifs modulate druggability of cellular targets

    PubMed Central

    Wu, Fan; Ma, Cong; Tan, Cheemeng

    2016-01-01

    Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology. PMID:27824147

  16. From 2D to 3D: novel nanostructured scaffolds to investigate signalling in reconstructed neuronal networks

    PubMed Central

    Bosi, Susanna; Rauti, Rossana; Laishram, Jummi; Turco, Antonio; Lonardoni, Davide; Nieus, Thierry; Prato, Maurizio; Scaini, Denis; Ballerini, Laura

    2015-01-01

    To recreate in vitro 3D neuronal circuits will ultimately increase the relevance of results from cultured to whole-brain networks and will promote enabling technologies for neuro-engineering applications. Here we fabricate novel elastomeric scaffolds able to instruct 3D growth of living primary neurons. Such systems allow investigating the emerging activity, in terms of calcium signals, of small clusters of neurons as a function of the interplay between the 2D or 3D architectures and network dynamics. We report the ability of 3D geometry to improve functional organization and synchronization in small neuronal assemblies. We propose a mathematical modelling of network dynamics that supports such a result. Entrapping carbon nanotubes in the scaffolds remarkably boosted synaptic activity, thus allowing for the first time to exploit nanomaterial/cell interfacing in 3D growth support. Our 3D system represents a simple and reliable construct, able to improve the complexity of current tissue culture models. PMID:25910072

  17. Towards understanding transcriptional networks in cellular reprogramming.

    PubMed

    Firas, Jaber; Polo, Jose M

    2017-10-01

    Most of the knowledge we have on the molecular mechanisms of transcription factor mediated reprogramming comes from studies conducted in induced pluripotency. Recently however, a few studies investigated the mechanisms of cellular reprogramming in direct and indirect transdifferentiation, which allows us to explore whether shared parallel mechanisms can be drawn. Moreover, there are currently several computational tools that have been developed to predict and enhance the reprogramming process by reconstructing the transcriptional networks of reprogramming cells. These new tools have the potential to greatly benefit the field of reprogramming, providing us with new approaches that can transform our understanding of the initiation, progression and successful completion of cellular fate transition. Copyright © 2017. Published by Elsevier Ltd.

  18. Heterogeneous Force Chains in Cellularized Biopolymer Network

    NASA Astrophysics Data System (ADS)

    Liang, Long; Jones, Christopher Allen Rucksack; Sun, Bo; Jiao, Yang

    Biopolymer Networks play an important role in coordinating and regulating collective cellular dynamics via a number of signaling pathways. Here, we investigate the mechanical response of a model biopolymer network due to the active contraction of embedded cells. Specifically, a graph (bond-node) model derived from confocal microscopy data is used to represent the network microstructure, and cell contraction is modeled by applying correlated displacements at specific nodes, representing the focal adhesion sites. A force-based stochastic relaxation method is employed to obtain force-balanced network under cell contraction. We find that the majority of the forces are carried by a small number of heterogeneous force chains emerged from the contracting cells. The force chains consist of fiber segments that either possess a high degree of alignment before cell contraction or are aligned due to the reorientation induced by cell contraction. Large fluctuations of the forces along different force chains are observed. Importantly, the decay of the forces along the force chains is significantly slower than the decay of radially averaged forces in the system, suggesting that the fibreous nature of biopolymer network structure could support long-range mechanical signaling between cells.

  19. Cellular recurrent deep network for image registration

    NASA Astrophysics Data System (ADS)

    Alam, M.; Vidyaratne, L.; Iftekharuddin, Khan M.

    2015-09-01

    Image registration using Artificial Neural Network (ANN) remains a challenging learning task. Registration can be posed as a two-step problem: parameter estimation and actual alignment/transformation using the estimated parameters. To date ANN based image registration techniques only perform the parameter estimation, while affine equations are used to perform the actual transformation. In this paper, we propose a novel deep ANN based image rigid registration that combines parameter estimation and transformation as a simultaneous learning task. Our previous work shows that a complex universal approximator known as Cellular Simultaneous Recurrent Network (CSRN) can successfully approximate affine transformations with known transformation parameters. This study introduces a deep ANN that combines a feed forward network with a CSRN to perform full rigid registration. Layer wise training is used to pre-train feed forward network for parameter estimation and followed by a CSRN for image transformation respectively. The deep network is then fine-tuned to perform the final registration task. Our result shows that the proposed deep ANN architecture achieves comparable registration accuracy to that of image affine transformation using CSRN with known parameters. We also demonstrate the efficacy of our novel deep architecture by a performance comparison with a deep clustered MLP.

  20. Simulation and analysis of solute transport in 2D fracture/pipe networks: The SOLFRAC program

    NASA Astrophysics Data System (ADS)

    Bodin, Jacques; Porel, Gilles; Delay, Fred; Ubertosi, Fabrice; Bernard, Stéphane; de Dreuzy, Jean-Raynald

    2007-01-01

    The Time Domain Random Walk (TDRW) method has been recently developed by Delay and Bodin [Delay, F. and Bodin, J., 2001. Time domain random walk method to simulate transport by advection-dispersion and matrix diffusion in fracture networks. Geophys. Res. Lett., 28(21): 4051-4054.] and Bodin et al. [Bodin, J., Porel, G. and Delay, F., 2003c. Simulation of solute transport in discrete fracture networks using the time domain random walk method. Earth Planet. Sci. Lett., 6566: 1-8.] for simulating solute transport in discrete fracture networks. It is assumed that the fracture network can reasonably be represented by a network of interconnected one-dimensional pipes (i.e. flow channels). Processes accounted for are: (1) advection and hydrodynamic dispersion in the channels, (2) matrix diffusion, (3) diffusion into stagnant zones within the fracture planes, (4) sorption reactions onto the fracture walls and in the matrix, (5) linear decay, and (6) mass sharing at fracture intersections. The TDRW method is handy and very efficient in terms of computation costs since it allows for the one-step calculation of the particle residence time in each bond of the network. This method has been programmed in C++, and efforts have been made to develop an efficient and user-friendly software, called SOLFRAC. This program is freely downloadable at the URL http://labo.univ-poitiers.fr/hydrasa/intranet/telechargement.htm. It calculates solute transport into 2D pipe networks, while considering different types of injections and different concepts of local dispersion within each flow channel. Post-simulation analyses are also available, such as the mean velocity or the macroscopic dispersion at the scale of the entire network. The program may be used to evaluate how a given transport mechanism influences the macroscopic transport behaviour of fracture networks. It may also be used, as is the case, e.g., with analytical solutions, to interpret laboratory or field tracer test experiments

  1. Simulation and analysis of solute transport in 2D fracture/pipe networks: the SOLFRAC program.

    PubMed

    Bodin, Jacques; Porel, Gilles; Delay, Fred; Ubertosi, Fabrice; Bernard, Stéphane; de Dreuzy, Jean-Raynald

    2007-01-05

    The Time Domain Random Walk (TDRW) method has been recently developed by Delay and Bodin [Delay, F. and Bodin, J., 2001. Time domain random walk method to simulate transport by advection-dispersion and matrix diffusion in fracture networks. Geophys. Res. Lett., 28(21): 4051-4054.] and Bodin et al. [Bodin, J., Porel, G. and Delay, F., 2003c. Simulation of solute transport in discrete fracture networks using the time domain random walk method. Earth Planet. Sci. Lett., 6566: 1-8.] for simulating solute transport in discrete fracture networks. It is assumed that the fracture network can reasonably be represented by a network of interconnected one-dimensional pipes (i.e. flow channels). Processes accounted for are: (1) advection and hydrodynamic dispersion in the channels, (2) matrix diffusion, (3) diffusion into stagnant zones within the fracture planes, (4) sorption reactions onto the fracture walls and in the matrix, (5) linear decay, and (6) mass sharing at fracture intersections. The TDRW method is handy and very efficient in terms of computation costs since it allows for the one-step calculation of the particle residence time in each bond of the network. This method has been programmed in C++, and efforts have been made to develop an efficient and user-friendly software, called SOLFRAC. This program is freely downloadable at the URL (labo.univ-poitiers.fr/hydrasa/intranet/telechargement.htm). It calculates solute transport into 2D pipe networks, while considering different types of injections and different concepts of local dispersion within each flow channel. Post-simulation analyses are also available, such as the mean velocity or the macroscopic dispersion at the scale of the entire network. The program may be used to evaluate how a given transport mechanism influences the macroscopic transport behaviour of fracture networks. It may also be used, as is the case, e.g., with analytical solutions, to interpret laboratory or field tracer test experiments performed

  2. Annexin-A5 organized in 2D-network at the plasmalemma eases human trophoblast fusion

    PubMed Central

    Degrelle, Severine A.; Gerbaud, Pascale; Leconte, Ludovic; Ferreira, Fatima; Pidoux, Guillaume

    2017-01-01

    Only a limited number of human cells can fuse to form a multinucleated syncytium. Cell fusion occurs as part of the differentiation of some cell types, including myotubes in muscle and osteoclasts in remodeling bone. In the differentiation of the human placenta, mononuclear cytotrophoblasts aggregate and fuse to form endocrinologically active, non-proliferative, multinucleated syncytia. These syncytia allow the exchange of nutrients and gases between the maternal and fetal circulation. Alteration of syncytial formation during pregnancy affects fetal growth and the outcome of the pregnancy. Here, we demonstrate the role of annexin A5 (AnxA5) in syncytial formation by cellular delivery of recombinant AnxA5 and RNA interference. By a variety of co-immunoprecipitation, immunolocalization and proximity experiments, we show that a pool of AnxA5 organizes at the inner-leaflet of the plasma membrane in the vicinity of a molecular complex that includes E-Cadherin, α-Catenin and β-Catenin, three proteins previously shown to form adherens junctions implicated in cell fusion. A combination of knockdown and reconstitution experiments with AnxA5, with or without the ability to self-assemble in 2D-arrays, demonstrate that this AnxA5 2D-network mediates E-Cadherin mobility in the plasmalemma that triggers human trophoblasts aggregation and thereby cell fusion. PMID:28176826

  3. Confinement properties of 2D porous molecular networks on metal surfaces.

    PubMed

    Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

    2016-04-20

    Quantum effects that arise from confinement of electronic states have been extensively studied for the surface states of noble metals. Utilizing small artificial structures for confinement allows tailoring of the surface properties and offers unique opportunities for applications. So far, examples of surface state confinement include thin films, artificial nanoscale structures, vacancy and adatom islands, self-assembled 1D chains, vicinal surfaces, quantum dots and quantum corrals. In this review we summarize recent achievements in changing the electronic structure of surfaces by adsorption of nanoporous networks whose design principles are based on the concepts of supramolecular chemistry. Already in 1993, it was shown that quantum corrals made from Fe atoms on a Cu(1 1 1) surface using single atom manipulation with a scanning tunnelling microscope confine the Shockley surface state. However, since the atom manipulation technique for the construction of corral structures is a relatively time consuming process, the fabrication of periodic two-dimensional (2D) corral structures is practically impossible. On the other side, by using molecular self-assembly extended 2D porous structures can be achieved in a parallel process, i.e. all pores are formed at the same time. The molecular building blocks are usually held together by non-covalent interactions like hydrogen bonding, metal coordination or dipolar coupling. Due to the reversibility of the bond formation defect-free and long-range ordered networks can be achieved. However, recently also examples of porous networks formed by covalent coupling on the surface have been reported. By the choice of the molecular building blocks, the dimensions of the network (pore size and pore to pore distance) can be controlled. In this way, the confinement properties of the individual pores can be tuned. In addition, the effect of the confined state on the hosting properties of the pores will be discussed in this review article.

  4. Confinement properties of 2D porous molecular networks on metal surfaces

    NASA Astrophysics Data System (ADS)

    Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

    2016-04-01

    Quantum effects that arise from confinement of electronic states have been extensively studied for the surface states of noble metals. Utilizing small artificial structures for confinement allows tailoring of the surface properties and offers unique opportunities for applications. So far, examples of surface state confinement include thin films, artificial nanoscale structures, vacancy and adatom islands, self-assembled 1D chains, vicinal surfaces, quantum dots and quantum corrals. In this review we summarize recent achievements in changing the electronic structure of surfaces by adsorption of nanoporous networks whose design principles are based on the concepts of supramolecular chemistry. Already in 1993, it was shown that quantum corrals made from Fe atoms on a Cu(1 1 1) surface using single atom manipulation with a scanning tunnelling microscope confine the Shockley surface state. However, since the atom manipulation technique for the construction of corral structures is a relatively time consuming process, the fabrication of periodic two-dimensional (2D) corral structures is practically impossible. On the other side, by using molecular self-assembly extended 2D porous structures can be achieved in a parallel process, i.e. all pores are formed at the same time. The molecular building blocks are usually held together by non-covalent interactions like hydrogen bonding, metal coordination or dipolar coupling. Due to the reversibility of the bond formation defect-free and long-range ordered networks can be achieved. However, recently also examples of porous networks formed by covalent coupling on the surface have been reported. By the choice of the molecular building blocks, the dimensions of the network (pore size and pore to pore distance) can be controlled. In this way, the confinement properties of the individual pores can be tuned. In addition, the effect of the confined state on the hosting properties of the pores will be discussed in this review article.

  5. Wireless Fractal Ultra-Dense Cellular Networks.

    PubMed

    Hao, Yixue; Chen, Min; Hu, Long; Song, Jeungeun; Volk, Mojca; Humar, Iztok

    2017-04-12

    With the ever-growing number of mobile devices, there is an explosive expansion in mobile data services. This represents a challenge for the traditional cellular network architecture to cope with the massive wireless traffic generated by mobile media applications. To meet this challenge, research is currently focused on the introduction of a small cell base station (BS) due to its low transmit power consumption and flexibility of deployment. However, due to a complex deployment environment and low transmit power of small cell BSs, the coverage boundary of small cell BSs will not have a traditional regular shape. Therefore, in this paper, we discuss the coverage boundary of an ultra-dense small cell network and give its main features: aeolotropy of path loss fading and fractal coverage boundary. Simple performance analysis is given, including coverage probability and transmission rate, etc., based on stochastic geometry theory and fractal theory. Finally, we present an application scene and discuss challenges in the ultra-dense small cell network.

  6. Optimal flux patterns in cellular metabolic networks.

    PubMed

    Almaas, Eivind

    2007-06-01

    The availability of whole-cell-level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate the metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations has relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reactions are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central carbon metabolic pathways for the sample of random environments.

  7. Optimal flux patterns in cellular metabolic networks

    SciTech Connect

    Almaas, E

    2007-01-20

    The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.

  8. Optimal flux patterns in cellular metabolic networks

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind

    2007-06-01

    The availability of whole-cell-level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate the metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30 000 random cellular environments. The distribution of reaction fluxes is heavy tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations has relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reactions are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central carbon metabolic pathways for the sample of random environments.

  9. A Wireless Communications Laboratory on Cellular Network Planning

    ERIC Educational Resources Information Center

    Dawy, Z.; Husseini, A.; Yaacoub, E.; Al-Kanj, L.

    2010-01-01

    The field of radio network planning and optimization (RNPO) is central for wireless cellular network design, deployment, and enhancement. Wireless cellular operators invest huge sums of capital on deploying, launching, and maintaining their networks in order to ensure competitive performance and high user satisfaction. This work presents a lab…

  10. A Wireless Communications Laboratory on Cellular Network Planning

    ERIC Educational Resources Information Center

    Dawy, Z.; Husseini, A.; Yaacoub, E.; Al-Kanj, L.

    2010-01-01

    The field of radio network planning and optimization (RNPO) is central for wireless cellular network design, deployment, and enhancement. Wireless cellular operators invest huge sums of capital on deploying, launching, and maintaining their networks in order to ensure competitive performance and high user satisfaction. This work presents a lab…

  11. Spatial Dynamics of Multilayer Cellular Neural Networks

    NASA Astrophysics Data System (ADS)

    Wu, Shi-Liang; Hsu, Cheng-Hsiung

    2017-06-01

    The purpose of this work is to study the spatial dynamics of one-dimensional multilayer cellular neural networks. We first establish the existence of rightward and leftward spreading speeds of the model. Then we show that the spreading speeds coincide with the minimum wave speeds of the traveling wave fronts in the right and left directions. Moreover, we obtain the asymptotic behavior of the traveling wave fronts when the wave speeds are positive and greater than the spreading speeds. According to the asymptotic behavior and using various kinds of comparison theorems, some front-like entire solutions are constructed by combining the rightward and leftward traveling wave fronts with different speeds and a spatially homogeneous solution of the model. Finally, various qualitative features of such entire solutions are investigated.

  12. Insight into the crystallization of amorphous imine-linked polymer networks to 2D covalent organic frameworks.

    PubMed

    Smith, Brian J; Overholts, Anna C; Hwang, Nicky; Dichtel, William R

    2016-03-04

    We explore the crystallization of a high surface area imine-linked two-dimensional covalent organic framework (2D COF). The growth process reveals rapid initial formation of an amorphous network that subsequently crystallizes into the layered 2D network. The metastable amorphous polymer may be isolated and resubjected to growth conditions to form the COF. These experiments provide the first mechanistic insight into the mechanism of imine-linked 2D COF formation, which is distinct from that of boronate-ester linked COFs.

  13. Bernstein copula approach to model direction-length dependency for 2D discrete fracture network simulation

    NASA Astrophysics Data System (ADS)

    Mendoza-Torres, F.; Diaz-Viera, M. A.

    2015-12-01

    In many natural fractured porous media, such as aquifers, soils, oil and geothermal reservoirs, fractures play a crucial role in their flow and transport properties. An approach that has recently gained popularity for modeling fracture systems is the Discrete Fracture Network (DFN) model. This approach consists in applying a stochastic boolean simulation method, also known as object simulation method, where fractures are represented as simplified geometric objects (line segments in 2D and polygons in 3D). One of the shortcomings of this approach is that it usually does not consider the dependency relationships that may exist between the geometric properties of fractures (direction, length, aperture, etc), that is, each property is simulated independently. In this work a method for modeling such dependencies by copula theory is introduced. In particular, a nonparametric model using Bernstein copulas for direction-length fracture dependency in 2D is presented. The application of this method is illustrated in a case study for a fractured rock sample from a carbonate reservoir outcrop.

  14. 2D image classification for 3D anatomy localization: employing deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    de Vos, Bob D.; Wolterink, Jelmer M.; de Jong, Pim A.; Viergever, Max A.; Išgum, Ivana

    2016-03-01

    Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs -- heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.

  15. Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

    SciTech Connect

    Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu

    2011-10-15

    Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations

  16. Heterogeneous force network in 3D cellularized collagen networks.

    PubMed

    Liang, Long; Jones, Christopher; Chen, Shaohua; Sun, Bo; Jiao, Yang

    2016-10-25

    Collagen networks play an important role in coordinating and regulating collective cellular dynamics via a number of signaling pathways. Here, we investigate the transmission of forces generated by contractile cells in 3D collagen-I networks. Specifically, the graph (bond-node) representations of collagen networks with collagen concentrations of 1, 2 and 4 mg ml(-1) are derived from confocal microscopy data and used to model the network microstructure. Cell contraction is modeled by applying correlated displacements at specific nodes of the network, representing the focal adhesion sites. A nonlinear elastic model is employed to characterize the mechanical behavior of individual fiber bundles including strain hardening during stretching and buckling under compression. A force-based relaxation method is employed to obtain equilibrium network configurations under cell contraction. We find that for all collagen concentrations, the majority of the forces are carried by a small number of heterogeneous force chains emitted from the contracting cells, which is qualitatively consistent with our experimental observations. The force chains consist of fiber segments that either possess a high degree of alignment before cell contraction or are aligned due to fiber reorientation induced by cell contraction. The decay of the forces along the force chains is significantly slower than the decay of radially averaged forces in the system, suggesting that the fibreous nature of biopolymer network structure can support long-range force transmission. The force chains emerge even at very small cell contractions, and the number of force chains increases with increasing cell contraction. At large cell contractions, the fibers close to the cell surface are in the nonlinear regime, and the nonlinear region is localized in a small neighborhood of the cell. In addition, the number of force chains increases with increasing collagen concentration, due to the larger number of focal adhesion sites

  17. Heterogeneous force network in 3D cellularized collagen networks

    NASA Astrophysics Data System (ADS)

    Liang, Long; Jones, Christopher; Chen, Shaohua; Sun, Bo; Jiao, Yang

    2016-12-01

    Collagen networks play an important role in coordinating and regulating collective cellular dynamics via a number of signaling pathways. Here, we investigate the transmission of forces generated by contractile cells in 3D collagen-I networks. Specifically, the graph (bond-node) representations of collagen networks with collagen concentrations of 1, 2 and 4 mg ml-1 are derived from confocal microscopy data and used to model the network microstructure. Cell contraction is modeled by applying correlated displacements at specific nodes of the network, representing the focal adhesion sites. A nonlinear elastic model is employed to characterize the mechanical behavior of individual fiber bundles including strain hardening during stretching and buckling under compression. A force-based relaxation method is employed to obtain equilibrium network configurations under cell contraction. We find that for all collagen concentrations, the majority of the forces are carried by a small number of heterogeneous force chains emitted from the contracting cells, which is qualitatively consistent with our experimental observations. The force chains consist of fiber segments that either possess a high degree of alignment before cell contraction or are aligned due to fiber reorientation induced by cell contraction. The decay of the forces along the force chains is significantly slower than the decay of radially averaged forces in the system, suggesting that the fibreous nature of biopolymer network structure can support long-range force transmission. The force chains emerge even at very small cell contractions, and the number of force chains increases with increasing cell contraction. At large cell contractions, the fibers close to the cell surface are in the nonlinear regime, and the nonlinear region is localized in a small neighborhood of the cell. In addition, the number of force chains increases with increasing collagen concentration, due to the larger number of focal adhesion sites

  18. Supporting performance and configuration management of GTE cellular networks

    SciTech Connect

    Tan, Ming; Lafond, C.; Jakobson, G.; Young, G.

    1996-12-31

    GTE Laboratories, in cooperation with GTE Mobilnet, has developed and deployed PERFFEX (PERFormance Expert), an intelligent system for performance and configuration management of cellular networks. PERFEX assists cellular network performance and radio engineers in the analysis of large volumes of cellular network performance and configuration data. It helps them locate and determine the probable causes of performance problems, and provides intelligent suggestions about how to correct them. The system combines an expert cellular network performance tuning capability with a map-based graphical user interface, data visualization programs, and a set of special cellular engineering tools. PERFEX is in daily use at more than 25 GTE Mobile Switching Centers. Since the first deployment of the system in late 1993, PERFEX has become a major GTE cellular network performance optimization tool.

  19. GB(2D)2 PCA-based convolutional network for face recognition

    NASA Astrophysics Data System (ADS)

    Jiang, Min; Lu, Ruru; Kong, Jun; Wu, Xiao-Jun; Huo, Hongtao; Wang, Xiaofeng

    2017-03-01

    Face recognition is a challenging task in computer vision. Numerous efforts have been made to design low-level hand-crafted features for face recognition. Low-level hand-crafted features highly depend on prior knowledge, which is difficult to obtain without learning new domain knowledge. Recently, ConvNets have generated great attention for their ability of feature learning and achieved state-of-the-art results on many computer vision tasks. However, typical ConvNets are trained by a gradient descent method in supervised mode, which results in high computational complexity. To solve this problem, an efficient unsupervised deep learning network is proposed for face recognition in this paper, which combines both 2-D Gabor filters and 2 PCA to learn the multistage convolutional filters. To speed up the calculation, the learned high-dimensional features are further encoded using short binary hashes. Finally, the obtained output features are trained using LinearSVM. Extensive experimental results on several facial benchmark databases show that the proposed network can obtain competitive performance and robust distortion-tolerance for face recognition.

  20. Quantitative nanoscale visualization of heterogeneous electron transfer rates in 2D carbon nanotube networks

    PubMed Central

    Güell, Aleix G.; Ebejer, Neil; Snowden, Michael E.; McKelvey, Kim; Macpherson, Julie V.; Unwin, Patrick R.

    2012-01-01

    Carbon nanotubes have attracted considerable interest for electrochemical, electrocatalytic, and sensing applications, yet there remains uncertainty concerning the intrinsic electrochemical (EC) activity. In this study, we use scanning electrochemical cell microscopy (SECCM) to determine local heterogeneous electron transfer (HET) kinetics in a random 2D network of single-walled carbon nanotubes (SWNTs) on an Si/SiO2 substrate. The high spatial resolution of SECCM, which employs a mobile nanoscale EC cell as a probe for imaging, enables us to sample the responses of individual portions of a wide range of SWNTs within this complex arrangement. Using two redox processes, the oxidation of ferrocenylmethyl trimethylammonium and the reduction of ruthenium (III) hexaamine, we have obtained conclusive evidence for the high intrinsic EC activity of the sidewalls of the large majority of SWNTs in networks. Moreover, we show that the ends of SWNTs and the points where two SWNTs cross do not show appreciably different HET kinetics relative to the sidewall. Using finite element method modeling, we deduce standard rate constants for the two redox couples and demonstrate that HET based solely on characteristic defects in the SWNT side wall is highly unlikely. This is further confirmed by the analysis of individual line profiles taken as the SECCM probe scans over an SWNT. More generally, the studies herein demonstrate SECCM to be a powerful and versatile method for activity mapping of complex electrode materials under conditions of high mass transport, where kinetic assignments can be made with confidence. PMID:22635266

  1. 2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks.

    PubMed

    Caballero, Julio; Garriga, Miguel; Fernández, Michael

    2006-05-15

    Negative inotropic potency of 60 benzothiazepine-like calcium entry blockers (CEBs), Diltiazem analogs, was successfully modeled using Bayesian-regularized genetic neural networks (BRGNNs) and 2D autocorrelation vectors. This approach yielded reliable and robust models whilst by means of a linear genetic algorithm (GA) search routine no multilinear regression model was found describing more than 50% of the training set. On the contrary, the optimum neural network predictor with five inputs described about 84% and 65% variances of 50 randomly selected training and test sets. Autocorrelation vectors in the nonlinear model contained information regarding 2D spatial distributions on the CEB structure of van der Waals volumes, electronegativities, and polarizabilities. However, a sensitivity analysis of the network inputs pointed out to the electronegativity and polarizability 2D topological distributions at substructural fragments of sizes 3 and 4 as the most relevant features governing the nonlinear modeling of the negative inotropic potency.

  2. Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network.

    PubMed

    Tu, Ching-Ting; Chan, Yu-Hsien; Chen, Yi-Chung

    2016-08-01

    A facial sketch synthesis system is proposed, featuring a 2D direct combined model (2DDCM)-based face-specific Markov network. In contrast to the existing facial sketch synthesis systems, the proposed scheme aims to synthesize sketches, which reproduce the unique drawing style of a particular artist, where this drawing style is learned from a data set consisting of a large number of image/sketch pairwise training samples. The synthesis system comprises three modules, namely, a global module, a local module, and an enhancement module. The global module applies a 2DDCM approach to synthesize the global facial geometry and texture of the input image. The detailed texture is then added to the synthesized sketch in a local patch-based manner using a parametric 2DDCM model and a non-parametric Markov random field (MRF) network. Notably, the MRF approach gives the synthesized results an appearance more consistent with the drawing style of the training samples, while the 2DDCM approach enables the synthesis of outcomes with a more derivative style. As a result, the similarity between the synthesized sketches and the input images is greatly improved. Finally, a post-processing operation is performed to enhance the shadowed regions of the synthesized image by adding strong lines or curves to emphasize the lighting conditions. The experimental results confirm that the synthesized facial images are in good qualitative and quantitative agreement with the input images as well as the ground-truth sketches provided by the same artist. The representing power of the proposed framework is demonstrated by synthesizing facial sketches from input images with a wide variety of facial poses, lighting conditions, and races even when such images are not included in the training data set. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests.

  3. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    PubMed

    Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

    2015-01-01

    An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.

  4. Segmentation of Textures Defined on Flat vs. Layered Surfaces using Neural Networks: Comparison of 2D vs. 3D Representations.

    PubMed

    Oh, Sejong; Choe, Yoonsuck

    2007-08-01

    Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer's motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network's learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision.

  5. 2-D Optical CDMA Networks Using MWPM, Double Hard Limiters and Modified Carrier-Hopping Prime Sequences

    NASA Astrophysics Data System (ADS)

    Wang, Ping; Le-Ngoc, Tho

    2005-10-01

    This paper presents a two-dimensional optical code division multiple access (2-D-OCDMA) scheme using multiwavelength pulse modulation (MWPM), double optical hard limiters (DHL), and modified carrier-hopping prime sequences (MCHP) to increase the achievable system capacity. Design criteria to reduce multiaccess interference (MAI) are established and indicate that suitable signature sequences for 2-D-OCDMA/MWPM must have good cross-correlation property in terms of both time shift and wavelength shift. Performance analysis of 2-D-OCDMA/MWPM/DHL systems in the presence of MAI and photo detector shot noise is developed. Simulation and analytical results are in very good agreement and indicate that the proposed 2-D-OCDMA/MWPM/DHL systems using MCHP sequences can offer a much larger capacity than others, suitable for applications in broadband fiber-optic access networks.

  6. Environmental Monitoring using Measurements from Cellular Network Infrastructure

    NASA Astrophysics Data System (ADS)

    David, N.; Gao, O. H.

    2015-12-01

    Accurate measurements of atmospheric parameters at ground level are fundamentally essential for hazard warning, meteorological forecasting and for various applications in agriculture, hydrology, transportation and more. The accuracy of existing instruments, however, is often limited as a result of technical and practical constraints. Existing technologies such as satellite systems cover large areas but may experience lack of precision at near surface level. On the other hand, ground based in-situ sensors often suffer from low spatial representativity. In addition, these conventional monitoring instruments are costly to implement and maintain. At frequencies of tens of GHz, various atmospheric hydrometeors affect microwave beams, causing perturbations to radio signals. Consequently, commercial wireless links that constitute the infrastructure for data transport between cellular base stations can be considered as a built in environmental monitoring facility (Messer et al., Science, 2006). These microwave links are widely deployed worldwide at surface level altitudes and can provide measurements of various atmospheric phenomena. The implementation costs are minimal since the infrastructure is already situated in the field. This technique has been shown to be applicable for 2D rainfall monitoring (e.g. Overeem et al., PNAS, 2013; Liberman et al., AMT, 2014) and potentially for water vapor observations (David et al., ACP, 2009; Chwala et al., Atmos. Res., 2013). Moreover, it has been recently shown that the technology has strong potential for detection of fog and estimation of its intensity (David et al., JGR-Atmos., 2013; David et al., BAMS, 2014). The research conducted to this point forms the basis for the initiation of a research project in this newly emerging field at the School of Civil and Environmental Engineering of Cornell University. The presentation will provide insights into key capabilities of the novel approach. The potential to monitor various

  7. Formats and Network Protocols for Browser Access to 2D Raster Data

    NASA Astrophysics Data System (ADS)

    Plesea, L.

    2015-12-01

    Tiled web maps in browsers are a major success story, forming the foundation of many current web applications. Enabling tiled data access is the next logical step, and is likely to meet with similar success. Many ad-hoc approaches have already started to appear, and something similar is explored within the Open Geospatial Consortium. One of the main obstacles in making browser data access a reality is the lack of a well-known data format. This obstacle also represents an opportunity to analyze the requirements and possible candidates, applying lessons learned from web tiled image services and protocols. Similar to the image counterpart, a web tile raster data format needs to have good intrinsic compression and be able to handle high byte count data types including floating point. An overview of a possible solution to the format problem, a 2D data raster compression algorithm called Limited Error Raster Compression (LERC) will be presented. In addition to the format, best practices for high request rate HTTP services also need to be followed. In particular, content delivery network (CDN) caching suitability needs to be part of any design, not an after-thought. Last but not least, HTML 5 browsers will certainly be part of any solution since they provide improved access to binary data, as well as more powerful ways to view and interact with the data in the browser. In a simple but relevant application, digital elevation model (DEM) raster data is served as LERC compressed data tiles which are used to generate terrain by a HTML5 scene viewer.

  8. Molecular tectonics: design of 2-D networks by simultaneous use of charge-assisted hydrogen and coordination bonds.

    PubMed

    Carpanese, Cristina; Ferlay, Sylvie; Kyritsakas, Nathalie; Henry, Marc; Hosseini, Mir Wais

    2009-11-28

    Using a combination of charge-assisted H- and coordination-bonds, a tetra component system composed of a dicationic and a dianionic organic tecton, Ag(+) cation and XF(6)(-) (X= P, As, Sb) anion behaves as planned and leads to the formation of 2-D isostructural networks for which the energetic contributions of the two recognition events dominate the construction process.

  9. Linking chordate gene networks to cellular behavior in ascidians.

    PubMed

    Davidson, Brad; Christiaen, Lionel

    2006-01-27

    Embryos of simple chordates called ascidians (sea squirts) have few cells, develop rapidly, and are transparent, enabling the in vivo fluorescent imaging of labeled cell lineages. Ascidians are also simple genetically, with limited redundancy and compact regulatory regions. This cellular and genetic simplicity is now being exploited to link comprehensive gene networks to the cellular events underlying morphogenesis.

  10. Biaxial Stretchability and Transparency of Ag Nanowire 2D Mass-Spring Networks Prepared by Floating Compression.

    PubMed

    Kim, Byoung Soo; Pyo, Jun Beom; Son, Jeong Gon; Zi, Goangseup; Lee, Sang-Soo; Park, Jong Hyuk; Lee, Jonghwi

    2017-03-29

    Networks of silver nanowires (Ag NWs) have been considered as promising materials for stretchable and transparent conductors. Despite various improvements of their optoelectronic and electromechanical properties over the past few years, Ag NW networks with a sufficient stretchability in multiple directions that is essential for the accommodation of the multidirectional strains of human movement have seldom been reported. For this paper, biaxially stretchable, transparent conductors were developed based on 2D mass-spring networks of wavy Ag NWs. Inspired by the traditional papermaking process, the 2D wavy networks were produced by floating Ag NW networks on the surface of water and subsequently applying biaxial compression to them. It was demonstrated that this floating-compression process can reduce the friction between the Ag NW-water interfaces, providing a uniform and isotropic in-plane waviness for the networks without buckling or cracking. The resulting Ag NW networks that were transferred onto elastomeric substrates successfully acted as conductors with an excellent transparency, conductivity, and electromechanical stability under a biaxial strain of 30%. The strain sensors that are based on the prepared conductors demonstrated a great potential for the enhanced performances of future wearable devices.

  11. Fc-optimized NKG2D-Fc constructs induce NK cell antibody-dependent cellular cytotoxicity against breast cancer cells independently of HER2/neu expression status.

    PubMed

    Raab, Stefanie; Steinbacher, Julia; Schmiedel, Benjamin J; Kousis, Philaretos C; Steinle, Alexander; Jung, Gundram; Grosse-Hovest, Ludger; Salih, Helmut R

    2014-10-15

    The ability of NK cells to mediate Ab-dependent cellular cytotoxicity (ADCC) largely contributes to the clinical success of antitumor Abs, including trastuzumab, which is approved for the treatment of breast cancer with HER2/neu overexpression. Notably, only ∼25% of breast cancer patients overexpress HER2/neu. Moreover, HER2/neu is expressed on healthy cells, and trastuzumab application is associated with side effects. In contrast, the ligands of the activating immunoreceptor NKG2D (NKG2DL) are selectively expressed on malignant cells. In this study, we took advantage of the tumor-associated expression of NKG2DL by using them as target Ags for NKG2D-IgG1 fusion proteins optimized by amino acid exchange S239D/I332E in their Fc part. Compared to constructs with wild-type Fc parts, fusion proteins carrying the S239D/I332E modification (NKG2D-Fc-ADCC) mediated highly enhanced degranulation, ADCC, and IFN-γ production of NK cells in response to breast cancer cells. NKG2D-Fc-ADCC substantially enhanced NK reactivity also against HER2/neu-low targets that were unaffected by trastuzumab, as both compounds mediated their immunostimulatory effects in strict dependence of target Ag expression levels. Thus, in line with the hierarchically organized potential of the various activating receptors governing NK reactivity and due to its highly increased affinity to CD16, NKG2D-Fc-ADCC potently enhances NK cell reactivity despite the inevitable reduction of activating signals upon binding to NKG2DL. Due to the tumor-restricted expression of NKG2DL, NKG2D-Fc-ADCC may constitute an attractive means for immunotherapy especially of HER2/neu-low or -negative breast cancer.

  12. Estimation of 3-D pore network coordination number of rocks from watershed segmentation of a single 2-D image

    NASA Astrophysics Data System (ADS)

    Rabbani, Arash; Ayatollahi, Shahab; Kharrat, Riyaz; Dashti, Nader

    2016-08-01

    In this study, we have utilized 3-D micro-tomography images of real and synthetic rocks to introduce two mathematical correlations which estimate the distribution parameters of 3-D coordination number using a single 2-D cross-sectional image. By applying a watershed segmentation algorithm, it is found that the distribution of 3-D coordination number is acceptably predictable by statistical analysis of the network extracted from 2-D images. In this study, we have utilized 25 volumetric images of rocks in order to propose two mathematical formulas. These formulas aim to approximate the average and standard deviation of coordination number in 3-D pore networks. Then, the formulas are applied for five independent test samples to evaluate the reliability. Finally, pore network flow modeling is used to find the error of absolute permeability prediction using estimated and measured coordination numbers. Results show that the 2-D images are considerably informative about the 3-D network of the rocks and can be utilized to approximate the 3-D connectivity of the porous spaces with determination coefficient of about 0.85 that seems to be acceptable considering the variety of the studied samples.

  13. Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB application

    NASA Astrophysics Data System (ADS)

    Neyamadpour, Ahmad; Taib, Samsudin; Wan Abdullah, W. A. T.

    2009-11-01

    MATLAB is a high-level matrix/array language with control flow statements and functions. MATLAB has several useful toolboxes to solve complex problems in various fields of science, such as geophysics. In geophysics, the inversion of 2D DC resistivity imaging data is complex due to its non-linearity, especially for high resistivity contrast regions. In this paper, we investigate the applicability of MATLAB to design, train and test a newly developed artificial neural network in inverting 2D DC resistivity imaging data. We used resilient propagation to train the network. The model used to produce synthetic data is a homogeneous medium of 100 Ω m resistivity with an embedded anomalous body of 1000 Ω m. The location of the anomalous body was moved to different positions within the homogeneous model mesh elements. The synthetic data were generated using a finite element forward modeling code by means of the RES2DMOD. The network was trained using 21 datasets and tested on another 16 synthetic datasets, as well as on real field data. In field data acquisition, the cable covers 120 m between the first and the last take-out, with a 3 m x-spacing. Three different electrode spacings were measured, which gave a dataset of 330 data points. The interpreted result shows that the trained network was able to invert 2D electrical resistivity imaging data obtained by a Wenner-Schlumberger configuration rapidly and accurately.

  14. Extracting insight from noisy cellular networks.

    PubMed

    Landry, Christian R; Levy, Emmanuel D; Abd Rabbo, Diala; Tarassov, Kirill; Michnick, Stephen W

    2013-11-21

    Network biologists attempt to extract meaningful relationships among genes or their products from very noisy data. We argue that what we categorize as noisy data may sometimes reflect noisy biology and therefore may shield a hidden meaning about how networks evolve and how matter is organized in the cell. We present practical solutions, based on existing evolutionary and biophysical concepts, through which our understanding of cell biology can be enormously enriched.

  15. Energy efficiency analysis of relay-assisted cellular networks

    NASA Astrophysics Data System (ADS)

    Yu, Huan; Li, Yunzhou; Kountouris, Marios; Xu, Xibin; Wang, Jing

    2014-12-01

    To meet the demand for higher throughput, improved coverage and enhanced reliability, future wireless cellular networks face significant technical challenges. One promising solution is to place relay stations between transmitters and receivers in the cellular network. Meanwhile, as energy consumption reduction has been an important concern for the wireless industry, energy-efficient communications is of prime interest for future networks. In this paper, we study whether and how relays can improve the energy efficiency of cellular networks. Specifically, the energy efficiency of relay-assisted cellular networks is analyzed using tools of stochastic geometry. We first derive the coverage probability for the macro base station (MBS) to user (UE), the MBS to relay station (RS), and the RS to UE links, and then we model the power consumption at the MBS and RS. Based on the analytical model and expressions, the energy efficiency of relay-assisted cellular networks is then evaluated and is shown to be strictly quasi-concave on the transmit power for MBS to UE link or the RS to UE link. Numerical results show that the energy efficiency first improves while it hits a ceiling as the MBS density increases.

  16. 2D warp-and-woof interwoven networks constructed by helical chains with different chirality.

    PubMed

    Feng, Yuhua; Guo, Yang; OuYang, Yan; Liu, Zhanquan; Liao, Daizheng; Cheng, Peng; Yan, Shiping; Jiang, Zonghui

    2007-09-21

    Two unprecedented 2D entangled layers of warp-and-woof threads interwoven by left- and right-handed helical chains, {[Mn(salen)Au(CN)2]4(H2O)}n (salen = N,N'-ethylenebis(salicylideneaminato)) and {Mn(acacen)Ag(CN)2}n (acacen = N,N'-ethylenebis(acetylacetonylideneiminate)) 2, have been synthesized and characterized.

  17. A New Family of 2-D Optical Orthogonal Codes and Analysis of Its Performance in Optical CDMA Access Networks

    NASA Astrophysics Data System (ADS)

    Shurong, Sun; Yin, Hongxi; Wang, Ziyu; Xu, Anshi

    2006-04-01

    A new family of two-dimensional optical orthogonal code (2-D OOC), one-coincidence frequency hop code (OCFHC)/OOC, which employs OCFHC and OOC as wavelengthhopping and time-spreading patterns, respectively, is proposed in this paper. In contrary to previously constructed 2-D OOCs, OCFHC/OOC provides more choices on the number of available wavelengths and its cardinality achieves the upper bound in theory without sacrificing good auto-and-cross correlation properties, i.e., the correlation properties of the code is still ideal. Meanwhile, we utilize a new method, called effective normalized throughput, to compare the performance of diverse codes applicable to optical code division multiple access (OCDMA) systems besides conventional measure bit error rate, and the results indicate that our code performs better than obtained OCDMA codes and is truly applicable to OCDMA networks as multiaccess codes and will greatly facilitate the implementation of OCDMA access networks.

  18. Extrapolation of fractal dimensions of natural fracture networks in dolomites from 1-D to 2-D environment

    NASA Astrophysics Data System (ADS)

    Verbovšek, T.

    2009-04-01

    Fractal dimensions of fracture networks (D) are usually determined from 2-D objects, like the digitized fracture traces in outcrops. Sometimes, extrapolations to higher dimensions are required if the measurements (for example fracture traces in the boreholes or in the scanlines) are performed in 1-D environment (D1-D) and are later upscaled to higher dimensions (D2-D). For isotropic fractals this relation should be straight-forward according to the theory: D2-D = D1-D +1, as the intersection of a 2-D fractal with a plane results in a fractal with D1-D equal to D2-D minus one. Some authors have questioned this relation and proposed different empirical relationships. Still, there exist very few field studies of natural fracture networks to support or test such a relationship. The study was therefore focused on the analysis of 23 natural fracture networks in Triassic dolomites in Slovenia. The traces of these fractures were analyzed separately in both 1-D and 2-D environments, and relationships between the obtained fractal dimensions were determined. For 2-D data, the digitized images of fracture traces in 2048x2048 pixel resolution were analyzed by the box-counting method, considering truncation and censoring effects (the 'cut-off' method, using only the valid data right of the cut-off points) and also by considering the complete data range interval (the 'full' method). These values were consequently compared to 1-D values. Those were obtained by dissecting images in both x- and y-directions into 2048 smaller linear images of 1-pixel width, simulating the intersection with a plane. Such line images were then examined by the fracture line-counting method, a 1-D equivalent of the box-counting technique. Results show that the values of all fractal dimensions, regardless of the different fracture networks or the method used, lie in a very narrow data range, and the standard deviations are very small (up to 0.03). The small range can be attributed to a similar fracturing

  19. Optimal Prediction by Cellular Signaling Networks

    NASA Astrophysics Data System (ADS)

    Becker, Nils B.; Mugler, Andrew; ten Wolde, Pieter Rein

    2015-12-01

    Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

  20. Joint Mode Selection and Resource Allocation for Cellular Controlled Short-Range Communication in OFDMA Networks

    NASA Astrophysics Data System (ADS)

    Deng, Hui; Tao, Xiaoming; Ge, Ning; Lu, Jianhua

    This letter studies cellular controlled short-range communication in OFDMA networks. The network needs to decide when to allow direct communication between a closely located device-to-device (D2D) pair instead of conveying data from one device to the other via the base station and when not to, in addition to subchannel and power allocation. Our goal is to maximize the total network throughput while guaranteeing the rate requirements of all users. For that purpose, we formulate an optimization problem subject to subchannel and power constraints. A scheme which combines a joint mode selection and subchannel allocation algorithm based on equal power allocation with a power reallocation scheme is proposed. Simulation results show that our proposed scheme can improve the network throughput and outage probability compared with other schemes.

  1. Personal communication in traditional cellular networks

    NASA Astrophysics Data System (ADS)

    Neuer, Ellwood I.

    1996-01-01

    The purpose of this paper is to describe the flow of calls through the mobile network as it applies to the operation of Basic and Enhanced Services. Included in the discussion is the overall network layout, the physical connections between the network entities, and the signaling protocols which allow the entities to be integrated. The specific functionality of the applications and services are not detailed as the specific implementation varies from vendor to vendor and from service provider to service provider. The Enhanced Services Platform is installed in a service providers network in order to offer mobile subscribers services and applications which would otherwise not be available. The service providers' objective is to increase revenue/subscriber, increase subscriber loyalty/decrease churn, and build competitive advantages through differentiation. The services provided on the Enhanced Services platform can be viewed as either Basic or Enhanced. For the purpose of this paper, Basic Services refers to Numeric Paging, Call Answering, and Voice Messaging while Enhanced Services refers to FAX Messaging, One Number Service, Voice Dialing and other Voice Recognition applications, Information Services including FAX on Demand, and Automated Call Routing.

  2. Interferometric noise characterization of a 2-D time-spreading wavelength-hopping OCDMA network using FBG encoding and decoding

    NASA Astrophysics Data System (ADS)

    Michie, Craig; Andonovic, Ivan; Atkinson, R.; Deng, Yanhua; Szefer, Jakub; Bres, Camille-Sophie; Huang, Yue Kai; Glesk, Ivan; Prucnal, Paul; Sasaki, Kensuke; Gupta, Gyaneshwar

    2007-06-01

    The results of a range of experimental characterization exercises of interferometric noise for the case of a representative 2-D time-spreading wavelength-hopping optical code family are presented. Interferometric noise is evaluated at a data rate of 2.5 Gbits/s within an OCDMA network emulation test bed established utilizing fiber Bragg grating encoders/decoders. The results demonstrate that this form of noise introduces significant system power penalties and must be taken into consideration in any OCDMA network designs and implementations.

  3. Design of multi-functional 2D open-shell organic networks with mechanically controllable properties.

    PubMed

    Alcón, Isaac; Reta, Daniel; Moreira, Iberio de P R; Bromley, Stefan T

    2017-02-01

    Triarylmethyls (TAMs) are prominent highly attractive open shell organic molecular building blocks for materials science, having been used in breakthrough syntheses of organic magnetic polymers and metal organic frameworks. With their radical π-conjugated nature and a proven capacity to possess high stability via suitable chemical design, TAMs display a variety of desirable characteristics which can be exploited for a wide range of applications. Due to their particular molecular and electronic structure, the spin localization in TAMs almost entirely depends on the dihedral angles of their three aryl rings with respect to the central methyl carbon atom plane, which opens up the possibility of controlling their fundamental properties by twisting the three aryl rings. Aryl ring twist angles can be tuned to a single value by specific chemical functionalisation but controlling them by external means in organic materials or devices represents a challenging task which has not yet been experimentally achieved. Herein, through rational chemical design we propose two 2D covalent organic frameworks (2D-COFs) based on specific TAM building blocks. By employing ab initio computational modeling we demonstrate that it is possible to externally manipulate the aryl ring twist angles in these 2D-linked TAM frameworks by external mechanical means. Furthermore, we show this structural manipulation allows for finely tuning the most important characteristics of these materials such as spin localization, optical electronic transitions and magnetic interactions. Due to the enormous technological potential offered by this new class of material and the fact that our work is guided by real advances in organic materials synthesis, we believe that our predictions will inspire the experimental realization of radical-2D-COFs with externally controllable characteristics.

  4. An entropic characterization of protein interaction networks and cellular robustness.

    PubMed

    Manke, Thomas; Demetrius, Lloyd; Vingron, Martin

    2006-12-22

    The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans. Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.

  5. Channel modeling for fifth generation cellular networks and wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Torabi, Amir

    In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance.

  6. Widrow-cellular neural network and optoelectronic implementation

    NASA Astrophysics Data System (ADS)

    Bal, Abdullah

    A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.

  7. Cellular and network mechanisms of electrographic seizures

    PubMed Central

    Bazhenov, Maxim; Timofeev, Igor; Fröhlich, Flavio; Sejnowski, Terrence J.

    2008-01-01

    Epileptic seizures constitute a complex multiscale phenomenon that is characterized by synchronized hyperexcitation of neurons in neuronal networks. Recent progress in understanding pathological seizure dynamics provides crucial insights into underlying mechanisms and possible new avenues for the development of novel treatment modalities. Here we review some recent work that combines in vivo experiments and computational modeling to unravel the pathophysiology of seizures of cortical origin. We particularly focus on how activity-dependent changes in extracellular potassium concentration affects the intrinsic dynamics of neurons involved in cortical seizures characterized by spike/wave complexes and fast runs. PMID:19190736

  8. TRANSWESD: inferring cellular networks with transitive reduction

    PubMed Central

    Klamt, Steffen; Flassig, Robert J.; Sundmacher, Kai

    2010-01-01

    Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic. Results: We present TRANSWESD, an elaborated variant of TRANSitive reduction for WEighted Signed Digraphs that overcomes conceptual problems of existing versions. Major changes and improvements concern: (i) new statistical approaches for generating high-quality perturbation graphs from systematic perturbation experiments; (ii) the use of edge weights (association strengths) for recognizing true redundant structures; (iii) causal interpretation of cycles; (iv) relaxed definition of transitive reduction; and (v) approximation algorithms for large networks. Using standardized benchmark tests, we demonstrate that our method outperforms existing variants of transitive reduction and is, despite its conceptual simplicity, highly competitive with other reverse engineering methods. Contact: klamt@mpi-magdeburg.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20605927

  9. Optimising base station location for UMTS cellular networks

    NASA Astrophysics Data System (ADS)

    Kalata, G.; Pozniak-Koszalka, I.; Koszalka, L.; Kasprzak, A.

    2014-12-01

    Rapid development of universal mobile telecommunication systems put demands on tools for assisting planning of cellular network infrastructure. The tools need to focus on critical issues in modern cellular networks and techniques used for previous generation system no longer serve useful. In this paper, an algorithm based on Branch & Bound approach is proposed for solving base station location problem, covering interference levels, traffic demands and power control mechanism. The efficiency of the algorithm is evaluated with respect to existing approaches for solving this problem - using the designed and implemented experimentation system.

  10. Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

    PubMed Central

    Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

    2013-01-01

    The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more

  11. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    PubMed

    Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

    2013-01-01

    The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more

  12. NEDD9 stabilizes focal adhesions, increases binding to the extra-cellular matrix and differentially effects 2D versus 3D cell migration.

    PubMed

    Zhong, Jessie; Baquiran, Jaime B; Bonakdar, Navid; Lees, Justin; Ching, Yu Wooi; Pugacheva, Elena; Fabry, Ben; O'Neill, Geraldine M

    2012-01-01

    The speed of cell migration on 2-dimensional (2D) surfaces is determined by the rate of assembly and disassembly of clustered integrin receptors known as focal adhesions. Different modes of cell migration that have been described in 3D environments are distinguished by their dependence on integrin-mediated interactions with the extra-cellular matrix. In particular, the mesenchymal invasion mode is the most dependent on focal adhesion dynamics. The focal adhesion protein NEDD9 is a key signalling intermediary in mesenchymal cell migration, however whether NEDD9 plays a role in regulating focal adhesion dynamics has not previously been reported. As NEDD9 effects on 2D migration speed appear to depend on the cell type examined, in the present study we have used mouse embryo fibroblasts (MEFs) from mice in which the NEDD9 gene has been depleted (NEDD9 -/- MEFs). This allows comparison with effects of other focal adhesion proteins that have previously been demonstrated using MEFs. We show that focal adhesion disassembly rates are increased in the absence of NEDD9 expression and this is correlated with increased paxillin phosphorylation at focal adhesions. NEDD9-/- MEFs have increased rates of migration on 2D surfaces, but conversely, migration of these cells is significantly reduced in 3D collagen gels. Importantly we show that myosin light chain kinase is activated in 3D in the absence of NEDD9 and is conversely inhibited in 2D cultures. Measurement of adhesion strength reveals that NEDD9-/- MEFs have decreased adhesion to fibronectin, despite upregulated α5β1 fibronectin receptor expression. We find that β1 integrin activation is significantly suppressed in the NEDD9-/-, suggesting that in the absence of NEDD9 there is decreased integrin receptor activation. Collectively our data suggest that NEDD9 may promote 3D cell migration by slowing focal adhesion disassembly, promoting integrin receptor activation and increasing adhesion force to the ECM.

  13. Designed Proteins To Modulate Cellular Networks

    PubMed Central

    Cortajarena, Aitziber L.; Liu, Tina Y.; Hochstrasser, Mark; Regan, Lynne

    2012-01-01

    A major challenge of protein design is to create useful new proteins that interact specifically with biological targets in living cells. Such binding modules have many potential applications, including the targeted perturbation of protein networks. As a general approach to create such modules, we designed a library with approximately 109 different binding specificities based on a small 3-tetratricopeptide repeat (TPR) motif framework. We employed a novel strategy, based on split GFP reassembly, to screen the library for modules with the desired binding specificity. Using this approach, we identified modules that bind tightly and specifically to Dss1, a small human protein that interacts with the tumor suppressor protein BRCA2. We showed that these modules also bind the yeast homologue of Dss1, Sem1. Furthermore, we demonstrated that these modules inhibit Sem1 activity in yeast. This strategy will be generally applicable to make novel genetically encoded tools for systems/synthetic biology applications. PMID:20020775

  14. Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

    PubMed

    Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

    2016-10-01

    This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

  15. The community structure of human cellular signaling network.

    PubMed

    Diao, Yuanbo; Li, Menglong; Feng, Zinan; Yin, Jiajian; Pan, Yi

    2007-08-21

    Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

  16. Tuning the resonance properties of 2D carbon nanotube networks towards a mechanical resonator

    NASA Astrophysics Data System (ADS)

    Zhan, Haifei; Zhang, Guiyong; Zhang, Baocheng; Bell, John M.; Gu, Yuantong

    2015-08-01

    The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purposes. Based on large-scale molecular dynamics simulations, we have assessed the applicability of a new class of carbon nanomaterials for nanoresonator usage, i.e. the single-wall carbon nanotube (SWNT) network. It is found that SWNT networks inherit excellent mechanical properties from the constituent SWNTs, possessing a high natural frequency. However, although a high quality factor is suggested from the simulation results, it is hard to obtain an unambiguous Q-factor due to the existence of vibration modes in addition to the dominant mode. The nonlinearities resulting from these extra vibration modes are found to exist uniformly under various testing conditions including different initial actuations and temperatures. Further testing shows that these modes can be effectively suppressed through the introduction of axial strain, leading to an extremely high quality factor in the order of 109 estimated from the SWNT network with 2% tensile strain. Additional studies indicate that the carbon rings connecting the SWNTs can also be used to alter the vibrational properties of the resulting network. This study suggests that the SWNT network can be a good candidate for applications as nanoresonators.

  17. A 2D zinc-organic network being easily exfoliated into isolated sheets

    NASA Astrophysics Data System (ADS)

    Yu, Guihong; Li, Ruiqing; Leng, Zhihua; Gan, Shucai

    2016-08-01

    A metal-organic aggregate, namely {Zn2Cl2(BBC)}n (BBC = 4,4‧,4‧‧-(benzene-1,3,5-triyl-tris(benzene-4,1-diyl))tribenzoate) was obtained by solvothermal synthesis. Its structure is featured with the Zn2(COO)3 paddle-wheels with two chloride anions on axial positions and hexagonal pores in the layers. The exclusion of water in the precursor and the solvent plays a crucial role in the formation of target compound. This compound can be easily dissolved in alkaline solution and exfoliated into isolated sheets, which shows a novel way for the preparation of 2D materials.

  18. 2D wireless sensor network deployment based on Centroidal Voronoi Tessellation

    NASA Astrophysics Data System (ADS)

    Iliodromitis, Athanasios; Pantazis, George; Vescoukis, Vasileios

    2017-06-01

    In recent years, Wireless Sensor Networks (WSNs) have rapidly evolved and now comprise a powerful tool in monitoring and observation of the natural environment, among other fields. The use of WSNs is critical in early warning systems, which are of high importance today. In fact, WSNs are adopted more and more in various applications, e.g. for fire or deformation detection. The optimum deployment of sensors is a multi-dimensional problem, which has two main components; network and positioning approach. Although lots of work has dealt with the issue, most of it emphasizes on mere network approach (communication, energy consumption) and not on the topography (positioning) of the sensors in achieving ideal geometry. In some cases, it is hard or even impossible to achieve perfect geometry in nodes' deployment. The ideal and desirable scenario of nodes arranged in square or hexagonal grid would raise extremely the cost of the network, especially in unfriendly or hostile environments. In such environments the positions of the sensors have to be chosen among a list of possible points, which in most cases are randomly distributed. This constraint has to be taken under consideration during the WSN planning. Full geographical coverage is in some applications of the same, if not of greater, importance than the network coverage. Cost is a crucial factor at network planning and given that resources are often limited, what matters, is to cover the whole area with the minimum number of sensors. This paper suggests a deployment method for nodes, in large scale and high density WSNs, based on Centroidal Voronoi Tessellation (CVT). It approximates the solution through the geometry of the random points and proposes a deployment plan, for the given characteristics of the study area, in order to achieve a deployment as near as possible to the ideal one.

  19. From molecules to cellular networks: past and future outlook

    NASA Astrophysics Data System (ADS)

    Fang, Xiaona; Wang, Jin

    2017-02-01

    Cellular networks have been the focus of studies in modern systems biology. They are crucial in understanding cell functions and related diseases. We review some past progress in both the theory and experiments, and we also provide several future perspectives for the field.

  20. From molecules to cellular networks: past and future outlook.

    PubMed

    Fang, Xiaona; Wang, Jin

    2017-02-16

    Cellular networks have been the focus of studies in modern systems biology. They are crucial in understanding cell functions and related diseases. We review some past progress in both the theory and experiments, and we also provide several future perspectives for the field.

  1. Millimeter-Wave Evolution for 5G Cellular Networks

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Kei; Tran, Gia Khanh; Shimodaira, Hidekazu; Nanba, Shinobu; Sakurai, Toshiaki; Takinami, Koji; Siaud, Isabelle; Strinati, Emilio Calvanese; Capone, Antonio; Karls, Ingolf; Arefi, Reza; Haustein, Thomas

    Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5 GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.

  2. A competitive layer model for cellular neural networks.

    PubMed

    Zhou, Wei; Zurada, Jacek M

    2012-09-01

    This paper discusses a Competitive Layer Model (CLM) for a class of recurrent Cellular Neural Networks (CNNs) from continuous-time type to discrete-time type. The objective of the CLM is to partition a set of input features into salient groups. The complete convergence of such networks in continuous-time type has been discussed first. We give a necessary condition, and a necessary and sufficient condition, which allow the CLM performance existence in our networks. We also discuss the properties of such networks of discrete-time type, and propose a novel CLM iteration method. Such method shows similar performance and storage allocation but faster convergence compared with the previous CLM iteration method (Wersing, Steil, & Ritter, 2001a). Especially for a large scale network with many features and layers, it can significantly reduce the computing time. Examples and simulation results are used to illustrate the developed theory, the comparison between two CLM iteration methods, and the application in image segmentation.

  3. Topological defect formation in 1D and 2D spin chains realized by network of optical parametric oscillators

    NASA Astrophysics Data System (ADS)

    Hamerly, Ryan; Inaba, Kensuke; Inagaki, Takahiro; Takesue, Hiroki; Yamamoto, Yoshihisa; Mabuchi, Hideo

    2016-09-01

    A network of optical parametric oscillators (OPOs) is used to simulate classical Ising and XY spin chains. The collective nonlinear dynamics of this network, driven by quantum noise rather than thermal fluctuations, seeks out the Ising/XY ground state as the system transitions from below to above the lasing threshold. We study the behavior of this “Ising machine” for three canonical problems: a 1D ferromagnetic spin chain, a 2D square lattice and problems where next-nearest-neighbor couplings give rise to frustration. If the pump turn-on time is finite, topological defects form (domain walls for the Ising model, winding number and vortices for XY) and their density can be predicted from a numerical model involving a linear “growth stage” and a nonlinear “saturation stage”. These predictions are compared against recent data for a 10,000-spin 1D Ising machine.

  4. Ferrimagnetism in 2D networks of porphyrin-X and -XO (X=Sc,...,Zn) with acetylene bridges

    NASA Astrophysics Data System (ADS)

    Wierzbowska, Małgorzata; Sobolewski, Andrzej L.

    2016-03-01

    Magnetism in 2D networks of the acetylene-bridged transition metal porphyrins M(P)-2(C-C)-2 (denoted P-TM), and oxo-TM-porphyrins OM(P)-2(C-C)-2 (denoted P-TMO), is studied with the density functional theory (DFT) and the self-interaction corrected pseudopotential scheme (pSIC). Addition of oxygen lowers magnetism of P-TMO with respect to the corresponding P-TM for most of the first-half 3d-row TMs. In contrast, binding O with the second-half 3d-row TMs or Sc increases the magnetic moments. Ferrimagnetism is found for the porphyrin networks with the TMs from V to Co and also for these cases with oxygen. This is a long-range effect of the delocalized spin-polarization, extended even to the acetylene bridges.

  5. Selfish cellular networks and the evolution of complex organisms.

    PubMed

    Kourilsky, Philippe

    2012-03-01

    Human gametogenesis takes years and involves many cellular divisions, particularly in males. Consequently, gametogenesis provides the opportunity to acquire multiple de novo mutations. A significant portion of these is likely to impact the cellular networks linking genes, proteins, RNA and metabolites, which constitute the functional units of cells. A wealth of literature shows that these individual cellular networks are complex, robust and evolvable. To some extent, they are able to monitor their own performance, and display sufficient autonomy to be termed "selfish". Their robustness is linked to quality control mechanisms which are embedded in and act upon the individual networks, thereby providing a basis for selection during gametogenesis. These selective processes are equally likely to affect cellular functions that are not gamete-specific, and the evolution of the most complex organisms, including man, is therefore likely to occur via two pathways: essential housekeeping functions would be regulated and evolve during gametogenesis within the parents before being transmitted to their progeny, while classical selection would operate on other traits of the organisms that shape their fitness with respect to the environment. Copyright © 2012 Académie des sciences. Published by Elsevier SAS. All rights reserved.

  6. Artificial neural networks and model-based recognition of 3-D objects from 2-D images

    NASA Astrophysics Data System (ADS)

    Chao, Chih-Ho; Dhawan, Atam P.

    1992-09-01

    A computer vision system is developed for 3-D object recognition using artificial neural networks and a knowledge-based top-down feedback analysis system. This computer vision system can adequately analyze an incomplete edge map provided by a low-level processor for 3-D representation and recognition using key features. The key features are selected using a priority assignment and then used in an artificial neural network for matching with model key features. The result of such matching is utilized in generating the model-driven top-down feedback analysis. From the incomplete edge map we try to pick a candidate pattern utilizing the key feature priority assignment. The highest priority is given for the most connected node and associated features. The features are space invariant structures and sets of orientation for edge primitives. These features are now mapped into real numbers. A Hopfield network is then applied with two levels of matching to reduce the search time. The first match is to choose the class of possible model, the second match is then to find the model closest to the data patterns. This model is then rotated in 3-D to find the best match with the incomplete edge patterns and to provide the additional features in 3-D. In the case of multiple objects, a dynamically interconnected search strategy is designed to recognize objects using one pattern at a time. This strategy is also useful in recognizing occluded objects. The experimental results presented show the capability and effectiveness of this system.

  7. Probing Magnetism in 2D Molecular Networks after in Situ Metalation by Transition Metal Atoms.

    PubMed

    Schouteden, K; Ivanova, Ts; Li, Z; Iancu, V; Janssens, E; Van Haesendonck, C

    2015-03-19

    Metalated molecules are the ideal building blocks for the bottom-up fabrication of, e.g., two-dimensional arrays of magnetic particles for spintronics applications. Compared to chemical synthesis, metalation after network formation by an atom beam can yield a higher degree of control and flexibility and allows for mixing of different types of magnetic atoms. We report on successful metalation of tetrapyridyl-porphyrins (TPyP) by Co and Cr atoms, as demonstrated by scanning tunneling microscopy experiments. For the metalation, large periodic networks formed by the TPyP molecules on a Ag(111) substrate are exposed in situ to an atom beam. Voltage-induced dehydrogenation experiments support the conclusion that the porphyrin macrocycle of the TPyP molecule incorporates one transition metal atom. The newly synthesized Co-TPyP and Cr-TPyP complexes exhibit striking differences in their electronic behavior, leading to a magnetic character for Cr-TPyP only as evidenced by Kondo resonance measurements.

  8. The role of actin networks in cellular mechanosensing

    NASA Astrophysics Data System (ADS)

    Azatov, Mikheil

    Physical processes play an important role in many biological phenomena, such as wound healing, organ development, and tumor metastasis. During these processes, cells constantly interact with and adapt to their environment by exerting forces to mechanically probe the features of their surroundings and generating appropriate biochemical responses. The mechanisms underlying how cells sense the physical properties of their environment are not well understood. In this thesis, I present my studies to investigate cellular responses to the stiffness and topography of the environment. In order to sense the physical properties of their environment, cells dynamically reorganize the structure of their actin cytoskeleton, a dynamic network of biopolymers, altering the shape and spatial distribution of protein assemblies. Several observations suggest that proteins that crosslink actin filaments may play an important role in cellular mechanosensitivity. Palladin is an actin-crosslinking protein that is found in the lamellar actin network, stress fibers and focal adhesions, cellular structures that are critical for mechanosensing of the physical environment. By virtue of its close interactions with these structures in the cell, palladin may play an important role in cell mechanics. However, the role of actin crosslinkers in general, and palladin in particular, in cellular force generation and mechanosensing is not well known. I have investigated the role of palladin in regulating the plasticity of the actin cytoskeleton and cellular force generation in response to alterations in substrate stiffness. I have shown that the expression levels of palladin modulate the forces exerted by cells and their ability to sense substrate stiffness. Perturbation experiments also suggest that palladin levels in cells altered myosin motor activity. These results suggest that the actin crosslinkers, such as palladin, and myosin motors coordinate for optimal cell function and to prevent aberrant

  9. Fracture network evaluation program (FraNEP): A software for analyzing 2D fracture trace-line maps

    NASA Astrophysics Data System (ADS)

    Zeeb, Conny; Gomez-Rivas, Enrique; Bons, Paul D.; Virgo, Simon; Blum, Philipp

    2013-10-01

    Fractures, such as joints, faults and veins, strongly influence the transport of fluids through rocks by either enhancing or inhibiting flow. Techniques used for the automatic detection of lineaments from satellite images and aerial photographs, LIDAR technologies and borehole televiewers significantly enhanced data acquisition. The analysis of such data is often performed manually or with different analysis software. Here we present a novel program for the analysis of 2D fracture networks called FraNEP (Fracture Network Evaluation Program). The program was developed using Visual Basic for Applications in Microsoft Excel™ and combines features from different existing software and characterization techniques. The main novelty of FraNEP is the possibility to analyse trace-line maps of fracture networks applying the (1) scanline sampling, (2) window sampling or (3) circular scanline and window method, without the need of switching programs. Additionally, binning problems are avoided by using cumulative distributions, rather than probability density functions. FraNEP is a time-efficient tool for the characterisation of fracture network parameters, such as density, intensity and mean length. Furthermore, fracture strikes can be visualized using rose diagrams and a fitting routine evaluates the distribution of fracture lengths. As an example of its application, we use FraNEP to analyse a case study of lineament data from a satellite image of the Oman Mountains.

  10. Temperature dependence of the partially localized state in a 2D molecular nanoporous network

    NASA Astrophysics Data System (ADS)

    Piquero-Zulaica, Ignacio; Nowakowska, Sylwia; Ortega, J. Enrique; Stöhr, Meike; Gade, Lutz H.; Jung, Thomas A.; Lobo-Checa, Jorge

    2017-01-01

    Two-dimensional organic and metal-organic nanoporous networks can scatter surface electrons, leading to their partial localization. Such quantum states are related to intrinsic surface states of the substrate material. We further corroborate this relation by studying the thermally induced energy shifts of the electronic band stemming from coupled quantum states hosted in a metal-organic array formed by a perylene derivative on Cu(111). We observe by angle-resolved photoemission spectroscopy (ARPES), that both, the Shockley and the partially localized states, shift by the same amount to higher binding energies upon decreasing the sample temperature, providing evidence of their common origin. Our experimental approach and results further support the use of surface states for modelling these systems, which are expected to provide new insight into the physics concerning partially confined electronic states: scattering processes, potential barrier strengths, excited state lifetimes or the influence of guest molecules.

  11. Network Medicine: From Cellular Networks to the Human Diseasome

    NASA Astrophysics Data System (ADS)

    Barabasi, Albert-Laszlo

    2014-03-01

    Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The tools of network science offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction not only enrich our understanding of complex systems, but are also essential to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases.

  12. Cellular-based modeling of oscillatory dynamics in brain networks.

    PubMed

    Skinner, Frances K

    2012-08-01

    Oscillatory, population activities have long been known to occur in our brains during different behavioral states. We know that many different cell types exist and that they contribute in distinct ways to the generation of these activities. I review recent papers that involve cellular-based models of brain networks, most of which include theta, gamma and sharp wave-ripple activities. To help organize the modeling work, I present it from a perspective of three different types of cellular-based modeling: 'Generic', 'Biophysical' and 'Linking'. Cellular-based modeling is taken to encompass the four features of experiment, model development, theory/analyses, and model usage/computation. The three modeling types are shown to include these features and interactions in different ways. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Tensegrity II. How structural networks influence cellular information processing networks

    NASA Technical Reports Server (NTRS)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  14. Tensegrity II. How structural networks influence cellular information processing networks

    NASA Technical Reports Server (NTRS)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  15. Analysing Dynamical Behavior of Cellular Networks via Stochastic Bifurcations

    PubMed Central

    Zakharova, Anna; Kurths, Jürgen; Vadivasova, Tatyana; Koseska, Aneta

    2011-01-01

    The dynamical structure of genetic networks determines the occurrence of various biological mechanisms, such as cellular differentiation. However, the question of how cellular diversity evolves in relation to the inherent stochasticity and intercellular communication remains still to be understood. Here, we define a concept of stochastic bifurcations suitable to investigate the dynamical structure of genetic networks, and show that under stochastic influence, the expression of given proteins of interest is defined via the probability distribution of the phase variable, representing one of the genes constituting the system. Moreover, we show that under changing stochastic conditions, the probabilities of expressing certain concentration values are different, leading to different functionality of the cells, and thus to differentiation of the cells in the various types. PMID:21647432

  16. Cellular Automata with network incubation in information technology diffusion

    NASA Astrophysics Data System (ADS)

    Guseo, Renato; Guidolin, Mariangela

    2010-06-01

    Innovation diffusion of network goods determines direct network externalities that depress sales for long periods and delay full benefits. We model this effect through a multiplicative dynamic market potential driven by a latent individual threshold embedded in a special Cellular Automata representation. The corresponding mean field approximation of its aggregate version is a Riccati equation with a closed form solution. This allows the detection of a change-point time separating an incubation period from a subsequent take-off due to a collective threshold (critical mass). Weighted nonlinear least squares are the main inferential methodology. An application is analysed with reference to USA fax machine diffusion.

  17. Nanotribology Results Show that DNA Forms a Mechanically Resistant 2D Network in Metaphase Chromatin Plates

    PubMed Central

    Gállego, Isaac; Oncins, Gerard; Sisquella, Xavier; Fernàndez-Busquets, Xavier; Daban, Joan-Ramon

    2010-01-01

    In a previous study, we found that metaphase chromosomes are formed by thin plates, and here we have applied atomic force microscopy (AFM) and friction force measurements at the nanoscale (nanotribology) to analyze the properties of these planar structures in aqueous media at room temperature. Our results show that high concentrations of NaCl and EDTA and extensive digestion with protease and nuclease enzymes cause plate denaturation. Nanotribology studies show that native plates under structuring conditions (5 mM Mg2+) have a relatively high friction coefficient (μ ≈ 0.3), which is markedly reduced when high concentrations of NaCl or EDTA are added (μ ≈ 0.1). This lubricant effect can be interpreted considering the electrostatic repulsion between DNA phosphate groups and the AFM tip. Protease digestion increases the friction coefficient (μ ≈ 0.5), but the highest friction is observed when DNA is cleaved by micrococcal nuclease (μ ≈ 0.9), indicating that DNA is the main structural element of plates. Whereas nuclease-digested plates are irreversibly damaged after the friction measurement, native plates can absorb kinetic energy from the AFM tip without suffering any damage. These results suggest that plates are formed by a flexible and mechanically resistant two-dimensional network which allows the safe storage of DNA during mitosis. PMID:21156137

  18. Leader neurons in population bursts of 2D living neural networks

    NASA Astrophysics Data System (ADS)

    Eckmann, J.-P.; Jacobi, Shimshon; Marom, Shimon; Moses, Elisha; Zbinden, Cyrille

    2008-01-01

    Eytan and Marom (2006 J. Neurosci. 26 8465-76) recently showed that the spontaneous bursting activity of rat neuron cultures includes 'first-to-fire' cells that consistently fire earlier than others. Here, we analyze the behavior of these neurons in long-term recordings of spontaneous activity of rat hippocampal and rat cortical neuron cultures from three different laboratories. We identify precursor events that may either subside ('aborted bursts') or can lead to a full-blown burst ('pre-bursts'). We find that the activation in the pre-burst typically has a first neuron ('leader'), followed by a localized response in its neighborhood. Locality is diminished in the bursts themselves. The long-term dynamics of the leaders is relatively robust, evolving with a half-life of 23-34 h. Stimulation of the culture alters the leader distribution, but the distribution stabilizes within about 1 h. We show that the leaders carry information about the identity of the burst, as measured by the signature of the number of spikes per neuron in a burst. The number of spikes from leaders in the first few spikes of a precursor event is furthermore shown to be predictive with regard to the transition into a burst (pre-burst versus aborted burst). We conclude that the leaders play a role in the development of the bursts and conjecture that they are part of an underlying sub-network that is excited first and then acts as a nucleation center for the burst.

  19. Digital implementation of shunting-inhibitory cellular neural network

    NASA Astrophysics Data System (ADS)

    Hammadou, Tarik; Bouzerdoum, Abdesselam; Bermak, Amine

    2000-05-01

    Shunting inhibition is a model of early visual processing which can provide contrast and edge enhancement, and dynamic range compression. An architecture of digital Shunting Inhibitory Cellular Neural Network for real time image processing is presented. The proposed architecture is intended to be used in a complete vision system for edge detection and image enhancement. The present hardware architecture, is modeled and simulated in VHDL. Simulation results show the functional validity of the proposed architecture.

  20. Estimating nonlinear interdependences in dynamical systems using cellular nonlinear networks

    NASA Astrophysics Data System (ADS)

    Krug, Dieter; Osterhage, Hannes; Elger, Christian E.; Lehnertz, Klaus

    2007-10-01

    We propose a method for estimating nonlinear interdependences between time series using cellular nonlinear networks. Our approach is based on the nonlinear dynamics of interacting nonlinear elements. We apply it to time series of coupled nonlinear model systems and to electroencephalographic time series from an epilepsy patient, and we show that an accurate approximation of symmetric and asymmetric realizations of a nonlinear interdependence measure can be achieved, thus allowing one to detect the strength and direction of couplings.

  1. Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks.

    PubMed

    Nuñez de Villavicencio-Diaz, Teresa; Rabalski, Adam J; Litchfield, David W

    2017-03-05

    Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks.

  2. Protein Kinase CK2: Intricate Relationships within Regulatory Cellular Networks

    PubMed Central

    Nuñez de Villavicencio-Diaz, Teresa; Rabalski, Adam J.; Litchfield, David W.

    2017-01-01

    Protein kinase CK2 is a small family of protein kinases that has been implicated in an expanding array of biological processes. While it is widely accepted that CK2 is a regulatory participant in a multitude of fundamental cellular processes, CK2 is often considered to be a constitutively active enzyme which raises questions about how it can be a regulatory participant in intricately controlled cellular processes. To resolve this apparent paradox, we have performed a systematic analysis of the published literature using text mining as well as mining of proteomic databases together with computational assembly of networks that involve CK2. These analyses reinforce the notion that CK2 is involved in a broad variety of biological processes and also reveal an extensive interplay between CK2 phosphorylation and other post-translational modifications. The interplay between CK2 and other post-translational modifications suggests that CK2 does have intricate roles in orchestrating cellular events. In this respect, phosphorylation of specific substrates by CK2 could be regulated by other post-translational modifications and CK2 could also have roles in modulating other post-translational modifications. Collectively, these observations suggest that the actions of CK2 are precisely coordinated with other constituents of regulatory cellular networks. PMID:28273877

  3. Hybrid Spectral Efficient Cellular Network Deployment to Reduce RF Pollution

    NASA Astrophysics Data System (ADS)

    Katiyar, Sumit; K. Jain, R.; K. Agrawal, N.

    2012-09-01

    As the mobile telecommunication systems are growing tremendously all over the world, the numbers of handheld and base stations are also rapidly growing and it became very popular to see these base stations distributed everywhere in the neighborhood and on roof tops which has caused a considerable amount of panic to the public in Palestine concerning wither the radiated electromagnetic fields from these base stations may cause any health effect or hazard. Recently UP High Court in India ordered for removal of BTS towers from residential area, it has created panic among cellular communication network designers too. Green cellular networks could be a solution for the above problem. This paper deals with green cellular networks with the help of multi-layer overlaid hierarchical structure (macro / micro / pico / femto cells). Macrocell for area coverage, micro for pedestrian and a slow moving traffic while pico for indoor use and femto for individual high capacity users. This could be the answer of the problem of energy conservation and enhancement of spectral density also.

  4. Industrial grade 2D molybdenum disulphide (MoS2): an in vitro exploration of the impact on cellular uptake, cytotoxicity, and inflammation

    NASA Astrophysics Data System (ADS)

    Moore, Caroline; Movia, Dania; Smith, Ronan J.; Hanlon, Damien; Lebre, Filipa; Lavelle, Ed C.; Byrne, Hugh J.; Coleman, Jonathan N.; Volkov, Yuri; McIntyre, Jennifer

    2017-06-01

    The recent surge in graphene research, since its liquid phase monolayer isolation and characterization in 2004, has led to advancements which are accelerating the exploration of alternative 2D materials such as molybdenum disulphide (MoS2), whose unique physico-chemical properties can be exploited in applications ranging from cutting edge electronic devices to nanomedicine. However, to assess any potential impact on human health and the environment, the need to understand the bio-interaction of MoS2 at a cellular and sub-cellular level is critical. Notably, it is important to assess such potential impacts of materials which are produced by large scale production techniques, rather than research grade materials. The aim of this study was to explore cytotoxicity, cellular uptake and inflammatory responses in established cell-lines that mimic different potential exposure routes (inhalation, A549; ingestion, AGS; monocyte, THP-1) following incubation with MoS2 flakes of varying sizes (50 nm, 117 nm and 177 nm), produced by liquid phase exfoliation. Using high content screening (HCS) and Live/Dead assays, it was established that 1 µg ml-1 (for the three different MoS2 sizes) did not induce toxic effects on any of the cell-lines. Confocal microscopy images revealed a normal cellular morphology in all cases. Transmission electron microscopy (TEM) confirmed the uptake of all MoS2 nanomaterials in all the cell-lines, the MoS2 ultimately locating in single membrane vesicles. At such sub-lethal doses, inflammatory responses are observed, however, associated, at least partially, with the presence of lipopolysaccharide endotoxin in nanomaterial suspensions and surfactant samples. Therefore, the inflammatory response of the cells to the MoS2 or endotoxin contamination was interrogated using a 10-plex ELISA which illustrates cytokine production. The experiments carried out using wild-type and endotoxin hyporesponsive bone marrow derived dendritic cells confirmed that the

  5. Distinctive Behaviors of Druggable Proteins in Cellular Networks

    PubMed Central

    Workman, Paul; Al-Lazikani, Bissan

    2015-01-01

    The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/. PMID:26699810

  6. ATR inhibition rewires cellular signaling networks induced by replication stress.

    PubMed

    Wagner, Sebastian A; Oehler, Hannah; Voigt, Andrea; Dalic, Denis; Freiwald, Anja; Serve, Hubert; Beli, Petra

    2016-02-01

    The slowing down or stalling of replication forks is commonly known as replication stress and arises from multiple causes such as DNA lesions, nucleotide depletion, RNA-DNA hybrids, and oncogene activation. The ataxia telangiectasia and Rad3-related kinase (ATR) plays an essential role in the cellular response to replication stress and inhibition of ATR has emerged as therapeutic strategy for the treatment of cancers that exhibit high levels of replication stress. However, the cellular signaling induced by replication stress and the substrate spectrum of ATR has not been systematically investigated. In this study, we employed quantitative MS-based proteomics to define the cellular signaling after nucleotide depletion-induced replication stress and replication fork collapse following ATR inhibition. We demonstrate that replication stress results in increased phosphorylation of a subset of proteins, many of which are involved in RNA splicing and transcription and have previously not been associated with the cellular replication stress response. Furthermore, our data reveal the ATR-dependent phosphorylation following replication stress and discover novel putative ATR target sites on MCM6, TOPBP1, RAD51AP1, and PSMD4. We establish that ATR inhibition rewires cellular signaling networks induced by replication stress and leads to the activation of the ATM-driven double-strand break repair signaling.

  7. Extreme narrow photonic bands and strong photonic localization produced by 2D defect two-segment-connected quadrangular waveguide networks

    NASA Astrophysics Data System (ADS)

    Li, Zhaoyang; Yang, Xiangbo; Timon Liu, Chengyi

    2014-09-01

    In this paper, we investigate the properties of optical transmission and photonic localization of two-dimensional (2D) defect two-segment-connected quadrangular waveguide networks (DTSCQWNs) and find that many groups of extreme narrow photonic bands are created in the middle of the transmission spectra. The electromagnetic (EM) waves in DTSCQWNs with the frequencies of extreme narrow photonic bands can produce strong photonic localizations by adjusting defect broken degree. On the other hand, we obtain the formula of extreme narrow photonic bands' frequencies dependent on defect broken degree and the formula of the largest intensity of photonic localization dependent on defect broken degree, respectively. It may possess potential application for designing all-optical devices based on strong photonic localizations. Additionally, we propose a so-called defecton mode to study the splitting rules of extreme narrow photonic bands, where decomposition-decimation method is expanded from the field of electronic energy spectra to that of optical transmission spectra.

  8. Cellular and network mechanisms of spike-wave seizures.

    PubMed

    Blumenfeld, Hal

    2005-01-01

    Spike-wave seizures are often considered a relatively "pure" form of epilepsy, with a uniform defect present in all patients and involvement of the whole brain homogeneously. Here, we present evidence against these common misconceptions. Rather than a uniform disorder, spike-wave rhythms arise from the normal inherent network properties of brain excitatory and inhibitory circuits, where they can be provoked by many different insults in several different brain networks. Here we discuss several different cellular and molecular mechanisms that may contribute to the generation of spike-wave seizures, particularly in idiopathic generalized epilepsy. In addition, we discuss growing evidence that electrical, neuroimaging, and molecular changes in spike-wave seizures do not involve the entire brain homogeneously. Rather, spike-wave discharges occur selectively in some thalamocortical networks, while sparing others. It is hoped that improved understanding of the heterogeneous defects and selective brain regions involved will ultimately lead to more effective treatments for spike-wave seizures.

  9. Reverse Engineering Cellular Networks with Information Theoretic Methods

    PubMed Central

    Villaverde, Alejandro F.; Ross, John; Banga, Julio R.

    2013-01-01

    Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets. PMID:24709703

  10. An investigation of spatial signal transduction in cellular networks.

    PubMed

    Alam-Nazki, Aiman; Krishnan, J

    2012-07-05

    Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary. In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling. Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.

  11. An investigation of spatial signal transduction in cellular networks

    PubMed Central

    2012-01-01

    Background Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary. Results In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling. Conclusions Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual

  12. Mechanics of composite actin networks: in vitro and cellular perspectives

    NASA Astrophysics Data System (ADS)

    Upadhyaya, Arpita

    2014-03-01

    Actin filaments and associated actin binding proteins play an essential role in governing the mechanical properties of eukaryotic cells. Even though cells have multiple actin binding proteins (ABPs) that exist simultaneously to maintain the structural and mechanical integrity of the cellular cytoskeleton, how these proteins work together to determine the properties of actin networks is not well understood. The ABP, palladin, is essential for the integrity of cell morphology and movement during development. Palladin coexists with alpha-actinin in stress fibers and focal adhesions and binds to both actin and alpha-actinin. To obtain insight into how mutually interacting actin crosslinking proteins modulate the properties of actin networks, we have characterized the micro-structure and mechanics of actin networks crosslinked with palladin and alpha-actinin. Our studies on composite networks of alpha-actinin/palladin/actin show that palladin and alpha-actinin synergistically determine network viscoelasticity. We have further examined the role of palladin in cellular force generation and mechanosensing. Traction force microscopy revealed that TAFs are sensitive to substrate stiffness as they generate larger forces on substrates of increased stiffness. Contrary to expectations, knocking down palladin increased the forces generated by cells, and also inhibited the ability to sense substrate stiffness for very stiff gels. This was accompanied by significant differences in the actin organization and adhesion dynamics of palladin knock down cells. Perturbation experiments also suggest altered myosin activity in palladin KD cells. Our results suggest that the actin crosslinkers such as palladin and myosin motors coordinate for optimal cell function and to prevent aberrant behavior as in cancer metastasis.

  13. Cutting the wires: modularization of cellular networks for experimental design.

    PubMed

    Lang, Moritz; Summers, Sean; Stelling, Jörg

    2014-01-07

    Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  14. Cutting the Wires: Modularization of Cellular Networks for Experimental Design

    PubMed Central

    Lang, Moritz; Summers, Sean; Stelling, Jörg

    2014-01-01

    Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. PMID:24411264

  15. Country-wide rainfall maps from cellular communication networks

    PubMed Central

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-01-01

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. PMID:23382210

  16. Study and Simulation of Traffic Behavior in Cellular Network

    NASA Astrophysics Data System (ADS)

    Madhup, D. K.; Shrestha, C. L.; Sharma, R. K.

    2007-07-01

    Cellular radio systems accommodate a large number of users with a limited radio spectrum. The concept of trunking allows a large number of users to share the relatively small number of channels in a cell by providing access to each user, on demand, from a pool of available channels. Traffic engineering deals with provisioning of communication circuits in a given area for a number of subscribers with a required grade of service. Traffic in any cell depends upon the number of users, the average request rate and average call duration. Certain number of channels is required for the required GOS. To design an optimum capacity cellular system, traffic behavior on that system is important. The number of channel required can be estimated by using Erlang formula and Erlang table. Erlang table is not always useful to calculate the probability of blocking in various complex scenarios such as channel borrowing strategies. When the total number of channel available in a given cell are divided to serve partly for newly generated calls and partly for handover calls, and if they use dynamic channel assignment strategies like channel borrowing, then the probability of blocking can't be calculated from Erlang table. Simulation model of the behavior help us to determine the blocking and the channel utilization while using various channel assignment strategies. The title "Study and Simulation of Traffic Behavior in Cellular Network" entail the study of the blocking probability of traffic in cellular network for static channel assignment strategies and dynamic channel borrowing strategies through MATLAB programming language and graphic user interface (GUI). The result shows that the dynamic scheme can perform better than static maximizing the overall utilization of the circuits and minimizing the overall blocking.

  17. Neural network system for 3-D object recognition and pose estimation from a single arbitrary 2-D view

    NASA Astrophysics Data System (ADS)

    Khotanzad, Alireza R.; Liou, James H.

    1992-09-01

    In this paper, a robust, and fast system for recognition as well as pose estimation of a 3-D object from a single 2-D perspective of it taken from an arbitrary viewpoint is developed. The approach is invariant to location, orientation, and scale of the object in the perspective. The silhouette of the object in the 2-D perspective is first normalized with respect to location and scale. A set of rotation invariant features derived from complex and orthogonal pseudo- Zernike moments of the image are then extracted. The next stage includes a bank of multilayer feed-forward neural networks (NN) each of which classifies the extracted features. The training set for these nets consists of perspective views of each object taken from several different viewing angles. The NNs in the bank differ in the size of their hidden layer nodes as well as their initial conditions but receive the same input. The classification decisions of all the nets are combined through a majority voting scheme. It is shown that this collective decision making yields better results compared to a single NN operating alone. After the object is classified, two of its pose parameters, namely elevation and aspect angles, are estimated by another module of NNs in a two-stage process. The first stage identifies the likely region of the space that the object is being viewed from. In the second stage, an NN estimator for the identified region is used to compute the pose angles. Extensive experimental studies involving clean and noisy images of seven military ground vehicles are carried out. The performance is compared to two other traditional methods, namely a nearest neighbor rule and a binary decision tree classifier and it is shown that our approach has major advantages over them.

  18. Application-Aware Dynamic Retransmission Control in Mobile Cellular Networks

    NASA Astrophysics Data System (ADS)

    Halima, Nadhir Ben; Kliazovich, Dzmitry; Granelli, Fabrizio

    This paper proposes an application-aware cross-layer approach between application/transport layers on the mobile terminal and link layer at the wireless base station to enable dynamic control on the strength of per-packet error protection for multimedia and data transfers. Specifically, in the context of cellular networks, the proposed scheme allows to control the desired level of Hybrid ARQ (HARQ) protection by using an in-band control feedback channel. Such protection is dynamically adapted on a per-packet basis and depends on the perceptual importance of different packets as well as on the reception history of the flow.

  19. GPM ground validation via commercial cellular networks: an exploratory approach

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Brasjen, Noud; Uijlenhoet, Remko

    2016-04-01

    The suitability of commercial microwave link networks for ground validation of GPM (Global Precipitation Measurement) data is evaluated here. Two state-of-the-art rainfall products are compared over the land surface of the Netherlands for a period of 7 months, i.e., rainfall maps from commercial cellular communication networks and Integrated Multi-satellite Retrievals for GPM (IMERG). Commercial microwave link networks are nowadays the core component in telecommunications worldwide. Rainfall rates can be retrieved from measurements of attenuation between transmitting and receiving antennas. If adequately set up, these networks enable rainfall monitoring tens of meters above the ground at high spatiotemporal resolutions (temporal sampling of seconds to tens of minutes, and spatial sampling of hundreds of meters to tens of kilometers). The GPM mission is the successor of TRMM (Tropical Rainfall Measurement Mission). For two years now, IMERG offers rainfall estimates across the globe (180°W - 180°E and 60°N - 60°S) at spatiotemporal resolutions of 0.1° x 0.1° every 30 min. These two data sets are compared against a Dutch gauge-adjusted radar data set, considered to be the ground truth given its accuracy, spatiotemporal resolution and availability. The suitability of microwave link networks in satellite rainfall evaluation is of special interest, given the independent character of this technique, its high spatiotemporal resolutions and availability. These are valuable assets for water management and modeling of floods, landslides, and weather extremes; especially in places where rain gauge networks are scarce or poorly maintained, or where weather radar networks are too expensive to acquire and/or maintain.

  20. Cellular telephone-based wide-area radiation detection network

    DOEpatents

    Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

    2009-06-09

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  1. Coupled 1-D sewer and street networks and 2-D flooding model to rapidly evaluate surface inundation

    NASA Astrophysics Data System (ADS)

    Kao, Hong-Ming; Hsu, Hao-Ming

    2017-04-01

    Flash floods have occurred frequently in the urban areas around the world and cause the infrastructure and people living to expose continuously in the high risk level of pluvial flooding. According to historical surveys, the major reasons of severe surface inundations in the urban areas can be attributed to heavy rainfall in the short time and/or drainage system failure. In order to obtain real-time flood forecasting with high accuracy and less uncertainty, an appropriate system for predicting floods is necessary. For the reason, this study coupled 1-D sewer and street networks and 2-D flooding model as an operational modelling system for rapidly evaluating surface inundation. The proposed system is constructed by three significant components: (1) all the rainfall-runoff of a sub-catchment collected via gullies is simulated by the RUNOFF module of the Storm Water Management Model (SWMM); (2) and directly drained to the 1-D sewer and street networks via manholes as inflow discharges to conduct flow routing by using the EXTRAN module of SWMM; (3) after the 1-D simulations, the surcharges from manholes are considered as point sources in 2-D overland flow simulations that are executed by the WASH123D model. It can thus be used for urban flood modelling that reflects the rainfall-runoff processes, and the dynamic flow interactions between the storm sewer system and the ground surface in urban areas. In the present study, we adopted the Huwei Science and Technology Park, located in the south-western part of Taiwan, as the demonstration area because of its high industrial values. The region has an area about 1 km2 with approximately 1 km in both length and width. It is as isolated urban drainage area in which there is a complete sewer system that collects the runoff and drains to the detention pond. Based on the simulated results, the proposed modelling system was found that the simulated floods fit to the survey records because the physical rainfall-runoff phenomena in

  2. Perturbation biology: inferring signaling networks in cellular systems.

    PubMed

    Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B; Pratilas, Christine A; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

  3. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    PubMed Central

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  4. Assessing the weather monitoring capabilities of cellular microwave link networks

    NASA Astrophysics Data System (ADS)

    Fencl, Martin; Vrzba, Miroslav; Rieckermann, Jörg; Bareš, Vojtěch

    2016-04-01

    Using of microwave links for rainfall monitoring was suggested already by (Atlas and Ulbrich, 1977). However, this technique attracted broader attention of scientific community only in the recent decade, with the extensive growth of cellular microwave link (CML) networks, which form the backbone of today's cellular telecommunication infrastructure. Several studies have already shown that CMLs can be conveniently used as weather sensors and have potential to provide near-ground path-integrated observations of rainfall but also humidity or fog. However, although research is still focusing on algorithms to improve the weather sensing capabilities (Fencl et al., 2015), it is not clear how to convince cellular operators to provide the power levels of their network. One step in this direction is to show in which regions or municipalities the networks are sufficiently dense to provide/develop good services. In this contribution we suggest a standardized approach to evaluate CML networks in terms of rainfall observation and to identify suitable regions for CML rainfall monitoring. We estimate precision of single CML based on its sensitivity to rainfall, i.e. as a function of frequency, polarization and path length. Capability of a network to capture rainfall spatial patterns is estimated from the CML coverage and path lengths considering that single CML provides path-integrated rain rates. We also search for suitable predictors for regions where no network topologies are available. We test our approach on several European networks and discuss the results. Our results show that CMLs are very dense in urban areas (> 1 CML/km2), but less in rural areas (< 0.02 CML/km2). We found a strong correlation between a population and CML network density (e.g. R2 = 0.97 in Czech Republic), thus population could be a simple proxy to identify suitable regions for CML weather monitoring. To enable a simple and efficient assessment of the CML monitoring potential for any region worldwide

  5. Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs.

    PubMed

    Allain, Ariane; Chauvot de Beauchêne, Isaure; Langenfeld, Florent; Guarracino, Yann; Laine, Elodie; Tchertanov, Luba

    2014-01-01

    Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non

  6. Multi-casting approach for vascular networks in cellularized hydrogels.

    PubMed

    Justin, Alexander W; Brooks, Roger A; Markaki, Athina E

    2016-12-01

    Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200-250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel.

  7. Multi-casting approach for vascular networks in cellularized hydrogels

    PubMed Central

    Justin, Alexander W.; Brooks, Roger A.

    2016-01-01

    Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200–250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel. PMID:27928031

  8. A learning algorithm for oscillatory cellular neural networks.

    PubMed

    Ho, C Y.; Kurokawa, H

    1999-07-01

    We present a cellular type oscillatory neural network for temporal segregation of stationary input patterns. The model comprises an array of locally connected neural oscillators with connections limited to a 4-connected neighborhood. The architecture is reminiscent of the well-known cellular neural network that consists of local connection for feature extraction. By means of a novel learning rule and an initialization scheme, global synchronization can be accomplished without incurring any erroneous synchrony among uncorrelated objects. Each oscillator comprises two mutually coupled neurons, and neurons share a piecewise-linear activation function characteristic. The dynamics of traditional oscillatory models is simplified by using only one plastic synapse, and the overall complexity for hardware implementation is reduced. Based on the connectedness of image segments, it is shown that global synchronization and desynchronization can be achieved by means of locally connected synapses, and this opens up a tremendous application potential for the proposed architecture. Furthermore, by using special grouping synapses it is demonstrated that temporal segregation of overlapping gray-level and color segments can also be achieved. Finally, simulation results show that the learning rule proposed circumvents the problem of component mismatches, and hence facilitates a large-scale integration.

  9. Low-cost chemiresistive sensor for volatile amines based on a 2D network of a zinc(II) Schiff-base complex

    NASA Astrophysics Data System (ADS)

    Mirabella, S.; Oliveri, I. P.; Ruffino, F.; Maccarrone, G.; Di Bella, S.

    2016-10-01

    A marked chemiresistive behavior is revealed in a nanostructured material obtained by spin-coating a solution of a bis(salycilaldiminato)Zn(II) Schiff-base (ZnSB) complex. The resulting submicron 2D network exhibits reversible changes in absorbance and resistance under the cycles of absorption and desorption of a volatile amine. These results are explained in terms of a Lewis donor-acceptor interaction between the ZnSB (acceptor) and the chemisorbed amine (donor). The 2D network of ZnSB was employed as a sensing element to fabricate a low-cost device for the volatile amines detection, showing promising results for food spoilage detection.

  10. Long-range alignments of single fullerenes by site-selective inclusion into a double-cavity 2D open network.

    PubMed

    Piot, Luc; Silly, Fabien; Tortech, Ludovic; Nicolas, Yohann; Blanchard, Philippe; Roncali, Jean; Fichou, Denis

    2009-09-16

    We show by means of STM that C(60) molecules can be trapped into specific sites of a 2D double-cavity open network, thus forming long-range alignments of single molecules. Since only one of the two cavities has the right size to host C(60), the smallest cavity remains empty and is thus available to trap additional species of smaller size. This novel 2D supramolecular network opens new perspectives in the design of multicomponent guest-host architectures with electronic functionalities.

  11. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

    PubMed

    Cheng, Feixiong; Liu, Chuang; Shen, Bairong; Zhao, Zhongming

    2016-08-26

    Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and

  12. Countrywide rainfall maps from a commercial cellular telecommunication network

    NASA Astrophysics Data System (ADS)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2012-12-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information. Many countries do not have continuously operating weather radars, and have no or few rain gauges. A new development is rainfall estimation from microwave links of commercial cellular telecommunication networks. Such networks cover large parts of the land surface of the earth and have a high density, especially in urban areas. The estimation of rainfall using commercial microwave links could therefore become a valuable source of information. The data produced by microwave links is essentially a by-product of the communication between mobile telephones. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. A dataset from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (1500) covering the land surface of the Netherlands (35500 km2). This dataset consists of 24 days with substantial rainfall in June - September 2011. A rainfall retrieval algorithm is presented to derive rainfall intensities from the microwave link data, which have a temporal resolution of 15 min. Rainfall maps (1 km spatial resolution) are generated from these rainfall intensities using Kriging. This algorithm is suited for real-time application, and is calibrated on a subset (12 days) of the dataset. The other 12 days in the dataset are used to validate the algorithm. Both calibration and validation are done using gauge-adjusted radar data

  13. A rigorous framework for multiscale simulation of stochastic cellular networks

    PubMed Central

    Chevalier, Michael W.; El-Samad, Hana

    2009-01-01

    Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-cell variability even in clonal populations. Stochastic biochemical networks are modeled as continuous time discrete state Markov processes whose probability density functions evolve according to a chemical master equation (CME). The CME is not solvable but for the simplest cases, and one has to resort to kinetic Monte Carlo techniques to simulate the stochastic trajectories of the biochemical network under study. A commonly used such algorithm is the stochastic simulation algorithm (SSA). Because it tracks every biochemical reaction that occurs in a given system, the SSA presents computational difficulties especially when there is a vast disparity in the timescales of the reactions or in the number of molecules involved in these reactions. This is common in cellular networks, and many approximation algorithms have evolved to alleviate the computational burdens of the SSA. Here, we present a rigorously derived modified CME framework based on the partition of a biochemically reacting system into restricted and unrestricted reactions. Although this modified CME decomposition is as analytically difficult as the original CME, it can be naturally used to generate a hierarchy of approximations at different levels of accuracy. Most importantly, some previously derived algorithms are demonstrated to be limiting cases of our formulation. We apply our methods to biologically relevant test systems to demonstrate their accuracy and efficiency. PMID:19673546

  14. Myosin lever arm directs collective motion on cellular actin network

    PubMed Central

    Hariadi, Rizal F.; Cale, Mario; Sivaramakrishnan, Sivaraj

    2014-01-01

    The molecular motor myosin teams up to drive muscle contraction, membrane traffic, and cell division in biological cells. Myosin function in cells emerges from the interaction of multiple motors tethered to a scaffold, with surrounding actin filaments organized into 3D networks. Despite the importance of myosin function, the influence of intermotor interactions on collective motion remains poorly understood. In this study, we used precisely engineered myosin assemblies to examine emergence in collective myosin movement. We report that tethering multiple myosin VI motors, but not myosin V motors, modifies their movement trajectories on keratocyte actin networks. Single myosin V and VI dimers display similar skewed trajectories, albeit in opposite directions, when traversing the keratocyte actin network. In contrast, tethering myosin VI motors, but not myosin V motors, progressively straightens the trajectories with increasing myosin number. Trajectory shape of multimotor scaffolds positively correlates with the stiffness of the myosin lever arm. Swapping the flexible myosin VI lever arm for the relatively rigid myosin V lever increases trajectory skewness, and vice versa. A simplified model of coupled motor movement demonstrates that the differences in flexural rigidity of the two myosin lever arms is sufficient to account for the differences in observed behavior of groups of myosin V and VI motors. In accordance with this model trajectory, shapes for scaffolds containing both myosin V and VI are dominated by the myosin with a stiffer lever arm. Our findings suggest that structural features unique to each myosin type may confer selective advantages in cellular functions. PMID:24591646

  15. Myosin lever arm directs collective motion on cellular actin network.

    PubMed

    Hariadi, Rizal F; Cale, Mario; Sivaramakrishnan, Sivaraj

    2014-03-18

    The molecular motor myosin teams up to drive muscle contraction, membrane traffic, and cell division in biological cells. Myosin function in cells emerges from the interaction of multiple motors tethered to a scaffold, with surrounding actin filaments organized into 3D networks. Despite the importance of myosin function, the influence of intermotor interactions on collective motion remains poorly understood. In this study, we used precisely engineered myosin assemblies to examine emergence in collective myosin movement. We report that tethering multiple myosin VI motors, but not myosin V motors, modifies their movement trajectories on keratocyte actin networks. Single myosin V and VI dimers display similar skewed trajectories, albeit in opposite directions, when traversing the keratocyte actin network. In contrast, tethering myosin VI motors, but not myosin V motors, progressively straightens the trajectories with increasing myosin number. Trajectory shape of multimotor scaffolds positively correlates with the stiffness of the myosin lever arm. Swapping the flexible myosin VI lever arm for the relatively rigid myosin V lever increases trajectory skewness, and vice versa. A simplified model of coupled motor movement demonstrates that the differences in flexural rigidity of the two myosin lever arms is sufficient to account for the differences in observed behavior of groups of myosin V and VI motors. In accordance with this model trajectory, shapes for scaffolds containing both myosin V and VI are dominated by the myosin with a stiffer lever arm. Our findings suggest that structural features unique to each myosin type may confer selective advantages in cellular functions.

  16. Cellular Neural Network for Real Time Image Processing

    SciTech Connect

    Vagliasindi, G.; Arena, P.; Fortuna, L.; Mazzitelli, G.; Murari, A.

    2008-03-12

    Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)

  17. Cellular Neural Network for Real Time Image Processing

    NASA Astrophysics Data System (ADS)

    Vagliasindi, G.; Arena, P.; Fortuna, L.; Mazzitelli, G.; Murari, A.

    2008-03-01

    Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET).

  18. 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

  19. Image registration under affine transformation using cellular simultaneous recurrent networks

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan M.; Anderson, Keith

    2010-08-01

    Cellular simultaneous recurrent networks (CSRN)s have been traditionally exploited to solve the digital control and conventional maze traversing problems. In previous works, we investigated the use of CSRNs to register simulated binary images with in-plane rotations between +/-20° using two different CSRN architectures such as one with a general multi-layered perceptron (GMLP) architecture; and another with modified MLP architecture with multilayered feedback. We further exploit the CSRN for registration of realistic binary and gray scale images under rotation. In this current work we report results of applying CSRNs to perform image registration under affine transformations such as rotation and translation. We further provide extensive analyses of CSRN affine registration results for appropriate cost function formulation. Our CSRN results analyses show that formulation of locally varying cost function is desirable for robust image registration under affine transformation.

  20. Separation of Bouguer anomaly map using cellular neural network

    NASA Astrophysics Data System (ADS)

    Albora, A. Muhittin; Ucan, Osman N.; Ozmen, Atilla; Ozkan, Tulay

    2001-02-01

    In this paper, a modern image-processing technique, the Cellular Neural Network (CNN) has been firstly applied to Bouguer anomaly map of synthetic examples and then to data from the Sivas-Divrigi Akdag region. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behaviour of the CNN is defined by two template matrices and a template vector. We have optimised the weight coefficients of these templates using the Recurrent Perceptron Learning Algorithm (RPLA). After testing CNN performance on synthetic examples, the CNN approach has been applied to the Bouguer anomaly of Sivas-Divrigi Akdag region and the results match drilling logs done by Mineral Research and Exploration (MTA).

  1. Country-wide rainfall maps from cellular communication networks

    NASA Astrophysics Data System (ADS)

    Leijnse, Hidde; Overeem, Aart; Uijlenhoet, Remko

    2013-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular telecommunication networks may be employed for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall monitoring using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. Here we show how one cellular telecommunication network can be used to retrieve the space-time dynamics of rainfall for an entire country. A dataset from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (2400) covering the land surface of the Netherlands (35500 km2). This dataset consists of 24 days with substantial rainfall in June - September 2011. A rainfall retrieval algorithm is presented to derive rainfall intensities from the microwave link data, which have a temporal resolution of 15 min. Rainfall maps (1 km spatial resolution) are generated from these rainfall intensities using Kriging. This algorithm is suited for real-time application, and is calibrated on a subset (12 days) of the dataset. The other 12 days in the dataset are used to validate the algorithm. Both

  2. Spectral and structural properties of 2D network complex [Ni(4,4'-bipyridine) 2(NCS) 2] n

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Jianmin, L.; Nishiura, M.; Imamoto, T.

    2000-02-01

    The complex [Ni(4,4'-bipyridine) 2(NCS) 2] n, in which nickel atoms are linked by two different Ni-4,4'-bpy-Ni assemblies to form two-dimensional distorted square net structure and the most effective packing of layers, has been isolated and structurally characterized. It represents the first example of Ni(II)-4,4'-bpy complex possesses 2D network. Crystal data for I: Fw=487.23, a=12.156(3), b=11.38(2), c=16.646(8) Å, β=100.43(3), V=2265(1) Å3, Z=4, space group=C2/c, T=298 K, λ((Mo-K α)=0.71070 Å, ρ calc=1.429 g cm -3, μ=10.62 cm-1, F(000)=1000, R=0.054, Rw=0.086, GOF=3.98. The UV-VIS absorption spectrum of the title complex is also reported and explained perfectly by the scaling radial theory which proposed by us. The strong and broad absorption bands occurred at 10433, 16830, 26556 cm -1, and they are assigned as d-d transitions of Ni(II) ion in octahedral field: 3A2g→ 3T2ga,b+ 3T2gc; 3A2g→ 3T1gz+ 3T1gy,x; 3A2g→ 3T1gz+ 3T1gy,x. The calculated results of the d-d transition energy levels agree well with the experimental values.

  3. Preliminary 3d depth migration of a network of 2d seismic lines for fault imaging at a Pyramid Lake, Nevada geothermal prospect

    SciTech Connect

    Frary, R.; Louie, J.; Pullammanappallil, S.; Eisses, A.

    2016-08-01

    Roxanna Frary, John N. Louie, Sathish Pullammanappallil, Amy Eisses, 2011, Preliminary 3d depth migration of a network of 2d seismic lines for fault imaging at a Pyramid Lake, Nevada geothermal prospect: presented at American Geophysical Union Fall Meeting, San Francisco, Dec. 5-9, abstract T13G-07.

  4. Cellular computational networks--a scalable architecture for learning the dynamics of large networked systems.

    PubMed

    Luitel, Bipul; Venayagamoorthy, Ganesh Kumar

    2014-02-01

    Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Rainfall maps from cellular communication networks: Assessing uncertainties

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2014-05-01

    Several studies show the potential applicability of commercial cellular communication networks in the retrieval of rainfall fields, sometimes even for an entire country. The key principle of rainfall monitoring using microwave links is based on the attenuation, due to rainfall, of the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link, as a function of time, the path-averaged rainfall intensity can be estimated. This study focuses on the quality of country-wide rainfall maps derived from commercial microwave link data compared to a quality-controlled gauge-adjusted radar rainfall data set, considered as ground-truth. Part of the differences can be attributed to the interpolation methodology, as well as to the much higher spatial resolution (≡38.000 pixels of 0.9 by 0.9 km2) of the radar data compared to the relatively low density of the microwave link network (≡1700 microwave links with an average length of 3.1 km). The magnitude of these factors is assessed by simulating microwave link rainfall depths from the radar rainfall data set. The Ordinary-Kriging (OK) methodology is used to obtain rainfall maps based on the simulated and real microwave link data. This work quantifies what percentage of the errors in link-based rainfall maps can be attributed to the interpolation methodology itself and the limited spatial density of the microwave link network. Moreover, the spatial distribution of the error in rainfall maps is quantified in relation to the spatial density and temporally variable availability of links, which is highly relevant since the microwave link data are non-uniformly distributed in space or time. Finally, the applicability of the OK-methodology is tested over Dutch areas with different spatial densities of commercial microwave links.

  6. Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling.

    PubMed

    Bazeley, Peter S; Prithivi, Sridevi; Struble, Craig A; Povinelli, Richard J; Sem, Daniel S

    2006-01-01

    Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known active site. Docking scores for the active site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85+/-6% is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound's formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12% improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23% above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding.

  7. Segmentation of Textures Defined on Flat vs. Layered Surfaces using Neural Networks: Comparison of 2D vs. 3D Representations 1

    PubMed Central

    Oh, Sejong; Choe, Yoonsuck

    2009-01-01

    Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer’s motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network’s learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision. PMID:19562098

  8. Modeling and Analysis of Granite Matrix Pore Structure and Hydraulic Characteristics in 2D and 3D Networks

    NASA Astrophysics Data System (ADS)

    Gvozdik, L.; Polak, M.; Zaruba, J.; Vanecek, M.

    2010-12-01

    A geological environment labeled as a Granite massif represents in terms of groundwater flow and transport a distinct hydrogeological environment from that of sedimentary basins, the characterisation of which is generally more complex and uncertain. Massifs are composed of hard crystalline rocks with the very low effective porosity. Due to their rheological properties such rocks are predisposed to brittle deformation resulting from changes in stress conditions. Our specific research project (Research on the influence of intergrangular porosity on deep geological disposal: geological formations, methodology and the development of measurement apparatus) is focussed on the problem of permeable zones within apparently undisturbed granitic rock matrix. The project including the both laboratory and in-situ tracer tests study migration along and through mineral grains in fresh and altered granite. The objective of the project is to assess whether intergranular porosity is a general characteristic of the granitic rock matrix or subject to significant evolution resulting from geochemical and/or hydrogeochemical processes, geotechnical and/or mechanical processes. Moreover, the research is focussed on evaluating methods quantifying intergranular porosity by both physical testing and mathematical modelling using verified standard hydrological software tools. Groundwater flow in microfractures and intergranular pores in granite rock matrix were simulated in three standard hydrogeological modeling programs with completely different conceptual approaches: MODFLOW (Equivalent Continuum concept), FEFLOW (Discrete Fracture and Equivalent Continuum concepts) and NAPSAC (Discrete Fracture Network concept). Specialized random fracture generators were used for creation of several 2D and 3D models in each of the chosen program. Percolation characteristics of these models were tested and analyzed. Several scenarios of laboratory tests of the rock samples permeability made in triaxial

  9. Discrete hexamer water clusters and 2D water layer trapped in three luminescent Ag/tetramethylpyrazine/benzene-dicarboxylate hosts: 1D chain, 2D layer and 3D network

    NASA Astrophysics Data System (ADS)

    Mei, Hong-Xin; Zhang, Ting; Huang, Hua-Qi; Huang, Rong-Bin; Zheng, Lan-Sun

    2016-03-01

    Three mix-ligand Ag(I) coordination compounds, namely, {[Ag10(tpyz) 5(L1) 5(H2 O)2].(H2 O)4}n (1, tpyz = 2,3,4,5-tetramethylpyrazine, H2 L1 = phthalic acid), [Ag4(tpyz) 2(L2) 2(H2 O)].(H2 O)5}n (2, H2 L2 = isophthalic acid) {[Ag2(tpyz) 2(L3) (H2 O)4].(H2 O)8}n (3, H2 L3 = terephthalic acid), have been synthesized and characterized by elemental analysis, IR, PXRD and X-ray single-crystal diffraction. 1 exhibits a 2D layer which can be simplified as a (4,4) net. 2 is a 3D network which can be simplified as a (3,3)-connected 2-nodal net with a point symbol of {102.12}{102}. 3 consists of linear [Ag(tpyz) (H2 O)2]n chain. Of particular interest, discrete hexamer water clusters were observed in 1 and 2, while a 2D L10(6) water layer exists in 3. The results suggest that the benzene dicarboxylates play pivotal roles in the formation of the different host architectures as well as different water aggregations. Moreover, thermogravimetric analysis (TGA) and emissive behaviors of these compounds were investigated.

  10. Using cellular network diagrams to interpret large-scale datasets: past progress and future challenges

    NASA Astrophysics Data System (ADS)

    Karp, Peter D.; Latendresse, Mario; Paley, Suzanne

    2011-03-01

    Cellular networks are graphs of molecular interactions within the cell. Thanks to the confluence of genome sequencing and bioinformatics, scientists are now able to reconstruct cellular network models for more than 1,000 organisms. A variety of bioinformatics tools have been developed to support the visualization and navigation of cellular network data. Another important application is the use of cellular network diagrams to visualize and interpret large-scale datasets, such as gene-expression data. We present the Cellular Overview, a network visualization tool developed at SRI International (SRI) to support visualization, navigation, and interpretation of large-scale datasets on metabolic networks. Different variations of the diagram have been generated algorithmically for more than 1,000 organisms. We discuss the graphical design of the diagram and its interactive capabilities.

  11. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  12. Binary image registration using cellular simultaneous recurrent networks

    NASA Astrophysics Data System (ADS)

    Anderson, Keith; Iftekharuddin, Khan; Kim, Paul

    2009-08-01

    Cellular simultaneous recurrent networks (CSRN)s have been traditionally exploited to solve digital control and conventional maze traversing problems. In a previous work [1], we investigate the use of CSRNs for image registration under affine transformations for binary images. In Ref. [1], we attempt to register simulated binary images with in-plane rotations between +/-20° using two different CSRN implementations such as with 1) a general multi-layered perceptron (GMLP) architecture; and (2) a modified MLP architecture with multi-layered feedback. Our results in Ref. 1 show that both architectures achieve moderate local registration, with our modified MLP architecture producing a best result of around 64% for cost function accuracy and 98% for image registration accuracy. In this current work, for the first time in literature, we investigate gray scale image registration using CSRNs. We exploit both types of CSRNs for registration of realistic images and perform complete evaluation of both binary and gray-scale image registration. Simulation results with both CSRN architectures show an average cost function accuracy of 40.5% and an average image accuracy of 33.2%, with a best result of 46.2% and 40.3%, respectively. Image results clearly show that the CSRN shows promise for use in registration of gray-scale images.

  13. Radio Resource Allocation on Complex 4G Wireless Cellular Networks

    NASA Astrophysics Data System (ADS)

    Psannis, Kostas E.

    2015-09-01

    In this article we consider the heuristic algorithm which improves step by step wireless data delivery over LTE cellular networks by using the total transmit power with the constraint on users’ data rates, and the total throughput with the constraints on the total transmit power as well as users’ data rates, which are jointly integrated into a hybrid-layer design framework to perform radio resource allocation for multiple users, and to effectively decide the optimal system parameter such as modulation and coding scheme (MCS) in order to adapt to the varying channel quality. We propose new heuristic algorithm which balances the accessible data rate, the initial data rates of each user allocated by LTE scheduler, the priority indicator which signals delay- throughput- packet loss awareness of the user, and the buffer fullness by achieving maximization of radio resource allocation for multiple users. It is noted that the overall performance is improved with the increase in the number of users, due to multiuser diversity. Experimental results illustrate and validate the accuracy of the proposed methodology.

  14. Hierarchical Interference Mitigation for Massive MIMO Cellular Networks

    NASA Astrophysics Data System (ADS)

    Liu, An; Lau, Vincent

    2014-09-01

    We propose a hierarchical interference mitigation scheme for massive MIMO cellular networks. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each BS (CSIT). The outer precoder controls the inter-cell interference and is adaptive to channel statistics. Such hierarchical precoding structure reduces the number of pilot symbols required for CSI estimation in massive MIMO downlink and is robust to the backhaul latency. We study joint optimization of the outer precoders, the user selection, and the power allocation to maximize a general concave utility which has no closed-form expression. We first apply random matrix theory to obtain an approximated problem with closed-form objective. We show that the solution of the approximated problem is asymptotically optimal with respect to the original problem as the number of antennas per BS grows large. Then using the hidden convexity of the problem, we propose an iterative algorithm to find the optimal solution for the approximated problem. We also obtain a low complexity algorithm with provable convergence. Simulations show that the proposed design has significant gain over various state-of-the-art baselines.

  15. Network signatures of cellular immortalization in human lymphoblastoid cell lines

    SciTech Connect

    Shim, Sung-Mi; Jung, So-Young; Nam, Hye-Young; Kim, Hye-Ryun; Lee, Mee-Hee; Kim, Jun-Woo; Han, Bok-Ghee; Jeon, Jae-Pil

    2013-11-15

    Highlights: •We identified network signatures of LCL immortalization from transcriptomic profiles. •More than 41% of DEGs are possibly regulated by miRNAs in LCLs. •MicroRNA target genes in LCLs are involved in apoptosis and immune-related functions. •This approach is useful to find functional miRNA targets in specific cell conditions. -- Abstract: Human lymphoblastoid cell line (LCL) has been used as an in vitro cell model in genetic and pharmacogenomic studies, as well as a good model for studying gene expression regulatory machinery using integrated genomic analyses. In this study, we aimed to identify biological networks of LCL immortalization from transcriptomic profiles of microRNAs and their target genes in LCLs. We first selected differentially expressed genes (DEGs) and microRNAs (DEmiRs) between early passage LCLs (eLCLs) and terminally differentiated late passage LCLs (tLCLs). The in silico and correlation analysis of these DEGs and DEmiRs revealed that 1098 DEG–DEmiR pairs were found to be positively (n = 591 pairs) or negatively (n = 507 pairs) correlated with each other. More than 41% of DEGs are possibly regulated by miRNAs in LCL immortalizations. The target DEGs of DEmiRs were enriched for cellular functions associated with apoptosis, immune response, cell death, JAK–STAT cascade and lymphocyte activation while non-miRNA target DEGs were over-represented for basic cell metabolisms. The target DEGs correlated negatively with miR-548a-3p and miR-219-5p were significantly associated with protein kinase cascade, and the lymphocyte proliferation and apoptosis, respectively. In addition, the miR-106a and miR-424 clusters located in the X chromosome were enriched in DEmiR–mRNA pairs for LCL immortalization. In this study, the integrated transcriptomic analysis of LCLs could identify functional networks of biologically active microRNAs and their target genes involved in LCL immortalization.

  16. Layered Video Transmission with Network Coding in a Cooperative Virtual MIMO Cellular Relay Networks

    NASA Astrophysics Data System (ADS)

    Kim, Yo-Han; Yoon, Jisun; Shin, Jitae; Yang, Janghoon

    In this letter, we propose a layered video streaming technique that combines network coding (NC), multiple-input multiple-output (MIMO), and hierarchical modulation (HM) over cellular relay networks. We provide a performance analysis of different transmission modes of NC and MIMO in terms of the error rate in video layers, which is reflected in the total layered-video quality. The HM is used to differentiate the error rates among video layers. The simulation results show that the NC, the MIMO spatial multiplexing (SM), and the combination of both the NC and MIMO-SM gives video-quality gains of about 1.9dB, 6dB, and 12dB maximally, respectively, compared to the conventional single-input and single-output (SISO) single relay network (SRN) system.

  17. Cellular and network mechanisms of genetically-determined absence seizures

    PubMed Central

    Pinault, Didier; O'Brien, Terence J.

    2005-01-01

    The absence epilepsies are characterized by recurrent episodes of loss of consciousness associated with generalized spike-and-wave discharges, with an abrupt onset and offset, in the thalamocortical system. In the absence of detailed neurophysiological studies in humans, many of the concepts regarding the pathophysiological basis of absence seizures are based on studies in animal models. Each of these models has its particular strengths and limitations, and the validity of findings from these models for the human condition cannot be assumed. Consequently, studies in different models have produced some conflicting findings and conclusions. A long-standing concept, based primarily from studies in vivo in cats and in vitro brain slices, is that these paroxysmal electrical events develop suddenly from sleep-related spindle oscillations. More specifically, it is proposed that the initial mechanisms that underlie absence-related spike-and-wave discharges are located in the thalamus, involving especially the thalamic reticular nucleus. By contrast, more recent studies in well-established, genetic models of absence epilepsy in rats demonstrate that spike-and-wave discharges originate in a cortical focus and develop from a wake-related natural corticothalamic sensorimotor rhythm. In this review we integrate recent findings showing that, in both the thalamus and the neocortex, genetically-determined, absence-related spike-and-wave discharges are the manifestation of hypersynchronized, cellular, rhythmic excitations and inhibitions that result from a combination of complex, intrinsic, synaptic mechanisms. Arguments are put forward supporting the hypothesis that layer VI corticothalamic neurons act as ‘drivers’ in the generation of spike-and-wave discharges in the somatosensory thalamocortical system that result in corticothalamic resonances particularly initially involving the thalamic reticular nucleus. However an important unresolved question is: what are the cellular

  18. Molecular tectonics: formation and structural studies on a 2-D directional coordination network based on a non-centric metacyclophane based tecton and zinc cation.

    PubMed

    Ehrhart, Jérôme; Planeix, Jean-Marc; Kyritsakas-Gruber, Nathalie; Hosseini, Mir Wais

    2010-02-28

    The combination of tectons based on the [1111]metacyclophane backbone blocked the 1,3-alternate conformation bearing two imidazoly or pyrazolyl groups located on the same side with metal halide complexes leads to the formation of either discrete metallmacrobicycles or infinite 1-D coordination networks. The same backbone bearing two sets of two different coordinating poles composed of two pyridyl and two pyrazolyl units, owing to its non-centrosymmetric nature, forms a directional 2-D network packed in an anti-parallel fashion.

  19. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment.

  20. Design mobile satellite system architecture as an integral part of the cellular access digital network

    NASA Technical Reports Server (NTRS)

    Chien, E. S. K.; Marinho, J. A.; Russell, J. E., Sr.

    1988-01-01

    The Cellular Access Digital Network (CADN) is the access vehicle through which cellular technology is brought into the mainstream of the evolving integrated telecommunications network. Beyond the integrated end-to-end digital access and per call network services provisioning of the Integrated Services Digital Network (ISDN), the CADN engenders the added capability of mobility freedom via wireless access. One key element of the CADN network architecture is the standard user to network interface that is independent of RF transmission technology. Since the Mobile Satellite System (MSS) is envisioned to not only complement but also enhance the capabilities of the terrestrial cellular telecommunications network, compatibility and interoperability between terrestrial cellular and mobile satellite systems are vitally important to provide an integrated moving telecommunications network of the future. From a network standpoint, there exist very strong commonalities between the terrestrial cellular system and the mobile satellite system. Therefore, the MSS architecture should be designed as an integral part of the CADN. This paper describes the concept of the CADN, the functional architecture of the MSS, and the user-network interface signaling protocols.

  1. Analysis of blocking rate and bandwidth usage of mobile IPTV services in wireless cellular networks.

    PubMed

    Li, Mingfu

    2014-01-01

    Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes.

  2. Analysis of Blocking Rate and Bandwidth Usage of Mobile IPTV Services in Wireless Cellular Networks

    PubMed Central

    Li, Mingfu

    2014-01-01

    Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes. PMID:25379521

  3. Molecular tectonics: p-H-thiacalix[4]arene pyridyl appended positional isomers as tectons for the formation of 1D and 2D mercury coordination networks.

    PubMed

    Ovsyannikov, A; Ferlay, S; Solovieva, S E; Antipin, I S; Konovalov, A I; Kyritsakas, N; Hosseini, M W

    2013-07-21

    Three p-H-thiacalix[4]arene pyridyl appended coordinating tectons (2-4) in a 1,3-alternate conformation have been prepared and structurally characterised in the solid state. These compounds are positional isomers differing only by the position of the nitrogen atom on the pyridyl ring. Their combinations with HgCl2 lead to the formation of 1- and 2-D neutral mercury coordination networks. Whereas for tecton 2 (ortho isomer) a 2D architecture resulting from the bridging of consecutive tectons by the mononuclear HgCl2 unit is obtained, for tecton 3 (meta isomer) again a 2D network is formed. However, in that case, the interconnection of consecutive organic tectons 3 takes place through a binuclear Hg2Cl4 species. Finally, in the case of tecton 4 (para position), a 1D ribbon type double chain arrangement resulting from the bridging of consecutive tectons by trinuclear Hg3Cl6 units followed by the interconnection of two chains through the fusion of the trinuclear centres into a hexanuclear node is observed.

  4. Cellular neural network analysis for two-dimensional bioheat transfer equation.

    PubMed

    Niu, J H; Wang, H Z; Zhang, H X; Yan, J Y; Zhu, Y S

    2001-09-01

    The cellular neural network (CNN) method is applied to solve the Pennes bioheat transfer equation, and its feasibility is demonstrated. Numerical solutions were obtained for a cellular neural network for a two-dimensional steady-state temperature field obtained from focused and unfocused ultrasound heat sources. Transient-state temperature fields were also studied and compared with experimental results obtained elsewhere. The cellular neural networks' key features of asynchronous parallel processing, continuous-time dynamics and local interaction enable real-time temperature field estimation for clinical hyperthermia.

  5. Traffic Driven Analysis of Cellular and WiFi Networks

    ERIC Educational Resources Information Center

    Paul, Utpal Kumar

    2012-01-01

    Since the days Internet traffic proliferated, measurement, monitoring and analysis of network traffic have been critical to not only the basic understanding of large networks, but also to seek improvements in resource management, traffic engineering and security. At the current times traffic in wireless local and wide area networks are facing…

  6. Traffic Driven Analysis of Cellular and WiFi Networks

    ERIC Educational Resources Information Center

    Paul, Utpal Kumar

    2012-01-01

    Since the days Internet traffic proliferated, measurement, monitoring and analysis of network traffic have been critical to not only the basic understanding of large networks, but also to seek improvements in resource management, traffic engineering and security. At the current times traffic in wireless local and wide area networks are facing…

  7. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

    PubMed

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-12-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

  8. A 2-D Pore-Network Model of the Drying of Single-Component Liquids in Porous Media

    SciTech Connect

    Yortsos, Yanic C.; Yiotis, A.G.; Stubos, A.K.; Boundovis, A.G.

    2000-01-20

    The drying of liquid-saturated porous media is typically approaching using macroscopic continuum models involving phenomenological coefficients. Insight on these coefficients can be obtained by a more fundamental study at the pore- and pore-network levels. In this report, a model based on pore-network representation of porous media that accounts for various process at the pore-scale is presented. These include mass transfer by advection and diffusion in the gas phase, viscous flow in liquid and gas phases and capillary effects at the gas-liquid menisci in the pore throats.

  9. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

    PubMed

    Naudé, Jérémie; Cessac, Bruno; Berry, Hugues; Delord, Bruno

    2013-09-18

    Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks.

  10. Analysis And Augmentation Of Timing Advance Based Geolocation In Lte Cellular Networks

    DTIC Science & Technology

    2016-12-01

    protection, and direct marketing. This work provides in-depth analysis of cellular positioning, which leverages the Long Term Evolution (LTE) signaling...provide improvements ranging from 10 to 254 m over TA-only positioning. 14. SUBJECT TERMS geolocation, Long Term Evolution (LTE), cellular networks...and direct marketing. This work provides in-depth analysis of cellular positioning, which leverages the Long Term Evolution (LTE) signaling plane

  11. On Window Control Algorithm over Wireless Cellular Networks with Large Delay Variation

    NASA Astrophysics Data System (ADS)

    Lee, Ho-Jin; Byun, Hee-Jung; Lim, Jong-Tae

    In addition to high bit error rates, large and sudden variations in delay often occur in wireless cellular networks. The delay can be several times the typical round-trip time, which can cause the spurious timeout. In this letter, we propose a new window control algorithm to improve TCP performance in wireless cellular networks with large delay variation and high bit error rates. Simulation results illustrate that our proposal improves the performance of TCP in terms of fairness and link utilization.

  12. Cues for cellular assembly of vascular elastin networks

    NASA Astrophysics Data System (ADS)

    Kothapalli, Chandrasekhar R.

    Elastin, a structural protein distributed in the extracellular matrix of vascular tissues is critical to the maintenance of vascular mechanics, besides regulation of cell-signaling pathways involved in injury response and morphogenesis. Thus, congenital absence or disease-mediated degradation of vascular elastin and its malformation within native vessels due to innately poor elastin synthesis by adult vascular cells compromise vascular homeostasis. Current elastin regenerative strategies using tissue engineering principles are limited by the progressive destabilization of tropoelastin mRNA expression in adult vascular cells and the unavailability of scaffolds that can provide cellular cues necessary to up-regulate elastin synthesis and regenerate faithful mimics of native elastin. Since our earlier studies demonstrated the elastogenic utility of hyaluronan (HA)-based cues, we have currently sought to identify a unique set of culture conditions based on HA fragments (0.756-2000 kDa), growth factors (TGF-beta1, IGF-1) and other biomolecules (Cu2+ ions, LOX), which will together enhance synthesis, crosslinking, maturation and fibrous elastin matrix formation by adult SMCs, under both healthy and inflammatory conditions. It was observed that TGF-beta1 (1 ng/mL) together with HA oligomers (0.2 microg/mL) synergistically suppressed SMC proliferation, enhanced tropoelastin (8-fold) and matrix elastin synthesis (5.5-fold), besides improving matrix yield (4.5-fold), possibly by increasing production and activity of lysyl oxidase (LOX). Though addition of IGF-1 alone did not offer any advantage, HA fragments (20-200 kDa) in the presence of IGF-1 stimulated tropoelastin and soluble elastin synthesis more than 2.2-fold, with HMW HA contributing for ˜5-fold increase in crosslinked matrix elastin synthesis. Similarly, 0.1 M of Cu2+ ions, alone or together with HA fragments stimulated synthesis of tropoelastin (4-fold) and crosslinked matrix elastin (4.5-fold), via increases in

  13. MGT-SM: A Method for Constructing Cellular Signal Transduction Networks.

    PubMed

    Li, Min; Zheng, Ruiqing; Li, Yaohang; Wu, Fang-Xiang; Wang, Jianxin

    2017-05-19

    A cellular signal transduction network is an important means to describe biological responses to environmental stimuli and exchange of biological signals. Constructing the cellular signal transduction network provides an important basis for the study of the biological activities, the mechanism of the diseases, drug targets and so on. The statistical approaches to network inference are popular in literature. Granger test has been used as an effective method for causality inference. Compared with bivariate granger tests, multivariate granger tests reduce the indirect causality and were used widely for the construction of cellular signal transduction networks. A multivariate Granger test requires that the number of time points in the time-series data is more than the number of nodes involved in the network. However, there are many real datasets with a few time points which are much less than the number of nodes in the network. In this study, we propose a new multivariate Granger test-based framework to construct cellular signal transduction network, called MGT-SM. Our MGT-SM uses SVD to compute the coefficient matrix from gene expression data and adopts Monte Carlo simulation to estimate the significance of directed edges in the constructed networks. We apply the proposed MGT-SM to Yeast Synthetic Network and MDA-MB-468, and evaluate its performance in terms of the recall and the AUC. The results show that MGT-SM achieves better results, compared with other popular methods (CGC2SPR, PGC and DBN).

  14. A metabolic-transcriptional network links sleep and cellular energetics in the brain.

    PubMed

    Wisor, Jonathan P

    2012-01-01

    This review proposes a mechanistic link between cellular metabolic status, transcriptional regulatory changes and sleep. Sleep loss is associated with changes in cellular metabolic status in the brain. Metabolic sensors responsive to cellular metabolic status regulate the circadian clock transcriptional network. Modifications of the transcriptional activity of circadian clock genes affect sleep/wake state changes. Changes in sleep state reverse sleep loss-induced changes in cellular metabolic status. It is thus proposed that the regulation of circadian clock genes by cellular metabolic sensors is a critical intermediate step in the link between cellular metabolic status and sleep. Studies of this regulatory relationship may offer insights into the function of sleep at the cellular level.

  15. A neural network-based 2D/3D image registration quality evaluator for pediatric patient setup in external beam radiotherapy.

    PubMed

    Wu, Jian; Su, Zhong; Li, Zuofeng

    2016-01-01

    Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting

  16. A neural network-based 2D/3D image registration quality evaluator for pediatric patient setup in external beam radiotherapy.

    PubMed

    Wu, Jian; Su, Zhong; Li, Zuofeng

    2016-01-08

    Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting

  17. Divide and Conquer Approach to Contact Map Overlap Problem Using 2D-Pattern Mining of Protein Contact Networks.

    PubMed

    Koneru, Suvarna Vani; Bhavani, Durga S

    2015-01-01

    A novel approach to Contact Map Overlap (CMO) problem is proposed using the two dimensional clusters present in the contact maps. Each protein is represented as a set of the non-trivial clusters of contacts extracted from its contact map. The approach involves finding matching regions between the two contact maps using approximate 2D-pattern matching algorithm and dynamic programming technique. These matched pairs of small contact maps are submitted in parallel to a fast heuristic CMO algorithm. The approach facilitates parallelization at this level since all the pairs of contact maps can be submitted to the algorithm in parallel. Then, a merge algorithm is used in order to obtain the overall alignment. As a proof of concept, MSVNS, a heuristic CMO algorithm is used for global as well as local alignment. The divide and conquer approach is evaluated for two benchmark data sets that of Skolnick and Ding et al. It is interesting to note that along with achieving saving of time, better overlap is also obtained for certain protein folds.

  18. Tools and Models for Integrating Multiple Cellular Networks

    SciTech Connect

    Gerstein, Mark

    2015-11-06

    In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed

  19. Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles

    EPA Science Inventory

    With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate t...

  20. Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles

    EPA Science Inventory

    With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate t...

  1. Smart time-pulse coding photoconverters as basic components 2D-array logic devices for advanced neural networks and optical computers

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Michalnichenko, Nikolay N.

    2004-04-01

    The article deals with a conception of building arithmetic-logic devices (ALD) with a 2D-structure and optical 2D-array inputs-outputs as advanced high-productivity parallel basic operational training modules for realization of basic operation of continuous, neuro-fuzzy, multilevel, threshold and others logics and vector-matrix, vector-tensor procedures in neural networks, that consists in use of time-pulse coding (TPC) architecture and 2D-array smart optoelectronic pulse-width (or pulse-phase) modulators (PWM or PPM) for transformation of input pictures. The input grayscale image is transformed into a group of corresponding short optical pulses or time positions of optical two-level signal swing. We consider optoelectronic implementations of universal (quasi-universal) picture element of two-valued ALD, multi-valued ALD, analog-to-digital converters, multilevel threshold discriminators and we show that 2D-array time-pulse photoconverters are the base elements for these devices. We show simulation results of the time-pulse photoconverters as base components. Considered devices have technical parameters: input optical signals power is 200nW_200μW (if photodiode responsivity is 0.5A/W), conversion time is from tens of microseconds to a millisecond, supply voltage is 1.5_15V, consumption power is from tens of microwatts to a milliwatt, conversion nonlinearity is less than 1%. One cell consists of 2-3 photodiodes and about ten CMOS transistors. This simplicity of the cells allows to carry out their integration in arrays of 32x32, 64x64 elements and more.

  2. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

    PubMed

    Watson, Emma; Walhout, Albertha J M

    2014-10-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here, we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

  3. Almost automorphic solutions for shunting inhibitory cellular neural networks with time-varying delays.

    PubMed

    Xu, Changjin; Liao, Maoxin

    2015-01-01

    This paper is concerned with the shunting inhibitory cellular neural networks with time-varying delays. Under some suitable conditions, we establish some criteria on the existence and global exponential stability of the almost automorphic solutions of the networks. Numerical simulations are given to support the theoretical findings.

  4. Networking with noise at the molecular, cellular, and population level

    NASA Astrophysics Data System (ADS)

    Vilar, Jose

    2002-03-01

    The intrinsic stochastic nature of biochemical reactions affects enzymatic and transcriptional networks at different levels. Yet, cells are able to function effectively and consistently amidst such random fluctuations. I will discuss some molecular mechanisms that are able to reduce the intrinsic noise of chemical reactions, how suitable designs can make networks resistant to noise, and what strategies can be used by populations to achieve precise functions.

  5. Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

    PubMed

    Hjorth, Johannes J J; Dawitz, Julia; Kroon, Tim; Pires, Johny; Dassen, Valerie J; Berkhout, Janna A; Emperador Melero, Javier; Nadadhur, Aish G; Alevra, Mihai; Toonen, Ruud F; Heine, Vivi M; Mansvelder, Huibert D; Meredith, Rhiannon M

    2016-04-01

    Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers.

  6. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Uijlenhoet, Remko; Leijnse, Hidde

    2016-04-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. We present a rainfall retrieval algorithm, which is employed to obtain rainfall maps from microwave links in a cellular communication network. We compare these rainfall maps to gauge-adjusted radar rainfall maps. The microwave link data set, as well as the developed code, a package in the open source scripting language "R", are freely available at GitHub (https://github.com/overeem11/RAINLINK). The purpose of this presentation is to promote rainfall mapping utilizing microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  7. Improved 2-D attenuation analysis for Northern Italy using a merged dataset from selected regional seismic networks

    NASA Astrophysics Data System (ADS)

    Morasca, Paola; Massa, Marco; Laprocina, Enrica; Mayeda, Kevin; Phillips, Scott; Malagnini, Luca; Spallarossa, Daniele; Costa, Giovanni; Augliera, Paolo

    2010-10-01

    A merged, high-quality waveform dataset from different seismic networks has been used to improve our understanding of lateral seismic attenuation for Northern Italy. In a previous study on the same region, Morasca et al. (Bull Seismol Soc Am 98:1936-1946, 2008) were able to resolve only a small area due to limited data coverage. For this reason, the interpretation of the attenuation anomalies was difficult given the complexity of the region and the poor resolution of the available data. In order to better understand the lateral changes in the crustal structure and thickness of this region, we selected 770 earthquakes recorded by 54 stations for a total of almost 16,000 waveforms derived from seismic networks operating totally or partially in Northern Italy. Direct S-wave and coda attenuation images were obtained using an amplitude ratio technique that eliminates source terms from the formulation. Both direct and early-coda amplitudes are used as input for the inversions, and the results are compared. Results were obtained for various frequency bands ranging between 0.3 and 25.0 Hz and in all cases show significant improvement with respect to the previous study since the resolved area has been extended and more crossing paths have been used to image smaller scale anomalies. Quality-factor estimates are consistent with the regional tectonic structure exhibiting a general trend of low attenuation under the Po Plain basin and higher values for the Western Alps and Northern Apennines. The interpretation of the results for the Eastern Alps is not simple, possibly because our resolution for this area is still not adequate to resolve small-scale structures.

  8. Application of Cellular Automata to Detection of Malicious Network Packets

    ERIC Educational Resources Information Center

    Brown, Robert L.

    2014-01-01

    A problem in computer security is identification of attack signatures in network packets. An attack signature is a pattern of bits that characterizes a particular attack. Because there are many kinds of attacks, there are potentially many attack signatures. Furthermore, attackers may seek to avoid detection by altering the attack mechanism so that…

  9. Resource Management in QoS-Aware Wireless Cellular Networks

    ERIC Educational Resources Information Center

    Zhang, Zhi

    2011-01-01

    Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study…

  10. Resource Management in QoS-Aware Wireless Cellular Networks

    ERIC Educational Resources Information Center

    Zhang, Zhi

    2011-01-01

    Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study…

  11. Application of Cellular Automata to Detection of Malicious Network Packets

    ERIC Educational Resources Information Center

    Brown, Robert L.

    2014-01-01

    A problem in computer security is identification of attack signatures in network packets. An attack signature is a pattern of bits that characterizes a particular attack. Because there are many kinds of attacks, there are potentially many attack signatures. Furthermore, attackers may seek to avoid detection by altering the attack mechanism so that…

  12. Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

    PubMed Central

    2013-01-01

    Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes. PMID:23663484

  13. Cellular networks involved in the influenza virus life cycle

    PubMed Central

    Watanabe, Tokiko; Watanabe, Shinji; Kawaoka, Yoshihiro

    2011-01-01

    Influenza viruses cause epidemics and pandemics. Like all other viruses, influenza viruses rely on the host cellular machinery to support their life cycle. Accordingly, identification of host functions that participate in viral replication steps is of great interest to understand the mechanisms of the virus life cycle as well as to find new targets for the development of antiviral compounds. Multiple laboratories have used various approaches to explore host factors involvement in the influenza virus replication cycle. One of the most powerful approaches is an RNAi-based genome-wide screen, which has thrown new light on the search for host factors involved in virus replication. In this review, we examine the cellular genes identified to date as important for influenza virus replication in genome-wide screens, assess pathways that were repeatedly identified in these studies, and discuss how these pathways might be involved in the individual steps of influenza virus replication, ultimately leading to a comprehensive understanding of the virus life cycle. PMID:20542247

  14. Frequency-dependent micromechanics of cellularized biopolymer networks

    NASA Astrophysics Data System (ADS)

    Jones, Chris; Kim, Jihan; McIntyre, David; Sun, Bo

    Mechanical interactions between cells and the extracellular matrix (ECM) influence many cellular behaviors such as growth, differentiation, and migration. These are dynamic processes in which the cells actively remodel the ECM. Reconstituted collagen gel is a common model ECM for studying cell-ECM interactions in vitro because collagen is the most abundant component of mammalian ECM and gives the ECM its material stiffness. We embed micron-sized particles in collagen and use holographic optical tweezers to apply forces to the particles in multiple directions and over a range of frequencies up to 10 Hz. We calculate the local compliance and show that it is dependent on both the direction and frequency of the applied force. Performing the same measurement on many particles allows us to characterize the spatial inhomogeneity of the mechanical properties and shows that the compliance decreases at higher frequencies. Performing these measurements on cell-populated collagen gels shows that cellular remodeling of the ECM changes the mechanical properties of the collagen and we investigate whether this change is dependent on the local strain and distance from nearby cells.

  15. Logical Modeling and Dynamical Analysis of Cellular Networks

    PubMed Central

    Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T.; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine

    2016-01-01

    The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle. PMID:27303434

  16. Dynamical modeling and analysis of large cellular regulatory networks

    NASA Astrophysics Data System (ADS)

    Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

    2013-06-01

    The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

  17. Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits.

    PubMed

    Kanner, Sivan; Bisio, Marta; Cohen, Gilad; Goldin, Miri; Tedesco, Marieteresa; Hanein, Yael; Ben-Jacob, Eshel; Barzilai, Ari; Chiappalone, Michela; Bonifazi, Paolo

    2015-04-15

    The brain operates through the coordinated activation and the dynamic communication of neuronal assemblies. A major open question is how a vast repertoire of dynamical motifs, which underlie most diverse brain functions, can emerge out of a fixed topological and modular organization of brain circuits. Compared to in vivo studies of neuronal circuits which present intrinsic experimental difficulties, in vitro preparations offer a much larger possibility to manipulate and probe the structural, dynamical and chemical properties of experimental neuronal systems. This work describes an in vitro experimental methodology which allows growing of modular networks composed by spatially distinct, functionally interconnected neuronal assemblies. The protocol allows controlling the two-dimensional (2D) architecture of the neuronal network at different levels of topological complexity. A desired network patterning can be achieved both on regular cover slips and substrate embedded micro electrode arrays. Micromachined structures are embossed on a silicon wafer and used to create biocompatible polymeric stencils, which incorporate the negative features of the desired network architecture. The stencils are placed on the culturing substrates during the surface coating procedure with a molecular layer for promoting cellular adhesion. After removal of the stencils, neurons are plated and they spontaneously redirected to the coated areas. By decreasing the inter-compartment distance, it is possible to obtain either isolated or interconnected neuronal circuits. To promote cell survival, cells are co-cultured with a supporting neuronal network which is located at the periphery of the culture dish. Electrophysiological and optical recordings of the activity of modular networks obtained respectively by using substrate embedded micro electrode arrays and calcium imaging are presented. While each module shows spontaneous global synchronizations, the occurrence of inter-module synchronization

  18. Development of hydraulic fracture network propagation model in shale gas reservoirs: 2D, single-phase and 3D, multi-phase model development, parametric studies, and verification

    NASA Astrophysics Data System (ADS)

    Ahn, Chong Hyun

    The most effective method for stimulating shale gas reservoirs is a massive hydraulic fracture treatment. Recent analysis using microseismic technology have shown that complex fracture networks are commonly created in the field as a result of the stimulation of shale wells. The interaction between pre-existing natural fractures and the propagating hydraulic fracture is a critical factor affecting the created complex fracture network; however, many existing numerical models simulate only planar hydraulic fractures without considering the pre-existing fractures in the formation. The shale formations already contain a large number of natural fractures, so an accurate fracture propagation model needs to be developed to optimize the fracturing process. In this research, we first characterized the mechanics of hydraulic fracturing and fluid flow in the shale gas reservoir. Then, a 2D, single-phase numerical model and a 3D, 2-phase coupled model were developed, which integrate dynamic fracture propagation, interactions between hydraulic fractures and pre-existing natural fractures, fracture fluid leakoff, and fluid flow in a petroleum reservoir. By using the developed model, we conducted parametric studies to quantify the effects of treatment rate, treatment size, fracture fluid viscosity, differential horizontal stress, natural fracture spacing, fracture toughness, matrix permeability, and proppant size on the geometry of the hydraulic fracture network. The findings elucidate important trends in hydraulic fracturing of shale reservoirs that are useful in improving the design of treatments for specific reservoir settings.

  19. Cellular Metabolic Network Analysis: Discovering Important Reactions in Treponema pallidum

    PubMed Central

    Chen, Xueying; Zhao, Min; Qu, Hong

    2015-01-01

    T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis. PMID:26495292

  20. Cellular network entropy as the energy potential in Waddington's differentiation landscape

    PubMed Central

    Banerji, Christopher R. S.; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X.; Teschendorff, Andrew E.

    2013-01-01

    Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape. PMID:24154593

  1. Cellular network entropy as the energy potential in Waddington's differentiation landscape.

    PubMed

    Banerji, Christopher R S; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X; Teschendorff, Andrew E

    2013-10-24

    Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape.

  2. Synthesis, structural determination and spectroscopic analysis of a novel 2D terbium coordination network with ethyleneglycol-bis-(2-aminoethylether)- N, N, N', N'-tetraacetic acid

    NASA Astrophysics Data System (ADS)

    Wu, Tong; Gao, Jingqun; Wang, Jun; Wang, Shujun; Bai, Yuan; Li, Ying; Li, Kai; Zhang, Xiangdong

    2012-02-01

    A novel two-dimensional (2D) mononuclear complex, namely, (enH 2)[Tb III(egta)(H 2O)] 2·6H 2O (H 4egta = ethyleneglycol-bis-(2-aminoethylether)- N, N, N', N'-tetraacetic acid and en = ethylenediamine), was successfully synthesized and characterized by infrared spectrum, UV-vis spectrum, fluorescence spectrum, thermal analysis and single-crystal X-ray diffraction techniques. Single-crystal X-ray diffraction analysis reveals that the central Tb(III) ion is nine-coordinate in geometry of pseudo-monocapped square antiprismatic polyhedron. Furthermore, the hydrogen bonds play an important role in the fabrication of layer structure of the complex. Through hydrogen bonds between ethylenediamine cation (enH 22+) and [Tb III(egta)(H 2O)] - complex anion, the title complex forms a 2D layer network along [1 1 1] crystallographic direction. Particularly, the fluorescent property is also fully investigated, which indicates that the title complex would be a potential candidate as fluorescent materials.

  3. Synthesis, structural determination and spectroscopic analysis of a novel 2D terbium coordination network with ethyleneglycol-bis-(2-aminoethylether)-N,N,N',N'-tetraacetic acid.

    PubMed

    Wu, Tong; Gao, Jingqun; Wang, Jun; Wang, Shujun; Bai, Yuan; Li, Ying; Li, Kai; Zhang, Xiangdong

    2012-02-01

    A novel two-dimensional (2D) mononuclear complex, namely, (enH(2))[Tb(III)(egta)(H(2)O)](2)·6H(2)O (H(4)egta=ethyleneglycol-bis-(2-aminoethylether)-N,N,N',N'-tetraacetic acid and en=ethylenediamine), was successfully synthesized and characterized by infrared spectrum, UV-vis spectrum, fluorescence spectrum, thermal analysis and single-crystal X-ray diffraction techniques. Single-crystal X-ray diffraction analysis reveals that the central Tb(III) ion is nine-coordinate in geometry of pseudo-monocapped square antiprismatic polyhedron. Furthermore, the hydrogen bonds play an important role in the fabrication of layer structure of the complex. Through hydrogen bonds between ethylenediamine cation (enH(2)(2+)) and [Tb(III)(egta)(H(2)O)](-) complex anion, the title complex forms a 2D layer network along [111] crystallographic direction. Particularly, the fluorescent property is also fully investigated, which indicates that the title complex would be a potential candidate as fluorescent materials. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. Stackelberg Game Based Power Allocation for Physical Layer Security of Device-to-device Communication Underlaying Cellular Networks

    NASA Astrophysics Data System (ADS)

    Qu, Junyue; Cai, Yueming; Wu, Dan; Chen, Hualiang

    2014-05-01

    The problem of power allocation for device-to-device (D2D) underlay communication to improve physical layer security is addressed. Specifically, to improve the secure communication of the cellular users, we introduce a Stackelberg game for allocating the power of the D2D link under a total power constraint and a rate constraint at the D2D pair. In the introduced Stackelberg game the D2D pair works as a seller and the cellular UEs work as buyers. Firstly, because the interference signals from D2D pair are unknown to both the legitimate receiver and the illegitimate eavesdropper, it is possible that a cellular UE decline to participate in the introduced Stackelberg game. So the condition under which a legitimate user will participate in the introduced Stackelberg game is discussed. Then, based on the Stackelberg game, we propose a semi-distributed power allocation algorithm, which is proved to conclude after finite-time iterations. In the end, some simulations are presented to verify the performance improvement in the physical layer security of cellular UEs using the proposed power allocation algorithm. We can determine that with the proposed algorithm, while the D2D pair's communication demand is met, the physical layer security of cellular UEs can be improved.

  5. Convolutional neural networks for automated annotation of cellular cryo-electron tomograms.

    PubMed

    Chen, Muyuan; Dai, Wei; Sun, Stella Y; Jonasch, Darius; He, Cynthia Y; Schmid, Michael F; Chiu, Wah; Ludtke, Steven J

    2017-10-01

    Cellular electron cryotomography offers researchers the ability to observe macromolecules frozen in action in situ, but a primary challenge with this technique is identifying molecular components within the crowded cellular environment. We introduce a method that uses neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yield in situ structures of molecular components of interest. The method is available in the EMAN2.2 software package.

  6. 2D and 3D soil moisture imaging using a sensor-based platform moving inside a subsurface network of pipes

    NASA Astrophysics Data System (ADS)

    Gravalos, I.; Moshou, D.; Loutridis, S.; Gialamas, Th.; Kateris, D.; Bompolas, E.; Tsiropoulos, Z.; Xyradakis, P.; Fountas, S.

    2013-08-01

    In this study a prototype sensor-based platform moving inside a subsurface network of pipes with the task of monitoring the soil moisture content is presented. It comprises of a mobile platform, a modified commercial soil moisture sensor (Diviner 2000), a network of subsurface polyvinylchloride (PVC) access pipes, driving hardware and image processing software. The software allows the composition of two-dimensional (2D) or three-dimensional (3D) images with high accuracy and at a large scale. The 3D soil moisture images are created by using 2D slices for better illustration of the soil moisture variability. Three case studies of varying soil moisture content using an experimental soil tank were examined. In the first case study, the irrigation water was applied uniformly on the entire tank surface. In second and third case studies, the irrigation water was applied uniformly only on the surface of the intermediate and last part of the soil tank respectively. The processed images give a detailed description of the soil moisture distribution of a layer at 15 cm depth under the soil surface in the tank. In all case studies that have been investigated, the distribution of soil moisture was characterized by a significant variability (difference between poorly and well-drained regions) of the soil tank. A very poorly-drained region was located in the middle of the soil tank, while well-drained soil areas were located southwest and northeast. The knowledge of the spatial and temporal distribution of soil moisture is a valuable tool for proper management of crop irrigation.

  7. Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling

    DTIC Science & Technology

    2016-05-31

    information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. West Virginia University...Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling Dr. Suzanne Bell, West Virginia University 1. Basis of the

  8. Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

    PubMed Central

    Rich, Scott; Booth, Victoria; Zochowski, Michal

    2016-01-01

    The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics

  9. Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks.

    PubMed

    Rich, Scott; Booth, Victoria; Zochowski, Michal

    2016-01-01

    The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics

  10. A New Cellular Automaton Model for Urban Two-Way Road Networks

    PubMed Central

    Shi, Junqing; Cheng, Lin; Liu, Yuanlin

    2014-01-01

    A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the “gridlock” phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested. PMID:25435868

  11. A new cellular automaton model for urban two-way road networks.

    PubMed

    Shi, Junqing; Cheng, Lin; Long, Jiancheng; Liu, Yuanlin

    2014-01-01

    A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the "gridlock" phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested.

  12. Molecular Signaling Network Motifs Provide a Mechanistic Basis for Cellular Threshold Responses

    PubMed Central

    Bhattacharya, Sudin; Conolly, Rory B.; Clewell, Harvey J.; Kaminski, Norbert E.; Andersen, Melvin E.

    2014-01-01

    Background: Increasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations. Objectives: Cellular response behaviors depend on the molecular pathway and circuitry in the cell and the manner in which chemicals perturb these circuits. Understanding circuit structures that are inherently capable of resisting small perturbations and producing threshold responses is an important step towards mechanistically interpreting in vitro testing data. Methods: Here we have examined dose–response characteristics for several biochemical network motifs. These network motifs are basic building blocks of molecular circuits underpinning a variety of cellular functions, including adaptation, homeostasis, proliferation, differentiation, and apoptosis. For each motif, we present biological examples and models to illustrate how thresholds arise from specific network structures. Discussion and Conclusion: Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays. Citation: Zhang Q, Bhattacharya S, Conolly RB, Clewell HJ III, Kaminski NE, Andersen ME. 2014. Molecular signaling network motifs provide a

  13. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    PubMed Central

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-01-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. PMID:27874024

  14. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  15. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks.

    PubMed

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-22

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  16. A hybrid consisting of coordination polymer and noncovalent organic networks: a highly ordered 2-D phenol network assembled by edge-to-face pi-pi interactions.

    PubMed

    Ko, Jung Woo; Min, Kil Sik; Suh, Myunghyun Paik

    2002-04-22

    A 2-D metal-organic open framework having 1-D channels, [Cu(C(10)H(26)N(6))](3)[C(6)H(3)(COO)(3)](2).18H(2)O (1), was constructed by the self-assembly of the Cu(II) complex of hexaazamacrocycle A (A = C(10)H(26)N(6)) with sodium 1,3,5-benzenetricarboxylate (BTC(3)(-)) in DMSO-H(2)O solution. 1 crystallizes in the trigonal space group P with a = b = 17.705(1) A, c = 6.940(1) A, alpha = beta = 90 degrees, gamma = 120 degrees, V = 1884.0(3) A(3), Z = 1, and rho(calcd) = 1.428 g cm(-3). The X-ray crystal structure of 1 indicates that each Cu(II) macrocyclic unit binds two BTC(3-) ions in a trans position and each BTC(3-) ion coordinates three Cu(II) macrocyclic complexes to form 2-D coordination polymer layers with honeycomb cavities (effective size 8.1 A), and the layers are packed to generate 1-D channels perpendicularly to the 2-D layers. Solid 1 binds guest molecules such as MeOH, EtOH, and PhOH with different binding constant and capacity. By the treatment of 1 with aqueous solution of phenol, a hybrid solid [Cu(C(10)H(26)N(6))](3)[C(6)H(3)(COO)(3)](2).9PhOH.6H(2)O (2) was assembled. 2 crystallizes in the trigonal R3 space group with a = b = 20.461(1) A, c = 24.159(1) A, alpha = beta = 90 degrees, gamma = 120 degrees, V = 8759.2(7) A(3), Z = 3, and rho(calcd) = 1.280 g cm(-3). In 2, highly ordered 2-D noncovalent phenol layers are formed by the edge-to-face pi-pi interactions between the phenol molecules and are alternately packed with the coordination polymer layers in the crystal lattice.

  17. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

  18. Rainfall measurements from cellular networks microwave links : an alternative ground reference for satellite validation and hydrology in Africa .

    NASA Astrophysics Data System (ADS)

    Gosset, Marielle; cazenave, frederic; Zougmore, françois; Doumounia, Ali; kacou, Modeste

    2015-04-01

    In many part of the Tropics the ground based gauge networks are sparse, often degrading and accessing this data for monitoring rainfall or for validating satellite products is sometime difficult. Here, an alternative rainfall measuring technique is proposed and tested in West Africa. It is based on using commercial microwave links from cellular telephone networks to detect and quantify rainfall. Rainfall monitoring based on commercial terrestrial microwave links has been tested for the first time in Burkina Faso, in Sahel. The rainfall regime is characterized by intense rainfall intensities brought by mesoscale Convective systems (MCS), generated by deep organized convection. The region is subjected to drought as well as dramatic floods associated with the intense rainfall provided by a few MCSs. The hydrometeorological risk is increasing and need to be monitored. In collaboration with the national cellular phone operator, Telecel Faso, the attenuation on 29 km long microwave links operating at 7 GHz was monitored at 1s time rate for the monsoon season 2012. The time series of attenuation is transformed into rain rates and compared with rain gauge data. The method is successful in quantifying rainfall: 95% of the rainy days are detected. The correlation with the daily raingauge series is 0.8 and the season bias is 5%. The correlation at the 5 min time step within each event is also high. We will present the quantitative results, discuss the uncertainties and compare the time series and the 2D maps with those derived from a polarimetric radar. The results demonstrate the potential interest of exploiting national and regional wireless telecommunication networks to provide rainfall maps for various applications : urban hydrology, agro-hydrological risk monitoring, satellite validation and development of combined rainfall products. We will also present the outcome of the first international Rain Cell Africa workshop held in Ouagadougou early 2015.

  19. Modeling psychiatric disorders at the cellular and network levels.

    PubMed

    Brennand, K J; Simone, A; Tran, N; Gage, F H

    2012-12-01

    Although psychiatric disorders such as autism spectrum disorders, schizophrenia and bipolar disorder affect a number of brain regions and produce a complex array of clinical symptoms, basic phenotypes likely exist at the level of single neurons and simple networks. Being highly heritable, it is hypothesized that these disorders are amenable to cell-based studies in vitro. Using induced pluripotent stem cell-derived neurons and/or induced neurons from fibroblasts, limitless numbers of live human neurons can now be generated from patients with a genetic background permissive to the disease state. We predict that cell-based studies will ultimately contribute to our understanding of the initiation, progression and treatment of these psychiatric disorders.

  20. Wireless cellular networks with Pareto-distributed call holding times

    NASA Astrophysics Data System (ADS)

    Rodriguez-Dagnino, Ramon M.; Takagi, Hideaki

    2001-07-01

    Nowadays, there is a growing interest in providing internet to mobile users. For instance, NTT DoCoMo in Japan deploys an important mobile phone network with that offers the Internet service, named 'i-mode', to more than 17 million subscribers. Internet traffic measurements show that the session duration of Call Holding Time (CHT) has probability distributions with heavy-tails, which tells us that they depart significantly from the traffic statistics of traditional voice services. In this environment, it is particularly important to know the number of handovers during a call for a network designer to make an appropriate dimensioning of virtual circuits for a wireless cell. The handover traffic has a direct impact on the Quality of Service (QoS); e.g. the service disruption due to the handover failure may significantly degrade the specified QoS of time-constrained services. In this paper, we first study the random behavior of the number of handovers during a call, where we assume that the CHT are Pareto distributed (heavy-tail distribution), and the Cell Residence Times (CRT) are exponentially distributed. Our approach is based on renewal theory arguments. We present closed-form formulae for the probability mass function (pmf) of the number of handovers during a Pareto distributed CHT, and obtain the probability of call completion as well as handover rates. Most of the formulae are expressed in terms of the Whittaker's function. We compare the Pareto case with cases of $k(subscript Erlang and hyperexponential distributions for the CHT.

  1. Computation of steady-state probability distributions in stochastic models of cellular networks.

    PubMed

    Hallen, Mark; Li, Bochong; Tanouchi, Yu; Tan, Cheemeng; West, Mike; You, Lingchong

    2011-10-01

    Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.

  2. Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks.

    PubMed

    Ferrazzi, Fulvia; Sebastiani, Paola; Ramoni, Marco F; Bellazzi, Riccardo

    2007-05-24

    Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.

  3. Characterization of one-dimensional cellular automata rules through topological network features

    NASA Astrophysics Data System (ADS)

    D'Alotto, Lou; Pizzuti, Clara

    2016-10-01

    The paper investigates the relationship between the classification schemes, defined by Wolfram and Gilman, of one-dimensional cellular automata through concepts coming from network theory. An automaton is represented with a network, generated from the elementary rule defining its behavior. Characteristic features of this graph are computed and machine learning classification models are built. Such models allow to classify automaton rules and to compare Wolfram's and Gilman's classes by comparing the classes predicted by these models.

  4. S-band NPOL and Iowa XPOL Radar Observations over Rain Gauge and 2D Video Disdrometer Networks during the IFloodS campaign

    NASA Astrophysics Data System (ADS)

    Thurai, Merhala; Bringi, Viswanathan; Galvez, Miguel; Vijay Mishra, Kumar; Krajewski, Witold; Goska, Radoslaw; Petersen, Walt

    2015-04-01

    As part of the GPM ground validation campaign, the Iowa Flood Studies (IFloodS) was conducted in eastern Iowa from May to June 2013. This was the first GPM campaign focused on hydrology studies and featured four units of Iowa XPOL radars and several ground-based instruments for in situ observations. In this paper, we analyze radar observations from the S-band NPOL radar and the Iowa XPOLs at locations that hosted a network of 2D video disdrometers. Three events during May 2013 have been analyzed using NPOL radar data and the measurements from the 2DVDs, in terms of drop size distribution parameters and rainfall rates. Based on these results, we derive rain rate estimators for both NPOL and XPOL radars. The estimators were then applied to radar observations for another event (12 June 2013) and compared with rain gauge measurements. Reasonable agreement is found for both NPOL and for XPOL radars, especially after taking into account the time taken for drops to fall from the radar pulse volume over the gauge network to ground level.

  5. A methodology for linking 2D overland flow models with the sewer network model SWMM 5.1 based on dynamic link libraries.

    PubMed

    Leandro, Jorge; Martins, Ricardo

    2016-01-01

    Pluvial flooding in urban areas is characterized by a gradually varying inundation process caused by surcharge of the sewer manholes. Therefore urban flood models need to simulate the interaction between the sewer network and the overland flow in order to accurately predict the flood inundation extents. In this work we present a methodology for linking 2D overland flow models with the storm sewer model SWMM 5. SWMM 5 is a well-known free open-source code originally developed in 1971. The latest major release saw its structure re-written in C ++ allowing it to be compiled as a command line executable or through a series of calls made to function inside a dynamic link library (DLL). The methodology developed herein is written inside the same DLL in C + +, and is able to simulate the bi-directional interaction between both models during simulation. Validation is done in a real case study with an existing urban flood coupled model. The novelty herein is that the new methodology can be added to SWMM without the need for editing SWMM's original code. Furthermore, it is directly applicable to other coupled overland flow models aiming to use SWMM 5 as the sewer network model.

  6. Reconstruction of multidimensional carbon hosts with combined 0D, 1D and 2D networks for enhanced lithium-sulfur batteries

    NASA Astrophysics Data System (ADS)

    Li, S. H.; Xia, X. H.; Wang, Y. D.; Wang, X. L.; Tu, J. P.

    2017-02-01

    It is a core task to find solutions to suppress the ;shuttle effect; of polysulfides and improve high rate capability at the sulfur cathode of lithium sulfur batteries. Herein we first time propose a concept of multileveled blocking ;dams; to suppress the diffusion of polysulfides. We report a facile and effective strategy to construct multidimensional conductive carbon hosts for accommodation of active sulfur. Multidimensional ternary carbon networks (MTCNs) with 0D nanospheres, 1D nanotubes and 2D nanoflakes are organically combined together to provide multileveled conductive channels to reserve active sulfur and promote stable sustained reactions. In the light of enhanced conductivity and multileveled blocking ;dams; for polysulfides, the designed MTCNs/S cathode has been demonstrated with noticeable improvement in discharge capacity (1472 mAh g-1 at 0.l C) and long-term cycling stability (65% retention at 5.0 C after 500 cycles). Our research may provide a new insight in the gradient blocking of polysulfides with the help of multidimensional carbon networks.

  7. A Mathematical Model to study the Dynamics of Epithelial Cellular Networks

    PubMed Central

    Abate, Alessandro; Vincent, Stéphane; Dobbe, Roel; Silletti, Alberto; Master, Neal; Axelrod, Jeffrey D.; Tomlin, Claire J.

    2013-01-01

    Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction). PMID:23221083

  8. A mathematical model to study the dynamics of epithelial cellular networks.

    PubMed

    Abate, Alessandro; Vincent, Stéphane; Dobbe, Roel; Silletti, Alberto; Master, Neal; Axelrod, Jeffrey D; Tomlin, Claire J

    2012-01-01

    Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution, we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction).

  9. High-frequency electric field and radiation characteristics of cellular microtubule network.

    PubMed

    Havelka, D; Cifra, M; Kučera, O; Pokorný, J; Vrba, J

    2011-10-07

    Microtubules are important structures in the cytoskeleton, which organizes the cell. Since microtubules are electrically polar, certain microtubule normal vibration modes efficiently generate oscillating electric field. This oscillating field may be important for the intracellular organization and intercellular interaction. There are experiments which indicate electrodynamic activity of variety of cells in the frequency region from kHz to GHz, expecting the microtubules to be the source of this activity. In this paper, results from the calculation of intensity of electric field and of radiated electromagnetic power from the whole cellular microtubule network are presented. The subunits of microtubule (tubulin heterodimers) are approximated by elementary electric dipoles. Mechanical oscillation of microtubule is represented by the spatial function which modulates the dipole moment of subunits. The field around oscillating microtubules is calculated as a vector superposition of contributions from all modulated elementary electric dipoles which comprise the cellular microtubule network. The electromagnetic radiation and field characteristics of the whole cellular microtubule network have not been theoretically analyzed before. For the perspective experimental studies, the results indicate that macroscopic detection system (antenna) is not suitable for measurement of cellular electrodynamic activity in the radiofrequency region since the radiation rate from single cells is very low (lower than 10⁻²⁰ W). Low noise nanoscopic detection methods with high spatial resolution which enable measurement in the cell vicinity are desirable in order to measure cellular electrodynamic activity reliably. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. Multi-Cell Cooperation with Fairness Constraint in the Downlink OFDMA Cellular Networks

    NASA Astrophysics Data System (ADS)

    Li, Hongxing; Luo, Hanwen; Chen, Wen; Guo, Jia

    In this letter, we study cell cooperation in the downlink OFDMA cellular networks. The proposed cooperation scheme is based on fractional frequency reuse (FFR), where a cooperation group consists of three sector antennas from three adjacent cells and the subchannels of each cooperation group are allocated coordinately to users. Simulation results demonstrate the effectiveness of the proposed schemes in terms of throughput and fairness.

  11. Gene and protein expression and cellular localisation of cytochrome P450 enzymes of the 1A, 2A, 2C, 2D and 2E subfamilies in equine intestine and liver.

    PubMed

    Tydén, Eva; Tjälve, Hans; Larsson, Pia

    2014-10-08

    Among the cytochrome P450 enzymes (CYP), families 1-3 constitute almost half of total CYPs in mammals and play a central role in metabolism of a wide range of pharmaceuticals. This study investigated gene and protein expression and cellular localisation of CYP1A, CYP2A, CYP2C, CYP2D and CYP2E in equine intestine and liver. Real-time polymerase chain reaction (RT-PCR) was used to analyse gene expression, western blot to examine protein expression and immunohistochemical analyses to investigate cellular localisation. CYP1A and CYP2C were the CYPs with the highest gene expression in the intestine and also showed considerable gene expression in the liver. CYP2E and CYP2A showed the highest gene expression in the liver. CYP2E showed moderate intestinal gene expression, whereas that of CYP2A was very low or undetectable. For CYP2D, rather low gene expression levels were found in both intestine and the liver. In the intestine, CYP gene expression levels, except for CYP2E, exhibited patterns resembling those of the proteins, indicating that intestinal protein expression of these CYPs is regulated at the transcriptional level. For CYP2E, the results showed that the intestinal gene expression did not correlate to any visible protein expression, indicating that intestinal protein expression of this CYP is regulated at the post-transcriptional level. Immunostaining of intestine tissue samples showed preferential CYP staining in enterocytes at the tips of intestinal villi in the small intestine. In the liver, all CYPs showed preferential localisation in the centrilobular hepatocytes. Overall, different gene expression profiles were displayed by the CYPs examined in equine intestine and liver. The CYPs present in the intestine may act in concert with those in the liver to affect the oral bioavailability and therapeutic efficiency of substrate drugs. In addition, they may play a role in first-pass metabolism of feed constituents and of herbal supplements used in equine practice.

  12. Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks.

    PubMed

    Li, Ming; Chen, Pengpeng; Gao, Shouwan

    2016-09-13

    Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes.

  13. Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks

    PubMed Central

    Li, Ming; Chen, Pengpeng; Gao, Shouwan

    2016-01-01

    Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes. PMID:27649170

  14. Identification of driving network of cellular differentiation from single sample time course gene expression data

    NASA Astrophysics Data System (ADS)

    Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

    Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

  15. Network-Guided Key Gene Discovery for a Given Cellular Process.

    PubMed

    He, Feng Q; Ollert, Markus

    2016-10-26

    Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data and the following-up network analysis, opens up new avenues to predict key genes driving a given biological process or cellular function. Here we review and compare the current approaches in predicting key genes, which have no chances to stand out by classic differential expression analysis, from gene-regulatory, protein-protein interaction, or gene expression correlation networks. We elaborate these network-based approaches mainly in the context of immunology and infection, and urge more usage of correlation network-based predictions. Such network-based key gene discovery approaches driven by information-enriched 'omics' data should be very useful for systematic key gene discoveries for any given biochemical process or cellular function, and also valuable for novel drug target discovery and novel diagnostic, prognostic and therapeutic-efficiency marker prediction for a specific disease or disorder.

  16. Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays

    PubMed Central

    Lu, Lin

    2016-01-01

    We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002). PMID:27840634

  17. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2015-08-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm and provide the corresponding code. Moreover, the code (in the scripting language "R") is made available including a data set of commercial microwave links. The purpose of this paper is to promote rainfall monitoring utilizing microwave links from cellular communication networks as an alternative or complementary means for global, continental-scale rainfall monitoring.

  18. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2016-06-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide (≈ 35 500 km2) 15 min rainfall maps can be derived from the signal attenuations of approximately 2400 microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm. Moreover, the documented, modular, and user-friendly code (a package in the scripting language "R") is made available, including a 2-day data set of approximately 2600 commercial microwave links from the Netherlands. The purpose of this paper is to promote rainfall mapping utilising microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  19. Novel Method for Detection of Air Pollution using Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    David, N.; Gao, O. H.

    2016-12-01

    Air pollution can lead to a wide spectrum of severe and chronic health impacts. Conventional tools for monitoring the phenomenon do not provide a sufficient monitoring solution in a global scale since they are, for example, not representative of the larger space or due to limited deployment as a result of practical limitations, such as: acquisition, installation, and ongoing maintenance costs. Near ground temperature inversions are directly identified with air pollution events since they suppress vertical atmospheric movement and trap pollutants near the ground. Wireless telecommunication links that comprise the data transfer infrastructure in cellular communication networks operate at frequencies of tens of GHz and are affected by different atmospheric phenomena. These systems are deployed near ground level across the globe, including in developing countries such as India, countries in Africa, etc. Many cellular providers routinely store data regarding the received signal levels in the network for quality assurance needs. Temperature inversions cause atmospheric layering, and change the refractive index of the air when compared to standard conditions. As a result, the ducts that are formed can operate, in essence, as atmospheric wave guides, and cause interference (signal amplification / attenuation) in the microwaves measured by the wireless network. Thus, this network is in effect, an existing system of environmental sensors for monitoring temperature inversions and the episodes of air pollution identified with them. This work presents the novel idea, and demonstrates it, in operation, over several events of air pollution which were detected by a standard cellular communication network during routine operation. Reference: David, N. and Gao, H.O. Using cellular communication networks to detect air pollution, Environmental Science & Technology, 2016 (accepted).

  20. Hardware Implementation of a Desktop Supercomputer for High Performance Image Processing. Color Image Processing Using Cellular Neural Networks

    DTIC Science & Technology

    1994-11-01

    This report addresses the functional behavior of Cellular Neural Networks (CNN). The impact of variable convergence times on the proper operation of...The report discusses the new fault model, presents the algorithmic procedures and shows simulated testing results. Cellular neural Networks , Testing.

  1. Quantum complexity: Quantum mutual information, complex networks, and emergent phenomena in quantum cellular automata

    NASA Astrophysics Data System (ADS)

    Vargas, David L.

    Emerging quantum simulator technologies provide a new challenge to quantum many body theory. Quantifying the emergent order in and predicting the dynamics of such complex quantum systems requires a new approach. We develop such an approach based on complex network analysis of quantum mutual information. First, we establish the usefulness of quantum mutual information complex networks by reproducing the phase diagrams of transverse Ising and Bose-Hubbard models. By quantifying the complexity of quantum cellular automata we then demonstrate the applicability of complex network theory to non-equilibrium quantum dynamics. We conclude with a study of student collaboration networks, correlating a student's role in a collaboration network with their grades. This work thus initiates a quantitative theory of quantum complexity and provides a new tool for physics education research. (Abstract shortened by ProQuest.).

  2. Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

    PubMed Central

    2013-01-01

    A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models. Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous. We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to

  3. Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation.

    PubMed

    van Winden, Wouter A; van Gulik, Walter M; Schipper, Dick; Verheijen, Peter J T; Krabben, Preben; Vinke, Jacobus L; Heijnen, Joseph J

    2003-07-05

    At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model. Copyright 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 83: 75-92, 2003.

  4. Cellular automata with object-oriented features for parallel molecular network modeling.

    PubMed

    Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

    2005-06-01

    Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

  5. The role of field coupling in nano-scale cellular nonlinear networks.

    PubMed

    Porod, Wolfgang; Csaba, Gyorgy; Csurgay, Arpad

    2003-12-01

    We review some of our previous work on field-coupling in nano-scale cellular arrays. Electronic devices based on metallic and magnetic nanoscale dots and molecular structures have been suggested, however, no technologically viable architecture for nanoelectronic circuit integration has emerged so far. A natural architecture on the nanoscale appears to be near-neighbor cellular networking, and we explore promising alternative ways of integrating nanodevices by direct physical field coupling, i.e. either by Coulomb or by magnetic interactions. We review new architectures for such field-coupled nanocircuits.

  6. Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network

    NASA Astrophysics Data System (ADS)

    Ma'ayan, Avi; Jenkins, Sherry L.; Neves, Susana; Hasseldine, Anthony; Grace, Elizabeth; Dubin-Thaler, Benjamin; Eungdamrong, Narat J.; Weng, Gehzi; Ram, Prahlad T.; Rice, J. Jeremy; Kershenbaum, Aaron; Stolovitzky, Gustavo A.; Blitzer, Robert D.; Iyengar, Ravi

    2005-08-01

    We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.

  7. Cellular and Network Contributions to Excitability of Layer 5 Neocortical Pyramidal Neurons in the Rat

    PubMed Central

    Bar-Yehuda, Dan; Korngreen, Alon

    2007-01-01

    There is a considerable gap between investigating the dynamics of single neurons and the computational aspects of neural networks. A growing number of studies have attempted to overcome this gap using the excitation in brain slices elicited by various chemical manipulations of the bath solution. However, there has been no quantitative study on the effects of these manipulations on the cellular and network factors controlling excitability. Using the whole-cell configuration of the patch-clamp technique we recorded the membrane potential from the soma of layer 5 pyramidal neurons in acute brain slices from the somatosensory cortex of young rats at 22°C and 35°C. Using blockers of synaptic transmission, we show distinct changes in cellular properties following modification of the ionic composition of the artificial cerebrospinal fluid (ACSF). Thus both cellular and network changes may contribute to the observed effects of slice excitation solutions on the physiology of single neurons. Furthermore, our data suggest that the difference in the ionic composition of current standard ACSF from that of CSF measured in vivo cause ACSF to depress network activity in acute brain slices. This may affect outcomes of experiments investigating biophysical and physiological properties of neurons in such preparations. Our results strongly advocate the necessity of redesigning experiments routinely carried out in the quiescent acute brain slice preparation. PMID:18030343

  8. Application of neural networks to channel assignment for cellular CDMA networks with multiple services and mobile base stations

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    1996-03-01

    The use of artificial neural networks to the channel assignment problem for cellular code- division multiple access (CDMA) telecommunications systems is considered. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth limited. Any reduction in interference in CDMA translates linearly into increased capacity. FDMA and TDMA use a frequency reuse pattern as a method to increase capacity, while CDMA reuses the same frequency for all cells and gains a reuse efficiency by means of orthogonal codes. The latter method can improve system capacity by factors of four to six over digital TDMA or FDMA. Cellular carriers are planning to provide multiple communication services using CDMA in the next generation cellular system infrastructure. The approach of this study is the use of neural network methods for automatic and local network control, based on traffic behavior in specific cell cites and demand history. The goal is to address certain problems associated with the management of mobile and personal communication services in a cellular radio communications environment. In planning a cellular radio network, the operator assigns channels to the radio cells so that the probability of the processed carrier-to-interference ratio, CII, exceeding a predefined value is sufficiently low. The RF propagation, determined from the topography and infrastructure in the operating area, is used in conjunction with the densities of expected communications traffic to formulate interference constraints. These constraints state which radio cells may use the same code (channel) or adjacent channels at a time. The traffic loading and the number of service grades can also be used to calculate the number of required channels (codes) for each cell. The general assignment problem is the task of assigning the required number

  9. Complete stability of cellular neural networks with unbounded time-varying delays.

    PubMed

    Wang, Lili; Chen, Tianping

    2012-12-01

    In this paper, we are concerned with the delayed cellular neural networks (DCNNs) in the case that the time-varying delays are unbounded. Under some conditions, it shows that the DCNNs can exhibit 3(n) equilibrium points. Then, we track the dynamics of u(t)(t>0) in two cases with respect to different types of subset regions in which u(0) is located. It concludes that every solution trajectory u(t) would converge to one of the equilibrium points despite the time-varying delays, that is, the delayed cellular neural networks are completely stable. The method is novel and the results obtained extend the existing ones. In addition, two illustrative examples are presented to verify the effectiveness of our results.

  10. Two programmed replicative lifespans of Saccharomyces cerevisiae formed by the endogenous molecular-cellular network.

    PubMed

    Hu, Jie; Zhu, Xiaomei; Wang, Xinan; Yuan, Ruoshi; Zheng, Wei; Xu, Minjuan; Ao, Ping

    2014-12-07

    Cellular replicative capacity is a therapeutic target for regenerative medicine as well as cancer treatment. The mechanism of replicative senescence and cell immortality is still unclear. We investigated the diauxic growth of Saccharomyces cerevisiae and demonstrate that the replicative capacity revealed by the yeast growth curve can be understood by using the dynamical property of the molecular-cellular network regulating S. cerevisiae. The endogenous network we proposed has a limit cycle when pheromone signaling is disabled, consistent with the exponential growth phase with an infinite replicative capacity. In the post-diauxic phase, the cooperative effect of the pheromone activated mitogen-activated protein kinase (MAPK) signaling pathway with the cell cycle leads to a fixed point attractor instead of the limit cycle. The cells stop dividing after several generations counting from the beginning of the post-diauxic growth. By tuning the MAPK pathway, S. cerevisiae therefore programs the number of offsprings it replicates.

  11. Sensitivity analysis of FBMC-based multi-cellular networks to synchronization errors and HPA nonlinearities

    NASA Astrophysics Data System (ADS)

    Elmaroud, Brahim; Faqihi, Ahmed; Aboutajdine, Driss

    2017-01-01

    In this paper, we study the performance of asynchronous and nonlinear FBMC-based multi-cellular networks. The considered system includes a reference mobile perfectly synchronized with its reference base station (BS) and K interfering BSs. Both synchronization errors and high-power amplifier (HPA) distortions will be considered and a theoretical analysis of the interference signal will be conducted. On the basis of this analysis, we will derive an accurate expression of signal-to-noise-plus-interference ratio (SINR) and bit error rate (BER) in the presence of a frequency-selective channel. In order to reduce the computational complexity of the BER expression, we applied an interesting lemma based on the moment generating function of the interference power. Finally, the proposed model is evaluated through computer simulations which show a high sensitivity of the asynchronous FBMC-based multi-cellular network to HPA nonlinear distortions.

  12. Adding learning to cellular genetic algorithms for training recurrent neural networks.

    PubMed

    Ku, K W; Mak, M W; Siu, W C

    1999-01-01

    This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.

  13. Exponential stability of delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions.

    PubMed

    Song, Xueli; Xin, Xing; Huang, Wenpo

    2012-05-01

    The paper discusses exponential stability of distributed delayed and impulsive cellular neural networks with partially Lipschitz continuous activation functions. By relative nonlinear measure method, some novel criteria are obtained for the uniqueness and exponential stability of the equilibrium point. Our method abandons usual assumptions on global Lipschitz continuity, boundedness and monotonicity of activation functions. Our results are generalization and improvement of some existing ones. Finally, two examples and their simulations are presented to illustrate the correctness of our analysis.

  14. Cellular Neural Network Models of Growth and Immune of Effector Cells Response to Cancer

    NASA Astrophysics Data System (ADS)

    Su, Yongmei; Min, Lequan

    Four reaction-diffusion cellular neural network (R-D CNN) models are set up based on the differential equation models for the growths of effector cells and cancer cells, and the model of the immune response to cancer proposed by Allison et al. The CNN models have different reaction-diffusion coefficients and coupling parameters. The R-D CNN models may provide possible quantitative interpretations, and are good in agreement with the in vitro experiment data reported by Allison et al.

  15. Cellular neural network implementation using a phase-only joint transform correlator

    NASA Astrophysics Data System (ADS)

    Zhang, Shuqun; Karim, Mohammad A.

    1999-04-01

    A phase-only joint transform correlator (JTC) is used to realize cellular neural networks (CNNs). The operation of summing cross-correlations of bipolar data in CNNs can be realized in parallel by phase-encoding bipolar data. Compared to other optical systems for implementing CNNs, the proposed method offers the advantages of easier implementation and robustness in terms of system alignment, and requires neither electronic precalculation nor data rearrangement. Simulation results of the proposed optical CNNs for edge detection are provided.

  16. Realization of couplings in a polynomial type mixed-mode cellular neural network.

    PubMed

    Laiho, Mika; Paasio, Ari; Kananen, Asko; Halonen, Kari

    2003-12-01

    In this paper realization of couplings between cells in a polynomial type mixed-mode cellular neural network (CNN) is analyzed. The choice of the multiplier is discussed and two multiplier types are analyzed. Also, two circuits for generating the second and third order polynomial terms of cell output are described. The accuracy of the multipliers and polynomial circuits at the presence of device mismatch is analyzed.

  17. Molecular tectonics: generation and structural studies on 1- and 2D coordination networks based on a meta-cyclophane in 1,3-alternate conformation bearing four pyrazolyl units and cobalt, zinc and copper cations.

    PubMed

    Ehrhart, Jérôme; Planeix, Jean-Marc; Kyritsakas-Gruber, Nathalie; Hosseini, Mir Wais

    2009-08-28

    The combination of a [1111] metacyclophane blocked in 1,3-alternate conformation and bearing four pyrazolyl coordinating units with MX(2) (M = Co, Zn and X = Cl or Br) leads to the formation of crystals formed by packing of 2D coordination networks. In the case of CuBr(2), the formation of a 1D network was observed. Structural studies by X-ray diffraction methods on single crystals were performed on all cases reported.

  18. An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

    NASA Astrophysics Data System (ADS)

    Al-karawi, Dhurgham; Sayasneh, A.; Al-Assam, Hisham; Jassim, Sabah; Page, N.; Timmerman, D.; Bourne, T.; Du, Hongbo

    2017-05-01

    Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.

  19. An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

    PubMed

    Matsubara, Takashi; Torikai, Hiroyuki

    2016-04-01

    Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

  20. Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

    PubMed

    Wei, Yawei; Venayagamoorthy, Ganesh Kumar

    2017-09-01

    To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Measuring information flow in cellular networks by the systems biology method through microarray data

    PubMed Central

    Chen, Bor-Sen; Li, Cheng-Wei

    2015-01-01

    In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells. PMID:26082788

  2. Measuring information flow in cellular networks by the systems biology method through microarray data.

    PubMed

    Chen, Bor-Sen; Li, Cheng-Wei

    2015-01-01

    In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.

  3. Creative elements: network-based predictions of active centres in proteins and cellular and social networks.

    PubMed

    Csermely, Peter

    2008-12-01

    Active centres and hot spots of proteins have a paramount importance in enzyme action, protein-complex formation and drug design. Recently, several publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show several properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks, from molecules to society at large. Using analogy, I propose that existing findings and methodology already enable us to detect active centres in cells and can be expanded to social networks and ecosystems. Members of these active centres are termed here as 'creative elements' of their respective networks, which can help them to survive unprecedented, novel challenges and play a key part in the development, survival and evolvability of complex systems.

  4. A New Cellular Architecture for Information Retrieval from Sensor Networks through Embedded Service and Security Protocols.

    PubMed

    Shahzad, Aamir; Landry, René; Lee, Malrey; Xiong, Naixue; Lee, Jongho; Lee, Changhoon

    2016-06-14

    Substantial changes have occurred in the Information Technology (IT) sectors and with these changes, the demand for remote access to field sensor information has increased. This allows visualization, monitoring, and control through various electronic devices, such as laptops, tablets, i-Pads, PCs, and cellular phones. The smart phone is considered as a more reliable, faster and efficient device to access and monitor industrial systems and their corresponding information interfaces anywhere and anytime. This study describes the deployment of a protocol whereby industrial system information can be securely accessed by cellular phones via a Supervisory Control And Data Acquisition (SCADA) server. To achieve the study goals, proprietary protocol interconnectivity with non-proprietary protocols and the usage of interconnectivity services are considered in detail. They support the visualization of the SCADA system information, and the related operations through smart phones. The intelligent sensors are configured and designated to process real information via cellular phones by employing information exchange services between the proprietary protocol and non-proprietary protocols. SCADA cellular access raises the issue of security flaws. For these challenges, a cryptography-based security method is considered and deployed, and it could be considered as a part of a proprietary protocol. Subsequently, transmission flows from the smart phones through a cellular network.

  5. A New Cellular Architecture for Information Retrieval from Sensor Networks through Embedded Service and Security Protocols

    PubMed Central

    Shahzad, Aamir; Landry, René; Lee, Malrey; Xiong, Naixue; Lee, Jongho; Lee, Changhoon

    2016-01-01

    Substantial changes have occurred in the Information Technology (IT) sectors and with these changes, the demand for remote access to field sensor information has increased. This allows visualization, monitoring, and control through various electronic devices, such as laptops, tablets, i-Pads, PCs, and cellular phones. The smart phone is considered as a more reliable, faster and efficient device to access and monitor industrial systems and their corresponding information interfaces anywhere and anytime. This study describes the deployment of a protocol whereby industrial system information can be securely accessed by cellular phones via a Supervisory Control And Data Acquisition (SCADA) server. To achieve the study goals, proprietary protocol interconnectivity with non-proprietary protocols and the usage of interconnectivity services are considered in detail. They support the visualization of the SCADA system information, and the related operations through smart phones. The intelligent sensors are configured and designated to process real information via cellular phones by employing information exchange services between the proprietary protocol and non-proprietary protocols. SCADA cellular access raises the issue of security flaws. For these challenges, a cryptography-based security method is considered and deployed, and it could be considered as a part of a proprietary protocol. Subsequently, transmission flows from the smart phones through a cellular network. PMID:27314351

  6. Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks

    PubMed Central

    Hallen, Mark; Li, Bochong; Tanouchi, Yu; Tan, Cheemeng; West, Mike; You, Lingchong

    2011-01-01

    Cellular processes are “noisy”. In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry. PMID:22022252

  7. Phase transitions in pancreatic islet cellular networks and implications for type-1 diabetes

    NASA Astrophysics Data System (ADS)

    Stamper, I. J.; Jackson, Elais; Wang, Xujing

    2014-01-01

    In many aspects the onset of a chronic disease resembles a phase transition in a complex dynamic system: Quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. In this study we examine a special case, the onset of type-1 diabetes (T1D), a disease that results from loss of the insulin-producing pancreatic islet β cells. Within each islet, the β cells are electrically coupled to each other via gap-junctional channels. This intercellular coupling enables the β cells to synchronize their insulin release, thereby generating the multiscale temporal rhythms in blood insulin that are critical to maintaining blood glucose homeostasis. Using percolation theory we show how normal islet function is intrinsically linked to network connectivity. In particular, the critical amount of β-cell death at which the islet cellular network loses site percolation is consistent with laboratory and clinical observations of the threshold loss of β cells that causes islet functional failure. In addition, numerical simulations confirm that the islet cellular network needs to be percolated for β cells to synchronize. Furthermore, the interplay between site percolation and bond strength predicts the existence of a transient phase of islet functional recovery after onset of T1D and introduction of treatment, potentially explaining the honeymoon phenomenon. Based on these results, we hypothesize that the onset of T1D may be the result of a phase transition of the islet β-cell network.

  8. Motion adaptive vertical handoff in cellular/WLAN heterogeneous wireless network.

    PubMed

    Li, Limin; Ma, Lin; Xu, Yubin; Fu, Yunhai

    2014-01-01

    In heterogeneous wireless network, vertical handoff plays an important role for guaranteeing quality of service and overall performance of network. Conventional vertical handoff trigger schemes are mostly developed from horizontal handoff in homogeneous cellular network. Basically, they can be summarized as hysteresis-based and dwelling-timer-based algorithms, which are reliable on avoiding unnecessary handoff caused by the terminals dwelling at the edge of WLAN coverage. However, the coverage of WLAN is much smaller compared with cellular network, while the motion types of terminals can be various in a typical outdoor scenario. As a result, traditional algorithms are less effective in avoiding unnecessary handoff triggered by vehicle-borne terminals with various speeds. Besides that, hysteresis and dwelling-timer thresholds usually need to be modified to satisfy different channel environments. For solving this problem, a vertical handoff algorithm based on Q-learning is proposed in this paper. Q-learning can provide the decider with self-adaptive ability for handling the terminals' handoff requests with different motion types and channel conditions. Meanwhile, Neural Fuzzy Inference System (NFIS) is embedded to retain a continuous perception of the state space. Simulation results verify that the proposed algorithm can achieve lower unnecessary handoff probability compared with the other two conventional algorithms.

  9. Motion Adaptive Vertical Handoff in Cellular/WLAN Heterogeneous Wireless Network

    PubMed Central

    Ma, Lin; Xu, Yubin; Fu, Yunhai

    2014-01-01

    In heterogeneous wireless network, vertical handoff plays an important role for guaranteeing quality of service and overall performance of network. Conventional vertical handoff trigger schemes are mostly developed from horizontal handoff in homogeneous cellular network. Basically, they can be summarized as hysteresis-based and dwelling-timer-based algorithms, which are reliable on avoiding unnecessary handoff caused by the terminals dwelling at the edge of WLAN coverage. However, the coverage of WLAN is much smaller compared with cellular network, while the motion types of terminals can be various in a typical outdoor scenario. As a result, traditional algorithms are less effective in avoiding unnecessary handoff triggered by vehicle-borne terminals with various speeds. Besides that, hysteresis and dwelling-timer thresholds usually need to be modified to satisfy different channel environments. For solving this problem, a vertical handoff algorithm based on Q-learning is proposed in this paper. Q-learning can provide the decider with self-adaptive ability for handling the terminals' handoff requests with different motion types and channel conditions. Meanwhile, Neural Fuzzy Inference System (NFIS) is embedded to retain a continuous perception of the state space. Simulation results verify that the proposed algorithm can achieve lower unnecessary handoff probability compared with the other two conventional algorithms. PMID:24741347

  10. Phase transitions in pancreatic islet cellular networks and implications for type-1 diabetes

    PubMed Central

    Stamper, I. J.; Jackson, Elais; Wang, Xujing

    2014-01-01

    In many aspects the onset of a chronic disease resembles a phase transition in a complex dynamic system: Quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. In this study we examine a special case, the onset of type-1 diabetes (T1D), a disease that results from loss of the insulin-producing pancreatic islet β cells. Within each islet, the β cells are electrically coupled to each other via gap-junctional channels. This intercellular coupling enables the β cells to synchronize their insulin release, thereby generating the multiscale temporal rhythms in blood insulin that are critical to maintaining blood glucose homeostasis. Using percolation theory we show how normal islet function is intrinsically linked to network connectivity. In particular, the critical amount of β-cell death at which the islet cellular network loses site percolation is consistent with laboratory and clinical observations of the threshold loss of β cells that causes islet functional failure. In addition, numerical simulations confirm that the islet cellular network needs to be percolated for β cells to synchronize. Furthermore, the interplay between site percolation and bond strength predicts the existence of a transient phase of islet functional recovery after onset of T1D and introduction of treatment, potentially explaining the honeymoon phenomenon. Based on these results, we hypothesize that the onset of T1D may be the result of a phase transition of the islet β-cell network. PMID:24580269

  11. Hybrid RF and Digital Beamformer for Cellular Networks: Algorithms, Microwave Architectures, and Measurements

    NASA Astrophysics Data System (ADS)

    Venkateswaran, Vijay; Pivit, Florian; Guan, Lei

    2016-07-01

    Modern wireless communication networks, particularly cellular networks utilize multiple antennas to improve the capacity and signal coverage. In these systems, typically an active transceiver is connected to each antenna. However, this one-to-one mapping between transceivers and antennas will dramatically increase the cost and complexity of a large phased antenna array system. In this paper, firstly we propose a \\emph{partially adaptive} beamformer architecture where a reduced number of transceivers with a digital beamformer (DBF) is connected to an increased number of antennas through an RF beamforming network (RFBN). Then, based on the proposed architecture, we present a methodology to derive the minimum number of transceivers that are required for marco-cell and small-cell base stations, respectively. Subsequently, in order to achieve optimal beampatterns with given cellular standard requirements and RF operational constraints, we propose efficient algorithms to jointly design DBF and RFBN. Starting from the proposed algorithms, we specify generic microwave RFBNs for optimal marco-cell and small-cell networks. In order to verify the proposed approaches, we compare the performance of RFBN using simulations and anechoic chamber measurements. Experimental measurement results confirm the robustness and performance of the proposed hybrid DBF-RFBN concept eventually ensuring that theoretical multi-antenna capacity and coverage are achieved at a little incremental cost.

  12. The translational machinery is an optimized molecular network that affects cellular homoeostasis and disease.

    PubMed

    Kazana, Eleanna; von der Haar, Tobias

    2014-02-01

    Translation involves interactions between mRNAs, ribosomes, tRNAs and a host of translation factors. Emerging evidence on the eukaryotic translational machinery indicates that these factors are organized in a highly optimized network, in which the levels of the different factors are finely matched to each other. This optimal factor network is essential for producing proteomes that result in optimal fitness, and perturbations to the optimal network that significantly affect translational activity therefore result in non-optimal proteomes, fitness losses and disease. On the other hand, experimental evidence indicates that translation and cell growth are relatively robust to perturbations, and viability can be maintained even upon significant damage to individual translation factors. How the eukaryotic translational machinery is optimized, and how it can maintain optimization in the face of changing internal parameters, are open questions relevant to the interaction between translation and cellular disease states.

  13. A global genetic interaction network maps a wiring diagram of cellular function.

    PubMed

    Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N; Baryshnikova, Anastasia; Pons, Carles; Tan, Guihong; Wang, Wen; Usaj, Matej; Hanchard, Julia; Lee, Susan D; Pelechano, Vicent; Styles, Erin B; Billmann, Maximilian; van Leeuwen, Jolanda; van Dyk, Nydia; Lin, Zhen-Yuan; Kuzmin, Elena; Nelson, Justin; Piotrowski, Jeff S; Srikumar, Tharan; Bahr, Sondra; Chen, Yiqun; Deshpande, Raamesh; Kurat, Christoph F; Li, Sheena C; Li, Zhijian; Usaj, Mojca Mattiazzi; Okada, Hiroki; Pascoe, Natasha; San Luis, Bryan-Joseph; Sharifpoor, Sara; Shuteriqi, Emira; Simpkins, Scott W; Snider, Jamie; Suresh, Harsha Garadi; Tan, Yizhao; Zhu, Hongwei; Malod-Dognin, Noel; Janjic, Vuk; Przulj, Natasa; Troyanskaya, Olga G; Stagljar, Igor; Xia, Tian; Ohya, Yoshikazu; Gingras, Anne-Claude; Raught, Brian; Boutros, Michael; Steinmetz, Lars M; Moore, Claire L; Rosebrock, Adam P; Caudy, Amy A; Myers, Chad L; Andrews, Brenda; Boone, Charles

    2016-09-23

    We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

  14. Issues for the integration of satellite and terrestrial cellular networks for mobile communications

    NASA Technical Reports Server (NTRS)

    Delre, Enrico; Mistretta, Ignazio; Dellipriscoli, Francesco; Settimo, Franco

    1991-01-01

    Satellite and terrestrial cellular systems naturally complement each other for land mobile communications, even though present systems have been developed independently. The main advantages of the integrated system are a faster wide area coverage, a better management of overloading traffic conditions, an extension to geographical areas not covered by the terrestrial network and, in perspective, the provision of only one integrated system for all mobile communications (land, aeronautical, and maritime). To achieve these goals, as far as possible the same protocols of the terrestrial network should be used also for the satellite network. Discussed here are the main issues arising from the requirements of the main integrated system. Some results are illustrated, and possible future improvements due to technical solutions are presented.

  15. Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

    PubMed

    Schröter, Manuel; Paulsen, Ole; Bullmore, Edward T

    2017-03-01

    Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.

  16. A Memristive Multilayer Cellular Neural Network With Applications to Image Processing.

    PubMed

    Hu, Xiaofang; Feng, Gang; Duan, Shukai; Liu, Lu

    2016-05-13

    The memristor has been extensively studied in electrical engineering and biological sciences as a means to compactly implement the synaptic function in neural networks. The cellular neural network (CNN) is one of the most implementable artificial neural network models and capable of massively parallel analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through simulations.

  17. Increasing the coverage area through relay node deployment in long term evolution advanced cellular networks

    NASA Astrophysics Data System (ADS)

    Aldhaibani, Jaafar A.; Ahmad, R. B.; Yahya, A.; Azeez, Suzan A.

    2015-05-01

    Wireless multi-hop relay networks have become very important technologies in mobile communications. These networks ensure high throughput and coverage extension with a low cost. The poor capacity at cell edges is not enough to meet with growing demand of high capacity and throughput irrespective of user's placement in the cellular network. In this paper we propose optimal placement of relay node that provides maximum achievable rate at users and enhances the throughput and coverage at cell edge region. The proposed scheme is based on the outage probability at users and taken on account the interference between nodes. Numerical analyses along with simulation results indicated there are an improvement in capacity for users at the cell edge is 40% increment from all cell capacity.

  18. A synthetic multi-cellular network of coupled self-sustained oscillators

    PubMed Central

    Giraldo, Daniel; Gomez-Porras, Judith Lucia; Dreyer, Ingo; González Barrios, Andrés Fernando; Arevalo-Ferro, Catalina

    2017-01-01

    Engineering artificial networks from modular components is a major challenge in synthetic biology. In the past years, single units, such as switches and oscillators, were successfully constructed and implemented. The effective integration of these parts into functional artificial self-regulated networks is currently on the verge of breakthrough. Here, we describe the design of a modular higher-order synthetic genetic network assembled from two independent self-sustained synthetic units: repressilators coupled via a modified quorum-sensing circuit. The isolated communication circuit and the network of coupled oscillators were analysed in mathematical modelling and experimental approaches. We monitored clustering of cells in groups of various sizes. Within each cluster of cells, cells oscillate synchronously, whereas the theoretical modelling predicts complete synchronization of the whole cellular population to be obtained approximately after 30 days. Our data suggest that self-regulated synchronization in biological systems can occur through an intermediate, long term clustering phase. The proposed artificial multicellular network provides a system framework for exploring how a given network generates a specific behaviour. PMID:28662174

  19. A synthetic multi-cellular network of coupled self-sustained oscillators.

    PubMed

    Fernández-Niño, Miguel; Giraldo, Daniel; Gomez-Porras, Judith Lucia; Dreyer, Ingo; González Barrios, Andrés Fernando; Arevalo-Ferro, Catalina

    2017-01-01

    Engineering artificial networks from modular components is a major challenge in synthetic biology. In the past years, single units, such as switches and oscillators, were successfully constructed and implemented. The effective integration of these parts into functional artificial self-regulated networks is currently on the verge of breakthrough. Here, we describe the design of a modular higher-order synthetic genetic network assembled from two independent self-sustained synthetic units: repressilators coupled via a modified quorum-sensing circuit. The isolated communication circuit and the network of coupled oscillators were analysed in mathematical modelling and experimental approaches. We monitored clustering of cells in groups of various sizes. Within each cluster of cells, cells oscillate synchronously, whereas the theoretical modelling predicts complete synchronization of the whole cellular population to be obtained approximately after 30 days. Our data suggest that self-regulated synchronization in biological systems can occur through an intermediate, long term clustering phase. The proposed artificial multicellular network provides a system framework for exploring how a given network generates a specific behaviour.

  20. Cellular pulse-coupled neural network with adaptive weights for image segmentation and its VLSI implementation

    NASA Astrophysics Data System (ADS)

    Schreiter, Juerg; Ramacher, Ulrich; Heittmann, Arne; Matolin, Daniel; Schuffny, Rene

    2004-05-01

    We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.

  1. Perturbation in mitochondrial network dynamics and in complex I dependent cellular respiration in schizophrenia.

    PubMed

    Rosenfeld, Marina; Brenner-Lavie, Hanit; Ari, Shunit Gal-Ben; Kavushansky, Alexandra; Ben-Shachar, Dorit

    2011-05-15

    Mitochondria have been suggested to be involved in the pathology of bipolar disorder (BD) and schizophrenia. However, the mechanism underlying mitochondrial dysfunction is unclear. Mitochondrial network dynamics, which reflects cellular metabolic state, is important for embryonic development, synapse formation, and neurodegeneration. This study aimed to investigate mitochondrial network dynamics and its plausible association with abnormal cellular oxygen consumption in schizophrenia. Viable Epstein-Barr virus (EBV)-transformed lymphocytes (lymphoblastoids) from DSM-IV diagnosed patients with schizophrenia (n = 17), BD (n = 15), and healthy control subjects (n = 15) were assessed for mitochondrial respiration, mitochondrial dynamics, and relevant protein levels by oxygraph, confocal microscopy, and immunoblotting, respectively. Respiration of schizophrenia-derived lymphoblastoids was significantly lower compared with control subjects, and was twice as sensitive to dopamine (DA)-induced inhibition. Unlike DA, haloperidol inhibited complex I-driven respiration to a similar extent in both schizophrenia and the control cells. Both drugs interact with complex I but at different sites. At the site of DA interaction, we found alterations in protein levels of three subunits of complex I in schizophrenia. In addition, we observed structural and connectivity perturbations in the mitochondrial network, associated with alterations in the profusion protein OPA1, which was similarly reduced in schizophrenia prefrontal cortex specimens. None of these alterations were observed in the BD cells, which were similar to control cells. We show impaired mitochondrial network dynamics associated with reduced cellular respiration and complex I abnormalities in schizophrenia but not in BD. If these findings represent disease-specific alterations, they may become an endophenotype biomarker for schizophrenia. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All

  2. Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

    PubMed Central

    Wallach, Thomas; Schellenberg, Katja; Maier, Bert; Kalathur, Ravi Kiran Reddy; Porras, Pablo; Wanker, Erich E.; Futschik, Matthias E.; Kramer, Achim

    2013-01-01

    Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner. PMID:23555304

  3. Towards operational rainfall monitoring with microwave links from cellular telecommunication networks

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2017-04-01

    The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. Microwave links from cellular communication networks have been proposed as a promising new rainfall measurement technique since one decade. They are particularly interesting for those countries where few surface rainfall observations are available. Yet too date no operational (real-time) link-based rainfall products are available. To advance the process towards operational application and upscaling of this technique, long time series should be analyzed for different networks and climates. Here the potential for long-term large-scale operational rainfall monitoring is demonstrated by utilizing a 2.5-year data set from a cellular communication network. The data set consists of roughly 2,000 links covering the land surface of the Netherlands (35,500 square kilometers). The quality of link rainfall maps is thoroughly quantified by an extensive validation against independent gauge-adjusted radar rainfall maps for, among others, different seasons and extremes. One of the goals is to quantify whether the cellular telecommunication network can yield rainfall maps of comparable quality as those based on automatic rain gauge data (with a density of 1 gauge per 1000 square kilometers). Developing countries will usually have rain gauge networks with a lower density (and little or no weather radars). This helps to assess the possibly added value of link-based rainfall estimates with respect to those from existing rain gauge networks. Moreover, this shows the possibly added value of link rainfall estimates for adjustment of radar rainfall images. The results further confirm

  4. Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data.

    PubMed

    Chen, Bor-Sen; Li, Cheng-Wei

    2016-02-20

    Epigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data. We investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein-protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone

  5. Multifunctional Superelastic Foam-Like Boron Nitride Nanotubular Cellular-Network Architectures.

    PubMed

    Xue, Yanming; Dai, Pengcheng; Zhou, Min; Wang, Xi; Pakdel, Amir; Zhang, Chao; Weng, Qunhong; Takei, Toshiaki; Fu, Xiuwei; Popov, Zakhar I; Sorokin, Pavel B; Tang, Chengchun; Shimamura, Kiyoshi; Bando, Yoshio; Golberg, Dmitri

    2017-01-24

    Construction of cellular architectures has been expected to enhance materials' mechanical tolerance and to stimulate and broaden their efficient utilizations in many potential fields. However, hitherto, there have been rather scarce developments in boron nitride (BN)-type cellular architectures because of well-known difficulties in the syntheses of BN-based structures. Herein, cellular-network multifunctional foams made of interconnective nanotubular hexagonal BN (h-BN) architectures are developed using carbothermal reduction-assisted in situ chemical vapor deposition conversion from N-doped tubular graphitic cellular foams. These ultralight, chemically inert, thermally stable, and robust-integrity (supporting about 25,000 times of their own weight) three-dimensional-BN foams exhibit a 98.5% porosity, remarkable shape recovery (even after cycling compressions with 90% deformations), excellent resistance to water intrusion, thermal diffusion stability, and high strength and stiffness. They remarkably reduce the coefficient of thermal expansion and dielectric constant of polymeric poly(methyl methacrylate) composites, greatly contribute to their thermal conductivity improvement, and effectively limit polymeric composite softening at elevated temperatures. The foams also demonstrate high-capacity adsorption-separation and removal ability for a wide range of oils and organic chemicals in oil/water systems and reliable recovery under their cycling usage as organic adsorbers. These created multifunctional foams should be valuable in many high-end practical applications.

  6. Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

    NASA Astrophysics Data System (ADS)

    Hinczewski, Michael; Thirumalai, D.

    2014-10-01

    Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.

  7. The Role of Voltage-Dependence of the NMDA Receptor in Cellular and Network Oscillation

    PubMed Central

    Martell, Amber L.; Ramirez, Jan-Marino; Lasky, Robert E.; Dwyer, Jennifer E.; Kohrman, Michael; van Drongelen, Wim

    2012-01-01

    Unraveling the mechanisms underlying oscillatory behavior is critical for understanding normal and pathological brain processes. Here we used electrophysiology in mouse neocortical slices and principles of nonlinear dynamics to demonstrate how an increase in the N-methyl-D-aspartic acid receptor (NMDAR) conductance can create a nonlinear whole-cell current-voltage ( I – V ) relationship, which leads to changes in cellular stability. We discovered two behaviorally and morphologically distinct pyramidal cell populations. Under control conditions, both cell types responded to depolarizing current injection with regular spiking patterns. However, upon NMDAR activation, an intrinsic oscillatory (IO) cell type (n = 44) showed a nonlinear whole-cell I – V relationship, intrinsic voltage-dependent oscillations plus amplification of alternating input current, and these properties persisted after disabling action potential generation with TTX. The other non-oscillatory (NO) neuronal population (n = 24) demonstrated none of these behaviors. Simultaneous intra- and extracellular recordings demonstrated the NMDAR’s capacity to promote low-frequency seizure-like network oscillations via its effects on intrinsic neuronal properties. The two pyramidal cell types demonstrated different relationships with network oscillation: the IO cells were leaders that were activated early in the population activity cycle while the activation of the NO cell type was distributed across network bursts. The properties of IO neurons disappeared in a low magnesium environment where the voltage-dependence of the receptor is abolished; concurrently, the cellular contribution to network oscillation switched to synchronous firing. Thus, depending upon the efficacy of NMDAR in altering the linearity of the whole-cell current-voltage relationship, the two cell populations played different roles in sustaining network oscillation. PMID:22805058

  8. Three Years of Country-Wide Rainfall Maps from Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Rios Gaona, Manuel Felipe; Leijnse, Hidde; Uijlenhoet, Remko

    2015-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular communication networks may be used for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. This is particularly interesting for those countries where few surface rainfall observations are available. A data set from a commercial microwave link network over the Netherlands is analyzed. The data set runs from January 2011 - January 2014 and consists of roughly 2,000 links covering the land surface of the Netherlands (35,500 square kilometers). From this 3-year data set country-wide rainfall maps are retrieved, which are compared to a gauge-adjusted radar data set. The ability of cellular communication networks to estimate rainfall is studied for different temporal and spatial scales (including the catchment scale). To summarize, the results further confirm the potential of these networks for rainfall monitoring for hydrological applications.

  9. Three Years of Country-Wide Rainfall Maps from Cellular Communication Networks

    NASA Astrophysics Data System (ADS)

    Uijlenhoet, R.; Overeem, A.; Leijnse, H.; Rios Gaona, M. F.

    2014-12-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular communication networks may be used for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. This is particularly interesting for those countries where few surface rainfall observations are available. A data set from a commercial microwave link network over the Netherlands is analyzed. The data set runs from January 2011 - January 2014 and consists of roughly 2000 links covering the land surface of the Netherlands (35,500 square kilometers). From this 3-year data set country-wide rainfall maps are retrieved, which are compared to a gauge-adjusted radar data set. The ability of cellular communication networks to estimate rainfall is studied for different temporal and spatial scales, as well as for several air temperature classes. Case studies are presented to investigate the performance of the algorithm during snow and sleet and to show the influence of dew formation on the antennas on the received signal levels. To summarize, the results further confirm the potential of these networks for rainfall monitoring.

  10. Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

    NASA Astrophysics Data System (ADS)

    Karabiber, Fethullah; Vecchio, Pietro; Grassi, Giuseppe

    2011-12-01

    The Bio-inspired (Bi-i) Cellular Vision System is a computing platform consisting of sensing, array sensing-processing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN) paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy.

  11. Reduction-oxidation network for flexible adjustment of cellular metabolism in photoautotrophic cells.

    PubMed

    Scheibe, Renate; Dietz, Karl-Josef

    2012-02-01

    Photosynthesis generates the energy carriers NADPH and ATP to be consumed in assimilatory processes. Continuous energy conversion and optimal use of the available light energy are only guaranteed when all reduction-oxidation (redox) processes are tightly controlled. A robust network links metabolism with regulation and signalling. Information on the redox situation is generated and transferred by various redox components that are parts of this network. Any imbalance in the network is sensed, and the information is transmitted in order to elicit a response at the various levels of regulation and in the different cellular compartments. Redox information within the chloroplast is derived from intersystem electron transport, the ferredoxin-NADP oxidoreductase (FNR)/NADPH branch of the redox network, the thioredoxin branch and from reactive oxygen species (ROS), resulting in a high diversity of responses that are able to adjust photosynthesis, as well as poising and antioxidant systems accordingly in each specific situation. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) represents a central step in CO(2) reduction and in carbohydrate oxidation involving both forms of energy, namely NAD(P)H and ATP, with its various isoforms that are located in plastids, cytosol and nucleus. GAPDH is used as an example to demonstrate complexity, flexibility and robustness of the regulatory redox network in plants. © 2011 Blackwell Publishing Ltd.

  12. High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels.

    PubMed

    Müller, Jan; Ballini, Marco; Livi, Paolo; Chen, Yihui; Radivojevic, Milos; Shadmani, Amir; Viswam, Vijay; Jones, Ian L; Fiscella, Michele; Diggelmann, Roland; Stettler, Alexander; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas

    2015-07-07

    Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

  13. Global optimization of data quality checks on 2-D and 3-D networks of GPR cross-well tomographic data for automatic correction of unknown well deviations

    SciTech Connect

    Sassen, D. S.; Peterson, J. E.

    2010-03-15

    .g. Bautu et al., 2006). In the technique of algebraic reconstruction tomography (ART), which is used herein for the travel time inversion (Peterson et al., 1985), a small relaxation parameter will smooth imaging artifacts caused by data errors at the expense of resolution and contrast (Figure 2). However, large data errors such as unaccounted well deviations cannot be adequately suppressed through inversion weighting schemes. Previously, problems with tomograms were treated manually. However, in large data sets and/or networks of data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Mislocation of the transmitter and receiver stations of GPR cross-well tomography data sets can lead to serious imaging artifacts if not accounted for prior to inversion. Previously, problems with tomograms have been treated manually prior to inversion. In large data sets and/or networks of tomographic data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Our approach is to use cross-well data quality checks and a simplified model of borehole deviation with particle swarm optimization (PSO) to automatically correct for source and receiver locations prior to tomographic inversion. We present a simple model of well deviation, which is designed to minimize potential corruption of actual data trends. We also provide quantitative quality control measures based on minimizing correlations between take-off angle and apparent velocity, and a quality check on the continuity of velocity between adjacent wells. This methodology is shown to be accurate and robust for simple 2-D synthetic test cases. Plus, we demonstrate the method on actual field data where it is compared to deviation logs. This study shows the promise for automatic correction of well deviations in GPR tomographic data. Analysis of synthetic data shows that very precise estimates of well deviation can be made for small deviations, even in the

  14. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

    NASA Astrophysics Data System (ADS)

    Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

    2009-10-01

    Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

  15. A new method for the re-implementation of threshold logic functions with cellular neural networks.

    PubMed

    Bénédic, Y; Wira, P; Mercklé, J

    2008-08-01

    A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.

  16. Moving object segmentation algorithm based on cellular neural networks in the H.264 compressed domain

    NASA Astrophysics Data System (ADS)

    Feng, Jie; Chen, Yaowu; Tian, Xiang

    2009-07-01

    A cellular neural network (CNN)-based moving object segmentation algorithm in the H.264 compressed domain is proposed. This algorithm mainly utilizes motion vectors directly extracted from H.264 bitstreams. To improve the robustness of the motion vector information, the intramodes in I-frames are used for smooth and nonsmooth region classification, and the residual coefficient energy of P-frames is used to update the classification results first. Then, an adaptive motion vector filter is used according to interpartition modes. Finally, many CNN models are applied to implement moving object segmentation based on motion vector fields. Experiment results are presented to verify the efficiency and the robustness of this algorithm.

  17. Evolving Transport Networks With Cellular Automata Models Inspired by Slime Mould.

    PubMed

    Tsompanas, Michail-Antisthenis I; Sirakoulis, Georgios Ch; Adamatzky, Andrew I

    2015-09-01

    Man-made transport networks and their design are closely related to the shortest path problem and considered amongst the most debated problems of computational intelligence. Apart from using conventional or bio-inspired computer algorithms, many researchers tried to solve this kind of problem using biological computing substrates, gas-discharge solvers, prototypes of a mobile droplet, and hot ice computers. In this aspect, another example of biological computer is the plasmodium of acellular slime mould Physarum polycephalum (P. polycephalum), which is a large single cell visible by an unaided eye and has been proven as a reliable living substrate for implementing biological computing devices for computational geometry, graph-theoretical problems, and optimization and imitation of transport networks. Although P. polycephalum is easy to experiment with, computing devices built with the living slime mould are extremely slow; it takes slime mould days to execute a computation. Consequently, mapping key computing mechanisms of the slime mould onto silicon would allow us to produce efficient bio-inspired computing devices to tackle with hard to solve computational intelligence problems like the aforementioned. Toward this direction, a cellular automaton (CA)-based, Physarum-inspired, network designing model is proposed. This novel CA-based model is inspired by the propagating strategy, the formation of tubular networks, and the computing abilities of the plasmodium of P. polycephalum. The results delivered by the CA model demonstrate a good match with several previously published results of experimental laboratory studies on imitation of man-made transport networks with P. polycephalum. Consequently, the proposed CA model can be used as a virtual, easy-to-access, and biomimicking laboratory emulator that will economize large time periods needed for biological experiments while producing networks almost identical to the tubular networks of the real-slime mould.

  18. Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

    PubMed Central

    Chen, Min; Hao, Yixue; Qiu, Meikang; Song, Jeungeun; Wu, Di; Humar, Iztok

    2016-01-01

    Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user’s quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities. PMID:27347975

  19. The cellular and signaling networks linking the immune system and metabolism in disease.

    PubMed

    Osborn, Olivia; Olefsky, Jerrold M

    2012-03-06

    It is now recognized that obesity is driving the type 2 diabetes epidemic in Western countries. Obesity-associated chronic tissue inflammation is a key contributing factor to type 2 diabetes and cardiovascular disease, and a number of studies have clearly demonstrated that the immune system and metabolism are highly integrated. Recent advances in deciphering the various cellular and signaling networks that participate in linking the immune and metabolic systems together have contributed to understanding of the pathogenesis of metabolic diseases and may also inform new therapeutic strategies based on immunomodulation. Here we discuss how these various networks underlie the etiology of the inflammatory component of insulin resistance, with a particular focus on the central roles of macrophages in adipose tissue and liver.

  20. Cellular telephone-based radiation sensor and wide-area detection network

    DOEpatents

    Craig, William W.; Labov, Simon E.

    2006-12-12

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  1. Modeling Immune Network Through Cellular Automata:. a Unified Mechanism of Immunological Memory

    NASA Astrophysics Data System (ADS)

    Chowdhury, Debashish; Deshpande, Varsha; Stauffer, Dietrich

    The populations of the various types of immunocompetent cells in the immune system are described as cellular automata and the population dynamics of these cells are formulated in terms of dynamical maps in discrete time. Both intra-clonal interactions (i.e., interactions among the cell types belonging to the same clone) and inter-clonal interactions (i.e., interactions among the cell types belonging to different clones) are included in the models proposed here. While the intra-clonal interactions are shown to play a crucial role in the primary response of some clones and in the formation of the immunological memory, the inter-clonal interactions are responsible for retaining the memory through a dynamical process driven by the mutual stimulation of the clones. We present the results for two different types of connectivity, namely, a “necklace” network and a network in “shape space”.

  2. Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

    PubMed Central

    2016-01-01

    For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example. PMID:26141830

  3. Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis

    NASA Astrophysics Data System (ADS)

    Trujillo San-Martin, Maite; Hlebarov, Vejen; Sadki, Mustapha

    2004-11-01

    Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.

  4. The Influence of Cold Temperature on Cellular Excitability of Hippocampal Networks

    PubMed Central

    Vara, Hugo; Caires, Rebeca; Ballesta, Juan J.; Belmonte, Carlos; Viana, Felix

    2012-01-01

    The hippocampus plays an important role in short term memory, learning and spatial navigation. A characteristic feature of the hippocampal region is its expression of different electrical population rhythms and activities during different brain states. Physiological fluctuations in brain temperature affect the activity patterns in hippocampus, but the underlying cellular mechanisms are poorly understood. In this work, we investigated the thermal modulation of hippocampal activity at the cellular network level. Primary cell cultures of mouse E17 hippocampus displayed robust network activation upon light cooling of the extracellular solution from baseline physiological temperatures. The activity generated was dependent on action potential firing and excitatory glutamatergic synaptic transmission. Involvement of thermosensitive channels from the transient receptor potential (TRP) family in network activation by temperature changes was ruled out, whereas pharmacological and immunochemical experiments strongly pointed towards the involvement of temperature-sensitive two-pore-domain potassium channels (K2P), TREK/TRAAK family. In hippocampal slices we could show an increase in evoked and spontaneous synaptic activity produced by mild cooling in the physiological range that was prevented by chloroform, a K2P channel opener. We propose that cold-induced closure of background TREK/TRAAK family channels increases the excitability of some hippocampal neurons, acting as a temperature-sensitive gate of network activation. Our findings in the hippocampus open the possibility that small temperature variations in the brain in vivo, associated with metabolism or blood flow oscillations, act as a switch mechanism of neuronal activity and determination of firing patterns through regulation of thermosensitive background potassium channel activity. PMID:23300680

  5. Long term country-wide rainfall monitoring employing cellular communication networks

    NASA Astrophysics Data System (ADS)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular telecommunication networks may be employed for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall monitoring using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. This is particularly interesting for those countries where few surface rainfall observations are available. Here we present preliminary results of long term country-wide rainfall monitoring employing cellular communication networks. A dataset from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (~ 2000) covering the land surface of the Netherlands (35500 square kilometres). This dataset spans from January 2011 through October 2012. Daily rainfall maps (1 km spatial resolution) are derived from the microwave link data and compared to maps from a gauge-adjusted radar dataset. The performance of the rainfall retrieval algorithm will be investigated, particularly a possible seasonal dependence.

  6. Effect of crystals and fibrous network polymer additives on cellular morphology of microcellular foams

    NASA Astrophysics Data System (ADS)

    Miyamoto, Ryoma; Utano, Tatsumi; Yasuhara, Shunya; Ishihara, Shota; Ohshima, Masahiro

    2015-05-01

    In this study, the core-back foam injection molding was used for preparing microcelluar polypropylene (PP) foam with either a 1,3:2,4 bis-O-(4-methylbenzylidene)-D-sorbitol gelling agent (Gel-all MD) or a fibros network polymer additive (Metablen 3000). Both agent and addiive could effectively control the celluar morphology in foams but somehow different ways. In course of cooling the polymer with Gel-all MD in the mold caity, the agent enhanced the crystal nucleation and resulted in the large number of small crystals. The crystals acted as effective bubble nucleation agent in foaming process. Thus, the agent reduced the cell size and increased the cell density, drastically. Furthermore, the small crystals provided an inhomogenuity to the expanding cell wall and produced the high open cell content with nano-scale fibril structure. Gell-all as well as Metablene 3000 formed a gel-like fibrous network in melt. The network increased the elongational viscosity and tended to prevent the cell wall from breaking up. The foaming temperature window was widened by the presence of the network. Especially, the temperature window where the macro-fibrous structure was formed was expanded to the higher temperature. The effects of crystal nucleating agent and PTFE on crystals' size and number, viscoelsticity, rheological propreties of PP and cellular morphology were compared and thorougly investigated.

  7. Identification of the direction of the neural network activation with a cellular resolution by fast two-photon imaging

    NASA Astrophysics Data System (ADS)

    Liu, Xiuli; Quan, Tingwei; Zeng, Shaoqun; Lv, Xiaohua

    2011-08-01

    Spatiotemporal activity patterns in local neural networks are fundamental to understanding how information is processed and stored in brain microcircuits. Currently, imaging techniques are able to map the directional activation of macronetworks across brain areas; however, these strategies still fail to resolve the activation direction for fine microcircuits with cellular spatial resolution. Here, we show the capability to identify the activation direction of a multicell network with a cellular resolution and millisecond precision by using fast two-photon microscopy and cross correlation procedures. As an example, we characterized a directional neuronal network in an epilepsy brain slice to provide different initiation delay among multiple neurons defined at a millisecond scale.

  8. ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.

    PubMed

    Veres, Daniel V; Gyurkó, Dávid M; Thaler, Benedek; Szalay, Kristóf Z; Fazekas, Dávid; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein-protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein-protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

  9. ComPPI: a cellular compartment-specific database for protein–protein interaction network analysis

    PubMed Central

    Veres, Daniel V.; Gyurkó, Dávid M.; Thaler, Benedek; Szalay, Kristóf Z.; Fazekas, Dávid; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein–protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein–protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein–protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design. PMID:25348397

  10. Condition monitoring of 3G cellular networks through competitive neural models.

    PubMed

    Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

    2005-09-01

    We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

  11. Country-wide rainfall maps from a commercial cellular telephone network

    NASA Astrophysics Data System (ADS)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2012-04-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for many applications, for instance, as input for hydrological models. Weather radars often provide data with sufficient spatial and temporal resolution, but usually need adjustment. In general, only few rain gauge measurements are available to adjust the radar data in real-time, for example, each hour. Physically based methods, such as a VPR correction, can be valuable and hold a promise. However, they are not always performed in real-time yet and can be difficult to implement. The estimation of rainfall using microwave links from commercial cellular telephone networks is a new and potentially valuable source of information. Such networks cover large parts of the land surface of the earth and have a high density. The data produced by the microwave links in such networks is essentially a by-product of the communication between mobile telephones. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. A recent study of us shows that urban rainfall can be estimated from commercial microwave link data for the Rotterdam region, a densely-populated delta city in the Netherlands. A data set from a commercial microwave link network over the Netherlands is analyzed, containing approximately 1500 links covering the land surface of the Netherlands (35500 km2). This data set consists of several days with extreme rainfall in June, July and August 2011. A methodology is presented to derive rainfall intensities and daily rainfall depths from the microwave link data, which have a temporal resolution of 15 min. The magnitude and dynamics of these rainfall intensities

  12. Vertical 2D Heterostructures

    NASA Astrophysics Data System (ADS)

    Lotsch, Bettina V.

    2015-07-01

    Graphene's legacy has become an integral part of today's condensed matter science and has equipped a whole generation of scientists with an armory of concepts and techniques that open up new perspectives for the postgraphene area. In particular, the judicious combination of 2D building blocks into vertical heterostructures has recently been identified as a promising route to rationally engineer complex multilayer systems and artificial solids with intriguing properties. The present review highlights recent developments in the rapidly emerging field of 2D nanoarchitectonics from a materials chemistry perspective, with a focus on the types of heterostructures available, their assembly strategies, and their emerging properties. This overview is intended to bridge the gap between two major—yet largely disjunct—developments in 2D heterostructures, which are firmly rooted in solid-state chemistry or physics. Although the underlying types of heterostructures differ with respect to their dimensions, layer alignment, and interfacial quality, there is common ground, and future synergies between the various assembly strategies are to be expected.

  13. Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular

    PubMed Central

    Okasaka, Shozo; Weiler, Richard J.; Keusgen, Wilhelm; Pudeyev, Andrey; Maltsev, Alexander; Karls, Ingolf; Sakaguchi, Kei

    2016-01-01

    The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access. PMID:27571074

  14. Firing patterns in a random network cellular automata model of the brain

    NASA Astrophysics Data System (ADS)

    Acedo, L.; Lamprianidou, E.; Moraño, J.-A.; Villanueva-Oller, J.; Villanueva, R.-J.

    2015-10-01

    One of the main challenges in the simulation of even reduced areas of the brain is the presence of a large number of neurons and a large number of connections among them. Even from a theoretical point of view, the behaviour of dynamical models of complex networks with high connectivity is unknown, precisely because the cost of computation is still unaffordable and it will likely be in the near future. In this paper we discuss the simulation of a cellular automata network model of the brain including up to one million sites with a maximum average of three hundred connections per neuron. This level of connectivity was achieved thanks to a distributed computing environment based on the BOINC (Berkeley Open Infrastructure for Network Computing) platform. Moreover, in this work we consider the interplay among excitatory neurons (which induce the excitation of their neighbours) and inhibitory neurons (which prevent resting neurons from firing and induce firing neurons to pass to the refractory state). Our objective is to classify the normal (noisy but asymptotically constant patterns) and the abnormal (high oscillations with spindle-like behaviour) patterns of activity in the model brain and their stability and parameter ranges in order to determine the role of excitatory and inhibitory compensatory effects in healthy and diseased individuals.

  15. Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

    NASA Astrophysics Data System (ADS)

    Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

    2016-10-01

    Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.

  16. Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

    PubMed Central

    Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

    2016-01-01

    Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

  17. Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular.

    PubMed

    Okasaka, Shozo; Weiler, Richard J; Keusgen, Wilhelm; Pudeyev, Andrey; Maltsev, Alexander; Karls, Ingolf; Sakaguchi, Kei

    2016-08-25

    The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access.

  18. A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways

    PubMed Central

    Taipale, Mikko; Tucker, George; Peng, Jian; Krykbaeva, Irina; Lin, Zhen-Yuan; Larsen, Brett; Choi, Hyungwon; Berger, Bonnie; Gingras, Anne-Claude; Lindquist, Susan

    2014-01-01

    Chaperones are abundant cellular proteins that promote the folding and function of their substrate proteins (clients). In vivo, chaperones also associate with a large and diverse set of co-factors (co-chaperones) that regulate their specificity and function. However, how these co-chaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. We have combined mass spectrometry and quantitative high-throughput LUMIER assays to systematically characterize the chaperone/co-chaperone/client interaction network in human cells. We uncover hundreds of novel chaperone clients, delineate their participation in specific co-chaperone complexes, and establish a surprisingly distinct network of protein/protein interactions for co-chaperones. As a salient example of the power of such analysis, we establish that NUDC family co-chaperones specifically associate with structurally related but evolutionarily distinct β-propeller folds. We provide a framework for deciphering the proteostasis network, its regulation in development and disease, and expand the use of chaperones as sensors for drug/target engagement. PMID:25036637

  19. An epidermal plakin that integrates actin and microtubule networks at cellular junctions.

    PubMed

    Karakesisoglou, I; Yang, Y; Fuchs, E

    2000-04-03

    Plakins are cytoskeletal linker proteins initially thought to interact exclusively with intermediate filaments (IFs), but recently were found to associate additionally with actin and microtubule networks. Here, we report on ACF7, a mammalian orthologue of the Drosophila kakapo plakin genetically involved in epidermal-muscle adhesion and neuromuscular junctions. While ACF7/kakapo is divergent from other plakins in its IF-binding domain, it has at least one actin (K(d) = 0.35 microM) and one microtubule (K(d) approximately 6 microM) binding domain. Similar to its fly counterpart, ACF7 is expressed in the epidermis. In well spread epidermal keratinocytes, ACF7 discontinuously decorates the cytoskeleton at the cell periphery, including microtubules (MTs) and actin filaments (AFs) that are aligned in parallel converging at focal contacts. Upon calcium induction of intercellular adhesion, ACF7 and the cytoskeleton reorganize at cell-cell borders but with different kinetics from adherens junctions and desmosomes. Treatments with cytoskeletal depolymerizing drugs reveal that ACF7's cytoskeletal association is dependent upon the microtubule network, but ACF7 also appears to stabilize actin at sites where microtubules and microfilaments meet. We posit that ACF7 may function in microtubule dynamics to facilitate actin-microtubule interactions at the cell periphery and to couple the microtubule network to cellular junctions. These attributes provide a clear explanation for the kakapo mutant phenotype in flies.

  20. An Epidermal Plakin That Integrates Actin and Microtubule Networks at Cellular Junctions

    PubMed Central

    Karakesisoglou, Iakowos; Yang, Yanmin; Fuchs, Elaine

    2000-01-01

    Plakins are cytoskeletal linker proteins initially thought to interact exclusively with intermediate filaments (IFs), but recently were found to associate additionally with actin and microtubule networks. Here, we report on ACF7, a mammalian orthologue of the Drosophila kakapo plakin genetically involved in epidermal–muscle adhesion and neuromuscular junctions. While ACF7/kakapo is divergent from other plakins in its IF-binding domain, it has at least one actin (Kd = 0.35 μM) and one microtubule (Kd ∼6 μM) binding domain. Similar to its fly counterpart, ACF7 is expressed in the epidermis. In well spread epidermal keratinocytes, ACF7 discontinuously decorates the cytoskeleton at the cell periphery, including microtubules (MTs) and actin filaments (AFs) that are aligned in parallel converging at focal contacts. Upon calcium induction of intercellular adhesion, ACF7 and the cytoskeleton reorganize at cell–cell borders but with different kinetics from adherens junctions and desmosomes. Treatments with cytoskeletal depolymerizing drugs reveal that ACF7's cytoskeletal association is dependent upon the microtubule network, but ACF7 also appears to stabilize actin at sites where microtubules and microfilaments meet. We posit that ACF7 may function in microtubule dynamics to facilitate actin–microtubule interactions at the cell periphery and to couple the microtubule network to cellular junctions. These attributes provide a clear explanation for the kakapo mutant phenotype in flies. PMID:10747097

  1. Network, cellular, and molecular mechanisms underlying long-term memory formation.

    PubMed

    Carasatorre, Mariana; Ramírez-Amaya, Víctor

    2013-01-01

    The neural network stores information through activity-dependent synaptic plasticity that occurs in populations of neurons. Persistent forms of synaptic plasticity may account for long-term memory storage, and the most salient forms are the changes in the structure of synapses. The theory proposes that encoding should use a sparse code and evidence suggests that this can be achieved through offline reactivation or by sparse initial recruitment of the network units. This idea implies that in some cases the neurons that underwent structural synaptic plasticity might be a subpopulation of those originally recruited; However, it is not yet clear whether all the neurons recruited during acquisition are the ones that underwent persistent forms of synaptic plasticity and responsible for memory retrieval. To determine which neural units underlie long-term memory storage, we need to characterize which are the persistent forms of synaptic plasticity occurring in these neural ensembles and the best hints so far are the molecular signals underlying structural modifications of the synapses. Structural synaptic plasticity can be achieved by the activity of various signal transduction pathways, including the NMDA-CaMKII and ACh-MAPK. These pathways converge with the Rho family of GTPases and the consequent ERK 1/2 activation, which regulates multiple cellular functions such as protein translation, protein trafficking, and gene transcription. The most detailed explanation may come from models that allow us to determine the contribution of each piece of this fascinating puzzle that is the neuron and the neural network.

  2. BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling

    PubMed Central

    Feng, Song; Ollivier, Julien F.; Swain, Peter S.; Soyer, Orkun S.

    2015-01-01

    Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. PMID:26101250

  3. Viral Replication Protein Inhibits Cellular Cofilin Actin Depolymerization Factor to Regulate the Actin Network and Promote Viral Replicase Assembly

    PubMed Central

    Kovalev, Nikolay; de Castro Martín, Isabel Fernández; Barajas, Daniel; Risco, Cristina; Nagy, Peter D.

    2016-01-01

    RNA viruses exploit host cells by co-opting host factors and lipids and escaping host antiviral responses. Previous genome-wide screens with Tomato bushy stunt virus (TBSV) in the model host yeast have identified 18 cellular genes that are part of the actin network. In this paper, we show that the p33 viral replication factor interacts with the cellular cofilin (Cof1p), which is an actin depolymerization factor. Using temperature-sensitive (ts) Cof1p or actin (Act1p) mutants at a semi-permissive temperature, we find an increased level of TBSV RNA accumulation in yeast cells and elevated in vitro activity of the tombusvirus replicase. We show that the large p33 containing replication organelle-like structures are located in the close vicinity of actin patches in yeast cells or around actin cable hubs in infected plant cells. Therefore, the actin filaments could be involved in VRC assembly and the formation of large viral replication compartments containing many individual VRCs. Moreover, we show that the actin network affects the recruitment of viral and cellular components, including oxysterol binding proteins and VAP proteins to form membrane contact sites for efficient transfer of sterols to the sites of replication. Altogether, the emerging picture is that TBSV, via direct interaction between the p33 replication protein and Cof1p, controls cofilin activities to obstruct the dynamic actin network that leads to efficient subversion of cellular factors for pro-viral functions. In summary, the discovery that TBSV interacts with cellular cofilin and blocks the severing of existing filaments and the formation of new actin filaments in infected cells opens a new window to unravel the way by which viruses could subvert/co-opt cellular proteins and lipids. By regulating the functions of cofilin and the actin network, which are central nodes in cellular pathways, viruses could gain supremacy in subversion of cellular factors for pro-viral functions. PMID:26863541

  4. Adaptive call admission control and resource allocation in multi server wireless/cellular network

    NASA Astrophysics Data System (ADS)

    Jain, Madhu; Mittal, Ragini

    2016-11-01

    The ever increasing demand of the subscribers has put pressure on the capacity of wireless networks around the world. To utilize the scare resources, in the present paper we propose an optimal allocation scheme for an integrated wireless/cellular model with handoff priority and handoff guarantee services. The suggested algorithm optimally allocates the resources in each cell and dynamically adjust threshold to control the admission. To give the priority to handoff calls over the new calls, the provision of guard channels and subrating scheme is taken into consideration. The handoff voice call may balk and renege from the system while waiting in the buffer. An iterative algorithm is implemented to generate the arrival rate of the handoff calls in each cell. Various performance indices are established in term of steady state probabilities. The sensitivity analysis has also been carried out to examine the tractability of algorithms and to explore the effects of system descriptors on the performance indices.

  5. Spike integration and cellular memory in a rhythmic network from Na+/K+ pump current dynamics

    PubMed Central

    Pulver, Stefan R.

    2009-01-01

    The output of a neural circuit results from an interaction between the intrinsic properties of neurons within the circuit and the features of the synaptic connections between them. The plasticity of intrinsic properties has been primarily attributed to modification of ion channel function and/or number. In this study, we demonstrate a mechanism for intrinsic plasticity in rhythmically active Drosophila neurons that is not conductance-based. Larval motor neurons show a long lasting sodium-dependent afterhyperpolarization (AHP) following bursts of action potentials that is mediated by the electrogenic activity of Na+/K+ ATPase. This AHP persists for multiple seconds following volleys of action potentials and is able to function as a pattern-insensitive integrator of spike number that is independent of external calcium. This current also interacts with endogenous Shal K+ conductances to modulate spike timing for multiple seconds following rhythmic activity, providing a cellular memory of network activity on a behaviorally relevant time scale. PMID:19966842

  6. Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

    NASA Technical Reports Server (NTRS)

    Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

    1997-01-01

    A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

  7. Multiscale systems analysis of root growth and development: modeling beyond the network and cellular scales.

    PubMed

    Band, Leah R; Fozard, John A; Godin, Christophe; Jensen, Oliver E; Pridmore, Tony; Bennett, Malcolm J; King, John R

    2012-10-01

    Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties.

  8. Modeling brain electrical activity in epilepsy by reaction-diffusion cellular neural networks

    NASA Astrophysics Data System (ADS)

    Gollas, F.; Tetzlaff, R.

    2005-06-01

    Reaction-Diffusion systems can be applied to describe a broad class of nonlinear phenomena, in particular in biological systems and in the propagation of nonlinear waves in excitable media. Especially, pattern formation and chaotic behavior are observed in Reaction-Diffusion systems and can be analyzed. Due to their structure multi-layer Cellular Neural Networks (CNN) are capable of representing Reaction-Diffusion systems effectively. In this contribution Reaction-Diffusion CNN are considered for modeling dynamics of brain activity in epilepsy. Thereby the parameters of Reaction-Diffusion systems are determined in a supervised optimization process, and brain electrical activity using invasive multi-electrode EEG recordings is analyzed with the aim to detect of precursors of impending epileptic seizures. A detailed discussion of first results and potentiality of the proposed approach will be given.

  9. A novel method to assess human population exposure induced by a wireless cellular network.

    PubMed

    Varsier, Nadège; Plets, David; Corre, Yoann; Vermeeren, Günter; Joseph, Wout; Aerts, Sam; Martens, Luc; Wiart, Joe

    2015-09-01

    This paper presents a new metric to evaluate electromagnetic exposure induced by wireless cellular networks. This metric takes into account the exposure induced by base station antennas as well as exposure induced by wireless devices to evaluate average global exposure of the population in a specific geographical area. The paper first explains the concept and gives the formulation of the Exposure Index (EI). Then, the EI computation is illustrated through simple phone call scenarios (indoor office, in train) and a complete macro urban data long-term evolution scenario showing how, based on simulations, radio-planning predictions, realistic population statistics, user traffic data, and specific absorption rate calculations can be combined to assess the index. Bioelectromagnetics. 36:451-463, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  10. Multiscale Systems Analysis of Root Growth and Development: Modeling Beyond the Network and Cellular Scales

    PubMed Central

    Band, Leah R.; Fozard, John A.; Godin, Christophe; Jensen, Oliver E.; Pridmore, Tony; Bennett, Malcolm J.; King, John R.

    2012-01-01

    Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties. PMID:23110897

  11. Parallelism on the Intel 860 Hypercube:. Ising Magnets, Hydrodynamical Cellular Automata and Neural Networks

    NASA Astrophysics Data System (ADS)

    Kohring, G. A.; Stauffer, D.

    Geometric parallelization was tested on the Intel Hypercube with 32 MIMD processors of 1860 type, each with 16 Mbytes of distributed memory. We applied it to Ising models in two and three dimensions as well as to neural networks and two-dimensional hydrodynamic cellular automata. For system sizes suited to this machine, up to 60960*60960 and 1410*1410*1408 Ising spins, we found nearly hundred percent parallel efficiency in spite of the needed inter-processor communications. For small systems, the observed deviations from full efficiency were compared with the scaling concepts of Heermann and Burkitt and of Jakobs and Gerling. For Ising models, we determined the Glauber kinetic exponent z≃2.18 in two dimensions and confirmed the stretched exponential relaxation of the magnetization towards the spontaneous magnetization below Tc. For three dimensions we found z≃2.09 and simple exponential relaxation.

  12. Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks

    NASA Astrophysics Data System (ADS)

    Di Marco, Mauro; Forti, Mauro; Grazzini, Massimo; Pancioni, Luca

    2009-12-01

    In several relevant applications to the solution of signal processing tasks in real time, a cellular neural network (CNN) is required to be convergent, that is, each solution should tend toward some equilibrium point. The paper develops a Lyapunov method, which is based on a generalized version of LaSalle's invariance principle, for studying convergence and stability of the differential inclusions modeling the dynamics of the full-range (FR) model of CNNs. The applicability of the method is demonstrated by obtaining a rigorous proof of convergence for symmetric FR-CNNs. The proof, which is a direct consequence of the fact that a symmetric FR-CNN admits a strict Lyapunov function, is much more simple than the corresponding proof of convergence for symmetric standard CNNs.

  13. Global detection of live virtual machine migration based on cellular neural networks.

    PubMed

    Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

    2014-01-01

    In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

  14. Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

    NASA Astrophysics Data System (ADS)

    Ozcan, H. Kurtulus; Bilgili, Erdem; Sahin, Ulku; Ucan, O. Nuri; Bayat, Cuma

    2007-09-01

    Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.

  15. Comparing the dynamics of stomatal networks to the problem-solving dynamics of cellular computers

    NASA Astrophysics Data System (ADS)

    West, Jevin D.; Peak, David; Mott, Keith; Messinger, Susanna

    Is the adaptive response to environmental stimuli of a biological system lacking a central nervous system a result of a formal computation? If so, these biological systems must conform to a different set of computational rules than those associated with central processing. To explore this idea, we examined the dynamics of stomatal patchiness in leaves. Stomata—tiny pores on the surface of a leaf—are biological processing units that a plant uses to solve an optimization problem—maximize CO 2 assimilation and minimize H 2 O loss. Under some conditions, groups of stomata coordinate in both space and time producing motile patches that can be visualized with chlorophyll fluorescence. These patches suggest that stomata are nonautonomous and that they form a network presumably engaged in the optimization task. In this study, we show that stomatal dynamics are statistically and qualitatively comparable to the emergent, collective, problem-solving dynamics of cellular computing systems.

  16. Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

    NASA Technical Reports Server (NTRS)

    Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

    1997-01-01

    A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

  17. Two years of country-wide rainfall maps employing cellular communication networks

    NASA Astrophysics Data System (ADS)

    Uijlenhoet, Remko; Overeem, Aart; Leijnse, Hidde; Rios Gaona, Manuel Felipe

    2014-05-01

    Accurate rainfall observations with high spatial and temporal resolutions are needed for hydrological applications, agriculture, meteorology, and climate monitoring. However, the majority of the land surface of the earth lacks accurate rainfall information and the number of rain gauges is even severely declining in Europe, South-America, and Africa. This calls for alternative sources of rainfall information. Various studies have shown that microwave links from operational cellular telecommunication networks may be employed for rainfall monitoring. Such networks cover 20% of the land surface of the earth and have a high density, especially in urban areas. The basic principle of rainfall monitoring using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated. Previous studies have shown that average rainfall intensities over the length of a link can be derived from the path-integrated attenuation. This is particularly interesting for those countries where few surface rainfall observations are available. Here we present almost two years of country-wide rainfall maps employing cellular communication networks. A data set from a commercial microwave link network over the Netherlands is analyzed, containing data from an unprecedented number of links (~ 2000) covering the land surface of the Netherlands (35500 square kilometers). This data set almost completely covers the years 2011 and 2012. Fifteen-minute and daily rainfall maps (1 km spatial resolution) are derived from the microwave link data and compared to maps from a gauge-adjusted radar data set. The performance of the rainfall retrieval algorithm will be studied, particularly differences in time and space. Time series of air temperature and snow from automatic weather stations, operated by the

  18. Computerized detection of pulmonary nodules using cellular neural networks in CT images

    NASA Astrophysics Data System (ADS)

    Zhang, Xiangwei; McLennan, Geoffrey; Hoffman, Eric A.; Sonka, Milan

    2004-05-01

    The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) non-pleural pulmonary nodules in high resolution helical CT scans. A new 3D automated scheme using cellular neural networks is presented. Different from most previous methods, this scheme employed the local shape property to perform voxel classification. The shape index feature successfully captured the local shape difference between nodules and non-nodules, especially vessels. A 3D discrete-time cellular neural network (DTCNN) was constructed to give a reliable voxel classification by collecting information in a neighborhood. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work, with 2 scans forming the training set, and the remaining 17 cases being the testing set. The evaluation was composed of two stages. During the first stage, a pulmonologist and our CAD system independently detected nodules in the testing set. Then, the suspected nodule areas located by the computer were reviewed by the pulmonologist to confirm nodules missed by the human in the first review. There were 32 true nodules detected by the computer but missed by the pulmonologist in the first review, in which 30 non-juxtapleural nodules were found. Considering the nodules detected by the pulmonologist during the first and second reviews as the truth, 52 of 62 non-pleural nodules were detected by the CAD system (sensitivity being 83.9%), with the number of false positives being 3.47 per case.

  19. Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions

    PubMed Central

    Bassel, George W.; Lan, Hui; Glaab, Enrico; Gibbs, Daniel J.; Gerjets, Tanja; Krasnogor, Natalio; Bonner, Anthony J.; Holdsworth, Michael J.; Provart, Nicholas J.

    2011-01-01

    Seed germination is a complex trait of key ecological and agronomic significance. Few genetic factors regulating germination have been identified, and the means by which their concerted action controls this developmental process remains largely unknown. Using publicly available gene expression data from Arabidopsis thaliana, we generated a condition-dependent network model of global transcriptional interactions (SeedNet) that shows evidence of evolutionary conservation in flowering plants. The topology of the SeedNet graph reflects the biological process, including two state-dependent sets of interactions associated with dormancy or germination. SeedNet highlights interactions between known regulators of this process and predicts the germination-associated function of uncharacterized hub nodes connected to them with 50% accuracy. An intermediate transition region between the dormancy and germination subdomains is enriched with genes involved in cellular phase transitions. The phase transition regulators SERRATE and EARLY FLOWERING IN SHORT DAYS from this region affect seed germination, indicating that conserved mechanisms control transitions in cell identity in plants. The SeedNet dormancy region is strongly associated with vegetative abiotic stress response genes. These data suggest that seed dormancy, an adaptive trait that arose evolutionarily late, evolved by coopting existing genetic pathways regulating cellular phase transition and abiotic stress. SeedNet is available as a community resource (http://vseed.nottingham.ac.uk) to aid dissection of this complex trait and gene function in diverse processes. PMID:21593420

  20. Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles.

    PubMed

    de la Iglesia, Idoia; Hernandez-Jayo, Unai; Osaba, Eneko; Carballedo, Roberto

    2017-09-27

    The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as an ecosystem where different types of users (vehicles, elements of the infrastructure, pedestrians) are connected. How to efficiently share the available radio resources among the different types of eligible users is one of the important issues to be addressed. This paper briefly analyzes various concepts presented hitherto in the literature and it proposes an enhanced algorithm for ensuring a robust co-existence of the aforementioned system users. Therefore, this paper introduces an underlay RRM (Radio Resource Management) methodology which is capable of (1) improving cellular spectral efficiency while making a minimal impact on cellular communications and (2) ensuring the different QoS (Quality of Service) requirements of ITS (Intelligent Transportation Systems) applications. Simulation results, where we compare the proposed algorithm to the other two RRM, show the promising spectral efficiency performance of the proposed RRM methodology.

  1. Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways.

    PubMed

    Zhang, Fan; Chen, Haoting; Zhao, Li Na; Liu, Hui; Przytycka, Teresa M; Zheng, Jie

    2016-01-11

    Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways. We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks. The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

  2. Intragranular cellular segregation network structure strengthening 316L stainless steel prepared by selective laser melting

    NASA Astrophysics Data System (ADS)

    Zhong, Yuan; Liu, Leifeng; Wikman, Stefan; Cui, Daqing; Shen, Zhijian

    2016-03-01

    A feasibility study was performed to fabricate ITER In-Vessel components by Selective Laser Melting (SLM) supported by Fusion for Energy (F4E). Almost fully dense 316L stainless steel (SS316L) components were prepared from gas-atomized powder and with optimized SLM processing parameters. Tensile tests and Charpy-V tests were carried out at 22 °C and 250 °C and the results showed that SLM SS316L fulfill the RCC-MR code. Microstructure characterization reveals the presence of hierarchical macro-, micro- and nano-structures in as-built samples that were very different from SS316L microstructures prepared by other established methods. The formation of a characteristic intragranular cellular segregation network microstructure appears to contribute to the increase of yield strength without losing ductility. Silicon oxide nano-inclusions were formed during the SLM process that generated a micro-hardness fluctuation in the building direction. The combined influence of a cellular microstructure and the nano-inclusions constraints the size of ductile dimples to nano-scale. The crack propagation is hindered by a pinning effect that improves the defect-tolerance of the SLM SS316L. This work proves that it was possible to manufacture SS316L with properties suitable for ITER First Wall panels. Further studies on irradiation properties of SLM SS316L and manufacturing of larger real-size components are needed.

  3. Roadmap to cellular reprogramming--manipulating transcriptional networks with DNA, RNA, proteins and small molecules.

    PubMed

    Wörsdörfer, P; Thier, M; Kadari, A; Edenhofer, F

    2013-06-01

    Recent reports demonstrate that the plasticity of mammalian somatic cells is much higher than previously assumed and that ectopic expression of transcription factors may have the potential to induce the conversion of any cell type into another. Fibroblast cells can be converted into embryonic stem cell-like cells, neural cells, cardiomyocytes, macrophage-like cells as well as blood progenitors. Additionally, the conversion of astrocytes into neurons or neural stem cells into monocytes has been demonstrated. Nowadays, in the era of systems biology, continuously growing holistic data sets are providing increasing insights into core transcriptional networks and cellular signaling pathways. This knowledge enables cell biologists to understand how cellular fate is determined and how it could be manipulated. As a consequence for biomedical applications, it might be soon possible to convert patient specific somatic cells directly into desired transplantable other cell types. The clinical value, however, of such reprogrammed cells is currently limited due to the invasiveness of methods applied to induce reprogramming factor activity. This review will focus on experimental strategies to ectopically induce cell fate modulators. We will emphasize those strategies that enable efficient and robust overexpression of transcription factors by minimal genetic alterations of the host genome. Furthermore, we will discuss procedures devoid of any genomic manipulation, such as the direct delivery of mRNA, proteins, or the use of small molecules. By this, we aim to give a comprehensive overview on state of the art techniques that harbor the potential to generate safe reprogrammed cells for clinical applications.

  4. Cationic Mn2+/H+ exchange leading a slow solid-state transformation of a 2D porphyrinic network at ambient conditions

    NASA Astrophysics Data System (ADS)

    Amayuelas, Eder; Fidalgo-Marijuan, Arkaitz; Bazán, Begoña; Urtiaga, Miren Karmele; Barandika, Gotzone; Lezama, Luis; Arriortua, María Isabel

    2017-03-01

    Metalloporphyrins exhibit outstanding chemical, physical and biological properties in dissolution, however, it is a challenge to synthesize them as stable solid frameworks. Long-time stability is crucial for future applications of these materials, and we have detected a slow, solid-state transformation of a 2D MnII-porphyrin at RT. The remarkable point is that this transformation showed up as a result of Electronic Paramagnetic Resonance measurements. Otherwise, the evolution of the system could have remained undetected. Thus, 2D [Mn3(TCPP)(H2O)4]·nD (1) (where TCPP is meso-tetra(4-carboxyphenyl)porphyrin and D is the solvent) has been synthesized hydrothermally, and characterised by means of X-ray diffraction (XRD), Thermogravimetry and X-ray thermodiffractometry (XRTD). This compound slowly transforms into [Mn(H4TCPP)(H2O)2]·nD (2) according to the equilibrium [Mn3(TCPP)]+4H+ ↔ [Mn(H4TCPP)]+2Mn2+. The evolution of the system has been studied through analysis of the distortion (both of the coordination sphere and the tetrapyrrolic macrocycle) and Density Functional Theory (DFT) quantum mechanical calculations.

  5. 2D semiconductor optoelectronics

    NASA Astrophysics Data System (ADS)

    Novoselov, Kostya

    The advent of graphene and related 2D materials has recently led to a new technology: heterostructures based on these atomically thin crystals. The paradigm proved itself extremely versatile and led to rapid demonstration of tunnelling diodes with negative differential resistance, tunnelling transistors, photovoltaic devices, etc. By taking the complexity and functionality of such van der Waals heterostructures to the next level we introduce quantum wells engineered with one atomic plane precision. Light emission from such quantum wells, quantum dots and polaritonic effects will be discussed.

  6. Input-dependent wave propagations in asymmetric cellular automata: possible behaviors of feed-forward loop in biological reaction network.

    PubMed

    Awazu, Akinori

    2008-07-01

    Dynamical aspects of the asymmetric cellular automata were investigated to consider the signaling processes in biological systems. As a meta-model of the cascade of feed-forward loop type network motifs in biological reaction networks, we consider the one dimensional asymmetric cellular automata where the state of each cell is controlled by a trio of cells, the cell itself, the nearest upstream cell and the next nearest upstream cell. Through the systematic simulations, some novel input-dependent wave propagations were found in certain asymmetric CA, which may be useful for the signaling processes like the distinction, the filtering and the memory of external stimuli.

  7. Trans-species learning of cellular signaling systems with bimodal deep belief networks.

    PubMed

    Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

    2015-09-15

    Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. xinghua@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  8. Turning gold into ‘junk’: transposable elements utilize central proteins of cellular networks

    PubMed Central

    Abrusán, György; Szilágyi, András; Zhang, Yang; Papp, Balázs

    2013-01-01

    The numerous discovered cases of domesticated transposable element (TE) proteins led to the recognition that TEs are a significant source of evolutionary innovation. However, much less is known about the reverse process, whether and to what degree the evolution of TEs is influenced by the genome of their hosts. We addressed this issue by searching for cases of incorporation of host genes into the sequence of TEs and examined the systems-level properties of these genes using the Saccharomyces cerevisiae and Drosophila melanogaster genomes. We identified 51 cases where the evolutionary scenario was the incorporation of a host gene fragment into a TE consensus sequence, and we show that both the yeast and fly homologues of the incorporated protein sequences have central positions in the cellular networks. An analysis of selective pressure (Ka/Ks ratio) detected significant selection in 37% of the cases. Recent research on retrovirus-host interactions shows that virus proteins preferentially target hubs of the host interaction networks enabling them to take over the host cell using only a few proteins. We propose that TEs face a similar evolutionary pressure to evolve proteins with high interacting capacities and take some of the necessary protein domains directly from their hosts. PMID:23341038

  9. Inter-cellular signaling network reveals a mechanistic transition in tumor microenvironment

    PubMed Central

    Wu, Yu; Garmire, Lana X.; Fan, Rong

    2012-01-01

    We conducted inter-cellular cytokine correlation and network analysis based upon a stochastic population dynamics model that comprises five cell types and fifteen signaling molecules inter-connected through a large number of cell-cell communication pathways. We observed that the signaling molecules are tightly correlated even at very early stages (e.g. the first month) of human glioma, but such correlation rapidly diminishes when tumor grows to a size that can be clinically detected. Further analysis suggests that paracrine is shown to be the dominant force during tumor initiation and priming, while autocrine supersedes it and supports a robust tumor expansion. In correspondence, the cytokine correlation network evolves through an increasing to decreasing complexity. This study indicates a possible mechanistic transition from the microenvironment-controlled, paracrine-based regulatory mechanism to self-sustained rapid progression to fetal malignancy. It also reveals key nodes that are responsible for such transition and can be potentially harnessed for the design of new anti-cancer therapies. PMID:23080410

  10. Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks

    PubMed Central

    Foremski, Paweł; Gorawski, Michał; Grochla, Krzysztof; Polys, Konrad

    2015-01-01

    The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown. PMID:26340633

  11. Trans-species learning of cellular signaling systems with bimodal deep belief networks

    PubMed Central

    Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

    2015-01-01

    Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25995230

  12. Ligand-controlled assembly of Cd(II) coordination polymers based on mixed ligands of naphthalene-dicarboxylate and dipyrido[3,2-d:2',3'-f]quinoxaline: From 0D+1D cocrystal, 2D rectangular network (4,4), to 3D PtS-type architecture

    SciTech Connect

    Liu Guocheng; Chen Yongqiang; Wang Xiuli Chen Baokuan; Lin Hongyan

    2009-03-15

    Three novel Cd(II) coordination polymers, namely, [Cd(Dpq)(1,8-NDC)(H{sub 2}O){sub 2}][Cd(Dpq)(1,8-NDC)].2H{sub 2}O (1), [Cd(Dpq)(1,4-NDC)(H{sub 2}O)] (2), and [Cd(Dpq)(2,6-NDC)] (3) have been obtained from hydrothermal reactions of cadmium(II) nitrate with the mixed ligands dipyrido [3,2-d:2',3'-f]quinoxaline (Dpq) and three structurally related naphthalene-dicarboxylate ligands [1,8-naphthalene-dicarboxylic acid (1,8-H{sub 2}NDC), 1,4-naphthalene-dicarboxylic acid (1,4-H{sub 2}NDC), and 2,6-naphthalene-dicarboxylic acid (2,6-H{sub 2}NDC)]. Single-crystal X-ray diffraction analysis reveals that the three polymers exhibit novel structures due to different naphthalene-dicarboxylic acid. Compound 1 is a novel cocrystal of left- and right-handed helical chains and binuclear complexes and ultimately packed into a 3D supramolecular structure through hydrogen bonds and {pi}-{pi} stacking interactions. Compound 2 shows a 2D rectangular network (4,4) bridged by 1,4-NDC with two kinds of coordination modes and ultimately packed into a 3D supramolecular structure through inter-layer {pi}-{pi} stacking interactions. Compound 3 is a new 3D coordination polymer with distorted PtS-type network. In addition, the title compounds exhibit blue/green emission in solid state at room temperature. - Graphical abstract: Three novel Cd(II) compounds have been synthesized under hydrothermal conditions exhibiting a systematic variation of architecture by the employment of three structurally related naphthalene-dicarboxylate ligands.

  13. Simultaneous imaging of the topography and electrochemical activity of a 2D carbon nanotube network using a dual functional L-shaped nanoprobe.

    PubMed

    Lee, Eunjoo; Sung, Jungwoo; An, Taechang; Shin, Heungjoo; Nam, Hong Gil; Lim, Geunbae

    2015-05-07

    The application of nanomaterials for biosensors and fuel cells is becoming more common, but it requires an understanding of the relationship between the structure and electrochemical characteristics of the materials at the nanoscale. Herein, we report the development of scanning electrochemical microscopy-atomic force microscopy (SECM-AFM) nanoprobes for collecting spatially resolved data regarding the electrochemical activity of nanomaterials such as carbon nanotube (CNT) networks. The fabrication of the nanoprobe begins with the integration of a CNT-bundle wire into a conventional AFM probe followed by the deposition of an insulating layer and cutting of the probe end. In addition, a protrusive insulating tip is integrated at the end of the insulated CNT-bundle wire to maintain a constant distance between the nanoelectrode and the substrate; this yields an L-shaped nanoprobe. The resulting nanoprobes produced well-fitted maps of faradaic current data with less than 300 nm spatial resolution and topographical images of CNT networks owing to the small effective distance (of the order of tens of nanometers) between the electrode and the substrate. Electrochemical imaging using the L-shaped nanoprobe revealed that the electrochemical activity of the CNT network is not homogeneous and provided further understanding of the relationship between the topography and electrochemical characteristics of CNT networks.

  14. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    NASA Astrophysics Data System (ADS)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  15. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    PubMed Central

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-01-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases. PMID:26608097

  16. Existence and global exponential stability of periodic solution of a cellular neural networks difference equation with delays and impulses.

    PubMed

    Yang, Xinsong; Cui, Xiangzhao; Long, Yao

    2009-09-01

    A class of cellular neural networks difference equation with delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of periodic solution are obtained by using contraction mapping theorem and inequality techniques. The results of this paper are completely new. A numerical example and its simulations are offered to show the effectiveness of our new results.

  17. Anisotropic cellular network formation in engineered muscle tissue through the self-organization of neurons and endothelial cells.

    PubMed

    Takahashi, Hironobu; Shimizu, Tatsuya; Nakayama, Masamichi; Yamato, Masayuki; Okano, Teruo

    2015-02-18

    Tissue anisotropy directed by cell sheets: Aligned myoblasts can be harvested as an anisotropic cell sheet using a micropatterned thermoresponsive substrate. Neurons and endothelial cells sandwiched between multiple anisotropic cell sheets self-organize oriented cellular networks in the tissue construct. This simple tissue engineering technique is useful for the creation of biomimetic microstructures in complex tissue, required for future advances in regenerative medicine.

  18. Simulation of Cardiac Arrhythmias Using a 2D Heterogeneous Whole Heart Model.

    PubMed

    Balakrishnan, Minimol; Chakravarthy, V Srinivasa; Guhathakurta, Soma

    2015-01-01

    Simulation studies of cardiac arrhythmias at the whole heart level with electrocardiogram (ECG) gives an understanding of how the underlying cell and tissue level changes manifest as rhythm disturbances in the ECG. We present a 2D whole heart model (WHM2D) which can accommodate variations at the cellular level and can generate the ECG waveform. It is shown that, by varying cellular-level parameters like the gap junction conductance (GJC), excitability, action potential duration (APD) and frequency of oscillations of the auto-rhythmic cell in WHM2D a large variety of cardiac arrhythmias can be generated including sinus tachycardia, sinus bradycardia, sinus arrhythmia, sinus pause, junctional rhythm, Wolf Parkinson White syndrome and all types of AV conduction blocks. WHM2D includes key components of the electrical conduction system of the heart like the SA (Sino atrial) node cells, fast conducting intranodal pathways, slow conducting atriovenctricular (AV) node, bundle of His cells, Purkinje network, atrial, and ventricular myocardial cells. SA nodal cells, AV nodal cells, bundle of His cells, and Purkinje cells are represented by the Fitzhugh-Nagumo (FN) model which is a reduced model of the Hodgkin-Huxley neuron model. The atrial and ventricular myocardial cells are modeled by the Aliev-Panfilov (AP) two-variable model proposed for cardiac excitation. WHM2D can prove to be a valuable clinical tool for understanding cardiac arrhythmias.

  19. Simulation of Cardiac Arrhythmias Using a 2D Heterogeneous Whole Heart Model

    PubMed Central

    Balakrishnan, Minimol; Chakravarthy, V. Srinivasa; Guhathakurta, Soma

    2015-01-01

    Simulation studies of cardiac arrhythmias at the whole heart level with electrocardiogram (ECG) gives an understanding of how the underlying cell and tissue level changes manifest as rhythm disturbances in the ECG. We present a 2D whole heart model (WHM2D) which can accommodate variations at the cellular level and can generate the ECG waveform. It is shown that, by varying cellular-level parameters like the gap junction conductance (GJC), excitability, action potential duration (APD) and frequency of oscillations of the auto-rhythmic cell in WHM2D a large variety of cardiac arrhythmias can be generated including sinus tachycardia, sinus bradycardia, sinus arrhythmia, sinus pause, junctional rhythm, Wolf Parkinson White syndrome and all types of AV conduction blocks. WHM2D includes key components of the electrical conduction system of the heart like the SA (Sino atrial) node cells, fast conducting intranodal pathways, slow conducting atriovenctricular (AV) node, bundle of His cells, Purkinje network, atrial, and ventricular myocardial cells. SA nodal cells, AV nodal cells, bundle of His cells, and Purkinje cells are represented by the Fitzhugh-Nagumo (FN) model which is a reduced model of the Hodgkin-Huxley neuron model. The atrial and ventricular myocardial cells are modeled by the Aliev-Panfilov (AP) two-variable model proposed for cardiac excitation. WHM2D can prove to be a valuable clinical tool for understanding cardiac arrhythmias. PMID:26733873

  20. DNA-damage response network at the crossroads of cell-cycle checkpoints, cellular senescence and apoptosis.

    PubMed

    Schmitt, Estelle; Paquet, Claudie; Beauchemin, Myriam; Bertrand, Richard

    2007-06-01

    Tissue homeostasis requires a carefully-orchestrated balance between cell proliferation, cellular senescence and cell death. Cells proliferate through a cell cycle that is tightly regulated by cyclin-dependent kinase activities. Cellular senescence is a safeguard program limiting the proliferative competence of cells in living organisms. Apoptosis eliminates unwanted cells by the coordinated activity of gene products that regulate and effect cell death. The intimate link between the cell cycle, cellular senescence, apoptosis regulation, cancer development and tumor responses to cancer treatment has become eminently apparent. Extensive research on tumor suppressor genes, oncogenes, the cell cycle and apoptosis regulatory genes has revealed how the DNA damage-sensing and -signaling pathways, referred to as the DNA-damage response network, are tied to cell proliferation, cell-cycle arrest, cellular senescence and apoptosis. DNA-damage responses are complex, involving "sensor" proteins that sense the damage, and transmit signals to "transducer" proteins, which, in turn, convey the signals to numerous "effector" proteins implicated in specific cellular pathways, including DNA repair mechanisms, cell-cycle checkpoints, cellular senescence and apoptosis. The Bcl-2 family of proteins stands among the most crucial regulators of apoptosis and performs vital functions in deciding whether a cell will live or die after cancer chemotherapy and irradiation. In addition, several studies have now revealed that members of the Bcl-2 family also interface with the cell cycle, DNA repair/recombination and cellular senescence, effects that are generally distinct from their function in apoptosis. In this review, we report progress in understanding the molecular networks that regulate cell-cycle checkpoints, cellular senescence and apoptosis after DNA damage, and discuss the influence of some Bcl-2 family members on cell-cycle checkpoint regulation.

  1. Astrocytic gap junctional networks suppress cellular damage in an in vitro model of ischemia

    SciTech Connect

    Shinotsuka, Takanori; Yasui, Masato; Nuriya, Mutsuo

    2014-02-07

    Highlights: • Astrocytes exhibit characteristic changes in [Ca{sup 2+}]{sub i} under OGD. • Astrocytic [Ca{sup 2+}]{sub i} increase is synchronized with a neuronal anoxic depolarization. • Gap junctional couplings protect neurons as well as astrocytes during OGD. - Abstract: Astrocytes play pivotal roles in both the physiology and the pathophysiology of the brain. They communicate with each other via extracellular messengers as well as through gap junctions, which may exacerbate or protect against pathological processes in the brain. However, their roles during the acute phase of ischemia and the underlying cellular mechanisms remain largely unknown. To address this issue, we imaged changes in the intracellular calcium concentration ([Ca{sup 2+}]{sub i}) in astrocytes in mouse cortical slices under oxygen/glucose deprivation (OGD) condition using two-photon microscopy. Under OGD, astrocytes showed [Ca{sup 2+}]{sub i} oscillations followed by larger and sustained [Ca{sup 2+}]{sub i} increases. While the pharmacological blockades of astrocytic receptors for glutamate and ATP had no effect, the inhibitions of gap junctional intercellular coupling between astrocytes significantly advanced the onset of the sustained [Ca{sup 2+}]{sub i} increase after OGD exposure. Interestingly, the simultaneous recording of the neuronal membrane potential revealed that the onset of the sustained [Ca{sup 2+}]{sub i} increase in astrocytes was synchronized with the appearance of neuronal anoxic depolarization. Furthermore, the blockade of gap junctional coupling resulted in a concurrent faster appearance of neuronal depolarizations, which remain synchronized with the sustained [Ca{sup 2+}]{sub i} increase in astrocytes. These results indicate that astrocytes delay the appearance of the pathological responses of astrocytes and neurons through their gap junction-mediated intercellular network under OGD. Thus, astrocytic gap junctional networks provide protection against tissue damage

  2. Memristor-based cellular nonlinear/neural network: design, analysis, and applications.

    PubMed

    Duan, Shukai; Hu, Xiaofang; Dong, Zhekang; Wang, Lidan; Mazumder, Pinaki

    2015-06-01

    Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.

  3. Early warning of illegal development for protected areas by integrating cellular automata with neural networks.

    PubMed

    Li, Xia; Lao, Chunhua; Liu, Yilun; Liu, Xiaoping; Chen, Yimin; Li, Shaoying; Ai, Bing; He, Zijian

    2013-11-30

    Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Adiponectin fine-tuning of liver regeneration dynamics revealed through cellular network modeling.

    PubMed

    Correnti, Jason M; Cook, Daniel; Aksamitiene, Edita; Swarup, Aditi; Ogunnaike, Babatunde; Vadigepalli, Rajanikanth; Hoek, Jan B

    2014-11-10

    Following partial hepatectomy, the liver initiates a regenerative program involving hepatocyte priming and replication driven by coordinated cytokine and growth factor actions. We investigated the mechanisms underlying Adiponectin's (Adn) regulation of liver regeneration through modulation of these mediators. Adn-/- mice showed delayed onset of hepatocyte replication, but accelerated cell cycle progression relative to wild-type mice, suggesting Adn has multiple effects fine-tuning the kinetics of liver regeneration. We developed a computational model describing the molecular and physiological kinetics of liver regeneration in Adn-/- mice. We employed this computational model to evaluate the underlying regulatory mechanisms. Our analysis predicted that Adn is required for an efficient early cytokine response to partial hepatectomy, but is inhibitory to later growth factor actions. Consistent with this prediction, Adn knockout reduced hepatocyte responses to IL-6 during the priming phase, but enhanced growth factor levels through peak hepatocyte replication. By contrast, supraphysiological concentrations of Adn resulting from rosiglitazone treatment suppressed regeneration by reducing growth factor levels during S phase, consistent with computational predictions. Together, these results revealed that Adn fine-tunes the progression of liver regeneration through dynamically modulating molecular mediator networks and cellular interactions within the liver. This article is protected by copyright. All rights reserved.

  5. Adiponectin fine-tuning of liver regeneration dynamics revealed through cellular network modelling.

    PubMed

    Correnti, Jason M; Cook, Daniel; Aksamitiene, Edita; Swarup, Aditi; Ogunnaike, Babatunde; Vadigepalli, Rajanikanth; Hoek, Jan B

    2015-01-15

    Following partial hepatectomy, the liver initiates a regenerative programme involving hepatocyte priming and replication driven by the coordinated actions of cytokine and growth factors. We investigated the mechanisms underlying adiponectin's (Adn) regulation of liver regeneration through modulation of these mediators. Adn(-/-) mice showed delayed onset of hepatocyte replication, but accelerated cell cycle progression relative to wild-type mice, suggesting Adn has multiple effects fine-tuning the kinetics of liver regeneration. We developed a computational model describing the molecular and physiological kinetics of liver regeneration in Adn(-/-) mice. We employed this computational model to evaluate the underlying regulatory mechanisms. Our analysis predicted that Adn is required for an efficient early cytokine response to partial hepatectomy, but is inhibitory to later growth factor actions. Consistent with this prediction, Adn knockout reduced hepatocyte responses to interleukin-6 during the priming phase, but enhanced growth factor levels through peak hepatocyte replication. By contrast, supraphysiological concentrations of Adn resulting from rosiglitazone treatment suppressed regeneration by reducing growth factor levels during S phase, consistent with computational predictions. Together, these results revealed that Adn fine-tunes the progression of liver regeneration through dynamically modulating molecular mediator networks and cellular interactions within the liver.

  6. Data fusion and classification using a hybrid intrinsic cellular inference network

    NASA Astrophysics Data System (ADS)

    Woodley, Robert; Walenz, Brett; Seiffertt, John; Robinette, Paul; Wunsch, Donald

    2010-04-01

    Hybrid Intrinsic Cellular Inference Network (HICIN) is designed for battlespace decision support applications. We developed an automatic method of generating hypotheses for an entity-attribute classifier. The capability and effectiveness of a domain specific ontology was used to generate automatic categories for data classification. Heterogeneous data is clustered using an Adaptive Resonance Theory (ART) inference engine on a sample (unclassified) data set. The data set is the Lahman baseball database. The actual data is immaterial to the architecture, however, parallels in the data can be easily drawn (i.e., "Team" maps to organization, "Runs scored/allowed" to Measure of organization performance (positive/negative), "Payroll" to organization resources, etc.). Results show that HICIN classifiers create known inferences from the heterogonous data. These inferences are not explicitly stated in the ontological description of the domain and are strictly data driven. HICIN uses data uncertainty handling to reduce errors in the classification. The uncertainty handling is based on subjective logic. The belief mass allows evidence from multiple sources to be mathematically combined to increase or discount an assertion. In military operations the ability to reduce uncertainty will be vital in the data fusion operation.

  7. Complex mental activity and the aging brain: molecular, cellular and cortical network mechanisms.

    PubMed

    Valenzuela, Michael J; Breakspear, Michael; Sachdev, Perminder

    2007-11-01

    There is strong evidence to suggest that high levels of complex mental activity can improve clinical outcome from brain injury. What are the neurobiological mechanisms underlying this observation? This paper proposes that complex mental activity induces a spectrum of biological changes on brain structure and function which can be best understood in a multiscalar spatiotemporal framework. Short-term molecular changes may include induction of BDNF, NGF and endopeptidase genes and elevation of the high-energy phosphocreatine-creatine resting state equilibrium. Animal models have implicated these processes in the reduction and even reversal of neurodegenerative changes secondary to mental work. These mechanisms can therefore be described as neuroprotective. Medium-term cellular changes are diverse and include neurogenesis, synaptogenesis, angiogenesis and formation of more complex dendritic branching patterns. Importantly, these effects parallel behavioral improvement, and thus a neurogenerative class of mechanisms is implicated. Finally, in the post-lesion context, computation principles such as efficiency, small world connectivity and functional adaptation are identified as important, with supportive clinical evidence from neuroimaging studies. Thus, dynamic compensatory cortical network mechanisms may also be relevant, yet take some time to evolve. This paper will explore the neurobiological and clinical implications of this framework, in particular in the context of age-related brain disease.

  8. A universal concept based on cellular neural networks for ultrafast and flexible solving of differential equations.

    PubMed

    Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere

    2015-04-01

    This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation.

  9. Interaction of cellular and network mechanisms for efficient pheromone coding in moths

    PubMed Central

    Belmabrouk, Hana; Nowotny, Thomas; Rospars, Jean-Pierre; Martinez, Dominique

    2011-01-01

    Sensory systems, both in the living and in machines, have to be optimized with respect to their environmental conditions. The pheromone subsystem of the olfactory system of moths is a particularly well-defined example in which rapid variations of odor content in turbulent plumes require fast, concentration-invariant neural representations. It is not clear how cellular and network mechanisms in the moth antennal lobe contribute to coding efficiency. Using computational modeling, we show that intrinsic potassium currents (IA and ISK) in projection neurons may combine with extrinsic inhibition from local interneurons to implement a dual latency code for both pheromone identity and intensity. The mean latency reflects stimulus intensity, whereas latency differences carry concentration-invariant information about stimulus identity. In accordance with physiological results, the projection neurons exhibit a multiphasic response of inhibition–excitation–inhibition. Together with synaptic inhibition, intrinsic currents IA and ISK account for the first and second inhibitory phases and contribute to a rapid encoding of pheromone information. The first inhibition plays the role of a reset to limit variability in the time to first spike. The second inhibition prevents responses of excessive duration to allow tracking of intermittent stimuli. PMID:22109556

  10. Cellular neural networks, the Navier-Stokes equation, and microarray image reconstruction.

    PubMed

    Zineddin, Bachar; Wang, Zidong; Liu, Xiaohui

    2011-11-01

    Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.

  11. New second-order difference algorithm for image segmentation based on cellular neural networks (CNNs)

    NASA Astrophysics Data System (ADS)

    Meng, Shukai; Mo, Yu L.

    2001-09-01

    Image segmentation is one of the most important operations in many image analysis problems, which is the process that subdivides an image into its constituents and extracts those parts of interest. In this paper, we present a new second order difference gray-scale image segmentation algorithm based on cellular neural networks. A 3x3 CNN cloning template is applied, which can make smooth processing and has a good ability to deal with the conflict between the capability of noise resistance and the edge detection of complex shapes. We use second order difference operator to calculate the coefficients of the control template, which are not constant but rather depend on the input gray-scale values. It is similar to Contour Extraction CNN in construction, but there are some different in algorithm. The result of experiment shows that the second order difference CNN has a good capability in edge detection. It is better than Contour Extraction CNN in detail detection and more effective than the Laplacian of Gauss (LOG) algorithm.

  12. Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach.

    PubMed

    Zeng, Nianyin; Wang, Zidong; Zineddin, Bachar; Li, Yurong; Du, Min; Xiao, Liang; Liu, Xiaohui; Young, Terry

    2014-05-01

    Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

  13. Residual Separation of Magnetic Fields Using a Cellular Neural Network Approach

    NASA Astrophysics Data System (ADS)

    Albora, A. M.; Özmen, A.; Uçan, O. N.

    - In this paper, a Cellular Neural Network (CNN) has been applied to a magnetic regional/residual anomaly separation problem. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behavior of the CNN is defined by the template matrices A, B and the template vector I. We have optimized weight coefficients of these templates using Recurrent Perceptron Learning Algorithm (RPLA). The advantages of CNN as a real-time stochastic method are that it introduces little distortion to the shape of the original image and that it is not effected significantly by factors such as the overlap of power spectra of residual fields. The proposed method is tested using synthetic examples and the average depth of the buried objects has been estimated by power spectrum analysis. Next the CNN approach is applied to magnetic data over the Golalan chromite mine in Elazig which lies East of Turkey. This area is among the largest and richest chromite masses of the world. We compared the performance of CNN to classical derivative approaches.

  14. BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling.

    PubMed

    Feng, Song; Ollivier, Julien F; Swain, Peter S; Soyer, Orkun S

    2015-10-30

    Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.

    PubMed

    Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Sobarzo-Sánchez, Eduardo; Yañez, Matilde; Riera-Fernandez, Pablo; González-Díaz, Humberto

    2011-12-01

    There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore

  16. Sparsity as cellular objective to infer directed metabolic networks from steady-state metabolome data: a theoretical analysis.

    PubMed

    Öksüz, Melik; Sadıkoğlu, Hasan; Çakır, Tunahan

    2013-01-01

    Since metabolome data are derived from the underlying metabolic network, reverse engineering of such data to recover the network topology is of wide interest. Lyapunov equation puts a constraint to the link between data and network by coupling the covariance of data with the strength of interactions (Jacobian matrix). This equation, when expressed as a linear set of equations at steady state, constitutes a basis to infer the network structure given the covariance matrix of data. The sparse structure of metabolic networks points to reactions which are active based on minimal enzyme production, hinting at sparsity as a cellular objective. Therefore, for a given covariance matrix, we solved Lyapunov equation to calculate Jacobian matrix by a simultaneous use of minimization of Euclidean norm of residuals and maximization of sparsity (the number of zeros in Jacobian matrix) as objective functions to infer directed small-scale networks from three kingdoms of life (bacteria, fungi, mammalian). The inference performance of the approach was found to be promising, with zero False Positive Rate, and almost one True positive Rate. The effect of missing data on results was additionally analyzed, revealing superiority over similarity-based approaches which infer undirected networks. Our findings suggest that the covariance of metabolome data implies an underlying network with sparsest pattern. The theoretical analysis forms a framework for further investigation of sparsity-based inference of metabolic networks from real metabolome data.

  17. Five inorganic–organic hybrids based on Keggin polyanion [SiMo{sub 12}O{sub 40}]{sup 4−}: From 0D to 2D network

    SciTech Connect

    Yu, Xiao-Yang; Cui, Xiao-Bing; Lu, Jing; Luo, Yu-Hui; Zhang, Hong; Gao, Wei-Ping

    2014-01-15

    Five new inorganic–organic hybrids based on 4,4′-bipyridine and Keggin-type polyoxometalate [SiMo{sub 12}O{sub 40}]{sup 4−}, (SiMo{sub 12}O{sub 40})(H{sub 2}bipy){sub 2}·2H{sub 2}O (1), [Cu(Hbipy){sub 4}(HSiMo{sub 12}O{sub 40})(SiMo{sub 12}O{sub 40})](H{sub 2}bipy){sub 0.5}·7H{sub 2}O (2), [Cu{sub 2}(Hbipy){sub 6}(bipy)(SiMo{sub 12}O{sub 40}){sub 3}](Hbipy){sub 2}·6H{sub 2}O (3), [Cu(bipy){sub 2}(SiMo{sub 12}O{sub 40})](H{sub 2}bipy)·2H{sub 2}O (4) and [Cu{sub 2}(bipy){sub 4}(H{sub 2}O){sub 4}](SiMo{sub 12}O{sub 40})·13H{sub 2}O (5) (bipy=4,4′-bipyridine), have been hydrothermally synthesized. 1 consists of H{sub 2}bipy{sup 2+} and [SiMo{sub 12}O{sub 40}]{sup 4−} units. In 2, two [SiMo{sub 12}O{sub 40}]{sup 4−} are bridged by [Cu(Hbipy){sub 4}]{sup 6+} to form a [Cu(Hbipy){sub 4}(SiMo{sub 12}O{sub 40}){sub 2}]{sup 2−} dimmer. In 3, [SiMo{sub 12}O{sub 40}]{sup 4−} polyanions acting as bidentated bridging ligands and monodentated auxiliary ligands connect [Cu{sub 2}(Hbipy){sub 6}(bipy)]{sup 8+} units into a 1D zigzag chain. In 4, [SiMo{sub 12}O{sub 40}]{sup 4−} polyanions bridge neighboring 1D [Cu(bipy){sub 2}]{sup 2+} double chains into a 2D extended layer. In 5, [SiMo{sub 12}O{sub 40}]{sup 4−} polyanions acting as templates site alternately upon the grids from both sides of the square grid [Cu{sub 2}(bipy){sub 4}(H{sub 2}O){sub 4}]{sup 4+} layer. In addition, the electrochemical behaviors of 1, 3 and 4 and the photocatalysis property of 1 have been investigated. - Graphical abstract: Five new compounds based on [SiMo{sub 12}O{sub 40}]{sup 4−} have been successfully generated. [SiMo{sub 12}O{sub 40}]{sup 4−} anions play different roles in the structures of the five compounds. Display Omitted - Highlights: • Five new compounds based on [SiMo{sub 12}O{sub 40}]{sup 4−} have been generated. • [SiMo{sub 12}O{sub 40}]{sup 4−} anions play different roles in the five structures. • The electrochemical behaviors of 1, 3 and 4 have been

  18. Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

    NASA Astrophysics Data System (ADS)

    Gollas, Frank; Tetzlaff, Ronald

    2009-05-01

    Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio

  19. Impedance Matching Network for High Frequency Ultrasonic Transducer for Cellular Applications

    PubMed Central

    Kim, Min Gon; Yoon, Sangpil; Kim, Hyung Ham; Shung, K. Kirk

    2015-01-01

    An approach for the design of an impedance matching network (IMN) for high frequency ultrasonic transducers with large apertures based on impedance analysis for cellular applications is presented in this paper. The main objectives were to maximize energy transmission from the excitation source to the ultrasonic transducers for cell manipulation and to achieve low input parameters for the safe operation of an ultrasonic transducer because the piezoelectric material in high frequency ultrasonic transducers is prone to breakage due to its being extremely thin. Two ultrasonic transducers, which were made of lithium niobate single crystal with the thickness of 15 μm, having apertures of 4.3 mm (fnumber = 1.23) and 2.6 mm (fnumber = 0.75) were tested. L-type IMN was selected for high sensitivity and compact design of the ultrasonic transducers. The target center frequency was chosen as the frequency where the electrical admittance (∣Y∣) and phase angle (θz) from impedance analysis was maximal and zero, respectively. The reference center frequency and reference echo magnitude were selected as the center frequency and echo magnitude, measured by pulse-echo testing, of the ultrasonic transducer without IMN. Initial component values and topology of IMN were determined using the Smith chart, and pulse-echo testing was analyzed to verify the performance of the ultrasonic transducers with and without IMN. After several iterations between changing component values and topology of IMN, and pulse-echo measurement of the ultrasonic transducer with IMN, optimized component values and topology of IMN were chosen when the measured center frequency from pulse-echo testing was comparable to the target frequency, and the measured echo magnitude was at least 30% larger than the reference echo magnitude. Performance of an ultrasonic transducer with and without IMN was tested by observing a tangible dent on the surface of a plastic petridish and single cell response after an acoustic

  20. Implementation of a cellular neural network-based segmentation algorithm on the bio-inspired vision system

    NASA Astrophysics Data System (ADS)

    Karabiber, Fethullah; Grassi, Giuseppe; Vecchio, Pietro; Arik, Sabri; Yalcin, M. Erhan

    2011-01-01

    Based on the cellular neural network (CNN) paradigm, the bio-inspired (bi-i) cellular vision system is a computing platform consisting of state-of-the-art sensing, cellular sensing-processing and digital signal processing. This paper presents the implementation of a novel CNN-based segmentation algorithm onto the bi-i system. The experimental results, carried out for different benchmark video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frame/sec. Comparisons with existing CNN-based methods show that, even though these methods are from two to six times faster than the proposed one, the conceived approach is more accurate and, consequently, represents a satisfying trade-off between real-time requirements and accuracy.

  1. Assessment of General Public Exposure to LTE signals compared to other Cellular Networks Present in Thessaloniki, Greece.

    PubMed

    Gkonis, Fotios; Boursianis, Achilles; Samaras, Theodoros

    2016-12-15

    To assess general public exposure to electromagnetic fields from Long Term Evolution (LTE) base stations, measurements at 10 sites in Thessaloniki, Greece were performed. Results are compared with other mobile cellular networks currently in use. All exposure values satisfy the guidelines for general public exposure of the International Commission on Non-Ionizing Radiation Protection (ICNIRP), as well as the reference levels by the Greek legislation at all sites. LTE electric field measurements were recorded up to 0.645 V/m. By applying the ICNIRP guidelines, the exposure ratio for all LTE signals is between 2.9 × 10(-5) and 2.8 × 10(-2) From the measurements results it is concluded that the average and maximum power density contribution of LTE downlink signals to the overall cellular networks signals are 7.8% and 36.7%, respectively.

  2. Geometric phase transition in the cellular network of the pancreatic islets may underlie the onset of type 1diabetes

    NASA Astrophysics Data System (ADS)

    Wang, Xujing

    Living systems are characterized by complexity in structure and emergent dynamic orders. In many aspects the onset of a chronic disease resembles phase transition in a dynamic system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. In this study we investigate this idea in a real example, the insulin-producing pancreatic islet β-cells and the onset of type 1 diabetes. Within each islet, the β-cells are electrically coupled to each other, and function as a network with synchronized actions. Using percolation theory we show how normal islet function is intrinsically linked to network connectivity, and the critical point where the islet cellular network loses site percolation, is consistent with laboratory and clinical observations of the threshold β-cell loss that causes islet functional failure. Numerical simulations confirm that the islet cellular network needs to be percolated for β-cells to synchronize. Furthermore, the interplay between site percolation and bond strength predicts the existence of a transient phase of islet functional recovery after disease onset and introduction of treatment, potentially explaining a long time mystery in the clinical study of type 1 diabetes: the honeymoon phenomenon. Based on these results, we hypothesized that the onset of T1D may be the result of a phase transition of the islet β-cell network. We further discuss the potential applications in identifying disease-driving factors, and the critical parameters that are predictive of disease onset.

  3. Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry.

    PubMed

    Powell, David W; Merchant, Michael L; Link, Andrew J

    2006-02-01

    An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.

  4. Impedance matching network for high frequency ultrasonic transducer for cellular applications.

    PubMed

    Kim, Min Gon; Yoon, Sangpil; Kim, Hyung Ham; Shung, K Kirk

    2016-02-01

    An approach for the design of an impedance matching network (IMN) for high frequency ultrasonic transducers with large apertures based on impedance analysis for cellular applications is presented in this paper. The main objectives were to maximize energy transmission from the excitation source to the ultrasonic transducers for cell manipulation and to achieve low input parameters for the safe operation of an ultrasonic transducer because the piezoelectric material in high frequency ultrasonic transducers is prone to breakage due to its being extremely thin. Two ultrasonic transducers, which were made of lithium niobate single crystal with the thickness of 15 μm, having apertures of 4.3 mm (fnumber=1.23) and 2.6mm (fnumber=0.75) were tested. L-type IMN was selected for high sensitivity and compact design of the ultrasonic transducers. The target center frequency was chosen as the frequency where the electrical admittance (|Y|) and phase angle (θz) from impedance analysis was maximal and zero, respectively. The reference center frequency and reference echo magnitude were selected as the center frequency and echo magnitude, measured by pulse-echo testing, of the ultrasonic transducer without IMN. Initial component values and topology of IMN were determined using the Smith chart, and pulse-echo testing was analyzed to verify the performance of the ultrasonic transducers with and without IMN. After several iterations between changing component values and topology of IMN, and pulse-echo measurement of the ultrasonic transducer with IMN, optimized component values and topology of IMN were chosen when the measured center frequency from pulse-echo testing was comparable to the target frequency, and the measured echo magnitude was at least 30% larger than the reference echo magnitude. Performance of an ultrasonic transducer with and without IMN was tested by observing a tangible dent on the surface of a plastic petridish and single cell response after an acoustic pulse was

  5. Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses.

    PubMed

    Mondal, Debasish; Dougherty, Edward; Mukhopadhyay, Abhishek; Carbo, Adria; Yao, Guang; Xing, Jianhua

    2014-01-01

    A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

  6. Mechanical models of the cellular cytoskeletal network for the analysis of intracellular mechanical properties and force distributions: a review.

    PubMed

    Chen, Ting-Jung; Wu, Chia-Ching; Su, Fong-Chin

    2012-12-01

    The cytoskeleton, which is the major mechanical component of cells, supports the cell body and regulates the cellular motility to assist the cell in performing its biological functions. Several cytoskeletal network models have been proposed to investigate the mechanical properties of cells. This review paper summarizes these models with a focus on the prestressed cable network, the semi-flexible chain network, the open-cell foam, the tensegrity, and the granular models. The components, material parameters, types of connection joints, tension conditions, and the advantages and disadvantages of each model are evaluated from a structural and biological point of view. The underlying mechanisms that are associated with the morphological changes of spreading cells are expected to be simulated using a cytoskeletal model; however, it is still paid less attention most likely due to the lack of a suitable cytoskeletal model that can accurately model the spreading process. In this review article, the established cytoskeletal models are hoped to provide useful information for the development of future cytoskeletal models with different degrees of cell attachment for the study of the mechanical mechanisms underlying the cellular behaviors in response to external stimulations.

  7. Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease

    PubMed Central

    Ding, Jingzhong; Reynolds, Lindsay M.; Zeller, Tanja; Müller, Christian; Lohman, Kurt; Nicklas, Barbara J.; Kritchevsky, Stephen B.; Huang, Zhiqing; de la Fuente, Alberto; Soranzo, Nicola; Settlage, Robert E.; Chuang, Chia-Chi; Howard, Timothy; Xu, Ning; Goodarzi, Mark O.; Chen, Y.-D. Ida; Rotter, Jerome I.; Siscovick, David S.; Parks, John S.; Murphy, Susan; Jacobs, David R.; Post, Wendy; Tracy, Russell P.; Wild, Philipp S.; Blankenberg, Stefan; Hoeschele, Ina; Herrington, David; McCall, Charles E.

    2015-01-01

    Obesity is linked to type 2 diabetes (T2D) and cardiovascular diseases; however, the underlying molecular mechanisms remain unclear. We aimed to identify obesity-associated molecular features that may contribute to obesity-related diseases. Using circulating monocytes from 1,264 Multi-Ethnic Study of Atherosclerosis (MESA) participants, we quantified the transcriptome and epigenome. We discovered that alterations in a network of coexpressed cholesterol metabolism genes are a signature feature of obesity and inflammatory stress. This network included 11 BMI-associated genes related to sterol uptake (↑LDLR, ↓MYLIP), synthesis (↑SCD, FADS1, HMGCS1, FDFT1, SQLE, CYP51A1, SC4MOL), and efflux (↓ABCA1, ABCG1), producing a molecular profile expected to increase intracellular cholesterol. Importantly, these alterations were associated with T2D and coronary artery calcium (CAC), independent from cardiometabolic factors, including serum lipid profiles. This network mediated the associations between obesity and T2D/CAC. Several genes in the network harbored C-phosphorus-G dinucleotides (e.g., ABCG1/cg06500161), which overlapped Encyclopedia of DNA Elements (ENCODE)-annotated regulatory regions and had methylation profiles that mediated the associations between BMI/inflammation and expression of their cognate genes. Taken together with several lines of previous experimental evidence, these data suggest that alterations of the cholesterol metabolism gene network represent a molecular link between obesity/inflammation and T2D/CAC. PMID:26153245

  8. Position and Velocity Tracking in Cellular Networks Using the Kalman Filter

    SciTech Connect

    Olama, Mohammed M; Djouadi, Seddik M; Kuruganti, Phani Teja

    2009-01-01

    Access to the right information anytime, anywhere is becoming the new driving force for the information technology revolution. The 'right' information's relevance is based on the user's profile and his/her current geographical position and/or time. Location Based Service (LBS) is an innovative technology that provides information or makes information available based on the geographical location of the mobile user. Analysts predict that LBSs will lead to new applications, generating billions of US dollars worldwide (Leite, 2001; Searle, 2001). The need for an efficient and accurate mobile station (MS) positioning system is growing day by day. The ability to pinpoint the location of an individual has an obvious and vital value in the context of emergency services (Chan, 2003; Olama et al., 2008). Pinpointing the location of people and other valuable assets also opens the door to a new world of previously unimagined information services and m-commerce probabilities. For example, availability of services like 'Where is the nearest ATM?', 'Check traffic conditions on the highway on my route', 'Find a parking lot nearby', as well as answers to 'Where is my advisor?', and 'Where is my car?' will be an everyday rule in our lives (Charalambous & Panayiotou, 2004). A technology independent LBS architecture can be considered as comprised by three main parts (Girodon, 2002): A user requesting information, a mobile network operator and its partners, and several content providers (e.g. data, maps). The subscriber requests a personalized service dependant on his geographic location. The system will ask the Location Services Manager (which is in charge of handling requests, i.e., send/receive to the Location Calculator and the Content Providers) to pinpoint the location of the mobile. The Location Services Manager (LSM), using the Location Calculator, will ask the Content Provider (CP) to supply qualified information according to the mobile's geographical position. The LSM will

  9. Toward a systems biology approach to investigate cellular networks in normal and malignant B cells.

    PubMed

    Basso, K

    2009-07-01

    In recent years, we experienced an increasing development of new technologies that aim to comprehensively dissect the molecular genetics of cellular phenotypes. Pioneering studies have been performed on leukemia and lymphoma and then extended to many other types of malignancies. Genome-wide technologies allow taking snapshots of defined cellular context from an unbiased angle highlighting a complexity that we still struggle to fully interpret. The increasing availability of technologies to detect genetic, transcriptional and post-transcriptional characteristics of cellular systems needs to be associated with the development of computational tools to fully investigate these data in an integrated way. The evolution of different genome-wide technologies as well as data mining and integration tools will be discussed following studies performed on normal and malignant human mature B cells.

  10. Exact quantification of cellular robustness in genome-scale metabolic networks

    PubMed Central

    Gerstl, Matthias P.; Klamt, Steffen; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2016-01-01

    Motivation: Robustness, the ability of biological networks to uphold their functionality in spite of perturbations, is a key characteristic of all living systems. Although several theoretical approaches have been developed to formalize robustness, it still eludes an exact quantification. Here, we present a rigorous and quantitative approach for the structural robustness of metabolic networks by measuring their ability to tolerate random reaction (or gene) knockouts. Results: In analogy to reliability theory, based on an explicit consideration of all possible knockout sets, we exactly quantify the probability of failure for a given network function (e.g. growth). This measure can be computed if the network’s minimal cut sets (MSCs) are known. We show that even in genome-scale metabolic networks the probability of (network) failure can be reliably estimated from MSCs with lowest cardinalities. We demonstrate the applicability of our theory by analyzing the structural robustness of multiple Enterobacteriaceae and Blattibacteriaceae and show a dramatically low structural robustness for the latter. We find that structural robustness develops from the ability to proliferate in multiple growth environments consistent with experimentally found knowledge. Conclusion: The probability of (network) failure provides thus a reliable and easily computable measure of structural robustness and redundancy in (genome-scale) metabolic networks. Availability and implementation: Source code is available under the GNU General Public License at https://github.com/mpgerstl/networkRobustnessToolbox. Contact: juergen.zanghellini@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26543173

  11. Comparative analysis of Salmonella susceptibility and tolerance to the biocide chlorhexidine identifies a complex cellular defense network

    PubMed Central

    Condell, Orla; Power, Karen A.; Händler, Kristian; Finn, Sarah; Sheridan, Aine; Sergeant, Kjell; Renaut, Jenny; Burgess, Catherine M.; Hinton, Jay C. D.; Nally, Jarlath E.; Fanning, Séamus

    2014-01-01

    Chlorhexidine is one of the most widely used biocides in health and agricultural settings as well as in the modern food industry. It is a cationic biocide of the biguanide class. Details of its mechanism of action are largely unknown. The frequent use of chlorhexidine has been questioned recently, amidst concerns that an overuse of this compound may select for bacteria displaying an altered susceptibility to antimicrobials, including clinically important anti-bacterial agents. We generated a Salmonella enterica serovar Typhimurium isolate (ST24CHX) that exhibited a high-level tolerant phenotype to chlorhexidine, following several rounds of in vitro selection, using sub-lethal concentrations of the biocide. This mutant showed altered suceptibility to a panel of clinically important antimicrobial compounds. Here we describe a genomic, transcriptomic, proteomic, and phenotypic analysis of the chlorhexidine tolerant S. Typhimurium compared with its isogenic sensitive progenitor. Results from this study describe a chlorhexidine defense network that functions in both the reference chlorhexidine sensitive isolate and the tolerant mutant. The defense network involved multiple cell targets including those associated with the synthesis and modification of the cell wall, the SOS response, virulence, and a shift in cellular metabolism toward anoxic pathways, some of which were regulated by CreB and Fur. In addition, results indicated that chlorhexidine tolerance was associated with more extensive modifications of the same cellular processes involved in this proposed network, as well as a divergent defense response involving the up-regulation of additional targets such as the flagellar apparatus and an altered cellular phosphate metabolism. These data show that sub-lethal concentrations of chlorhexidine induce distinct changes in exposed Salmonella, and our findings provide insights into the mechanisms of action and tolerance to this biocidal agent. PMID:25136333

  12. Delay-dependent global exponential robust stability for delayed cellular neural networks with time-varying delay.

    PubMed

    Liu, Pin-Lin

    2013-11-01

    This paper investigates a class of delayed cellular neural networks (DCNN) with time-varying delay. Based on the Lyapunov-Krasovski functional and integral inequality approach (IIA), a uniformly asymptotic stability criterion in terms of only one simple linear matrix inequality (LMI) is addressed, which guarantees stability for such time-varying delay systems. This LMI can be easily solved by convex optimization techniques. Unlike previous methods, the upper bound of the delay derivative is taken into consideration, even if larger than or equal to 1. It is proven that results obtained are less conservative than existing ones. Four numerical examples illustrate efficacy of the proposed methods.

  13. Delay-dependent exponential passivity of uncertain cellular neural networks with discrete and distributed time-varying delays.

    PubMed

    Du, Yuanhua; Zhong, Shouming; Xu, Jia; Zhou, Nan

    2015-05-01

    This paper is concerned with the delay-dependent exponential passivity analysis issue for uncertain cellular neural networks with discrete and distributed time-varying delays. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, a suitable augmented Lyapunov-Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the exponential passivity analysis issue for the considered system. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed results.

  14. A novel spatter detection algorithm based on typical cellular neural network operations for laser beam welding processes

    NASA Astrophysics Data System (ADS)

    Nicolosi, L.; Abt, F.; Blug, A.; Heider, A.; Tetzlaff, R.; Höfler, H.

    2012-01-01

    Real-time monitoring of laser beam welding (LBW) has increasingly gained importance in several manufacturing processes ranging from automobile production to precision mechanics. In the latter, a novel algorithm for the real-time detection of spatters was implemented in a camera based on cellular neural networks. The latter can be connected to the optics of commercially available laser machines leading to real-time monitoring of LBW processes at rates up to 15 kHz. Such high monitoring rates allow the integration of other image evaluation tasks such as the detection of the full penetration hole for real-time control of process parameters.

  15. Impact of Network Structure and Cellular Response on Spike Time Correlations

    PubMed Central

    Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir

    2012-01-01

    Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative – or correlated – activity in neural populations, and in the possible impact of such correlations on the neural code. A fundamental theoretical challenge is to understand how the architecture of network connectivity along with the dynamical properties of single cells shape the magnitude and timescale of correlations. We provide a general approach to this problem by extending prior techniques based on linear response theory. We consider networks of general integrate-and-fire cells with arbitrary architecture, and provide explicit expressions for the approximate cross-correlation between constituent cells. These correlations depend strongly on the operating point (input mean and variance) of the neurons, even when connectivity is fixed. Moreover, the approximations admit an expansion in powers of the matrices that describe the network architecture. This expansion can be readily interpreted in terms of paths between different cells. We apply our results to large excitatory-inhibitory networks, and demonstrate first how precise balance – or lack thereof – between the strengths and timescales of excitatory and inhibitory synapses is reflected in the overall correlation structure of the network. We then derive explicit expressions for the average correlation structure in randomly connected networks. These expressions help to identify the important factors that shape coordinated neural activity in such networks. PMID:22457608

  16. Mitigating Handoff Call Dropping in Wireless Cellular Networks: A Call Admission Control Technique

    NASA Astrophysics Data System (ADS)

    Ekpenyong, Moses Effiong; Udoh, Victoria Idia; Bassey, Udoma James

    2016-06-01

    Handoff management has been an important but challenging issue in the field of wireless communication. It seeks to maintain seamless connectivity of mobile users changing their points of attachment from one base station to another. This paper derives a call admission control model and establishes an optimal step-size coefficient (k) that regulates the admission probability of handoff calls. An operational CDMA network carrier was investigated through the analysis of empirical data collected over a period of 1 month, to verify the performance of the network. Our findings revealed that approximately 23 % of calls in the existing system were lost, while 40 % of the calls (on the average) were successfully admitted. A simulation of the proposed model was then carried out under ideal network conditions to study the relationship between the various network parameters and validate our claim. Simulation results showed that increasing the step-size coefficient degrades the network performance. Even at optimum step-size (k), the network could still be compromised in the presence of severe network crises, but our model was able to recover from these problems and still functions normally.

  17. Multi-Hop Link Capacity of Multi-Route Multi-Hop MRC Diversity for a Virtual Cellular Network

    NASA Astrophysics Data System (ADS)

    Daou, Imane; Kudoh, Eisuke; Adachi, Fumiyuki

    In virtual cellular network (VCN), proposed for high-speed mobile communications, the signal transmitted from a mobile terminal is received by some wireless ports distributed in each virtual cell and relayed to the central port that acts as a gateway to the core network. In this paper, we apply the multi-route MHMRC diversity in order to decrease the transmit power and increase the multi-hop link capacity. The transmit power, the interference power and the link capacity are evaluated for DS-CDMA multi-hop VCN by computer simulation. The multi-route MHMRC diversity can be applied to not only DS-CDMA but also other access schemes (i. e. MC-CDMA, OFDM, etc.).

  18. Rainfall monitoring based on microwave links from cellular telecommunication networks: First results from a West African test bed

    NASA Astrophysics Data System (ADS)

    Doumounia, Ali; Gosset, Marielle; Cazenave, Frederic; Kacou, Modeste; Zougmore, François

    2014-08-01

    Rainfall monitoring based on commercial terrestrial microwave links is tested for the first time in Burkina Faso, in Sahelian West Africa. In collaboration with one national cellular phone operator, Telecel Faso, the attenuation on a 29 km long microwave link operating at 7 GHz was monitored at 1 s time rate for the monsoon season 2012. The time series of attenuation is transformed into rain rates and compared with rain gauge data. The method is successful in quantifying rainfall: 95% of the rainy days are detected. The correlation with the daily rain gauge series is 0.8, and the season bias is 6%. The correlation at the 5 min time step within each event is also high. These results demonstrate the potential interest of exploiting national and regional wireless telecommunication networks for monitoring rainfall in Africa, where operational rain gauge networks are degrading and the hydrometeorological risk increasing.

  19. A double herd krill based algorithm for location area optimization in mobile wireless cellular network.

    PubMed

    Vincylloyd, F; Anand, B

    2015-01-01

    In wireless communication systems, mobility tracking deals with determining a mobile subscriber (MS) covering the area serviced by the wireless network. Tracking a mobile subscriber is governed by the two fundamental components called location updating (LU) and paging. This paper presents a novel hybrid method using a krill herd algorithm designed to optimize the location area (LA) within available spectrum such that total network cost, comprising location update (LU) cost and cost for paging, is minimized without compromise. Based on various mobility patterns of users and network architecture, the design of the LR area is formulated as a combinatorial optimization problem. Numerical results indicate that the proposed model provides a more accurate update boundary in real environment than that derived from a hexagonal cell configuration with a random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals.

  20. A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder.

    PubMed

    McEachin, Richard C; Chen, Haiming; Sartor, Maureen A; Saccone, Scott F; Keller, Benjamin J; Prossin, Alan R; Cavalcoli, James D; McInnis, Melvin G

    2010-11-19

    Lithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania. Using microarray and rt-QPCR assays, we identified candidate genes that are differentially expressed with lithium treatment. We used a systems biology approach to identify interactions among these candidate genes and develop a network of genes that interact with the differentially expressed candidates. Notably, we also identified cocaine as having a potential influence on the network, consistent with the observed high rate of comorbidity for BD and cocaine abuse. The resulting network represents a novel hypothesis on how multiple genetic influences on bipolar disorder are impacted by both lithium treatment and cocaine use. Testing this network for association with BD and related phenotypes, we find that it is significantly over-represented for genes that participate in signal transduction, consistent with our hypothesized-gene-by environment interaction. In addition, it models related pharmacogenomic, psychiatric, and chemical dependence phenotypes. We offer a network model of gene-by-environment interaction associated with lithium's effectiveness in treating BD mania, as well as the observed high rate of comorbidity of BD and cocaine abuse. We identified drug targets within this network that represent immediate candidates for therapeutic drug testing. Posing novel hypotheses for validation in future work, we prioritized SNPs near

  1. The dynamic and geometric phase transition in the cellular network of pancreatic islet

    NASA Astrophysics Data System (ADS)

    Wang, Xujing

    2013-03-01

    The pancreatic islet is a micro-organ that contains several thousands of endocrine cells, majority of which being the insulin releasing β - cells . - cellsareexcitablecells , andarecoupledtoeachother through gap junctional channels. Here, using percolation theory, we investigate the role of network structure in determining the dynamics of the β-cell network. We show that the β-cell synchronization depends on network connectivity. More specifically, as the site occupancy is reducing, initially the β-cell synchronization is barely affected, until it reaches around a critical value, where the synchronization exhibit a sudden rapid decline, followed by an slow exponential tail. This critical value coincides with the critical site open probability for percolation transition. The dependence over bond strength is similar, exhibiting critical-behavior like dependence around a certain value of bond strength. These results suggest that the β-cell network undergoes a dynamic phase transition when the network is percolated. We further apply the findings to study diabetes. During the development of diabetes, the β - cellnetworkconnectivitydecreases . Siteoccupancyreducesfromthe reducing β-cell mass, and the bond strength is increasingly impaired from β-cell stress and chronic hyperglycemia. We demonstrate that the network dynamics around the percolation transition explain the disease dynamics around onset, including a long time mystery in diabetes, the honeymoon phenomenon.

  2. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    NASA Astrophysics Data System (ADS)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  3. Optoelectronics with 2D semiconductors

    NASA Astrophysics Data System (ADS)

    Mueller, Thomas

    2015-03-01

    Two-dimensional (2D) atomic crystals, such as graphene and layered transition-metal dichalcogenides, are currently receiving a lot of attention for applications in electronics and optoelectronics. In this talk, I will review our research activities on electrically driven light emission, photovoltaic energy conversion and photodetection in 2D semiconductors. In particular, WSe2 monolayer p-n junctions formed by electrostatic doping using a pair of split gate electrodes, type-II heterojunctions based on MoS2/WSe2 and MoS2/phosphorene van der Waals stacks, 2D multi-junction solar cells, and 3D/2D semiconductor interfaces will be presented. Upon optical illumination, conversion of light into electrical energy occurs in these devices. If an electrical current is driven, efficient electroluminescence is obtained. I will present measurements of the electrical characteristics, the optical properties, and the gate voltage dependence of the device response. In the second part of my talk, I will discuss photoconductivity studies of MoS2 field-effect transistors. We identify photovoltaic and photoconductive effects, which both show strong photoconductive gain. A model will be presented that reproduces our experimental findings, such as the dependence on optical power and gate voltage. We envision that the efficient photon conversion and light emission, combined with the advantages of 2D semiconductors, such as flexibility, high mechanical stability and low costs of production, could lead to new optoelectronic technologies.

  4. A cardiac electrical activity model based on a cellular automata system in comparison with neural network model.

    PubMed

    Khan, Muhammad Sadiq Ali; Yousuf, Sidrah

    2016-03-01

    Cardiac Electrical Activity is commonly distributed into three dimensions of Cardiac Tissue (Myocardium) and evolves with duration of time. The indicator of heart diseases can occur randomly at any time of a day. Heart rate, conduction and each electrical activity during cardiac cycle should be monitor non-invasively for the assessment of "Action Potential" (regular) and "Arrhythmia" (irregular) rhythms. Many heart diseases can easily be examined through Automata model like Cellular Automata concepts. This paper deals with the different states of cardiac rhythms using cellular automata with the comparison of neural network also provides fast and highly effective stimulation for the contraction of cardiac muscles on the Atria in the result of genesis of electrical spark or wave. The specific formulated model named as "States of automaton Proposed Model for CEA (Cardiac Electrical Activity)" by using Cellular Automata Methodology is commonly shows the three states of cardiac tissues conduction phenomena (i) Resting (Relax and Excitable state), (ii) ARP (Excited but Absolutely refractory Phase i.e. Excited but not able to excite neighboring cells) (iii) RRP (Excited but Relatively Refractory Phase i.e. Excited and able to excite neighboring cells). The result indicates most efficient modeling with few burden of computation and it is Action Potential during the pumping of blood in cardiac cycle.

  5. Beta Cell Formation in vivo Through Cellular Networking, Integration and Processing (CNIP) in Wild Type Adult Mice.

    PubMed

    Doiron, Bruno; Hu, Wenchao; DeFronzo, Ralph A

    2016-01-01

    Insulin replacement therapy is essential in type 1 diabetic individuals and is required in ~40- 50% of type 2 diabetics during their lifetime. Prior attempts at beta cell regeneration have relied upon pancreatic injury to induce beta cell proliferation, dedifferentiation and activation of the embryonic pathway, or stem cell replacement. We report an alternative method to transform adult non-stem (somatic) cells into pancreatic beta cells. The Cellular Networking, Integration and Processing (CNIP) approach targets cellular mechanisms involved in pancreatic function in the organ's adult state and utilizes a synergistic mechanism that integrates three important levels of cellular regulation to induce beta cell formation: (i) glucose metabolism, (ii) membrane receptor function, and (iii) gene transcription. The aim of the present study was to induce pancreatic beta cell formation in vivo in adult animals without stem cells and without dedifferentiating cells to recapitulate the embryonic pathway as previously published (1-3). Our results employing CNIP demonstrate that: (i) insulin secreting cells can be generated in adult pancreatic tissue in vivo and circumvent the problem of generating endocrine (glucagon and somatostatin) cells that exert deleterious effects on glucose homeostasis, and (ii) longterm normalization of glucose tolerance and insulin secretion can be achieved in a wild type diabetic mouse model. The CNIP cocktail has the potential to be used as a preventative or therapeutic treatment or cure for both type 1 and type 2 diabetes.

  6. Coverage extension and balancing the transmitted power of the moving relay node at LTE-A cellular network.

    PubMed

    Aldhaibani, Jaafar A; Yahya, Abid; Ahmad, R Badlishah

    2014-01-01

    The poor capacity at cell boundaries is not enough to meet the growing demand and stringent design which required high capacity and throughput irrespective of user's location in the cellular network. In this paper, we propose new schemes for an optimum fixed relay node (RN) placement in LTE-A cellular network to enhance throughput and coverage extension at cell edge region. The proposed approach mitigates interferences between all nodes and ensures optimum utilization with the optimization of transmitted power. Moreover, we proposed a new algorithm to balance the transmitted power of moving relay node (MR) over cell size and providing required SNR and throughput at the users inside vehicle along with reducing the transmitted power consumption by MR. The numerical analysis along with the simulation results indicates that an improvement in capacity for users is 40% increment at downlink transmission from cell capacity. Furthermore, the results revealed that there is saving nearly 75% from transmitted power in MR after using proposed balancing algorithm. ATDI simulator was used to verify the numerical results, which deals with real digital cartographic and standard formats for terrain.

  7. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-11-21

    Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks. © 2013 Elsevier Ltd

  8. The role of glutathione reductase and related enzymes on cellular redox homoeostasis network.

    PubMed

    Couto, Narciso; Wood, Jennifer; Barber, Jill

    2016-06-01

    In this review article we examine the role of glutathione reductase in the regulation, modulation and maintenance of cellular redox homoeostasis. Glutathione reductase is responsible for maintaining the supply of reduced glutathione; one of the most abundant reducing thiols in the majority of cells. In its reduced form, glutathione plays key roles in the cellular control of reactive oxygen species. Reactive oxygen species act as intracellular and extracellular signalling molecules and complex cross talk between levels of reactive oxygen species, levels of oxidised and reduced glutathione and other thiols, and antioxidant enzymes such as glutathione reductase determine the most suitable conditions for redox control within a cell or for activation of programmed cell death. Additionally, we discuss the translation and expression of glutathione reductase in a number of organisms including yeast and humans. In yeast and human cells, a single gene expresses more than one form of glutathione reductase, destined for residence in the cytoplasm or for translocation to different organelles; in plants, however, two genes encoding this protein have been described. In general, insects and kinetoplastids (a group of protozoa, including Plasmodia and Trypanosoma) do not express glutathione reductase or glutathione biosynthetic enzymes. Instead, they express either the thioredoxin system or the trypanothione system. The thioredoxin system is also present in organisms that have the glutathione system and there may be overlapping functions with cross-talk between the two systems. Finally we evaluate therapeutic targets to overcome oxidative stress associated cellular disorders.

  9. Cellular and network-level adaptations to in utero methadone exposure along the ventral respiratory column in the neonate rat.

    PubMed

    Gourévitch, Boris; Cai, Jun; Mellen, Nicholas

    2016-03-20

    Neonatal abstinence syndrome (NAS) occurs in babies chronically exposed to opioids during pregnancy. NAS shares features with opioid withdrawal symptoms seen in adults, including autonomic dysregulation. Here, the effect of low-dose in utero methadone (MTD) exposure on respiration-modulated networks along the ventral respiratory column (VRC) in ventrolateral medulla was investigated in the neonate Sprague-Dawley rat. MTD was administered via drinking water (3mg/kg/day in drinking water of the mother E7-E21). Lower expression levels of myelin-associated proteins phosphorylated axonal neurofilament subunit H (pNFH), 2',3'-Cyclicnucleotide 3'-phosphodiesterase (CNPase) and myelin basic protein (MBP), in MTD-exposed pups compared to controls at P3, P6 and P10 indicated MTD transport across the placenta. We investigated whether in utero MTD exposure led to network-level excitability changes consistent with tolerance, and also probed for changes in endogenous opioid modulation of respiratory networks. To this end, high-speed (45.5Hz) optical recordings of respiratory network activity in control and MTD-exposed neonate (P0-P2) pups before and during administration of the μ-opioid receptor antagonist naloxone (NAL; 10μM) were carried out. Spike rate was estimated from optical traces via deconvolution, and coupling between all neuron pairs in recorded networks was quantified using the normalized transfer entropy (NTE). Recordings of local networks along the VRC, together with recordings of respiratory output from ventral root C1 did not reveal changes in respiratory activity at the system level, but cellular and network changes in MTD-exposed pups were consistent with the development of opioid tolerance. MTD-exposed pups were found to have i. higher neuronal firing rates; ii. higher covariance between neuronal activity and motor output; iii. more bidirectionally and unidirectionally coupled neurons, and fewer uncoupled neurons; iv. stronger coupling and shorter

  10. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

    PubMed

    Knapp, Bettina; Kaderali, Lars

    2013-01-01

    Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

  11. Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

    PubMed

    Ozekes, Serhat; Osman, Onur; Ucan, Osman N

    2008-01-01

    The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

  12. Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

    PubMed Central

    Osman, Onur; Ucan, Osman N.

    2008-01-01

    Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules. PMID:18253070

  13. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

    PubMed

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-14

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  14. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    NASA Astrophysics Data System (ADS)

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  15. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    PubMed Central

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-01-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

  16. Galvanic Vestibular Stimulation: Cellular Substrates and Response Patterns of Neurons in the Vestibulo-Ocular Network.

    PubMed

    Gensberger, Kathrin D; Kaufmann, Anna-Kristin; Dietrich, Haike; Branoner, Francisco; Banchi, Roberto; Chagnaud, Boris P; Straka, Hans

    2016-08-31

    Galvanic vestibular stimulation (GVS) uses modulated currents to evoke neuronal activity in vestibular endorgans in the absence of head motion. GVS is typically used for a characterization of vestibular pathologies; for studies on the vestibular influence of gaze, posture, and locomotion; and for deciphering the sensory-motor transformation underlying these behaviors. At variance with the widespread use of this method, basic aspects such as the activated cellular substrate at the sensory periphery or the comparability to motion-induced neuronal activity patterns are still disputed. Using semi-intact preparations of Xenopus laevis tadpoles, we determined the cellular substrate and the spatiotemporal specificity of GVS-evoked responses and compared sinusoidal GVS-induced activity patterns with motion-induced responses in all neuronal elements along the vestibulo-ocular pathway. As main result, we found that, despite the pharmacological block of glutamatergic hair cell transmission by combined bath-application of NMDA (7-chloro-kynurenic acid) and AMPA (CNQX) receptor blockers, GVS-induced afferent spike activity persisted. However, the amplitude modulation was reduced by ∼30%, suggesting that both hair cells and vestibular afferent fibers are normally recruited by GVS. Systematic alterations of electrode placement with respect to bilateral semicircular canal pairs or alterations of the bipolar stimulus phase timing yielded unique activity patterns in extraocular motor nerves, compatible with a spatially and temporally specific activation of vestibulo-ocular reflexes in distinct planes. Despite the different GVS electrode placement in semi-intact X. laevis preparations and humans and the more global activation of vestibular endorgans by the latter approach, this method is suitable to imitate head/body motion in both circumstances. Galvanic vestibular stimulation is used frequently in clinical practice to test the functionality of the sense of balance. The outcome of

  17. Extensions of 2D gravity

    SciTech Connect

    Sevrin, A.

    1993-06-01

    After reviewing some aspects of gravity in two dimensions, I show that non-trivial embeddings of sl(2) in a semi-simple (super) Lie algebra give rise to a very large class of extensions of 2D gravity. The induced action is constructed as a gauged WZW model and an exact expression for the effective action is given.

  18. Highly crystalline 2D superconductors

    NASA Astrophysics Data System (ADS)

    Saito, Yu; Nojima, Tsutomu; Iwasa, Yoshihiro

    2017-02-01

    Recent advances in materials fabrication have enabled the manufacturing of ordered 2D electron systems, such as heterogeneous interfaces, atomic layers grown by molecular beam epitaxy, exfoliated thin flakes and field-effect devices. These 2D electron systems are highly crystalline, and some of them, despite their single-layer thickness, exhibit a sheet resistance more than an order of magnitude lower than that of conventional amorphous or granular thin films. In this Review, we explore recent developments in the field of highly crystalline 2D superconductors and highlight the unprecedented physical properties of these systems. In particular, we explore the quantum metallic state (or possible metallic ground state), the quantum Griffiths phase observed in out-of-plane magnetic fields and the superconducting state maintained in anomalously large in-plane magnetic fields. These phenomena are examined in the context of weakened disorder and/or broken spatial inversion symmetry. We conclude with a discussion of how these unconventional properties make highly crystalline 2D systems promising platforms for the exploration of new quantum physics and high-temperature superconductors.

  19. Highly crystalline 2D superconductors

    NASA Astrophysics Data System (ADS)

    Saito, Yu; Nojima, Tsutomu; Iwasa, Yoshihiro

    2016-12-01

    Recent advances in materials fabrication have enabled the manufacturing of ordered 2D electron systems, such as heterogeneous interfaces, atomic layers grown by molecular beam epitaxy, exfoliated thin flakes and field-effect devices. These 2D electron systems are highly crystalline, and some of them, despite their single-layer thickness, exhibit a sheet resistance more than an order of magnitude lower than that of conventional amorphous or granular thin films. In this Review, we explore recent developments in the field of highly crystalline 2D superconductors and highlight the unprecedented physical properties of these systems. In particular, we explore the quantum metallic state (or possible metallic ground state), the quantum Griffiths phase observed in out-of-plane magnetic fields and the superconducting state maintained in anomalously large in-plane magnetic fields. These phenomena are examined in the context of weakened disorder and/or broken spatial inversion symmetry. We conclude with a discussion of how these unconventional properties make highly crystalline 2D systems promising platforms for the exploration of new quantum physics and high-temperature superconductors.

  20. The potential of cellular technology to mediate social networks for support of chronic disease self-management.

    PubMed

    Roblin, Douglas W

    2011-01-01

    Productive interactions among patients, friends/family, and health care providers, as outlined by the Chronic Care Model, are important for promoting adherence to recommended care and good health outcomes among adults with a chronic illness. Characteristics of these interactions--active participation, collaboration, and data sharing among constituents--are the same as those of social networks organized around Web 2.0 principles and technology. Thus, the Web 2.0 framework can be used to configure social networks without the inherent spatiotemporal constraints of face-to-face interactions that remain prevalent in health care delivery. In this article, the author outlines various design principles and decisions for a pilot study in which cellular technology was used to mediate interactions between adults with Type 2 diabetes and supporters (i.e., family members or friends selected by the patients who agree provide support) to motivate regular self-monitoring of blood glucose (among the diabetes participants). Participants generally found the network to be relatively easy to use. Some diabetes patients reported improved attention to self-monitoring; and, patient-selected supporters indicated improvements in emotional and instrumental support that should benefit diabetes patients' lifestyle and health.

  1. Cell Edge Capacity Improvement by Using Adaptive Base Station Cooperation in Cellular Networks with Fractional Frequency Reuse

    NASA Astrophysics Data System (ADS)

    Xu, Liang; Yamamoto, Koji; Murata, Hidekazu; Yoshida, Susumu

    The present paper focuses on the application of the base station cooperation (BSC) technique in fractional frequency reuse (FFR) networks. Fractional frequency reuse is considered to be a promising scheme for avoiding the inter-cell interference problem in OFDMA cellular systems, such as WiMAX, in which the edge mobile stations (MSs) of adjacent cells use different subchannels for separate transmission. However, the problem of FFR is that the cell edge spectral efficiency (SE) is much lower than that of the cell center. The BSC technique, in which adjacent BSs perform cooperative transmission for one cell edge MS with the same channel, may improve the cell edge SE. However, since more BSs transmit signals for one cell edge MS, the use of BSC can also increase the inter-cell interference, which might degrade the network performance. In this paper, with a focus on this tradeoff, we propose an adaptive BSC scheme in which BSC is only performed for the cell edge MSs that can achieve a significant capacity increase with only a slight increase in inter-cell interference. Moreover, a channel reallocation scheme is proposed in order to further improve the performance of the adaptive BSC scheme. The simulation results reveal that, compared to the conventional FFR scheme, the proposed schemes are effective for improving the performance of FFR networks.

  2. E-2D Advanced Hawkeye Aircraft (E-2D AHE)

    DTIC Science & Technology

    2015-12-01

    and Homeland Defense. As a part of the E-2D AHE radar modernization effort, the Navy also invested in integrating a full glass cockpit and full...Communication Navigation Surveillance/Air Traffic Management capability. The glass cockpit will also provide the capability for the pilot or co-pilot to...hours at a station distance of 200nm Flat Turn Service Ceiling =>25,000 feet above MSL at mission profile =>25,000 feet above MSL at mission

  3. Genomics, complexity and drug discovery: insights from Boolean network models of cellular regulation.

    PubMed

    Huang, S

    2001-08-01

    The completion of the first draft of the human genome sequence has revived the old notion that there is no one-to-one mapping between genotype and phenotype. It is now becoming clear that to elucidate the fundamental principles that govern how genomic information translates into organismal complexity, we must overcome the current habit of ad hoc explanations and instead embrace novel, formal concepts that will involve computer modelling. Most modelling approaches aim at recreating a living system via computer simulation, by including as much details as possible. In contrast, the Boolean network model reviewed here represents an abstraction and a coarse-graining, such that it can serve as a simple, efficient tool for the extraction of the very basic design principles of molecular regulatory networks, without having to deal with all the biochemical details. We demonstrate here that such a discrete network model can help to examine how genome-wide molecular interactions generate the coherent, rule-like behaviour of a cell - the first level of integration in the multi-scale complexity of the living organism. Hereby the various cell fates, such as differentiation, proliferation and apoptosis, are treated as attractor states of the network. This modelling language allows us to integrate qualitative gene and protein interaction data to explain a series of hitherto non-intuitive cell behaviours. As the human genome project starts to reveal the limits of the current simplistic 'one gene - one function - one target' paradigm, the development of conceptual tools to increase our understanding of how the intricate interplay of genes gives rise to a global 'biological observable' will open a new perspective for post-genomic drug target discovery.

  4. Hierarchical random cellular neural networks for system-level brain-like signal processing.

    PubMed

    Kozma, Robert; Puljic, Marko

    2013-09-01

    Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain's dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms.

  5. 2-D or not 2-D, that is the question: A Northern California test

    SciTech Connect

    Mayeda, K; Malagnini, L; Phillips, W S; Walter, W R; Dreger, D

    2005-06-06

    Reliable estimates of the seismic source spectrum are necessary for accurate magnitude, yield, and energy estimation. In particular, how seismic radiated energy scales with increasing earthquake size has been the focus of recent debate within the community and has direct implications on earthquake source physics studies as well as hazard mitigation. The 1-D coda methodology of Mayeda et al. has provided the lowest variance estimate of the source spectrum when compared against traditional approaches that use direct S-waves, thus making it ideal for networks that have sparse station distribution. The 1-D coda methodology has been mostly confined to regions of approximately uniform complexity. For larger, more geophysically complicated regions, 2-D path corrections may be required. The complicated tectonics of the northern California region coupled with high quality broadband seismic data provides for an ideal ''apples-to-apples'' test of 1-D and 2-D path assumptions on direct waves and their coda. Using the same station and event distribution, we compared 1-D and 2-D path corrections and observed the following results: (1) 1-D coda results reduced the amplitude variance relative to direct S-waves by roughly a factor of 8 (800%); (2) Applying a 2-D correction to the coda resulted in up to 40% variance reduction from the 1-D coda results; (3) 2-D direct S-wave results, though better than 1-D direct waves, were significantly worse than the 1-D coda. We found that coda-based moment-rate source spectra derived from the 2-D approach were essentially identical to those from the 1-D approach for frequencies less than {approx}0.7-Hz, however for the high frequencies (0.7{le} f {le} 8.0-Hz), the 2-D approach resulted in inter-station scatter that was generally 10-30% smaller. For complex regions where data are plentiful, a 2-D approach can significantly improve upon the simple 1-D assumption. In regions where only 1-D coda correction is available it is still preferable over 2

  6. An asymmetric image cryptosystem based on the adaptive synchronization of an uncertain unified chaotic system and a cellular neural network

    NASA Astrophysics Data System (ADS)

    Cheng, Chao-Jung; Cheng, Chi-Bin

    2013-10-01

    Chaotic dynamics provide a fast and simple means to create an excellent image cryptosystem, because it is extremely sensitive to initial conditions and system parameters, pseudorandomness, and non-periodicity. However, most chaos-based image encryption schemes are symmetric cryptographic techniques, which have been proven to be more vulnerable, compared to an asymmetric cryptosystem. This paper develops an asymmetric image cryptosystem, based on the adaptive synchronization of two different chaotic systems, namely a unified chaotic system and a cellular neural network. An adaptive controller with parameter update laws is formulated, using the Lyapunov stability theory, to asymptotically synchronize the two chaotic systems. The synchronization controller is embedded in the image cryptosystem and generates a pair of asymmetric keys, for image encryption and decryption. Using numerical simulations, three sets of experiments are conducted to evaluate the feasibility and reliability of the proposed chaos-based image cryptosystem.

  7. A dynamic genetic-hormonal regulatory network model explains multiple cellular behaviors of the root apical meristem of Arabidopsis thaliana.

    PubMed

    García-Gómez, Mónica L; Azpeitia, Eugenio; Álvarez-Buylla, Elena R

    2017-04-01

    The study of the concerted action of hormones and transcription factors is fundamental to understand cell differentiation and pattern formation during organ development. The root apical meristem of Arabidopsis thaliana is a useful model to address this. It has a stem cell niche near its tip conformed of a quiescent organizer and stem or initial cells around it, then a proliferation domain followed by a transition domain, where cells diminish division rate before transiting to the elongation zone; here, cells grow anisotropically prior to their final differentiation towards the plant base. A minimal model of the gene regulatory network that underlies cell-fate specification and patterning at the root stem cell niche was proposed before. In this study, we update and couple such network with both the auxin and cytokinin hormone signaling pathways to address how they collectively give rise to attractors that correspond to the genetic and hormonal activity profiles that are characteristic of different cell types along A. thaliana root apical meristem. We used a Boolean model of the genetic-hormonal regulatory network to integrate known and predicted regulatory interactions into alternative models. Our analyses show that, after adding some putative missing interactions, the model includes the necessary and sufficient components and regulatory interactions to recover attractors characteristic of the root cell types, including the auxin and cytokinin activity profiles that correlate with different cellular behaviors along the root apical meristem. Furthermore, the model predicts the existence of activity configurations that could correspond to the transition domain. The model also provides a possible explanation for apparently paradoxical cellular behaviors in the root meristem. For example, how auxin may induce and at the same time inhibit WOX5 expression. According to the model proposed here the hormonal regulation of WOX5 might depend on the cell type. Our results

  8. Altered protein networks and cellular pathways in severe west nile disease in mice.

    PubMed

    Fraisier, Christophe; Camoin, Luc; Lim, Stephanie M; Lim, Stéphanie; Bakli, Mahfoud; Belghazi, Maya; Fourquet, Patrick; Granjeaud, Samuel; Osterhaus, Ab D M E; Koraka, Penelope; Martina, Byron; Almeras, Lionel

    2013-01-01

    The recent West Nile virus (WNV) outbreaks in developed countries, including Europe and the United States, have been associated with significantly higher neuropathology incidence and mortality rate than previously documented. The changing epidemiology, the constant risk of (re-)emergence of more virulent WNV strains, and the lack of effective human antiviral therapy or vaccines makes understanding the pathogenesis of severe disease a priority. Thus, to gain insight into the pathophysiological processes in severe WNV infection, a kinetic analysis of protein expression profiles in the brain of WNV-infected mice was conducted using samples prior to and after the onset of clinical symptoms. To this end, 2D-DIGE and gel-free iTRAQ labeling approaches were combined, followed by protein identification by mass spectrometry. Using these quantitative proteomic approaches, a set of 148 proteins with modified abundance was identified. The bioinformatics analysis (Ingenuity Pathway Analysis) of each protein dataset originating from the different time-point comparisons revealed that four major functions were altered during the course of WNV-infection in mouse brain tissue: i) modification of cytoskeleton maintenance associated with virus circulation; ii) deregulation of the protein ubiquitination pathway; iii) modulation of the inflammatory response; and iv) alteration of neurological development and neuronal cell death. The differential regulation of selected host protein candidates as being representative of these biological processes were validated by western blotting using an original fluorescence-based method. This study provides novel insights into the in vivo kinetic host reactions against WNV infection and the pathophysiologic processes involved, according to clinical symptoms. This work offers useful clues for anti-viral research and further evaluation of early biomarkers for the diagnosis and prevention of severe neurological disease caused by WNV.

  9. Altered Protein Networks and Cellular Pathways in Severe West Nile Disease in Mice

    PubMed Central

    Fraisier, Christophe; Camoin, Luc; Lim, Stéphanie; Bakli, Mahfoud; Belghazi, Maya; Fourquet, Patrick; Granjeaud, Samuel; Osterhaus, Ab D. M. E.; Koraka, Penelope; Martina, Byron; Almeras, Lionel

    2013-01-01

    Background The recent West Nile virus (WNV) outbreaks in developed countries, including Europe and the United States, have been associated with significantly higher neuropathology incidence and mortality rate than previously documented. The changing epidemiology, the constant risk of (re-)emergence of more virulent WNV strains, and the lack of effective human antiviral therapy or vaccines makes understanding the pathogenesis of severe disease a priority. Thus, to gain insight into the pathophysiological processes in severe WNV infection, a kinetic analysis of protein expression profiles in the brain of WNV-infected mice was conducted using samples prior to and after the onset of clinical symptoms. Methodology/Principal Findings To this end, 2D-DIGE and gel-free iTRAQ labeling approaches were combined, followed by protein identification by mass spectrometry. Using these quantitative proteomic approaches, a set of 148 proteins with modified abundance was identified. The bioinformatics analysis (Ingenuity Pathway Analysis) of each protein dataset originating from the different time-point comparisons revealed that four major functions were altered during the course of WNV-infection in mouse brain tissue: i) modification of cytoskeleton maintenance associated with virus circulation; ii) deregulation of the protein ubiquitination pathway; iii) modulation of the inflammatory response; and iv) alteration of neurological development and neuronal cell death. The differential regulation of selected host protein candidates as being representative of these biological processes were validated by western blotting using an original fluorescence-based method. Conclusion/Significance This study provides novel insights into the in vivo kinetic host reactions against WNV infection and the pathophysiologic processes involved, according to clinical symptoms. This work offers useful clues for anti-viral research and further evaluation of early biomarkers for the diagnosis and prevention

  10. Co-expression network analysis of toxin-antitoxin loci in Mycobacterium tuberculosis reveals key modulators of cellular stress.

    PubMed

    Gupta, Amita; Venkataraman, Balaji; Vasudevan, Madavan; Gopinath Bankar, Kiran

    2017-07-19

    Research on toxin-antitoxin loci (TA loci) is gaining impetus due to their ubiquitous presence in bacterial genomes and their observed roles in stress survival, persistence and drug tolerance. The present study investigates the expression profile of all the seventy-nine TA loci found in Mycobacterium tuberculosis. The bacterium was subjected to multiple stress conditions to identify key players of cellular stress response and elucidate a TA-coexpression network. This study provides direct experimental evidence for transcriptional activation of each of the seventy-nine TA loci following mycobacterial exposure to growth-limiting environments clearly establishing TA loci as stress-responsive modules in M. tuberculosis. TA locus activation was found to be stress-specific with multiple loci activated in a duration-based response to a particular stress. Conditions resulting in arrest of cellular translation led to greater up-regulation of TA genes suggesting that TA loci have a primary role in arresting translation in the cell. Our study identifed higBA2 and vapBC46 as key loci that were activated in all the conditions tested. Besides, relBE1, higBA3, vapBC35, vapBC22 and higBA1 were also upregulated in multpile stresses. Certain TA modules exhibited co-activation across multiple conditions suggestive of a common regulatory mechanism.

  11. Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease.

    PubMed

    Ding, Jingzhong; Reynolds, Lindsay M; Zeller, Tanja; Müller, Christian; Lohman, Kurt; Nicklas, Barbara J; Kritchevsky, Stephen B; Huang, Zhiqing; de la Fuente, Alberto; Soranzo, Nicola; Settlage, Robert E; Chuang, Chia-Chi; Howard, Timothy; Xu, Ning; Goodarzi, Mark O; Chen, Y-D Ida; Rotter, Jerome I; Siscovick, David S; Parks, John S; Murphy, Susan; Jacobs, David R; Post, Wendy; Tracy, Russell P; Wild, Philipp S; Blankenberg, Stefan; Hoeschele, Ina; Herrington, David; McCall, Charles E; Liu, Yongmei

    2015-10-01

    Obesity is linked to type 2 diabetes (T2D) and cardiovascular diseases; however, the underlying molecular mechanisms remain unclear. We aimed to identify obesity-associated molecular features that may contribute to obesity-related diseases. Using circulating monocytes from 1,264 Multi-Ethnic Study of Atherosclerosis (MESA) participants, we quantified the transcriptome and epigenome. We discovered that alterations in a network of coexpressed cholesterol metabolism genes are a signature feature of obesity and inflammatory stress. This network included 11 BMI-associated genes related to sterol uptake (↑LDLR, ↓MYLIP), synthesis (↑SCD, FADS1, HMGCS1, FDFT1, SQLE, CYP51A1, SC4MOL), and efflux (↓ABCA1, ABCG1), producing a molecular profile expected to increase intracellular cholesterol. Importantly, these alterations were associated with T2D and coronary artery calcium (CAC), independent from cardiometabolic factors, including serum lipid profiles. This network mediated the associations between obesity and T2D/CAC. Several genes in the network harbored C-phosphorus-G dinucleotides (e.g., ABCG1/cg06500161), which overlapped Encyclopedia of DNA Elements (ENCODE)-annotated regulatory regions and had methylation profiles that mediated the associations between BMI/inflammation and expression of their cognate genes. Taken together with several lines of previous experimental evidence, these data suggest that alterations of the cholesterol metabolism gene network represent a molecular link between obesity/inflammation and T2D/CAC. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

  12. Assigning cells to switches in cellular mobile networks using taboo search.

    PubMed

    Pierre, S; Houeto, F

    2002-01-01

    The design of wireless telecommunications networks is a complex process, which requires solving simultaneously many difficult combinatorial optimization problems. We propose a taboo-search approach dedicated to one of the aforementioned design optimization problems, namely the cell assignment problem. Our approach defines a series of moves applicable to an initial solution in order to improve the cost and establish the feasibility of the solution. For this purpose, we identify a gain structure with update procedures to efficiently choose the best solution in the current neighborhood. The results are generally good in comparison with those obtained through other heuristic methods.

  13. Features of the Chaperone Cellular Network Revealed through Systematic Interaction Mapping.

    PubMed

    Rizzolo, Kamran; Huen, Jennifer; Kumar, Ashwani; Phanse, Sadhna; Vlasblom, James; Kakihara, Yoshito; Zeineddine, Hussein A; Minic, Zoran; Snider, Jamie; Wang, Wen; Pons, Carles; Seraphim, Thiago V; Boczek, Edgar Erik; Alberti, Simon; Costanzo, Michael; Myers, Chad L; Stagljar, Igor; Boone, Charles; Babu, Mohan; Houry, Walid A

    2017-09-12

    A comprehensive view of molecular chaperone function in the cell was obtained through a systematic global integrative network approach based on physical (protein-protein) and genetic (gene-gene or epistatic) interaction mapping. This allowed us to decipher interactions involving all core chaperones (67) and cochaperones (15) of Saccharomyces cerevisiae. Our analysis revealed the presence of a large chaperone functional supercomplex, which we named the naturally joined (NAJ) chaperone complex, encompassing Hsp40, Hsp70, Hsp90, AAA+, CCT, and small Hsps. We further found that many chaperones interact with proteins that form foci or condensates under stress conditions. Using an in vitro reconstitution approach, we demonstrate condensate formation for the highly conserved AAA+ ATPases Rvb1 and Rvb2, which are part of the R2TP complex that interacts with Hsp90. This expanded view of the chaperone network in the cell clearly demonstrates the distinction between chaperones having broad versus narrow substrate specificities in protein homeostasis. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  14. Structural data in synthetic biology approaches for studying general design principles of cellular signaling networks.

    PubMed

    Kiel, Christina; Serrano, Luis

    2012-11-07

    In recent years, high-throughput discovery of macromolecular protein structures and complexes has played a major role in advancing a more systems-oriented view of protein interaction and signaling networks. The design of biological systems often employs structural information or structure-based protein design to successfully implement synthetic signaling circuits or for rewiring signaling flows. Here, we summarize the latest advances in using structural information for studying protein interaction and signaling networks, and in synthetic biology approaches. We then provide a perspective of how combining structural biology with engineered cell signaling modules--using additional information from quantitative biochemistry and proteomics, gene evolution, and mathematical modeling--can provide insight into signaling modules and the general design principles of cell signaling. Ultimately, this will improve our understanding of cell- and tissue-type-specific signal transduction. Integrating the quantitative effects of disease mutations into these systems may provide a basis for elucidating the molecular mechanisms of diseases. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. An Integrative Model of the Cardiovascular System Coupling Heart Cellular Mechanics with Arterial Network Hemodynamics

    PubMed Central

    Kim, Young-Tae; Lee, Jeong Sang; Youn, Chan-Hyun; Choi, Jae-Sung

    2013-01-01

    The current study proposes a model of the cardiovascular system that couples heart cell mechanics with arterial hemodynamics to examine the physiological role of arterial blood pressure (BP) in left ventricular hypertrophy (LVH). We developed a comprehensive multiphysics and multiscale cardiovascular model of the cardiovascular system that simulates physiological events, from membrane excitation and the contraction of a cardiac cell to heart mechanics and arterial blood hemodynamics. Using this model, we delineated the relationship between arterial BP or pulse wave velocity and LVH. Computed results were compared with existing clinical and experimental observations. To investigate the relationship between arterial hemodynamics and LVH, we performed a parametric study based on arterial wall stiffness, which was obtained in the model. Peak cellular stress of the left ventricle and systolic blood pressure (SBP) in the brachial and central arteries also increased; however, further increases were limited for higher arterial stiffness values. Interestingly, when we doubled the value of arterial stiffness from the baseline value, the percentage increase of SBP in the central artery was about 6.7% whereas that of the brachial artery was about 3.4%. It is suggested that SBP in the central artery is more critical for predicting LVH as compared with other blood pressure measurements. PMID:23960442

  16. Cell Polarity in Cerebral Cortex Development-Cellular Architecture Shaped by Biochemical Networks.

    PubMed

    Hansen, Andi H; Duellberg, Christian; Mieck, Christine; Loose, Martin; Hippenmeyer, Simon

    2017-01-01

    The human cerebral cortex is the seat of our cognitive abilities and composed of an extraordinary number of neurons, organized in six distinct layers. The establishment of specific morphological and physiological features in individual neurons needs to be regulated with high precision. Impairments in the sequential developmental programs instructing corticogenesis lead to alterations in the cortical cytoarchitecture which is thought to represent the major underlying cause for several neurological disorders including neurodevelopmental and psychiatric diseases. In this review article we discuss the role of cell polarity at sequential stages during cortex development. We first provide an overview of morphological cell polarity features in cortical neural stem cells and newly-born postmitotic neurons. We then synthesize a conceptual molecular and biochemical framework how cell polarity is established at the cellular level through a break in symmetry in nascent cortical projection neurons. Lastly we provide a perspective how the molecular mechanisms applying to single cells could be probed and integrated in an in vivo and tissue-wide context.

  17. Cell Polarity in Cerebral Cortex Development—Cellular Architecture Shaped by Biochemical Networks

    PubMed Central

    Hansen, Andi H.; Duellberg, Christian; Mieck, Christine; Loose, Martin; Hippenmeyer, Simon

    2017-01-01

    The human cerebral cortex is the seat of our cognitive abilities and composed of an extraordinary number of neurons, organized in six distinct layers. The establishment of specific morphological and physiological features in individual neurons needs to be regulated with high precision. Impairments in the sequential developmental programs instructing corticogenesis lead to alterations in the cortical cytoarchitecture which is thought to represent the major underlying cause for several neurological disorders including neurodevelopmental and psychiatric diseases. In this review article we discuss the role of cell polarity at sequential stages during cortex development. We first provide an overview of morphological cell polarity features in cortical neural stem cells and newly-born postmitotic neurons. We then synthesize a conceptual molecular and biochemical framework how cell polarity is established at the cellular level through a break in symmetry in nascent cortical projection neurons. Lastly we provide a perspective how the molecular mechanisms applying to single cells could be probed and integrated in an in vivo and tissue-wide context. PMID:28701923

  18. Multimode fibers in millimeter-wave evolution for 5G cellular networks

    NASA Astrophysics Data System (ADS)

    Vázquez, C.; Montero, D. S.; Ponce, W.; Lallana, P. C.; Larrabeiti, D.; Montalvo, J.; Tapetado, A.; Pinzón, P. J.

    2016-02-01

    Small-cell and cloud-RAN systems along with the use of the millimeter-wave band have been considered as promising solutions to meet the capacity demand of the future wireless access networks. Radio over Multimode fibers (RoMMF) can play a role in the integrated optical-wireless access systems for next-generation wireless communications, mainly in within-building environments. The numerical results show the effectiveness of MMF to transmit at 60 GHz band with 7- GHz bandwidth for different link lengths and refractive index profiles under restricted mode launching and using narrow linewidth sources. The integration with optically powered remote antenna units is also proposed based on the large core effective area of MMF. Temperature impairments and graded index plastic optical fiber transmission are also discussed.

  19. Cellular and neurochemical basis of sleep stages in the thalamocortical network.

    PubMed

    Krishnan, Giri P; Chauvette, Sylvain; Shamie, Isaac; Soltani, Sara; Timofeev, Igor; Cash, Sydney S; Halgren, Eric; Bazhenov, Maxim

    2016-11-16

    The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.

  20. Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses.

    PubMed

    Yamaguchi-Shinozaki, Kazuko; Shinozaki, Kazuo

    2006-01-01

    Plant growth and productivity are greatly affected by environmental stresses such as drought, high salinity, and low temperature. Expression of a variety of genes is induced by these stresses in various plants. The products of these genes function not only in stress tolerance but also in stress response. In the signal transduction network from perception of stress signals to stress-responsive gene expression, various transcription factors and cis-acting elements in the stress-responsive promoters function for plant adaptation to environmental stresses. Recent progress has been made in analyzing the complex cascades of gene expression in drought and cold stress responses, especially in identifying specificity and cross talk in stress signaling. In this review article, we highlight transcriptional regulation of gene expression in response to drought and cold stresses, with particular emphasis on the role of transcription factors and cis-acting elements in stress-inducible promoters.

  1. Morphogenesis in sea urchin embryos: linking cellular events to gene regulatory network states

    PubMed Central

    Lyons, Deidre; Kaltenbach, Stacy; McClay, David R.

    2013-01-01

    Gastrulation in the sea urchin begins with ingression of the primary mesenchyme cells (PMCs) at the vegetal pole of the embryo. After entering the blastocoel the PMCs migrate, form a syncitium, and synthesize the skeleton of the embryo. Several hours after the PMCs ingress the vegetal plate buckles to initiate invagination of the archenteron. That morphogenetic process occurs in several steps. The non-skeletogenic cells produce the initial inbending of the vegetal plate. Endoderm cells then rearrange and extend the length of the gut across the blastocoel to a target near the animal pole. Finally, cells that will form part of the midgut and hindgut are added to complete gastrulation. Later, the stomodeum invaginates from the oral ectoderm and fuses with the foregut to complete the archenteron. In advance of, and during these morphogenetic events an increasingly complex gene regulatory network controls the specification and the cell biological events that conduct the gastrulation movements. PMID:23801438

  2. Evolving gene regulatory networks into cellular networks guiding adaptive behavior: an outline how single cells could have evolved into a centralized neurosensory system.

    PubMed

    Fritzsch, Bernd; Jahan, Israt; Pan, Ning; Elliott, Karen L

    2015-01-01

    Understanding the evolution of the neurosensory system of man, able to reflect on its own origin, is one of the major goals of comparative neurobiology. Details of the origin of neurosensory cells, their aggregation into central nervous systems and associated sensory organs and their localized patterning leading to remarkably different cell types aggregated into variably sized parts of the central nervous system have begun to emerge. Insights at the cellular and molecular level have begun to shed some light on the evolution of neurosensory cells, partially covered in this review. Molecular evidence suggests that high mobility group (HMG) proteins of pre-metazoans evolved into the definitive Sox [SRY (sex determining region Y)-box] genes used for neurosensory precursor specification in metazoans. Likewise, pre-metazoan basic helix-loop-helix (bHLH) genes evolved in metazoans into the group A bHLH genes dedicated to neurosensory differentiation in bilaterians. Available evidence suggests that the Sox and bHLH genes evolved a cross-regulatory network able to synchronize expansion of precursor populations and their subsequent differentiation into novel parts of the brain or sensory organs. Molecular evidence suggests metazoans evolved patterning gene networks early, which were not dedicated to neuronal development. Only later in evolution were these patterning gene networks tied into the increasing complexity of diffusible factors, many of which were already present in pre-metazoans, to drive local patterning events. It appears that the evolving molecular basis of neurosensory cell development may have led, in interaction with differentially expressed patterning genes, to local network modifications guiding unique specializations of neurosensory cells into sensory organs and various areas of the central nervous system.

  3. Evolving gene regulation networks into cellular networks guiding adaptive behavior: an outline how single cells could have evolved into a centralized neurosensory system

    PubMed Central

    Fritzsch, Bernd; Jahan, Israt; Pan, Ning; Elliott, Karen L.

    2014-01-01

    Understanding the evolution of the neurosensory system of man, able to reflect on its own origin, is one of the major goals of comparative neurobiology. Details of the origin of neurosensory cells, their aggregation into central nervous systems and associated sensory organs, their localized patterning into remarkably different cell types aggregated into variably sized parts of the central nervous system begin to emerge. Insights at the cellular and molecular level begin to shed some light on the evolution of neurosensory cells, partially covered in this review. Molecular evidence suggests that high mobility group (HMG) proteins of pre-metazoans evolved into the definitive Sox [SRY (sex determining region Y)-box] genes used for neurosensory precursor specification in metazoans. Likewise, pre-metazoan basic helix-loop-helix (bHLH) genes evolved in metazoans into the group A bHLH genes dedicated to neurosensory differentiation in bilaterians. Available evidence suggests that the Sox and bHLH genes evolved a cross-regulatory network able to synchronize expansion of precursor populations and their subsequent differentiation into novel parts of the brain or sensory organs. Molecular evidence suggests metazoans evolved patterning gene networks early and not dedicated to neuronal development. Only later in evolution were these patterning gene networks tied into the increasing complexity of diffusible factors, many of which were already present in pre-metazoans, to drive local patterning events. It appears that the evolving molecular basis of neurosensory cell development may have led, in interaction with differentially expressed patterning genes, to local network modifications guiding unique specializations of neurosensory cells into sensory organs and various areas of the central nervous system. PMID:25416504

  4. Photomagnetic properties of an Fe(ii) spin-crossover complex of 6-(3,5-diamino-2,4,6-triazinyl)-2,2'-bipyridine and its insertion into 2D and 3D bimetallic oxalate-based networks.

    PubMed

    Sánchez-Sánchez, C; Desplanches, C; Clemente-Juan, J M; Clemente-León, M; Coronado, E

    2017-02-21

    The Fe(ii) complex of the L1 ligand (L1 = 6-(3,5-diamino-2,4,6-triazinyl)-2,2'-bipyridine) has been used as a templating cation for the growth of oxalate-based networks. The magnetic characterization of the [Fe(II)(L1)2](ClO4)2·CH3CN (1) precursor in the solid state has been performed for the first time showing that the low-spin (LS) state is predominating from 2 to 400 K with 10% of Fe(ii), which undergoes a gradual and irreversible spin-crossover above 350 K. 1 presents the LIESST effect with a photo-conversion close to 25% and a T(LIESST) of 49 K. During the preparation of 1, a secondary product of the formula [Fe(II)(L1)(CH3CN)2(H2O)](ClO4)2·CH3CN (2) has been obtained. The magnetic characterization of 2 shows that it contains high-spin (HS) Fe(ii). 1 has afforded two novel oxalate-based compounds, the 2D compound of the formula [Fe(II)(L1)2][Mn(II)Cr(III)(ox)3]2·(CH3NO2)6·(CH3OH)·(H2O)2 (3) and the 3D compound of the formula [Fe(II)(L1)2][Mn(II)Cr(III)(ox)3]2·(CH3CN)3 (4), which have been obtained by changing the synthetic conditions. The magnetic properties show that in 3 the inserted Fe(ii) cation remains in the LS state from 2 to 340 K and presents a partial and irreversible spin-crossover of ∼20% at higher temperatures. In 4, most of the Fe(ii) complexes remain in the LS state from 2 to 230 K and present a partial and irreversible spin-crossover of ∼50% from 230 to 400 K. 3 and 4 do not present the LIESST effect.

  5. CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

    PubMed

    Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

    2017-09-13

    The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

  6. Energy Efficiency of D2D Multi-User Cooperation.

    PubMed

    Zhang, Zufan; Wang, Lu; Zhang, Jie

    2017-03-28

    The Device-to-Device (D2D) communication system is an important part of heterogeneous networks. It has great potential to improve spectrum efficiency, throughput and energy efficiency cooperation of multiple D2D users with the advantage of direct communication. When cooperating, D2D users expend extraordinary energy to relay data to other D2D users. Hence, the remaining energy of D2D users determines the life of the system. This paper proposes a cooperation scheme for multiple D2D users who reuse the orthogonal spectrum and are interested in the same data by aiming to solve the energy problem of D2D users. Considering both energy availability and the Signal to Noise Ratio (SNR) of each D2D user, the Kuhn-Munkres algorithm is introduced in the cooperation scheme to solve relay selection problems. Thus, the cooperation issue is transformed into a maximum weighted matching (MWM) problem. In order to enhance energy efficiency without the deterioration of Quality of Service (QoS), the link outage probability is derived according to the Shannon Equation by considering the data rate and delay. The simulation studies the relationships among the number of cooperative users, the length of shared data, the number of data packets and energy efficiency.

  7. Energy Efficiency of D2D Multi-User Cooperation

    PubMed Central

    Zhang, Zufan; Wang, Lu; Zhang, Jie

    2017-01-01

    The Device-to-Device (D2D) communication system is an important part of heterogeneous networks. It has great potential to improve spectrum efficiency, throughput and energy efficiency cooperation of multiple D2D users with the advantage of direct communication. When cooperating, D2D users expend extraordinary energy to relay data to other D2D users. Hence, the remaining energy of D2D users determines the life of the system. This paper proposes a cooperation scheme for multiple D2D users who reuse the orthogonal spectrum and are interested in the same data by aiming to solve the energy problem of D2D users. Considering both energy availability and the Signal to Noise Ratio (SNR) of each D2D user, the Kuhn-Munkres algorithm is introduced in the cooperation scheme to solve relay selection problems. Thus, the cooperation issue is transformed into a maximum weighted matching (MWM) problem. In order to enhance energy efficiency without the deterioration of Quality of Service (QoS), the link outage probability is derived according to the Shannon Equation by considering the data rate and delay. The simulation studies the relationships among the number of cooperative users, the length of shared data, the number of data packets and energy efficiency. PMID:28350374

  8. Structurally Dynamic Cellular Networks as Models for Planck Scale Physics and the Quantum Vacuum

    NASA Astrophysics Data System (ADS)

    Requardt, Manfred

    Starting from the working hypothesis that both physics and the corresponding mathematics have to be described by means of discrete concepts on the Planck scale, one of the many problems one has to face in this enterprise is to find the discrete protoforms of the building blocks of our ordinary continuum physics and mathematics. We regard these continuum concepts and continuum space-time (S-T) in particular as being emergent, coarse-grained and derived relative to an underlying erratic and disordered microscopic substratum which is expected to play by quite different rules. A central role in our analysis is played by a geometric renormalization group which creates (among other things) a kind of sparse translocal network of correlations in classical continuous space-time and underlies in our view such mysterious phenomena as holography and the black hole entropy-area law. The same point of view holds for quantum theory which we also regard as a low-energy, coarse-grained continuum theory, being emergent from something more fundamental.

  9. Mapping global sensitivity of cellular network dynamics: sensitivity heat maps and a global summation law.

    PubMed

    Rand, D A

    2008-08-06

    The dynamical systems arising from gene regulatory, signalling and metabolic networks are strongly nonlinear, have high-dimensional state spaces and depend on large numbers of parameters. Understanding the relation between the structure and the function for such systems is a considerable challenge. We need tools to identify key points of regulation, illuminate such issues as robustness and control and aid in the design of experiments. Here, I tackle this by developing new techniques for sensitivity analysis. In particular, I show how to globally analyse the sensitivity of a complex system by means of two new graphical objects: the sensitivity heat map and the parameter sensitivity spectrum. The approach to sensitivity analysis is global in the sense that it studies the variation in the whole of the model's solution rather than focusing on output variables one at a time, as in classical sensitivity analysis. This viewpoint relies on the discovery of local geometric rigidity for such systems, the mathematical insight that makes a practicable approach to such problems feasible for highly complex systems. In addition, we demonstrate a new summation theorem that substantially generalizes previous results for oscillatory and other dynamical phenomena. This theorem can be interpreted as a mathematical law stating the need for a balance between fragility and robustness in such systems.

  10. A Dynamic Programming Approach for Base Station Sleeping in Cellular Networks

    NASA Astrophysics Data System (ADS)

    Gong, Jie; Zhou, Sheng;